This acquisition consolidates the two leading providers of semantic discovery and search. It is also the culmination of my long and challenging venture to pioneer the adoption of the consumer Semantic Web.

As the CEO and founder of Radar Networks and Twine.com, I am both happy and relieved to have reached this milestone during what has been a difficult time of global recession. I am very proud of my team and the incredible work and accomplishments that we have made together, and I am grateful for the unflagging support of our investors, and the huge community of Twine users and supporters.

Selling Twine.com was not something we had planned on doing at this time, but given the economy and the fact that Twine.com is a long-term project that will require significant ongoing investment and work to reach our goals, it is the best decision for the business and our shareholders.

While we received several offers for the company, from multiple industry leaders in media, search and social software, we eventually selected Evri because not only did they make us the best offer, but we also felt Evri was the closest strategic and cultural fit to our mission and vision for Twine (note: Evri’s lead investor is Vulcan Capital, which is also the lead investor in Twine). Will Hunsinger, the CEO of Evri, and I have been speaking about the potential for teaming up since last year, and this deal made good sense for both companies.

The Twine team is joining Evri to continue our work there. Twine.com’s data and users are safe and sound and will be transitioned into the Evri.com service over time. This process will be done in a manner that protects privacy and data, and is minimally disruptive. I have great faith in the team at Evri and believe they will handle this with great care and respect for the Twine community.

It is always an emotional experience to sell a company. Building Twine.com has been a long, intense, challenging, rewarding, and all-consuming effort. There were incredible highpoints and some very deep lows along the way. But most of all, it has been an adventure I will never forget. I was fortunate to help pioneer a major new technology — the Semantic Web — with an incredible team, including many good friends. Bringing something as big, as ambitious, and as risky as Twine.com to market was exhilarating.

Twine has been one of the great learning experiences of my life. I am profoundly grateful to everyone I’ve worked with, and especially to those who supported us financially and personally with their moral support, ideas and advocacy.

I am also grateful to unsung heroes behind the project — the families of all of us who worked on it, who never failed to be supportive as we worked days, nights, weekends and vacations to bring Twine to market.

What I’m Doing Next

I will be continuing to advise Evri going forward, but will not be working full-time there. Instead, I will be turning my primary focus to several new projects, including some exciting new ventures:

• Live Matrix, a new venture that I co-founded  with Sanjay Reddy (CEO of Live Matrix; formerly SVP of Corp Dev for Gemstar TV Guide). We will be announcing Live Matrix and providing first public details at SXSW, this weekend. We’re going to give the Web a new dimension: time. More news about this soon.
• Klout, the leading provider of social analytics about influencers on Twitter and Facebook (which I was the first angel investor in, and which I now advise). Klout is a really hot  company and it’s growing fast.
• I’m starting a new early-stage fund with an innovative business model. It’s part incubator, part fund, part production company. Through this fund, my partners and I are planning to produce a number of original startups, and selected outside startups as well. There is a huge gap in the early-stage arena, and to fill this we need to modify the economics and model of early stage venture investing.
• I’m working on a book about major cultural shifts that are taking place, thanks in part to the real-time Web. And I’m looking forward to traveling and speaking about this topic widely in the coming year.
• I’m looking forward to working more on my non-profit interests, particularly those related to supporting democracy and human rights around the world, and one of my particular interests, Tibetan cultural preservation.
• And last but not least, I’m getting married later this month, which may turn out to be my best project of all.

If you want to keep up with what I am thinking about and working on, you should follow me on Twitter at @novaspivack, and also keep up with my blog here at novaspivack.com and my mailing list (accessible in the upper right hand corner of this page).

The Story Behind the Story

In making this transition, it seems appropriate to tell the Twine.com story. This will provide some insight into how we got here, including some of our triumphs, and our mistakes, and some of the difficulties we faced along the way. Hopefully this will shed some light on the story behind the story, and may even be useful to other entrepreneurs out there in what is perhaps one of the most difficult venture capital and startup environments in history.

The Early Years of the Project

The ideas that led to Twine were born in the 1990’s from my work as a co-founder of EarthWeb (which today continues as Dice.com), where among many things we prototyped a number of new knowledge-sharing and social networking tools, along with our primary work developing large Web portals and communities for customers, and eventually our own communities for IT professionals. My time with EarthWeb really helped me to understand that challenges and potential of sharing and growing knowledge socially on the Web. I became passionately interested in finding new ways to network people’s minds together, to solve information overload, and to enable the evolution of a future “global brain.”

After EarthWeb’s IPO I worked with SRI and Sarnoff to build their business incubator, nVention, and then eventually started my own incubator, Lucid Ventures, through which I co-founded Radar Networks with Kristin Thorisson, from the MIT Media Lab, and Jim Wissner (the continuing Chief Architect of Twine) in 2003. Our first implementation was a peer-to-peer Java-based knowledge sharing app called “Personal Radar.”

Personal Radar was a very cool app — it organized all the information on the desktop in a single semantic information space that was like an “iTunes for information” and then made it easy to share and annotate knowledge with others in a collaborative manner. There were some similarities to apps like Ray Ozzie’s Groove and the MIT Haystack project, but Personal Radar was built for consumers, entirely with Java, RDF, OWL and the standards of the emerging Semantic Web. You can see some screenshots pictures of this early work in this slideshow, here.

But due to the collapse of the first Internet bubble there was simply no venture funding available at the time and so instead, we ended up working as subcontractors on the DARPA CALO project at SRI. This kept our research alive through the downturn and also introduced us to a true Who’s Who of AI and Semantic Web gurus who worked on the CALO project. We eventually helped SRI build OpenIRIS, a personal semantic desktop application, which had many similarities to Personal Radar. All of our work for CALO was open-sourced under the LGPL license.

Becoming a Venture-Funded Company

Through CALO we met Deborah L. McGuinness, who was one of the co-designers of the OWL language (the Web Ontology Language, one of the foundations of the Semantic Web standards at the W3C). Deborah became one of our science advisers and kindly introduced us to Paul Allen, who invited us to present our work to his team at Vulcan Capital. The rest is history. Paul Allen and Ron Conway led an angel round to seed-fund us and we moved out of consulting to DARPA and began work on developing our own products and services.

Our long-term plan was to create a major online portal powered by the Semantic Web that would provide a new generation of Web-scale semantic search and discovery features to consumers. But for this to happen, first we had to build our own Web-scale commercial semantic applications platform, because there was no platform available at that time that could meet the requirements we had. In the process of building our platform numerous technical challenges had to be overcome.

At the time (the early 2000’s) there were few development tools in existence for creating ontologies or semantic applications, and in addition there were no commercial-quality databases capable of delivering high-performance Web-scale storage and retrieval of RDF triples. So we had to develop our own development tools, our own semantic applications framework, and our own federated high-performance semantic datastore.

This turned out to be a nearly endless amount of work. However we were fortunate to have Jim Wissner as our lead technical architect and chief scientist. Under his guidance we went through several iterations and numerous technical breakthroughs, eventually developing the most powerful and developer-friendly semantic applications platform in the world. This led to the  development of a portfolio of intellectual property that provides fundamental DNA for the Semantic Web.

During this process we raised a Series A round led by Vulcan Capital and Leapfrog Ventures, and our team was joined by interface designer and product management expert, Chris Jones (now leading strategy at HotStudio, a boutique design and user-experience firm in San Francisco). Under Chris’ guidance we developed Twine.com, our first application built on our semantic platform.

The mission of Twine.com was to help people keep up with their interests more efficiently, using the Semantic Web. The basic idea was that you could add content to Twine (most commonly by bookmarking it into the site, but also by authoring directly into it), and then Twine would use natural language processing and analysis, statistical methods, and graph and social network analysis, to automatically store, organize, link and semantically tag the content into various topical areas.

These topics could easily be followed by other users who wanted to keep up with specific types of content or interests. So basically you could author or add stuff to Twine and it would then do the work of making sense of it, organizing it, and helping you share it with others who were interested. The data was stored semantically and connected to ontologies, so that it could then be searched and reused in new ways.

With the help of Lew Tucker, Sonja Erickson and Candice Nobles, as well as an amazing team of engineers, product managers, systems admins and designers, Twine was announced at the Web 2.0 Summit in October of 2007 and went into full public beta in Q1 of 2008. Twine was well-received by the press and early-adopter users.

Soon after our initial beta launch we raised a Series B round, led by Vulcan Capital and Velocity Interactive Group (now named Fuse Capital), as well as DFJ and very significant co-investment from a large private angel investor. This gave us the capital to begin to grow Twine.com rapidly to become the major online destination we envisioned.

In the course of this work we made a number of additional technical breakthroughs, resulting in more than 20 patent filings in total, including several fundamental patents related to semantic data management, semantic portals, semantic social networking, semantic recommendations, semantic advertising, and semantic search.

Four of those patents have been granted so far and the rest are still pending — and perhaps the most interesting of these patents are related to our most recent work on “T2″ and are not yet visible.

At the time of beta launch and for almost six months after, Twine was still very much a work in progress. Fortunately our users and the press were fairly forgiving as we worked through evolving the GUI and feature set from what was initially just slightly better than an alpha site to the highly refined and graphical UI we have today.

During these early days of Twine.com we were fortunate to have a devoted user-base and this became a thriving community of power-users who really helped us to refine the product and develop great content within it.

Rapid Growth, and Scaling Challenges

As Twine grew the community went through many changes and some growing pains, and eventually crossed the chasm to a more mainstream user-base. Within less than a year from launch the site grew to around 3 million monthly visitors, 300,000 registered users, 25,000 “twines” about various interests, and almost 5 million pieces of user-contributed content. It was on its way to becoming the largest semantic web on the Web.

By all accounts Twine was looking like a “hit.” During this period the company staff increased to more than 40 people (inclusive of contractors and offshore teams) and our monthly burn rate increased to aggressive levels of spending to keep up with growth.

Despite this growth and spending we still could not keep up with demand for new features and at times we experienced major scaling and performance challenges. We had always planned for several more iterations of our backend architecture to facilitate scaling the system. But now we could see the writing on the wall — we had to begin to develop a more powerful, more scalable backend for Twine, much sooner than we had expected we would need to.

This required us to increase our engineering spending further in order to simultaneously support the live version of Twine and its very substantial backend, and run a parallel development team working on the next generation of the backend and the next version of Twine on top of it. Running two development teams instead of one was a challenging and costly endeavor. In addition, we entered into a strategic relationship with a company owned by one of our angel investors, in order to help them, improve our interface, and to fund our ongoing work — but that project turned out to be far more time consuming than planned. The engineering team was stretched thin and we were all putting in 12 to 15 hour days every day.

Breakthrough to “T2″

We began to work in earnest on a new iteration of our back-end architecture and application framework — one that could scale fast enough to keep up with our unexpectedly fast growth rate and the increasing demands on our servers that this was causing.

This initiative yielded unexpected fruit. Not only did we solve our scaling problems, but we were able to do so to such a degree that entirely new possibilities were opened up to us — ones that had previously been out of reach for purely technical reasons. In particular, semantic search.

Semantic search had always been a long-term goal of ours, however, in the first version of Twine (the one that is currently online) search was our weakest feature area, due to the challenge of scaling a semantic datastore to handle hundreds of billions of triples. But our user-studies revealed that it was in fact the feature our users wanted us to develop the most – search slowly became the dominant paradigm within Twine, especially when the content in our system reached critical mass.

Our new architecture initiative solved the semantic search problem to such a degree that we realized that not only could we scale Twine.com, we could scale it to eventually become a semantic search engine for the entire Web.

Instead of relying on users to crowdsource only a subset of the best content into our index, we could crawl large portions of the Web automatically and ingest millions and millions of Web pages, process them, and make them semantically searchable — using a true W3C Semantic Web compliant backend. (Note: Why did we even attempt to do this? We believed strongly in supporting open-standards for the Semantic Web, despite the fact that they posed major technical challenges and required tools that did not exist yet, because they promised to enable semantic application and data interoperability, one of the main potential benefits of the Semantic Web).

Based on our newfound ability to do Web-scale semantic search, we began planning the next version of Twine — Twine 2.0 (“T2″), with the help of Bob Morgan, Mark Erickson, Sasi Reddy, and a team of great designers.

The new T2 plan would merge new faceted semantic search features with the existing social, personalization and knowledge management features of Twine 1.0. It would be the best of both worlds: semantic search + social search. We began working intensively on developing T2, along with a new hosted developer tools that would make it easy for any webmaster to easily add their site into our semantic index. We were certain that with T2 we had finally “cracked the code” to the Semantic Web — we had a product plan and a strategy that could really bring the Semantic Web to everyone on the Web. It elegantly solved the key challenges to adoption and on a technical level, using SOLR instead of a giant triplestore, we were able to scale to unprecedented levels. It was an exciting plan and everyone on the team was confident in the direction.

To see screenshots that demo T2 and our hosted development tools click here.

The Global Recession

Our growth was fast, and so was our spending, but at the time this seemed logical because the future looked bright and we were in a race to keep ahead of our own curve. We were quickly nearing a point where we would soon need to raise another round of funding to sustain our pace, but we were confident that with our growth trends steadily increasing and our exciting plans for T2, the necessary funding would be forthcoming at favorable valuations.

We were wrong.

The global economy crashed unexpectedly, throwing a major curveball in our path. We had not planned on that happening and it certainly was inconvenient to say the least.

The recession not only hit Wall Street, it hit Silicon Valley. Venture capital funding dried up almost overnight. VC funds sent alarming letters to their portfolio companies warning of dire financial turmoil ahead. Many startups were forced to close their doors, while others made drastic sudden layoffs for better or for worse. We too made spending cuts, but we were limited in our ability to slash expenses until the new T2 platform could be completed. Once that was done, we would be able to move Twine to a much more scalable and less costly architecture, and we would no longer need parallel development teams. But until that happened, we still had to maintain a sizeable infrastructure and engineering effort.

As the recession dragged on, and the clock kept ticking down, the urgency of raising a C round increased, and finally we were faced with a painful decision. We had to drastically reduce our spending in order to wait out the recession and live to raise more funding in the future.

Unfortunately, the only way to accomplish such a drastic reduction in spending was to lay off almost 30% of our staff and cut our monthly spending by almost 40%. But by doing that we could not possibly continue to work on as many fronts as we had been doing. The result was that we had to stop most work on Twine 1.0 (the version that was currently online) and focus all our remaining development cycles and spending on the team needed to continue our work on T2.

This was extremely painful for me as the CEO, and for everyone on our team. But it was necessary for the survival of the business and it did buy us valuable time. However, it also slowed us down tremendously. The irony of making this decision was that it reduced our burn-rate but slowed us down, reduced productivity, and cost us time to such a degree that in the end it may have cost us the same amount of money anyway.

Due to this layoff and our reduced budgets and ability to support Twine.com, our traffic and usage fell off dramatically. In fact, in our growth charts, you can see the week in which we began cutting Twine spending – there is dramatic downturn in what had previously been a steady upward curve.

While much of our traffic had been organic and direct, we also had a number of partnerships and PR initiatives that we had to terminate. In addition, as part of this layoff we lost our amazing and talented marketing team, as well as half our product management team, our entire design team, our entire marketing and PR budget, and much of our support and community management team.

These losses meant we could no longer create compelling content or to manage the Twine community. So we put Twine.com on auto-pilot and let the traffic fall off. While painful to watch, this at least had the benefit of reducing the pressure to scale the system and support it under load, giving us time to focus all our energy on getting T2 finished and raising more funds.

But the recession dragged on and on and on, without end. VC’s remained extremely conservative and risk-averse. We focused our internal work on growing a large semantic index of the Web in T2, vertical by vertical, starting with food, then games, and then many other topics (technology, health, sports, etc.). We were quite confident that if we could bring T2 to market it would be a turning point for Web search, and funding would follow.

Meanwhile we met with VC’s in earnest. But nobody was able to invest in anything due to the recession. Furthermore we were a pre-revenue company working on a risky advanced technology and VC partnerships were far too terrified by the recession to make such a bet. We encountered the dreaded “wait and see” response.

The only way we could get the funding we needed to continue was to launch T2, grow it, and generate revenues from it, but the only way we could reach those milestones was to launch T2 in the first place: a classic catch-22 situation! We took comfort in the fact that we were not alone in this predicament. Almost every tech company at our stage was facing similar funding challenges. However, we were determined to find a solution despite the obstacles in our path.

Selling the Business

Had the recession not happened, I believe we would have raised a strong C round based on the momentum of the product and our technical achievements. Unfortunately, we, like many other early-stage technology ventures, found ourselves in the worst capital crunch in decades.

We eventually came to the conclusion that there was no viable path for the company but to use the runway we had left to sell to another entity that was more able to fund the ongoing development and marketing necessary to monetize T2.

While selling the company had always been a desirable exit strategy, we had hoped to do it after the launch and growth of T2. However, we could not afford to wait any longer. With some short-term bridge funding from our existing investors, we worked with Growth Point Technology Partners to sell the company.

We met with a number of the leading Internet and media companies and received numerous offers. In the end, the best and most strategically compatible offer came from Evri, one of our sibling companies in Vulcan Capital’s portfolio. While we had the option to sell to larger and more established companies with very compelling offers, it was simply the best option for our shareholders, and our team, and the Twine project, to join Evri.

And so we find ourselves at the present day. We got the best deal possible for our shareholders given the circumstances. Twine.com, my team, our users and their data are safe and sound. As an entrepreneur and CEO it is, as one advisor put it, of the utmost importance to always keep the company moving forward. I feel that I did manage to achieve this under extremely difficult economic circumstances. And for that I am grateful.

I am also positive about the future for Twine and Evri. I really do believe that Vulcan Capital and Evri have the vision, and the deep pockets, necessary to execute on the long-term vision of the Semantic Web and the work we have done so far at Twine. It’s the end of an era and the start of a new one, and I truly wish my team and the team at Evri great success going forward, and I’m glad to be a part of that as an advisor too.

Outlook for the Semantic Web

I’ve been one of the most outspoken advocates of the Semantic Web during my tenure at Twine. So what about my outlook for the Semantic Web now that Twine is being sold and I’m starting to do other things? Do I still believe in the promise of the Semantic Web vision? Where is it going? These are questions I expect to be asked, so I will attempt to answer them here.

I continue to believe in the promise of semantic technologies, and in particular the approach of the W3C semantic web standards (RDF, OWL, SPARQL). That said, having tried to bring them to market as hard as anyone ever has, I can truly say they present significant challenges both to developers and to end-users. These challenges all stem from one underlying problem: Data storage.

Existing SQL databases are not optimal for large-scale, high-performance semantic data storage and retrieval. Yet triplestores are still not ready for prime-time. New graph databases and column stores show a lot of promise, but they are still only beginning to emerge. This situation makes it incredibly difficult to bring Web-scale semantic applications to market cost-effectively.

Enterprise semantic applications are much more feasible today however — because existing and emerging databases and semantic storage solutions do scale to enterprise levels. But for consumer-grade, enormous, Web services, there are still challenges. This is single greatest technical obstacle that Twine faced and it cost us a large amount of our venture funding to surmount. Finally we did find a solution with our T2 architecture, but it is still not a general solution for all types of applications.

I have recently seen some new graph data storage products that may provide the levels of scale and performance needed, but pricing has not been determined yet. In short, storage and retrieval of semantic graph datasets is a big unsolved challenge that is holding back the entire industry. We need federated database systems that can handle hundreds of billions to trillions of triples under high load conditions, in the cloud, on commodity hardware and open source software. Only then will it be affordable to make semantic applications and services at Web-scale.

I believe that semantic metadata is essential for the growth and evolution of the Web. It is one of the only ways we can hope to dig out from the increasing problem of information overload. It is one of the only ways to make search, discovery, and collaboration smart enough to really be significantly better than it is today.

But the notion that everyone will learn and adopt standards for creating this metadata themselves is flawed in my opinion. They won’t. Instead, we must focus on solutions (like Twine and Evri) that make this metadata automatically by analyzing content semantically. I believe this is the most practical approach to bringing the value of semantic search and discovery to consumers, as well as Webmasters and content providers around the Web.

The major search engines are all working on various forms of semantic search, but to my knowledge none of them are fully supporting the W3C standards for the Semantic Web. In some cases this is because they are attempting to co-opt the standards for their own competitive advantage, and in other cases it is because it is simply easier not to use them. But in taking the easier path, they are giving up the long-term potential gains of a truly open and interoperable semantic ecosystem.

I do believe that whoever enables this open semantic ecosystem first will win in the end — because it will have greater and faster network effects than any closed competing system. That is the promise and beauty of open standards: everyone can feel safe using them since no single commercial interest controls them. At least that’s the vision I see for the Semantic Web.

As far as where the Semantic Web will add the most value in years to come, I think we will see it appear in some new areas. First and foremost is e-commerce, an area that is ripe with structured data that needs to be normalized, integrated and made more searchable. This is perhaps the most potentially profitable and immediately useful application of semantic technologies. It’s also one where there has been very little innovation. But imagine if eBay or Amazon or Salesforce.com provided open-standards-compliant semantic metadata and semantic search across all their data.

Another important opportunity is search and SEO — these are the areas that Twine’s T2 project focused on, by enabling webmasters to easily and semi-automatically add semantic descriptions of their content into search indexes, without forcing them to learn RDF and OWL and do it manually. This would create a better SEO ecosystem and would be beneficial not only to content providers and search engines, but also to advertisers. This is the approach that I believe the major search engines should take.

Another area where semantics could add a lot of value is social media — by providing semantic descriptions of user profiles and user profile data, as well as social relationships on the Web, it would be possible to integrate and search across all social networks in a unified manner.

Finally, another area where semantics will be beneficial is to enable easier integration of datasets and applications around the Web — currently every database is a separate island, but by using the Semantic Web appropriately data can be freed from databases and easily reused, remixed and repurposed by other applications. I look forward to the promise of a truly open data layer on the Web, when the Web becomes essentially one big open database that all applications can use.

Lessons Learned and Advice for Startups

While the outcome for Twine was decent under the circumstances, and was certainly far better than the alternative of simply running out of money, I do wonder how it could have been different. I ask myself what I learned and what I would do differently if I had the chance or could go back in time.

I think the most important lessons I learned, and the advice that I would give to other entrepreneurs can be summarized with a few key points:

1. Raise as little venture capital as possible. Raise less than you need, not more than you need. Don’t raise extra capital just because it is available. Later on it will make it harder to raise further capital when you really need it. If you can avoid raising venture capital at all, do so. It comes with many strings attached. Angel funding is far preferable. But best of all, self-fund from revenues as early as you can, if possible. If you must raise venture capital, raise as little as you can get by on — even if they offer you more. But make sure you have at least enough to reach your next funding round — and assume that it will take twice as long to close as you think. It is no easy task to get a startup funded and launched in this economy — the odds are not in your favor — so play defense, not offense, until conditions improve (years from now).
2. Build for lower exits. Design your business model and capital strategy so that you can deliver a good ROI to your investors at an exit under $30mm. Exit prices are going lower, not higher. There is less competition and fewer buyers and they know it’s a buyer’s market. So make sure your capital strategy gives the option to sell in lower price ranges. If you raise too much you create a situation where you either have to sell at a loss, or raise even more funding which only makes the exit goal that much harder to reach.
3. Spend less. Spend less than you want to, less than you need to, and less than you can. When you are flush with capital it is tempting to spend it and grow aggressively, but don’t. Assume the market will crash — downturns are more frequent and last longer than they used to. Expect that. Plan on it. And make sure you keep enough capital in reserve to spend 9 to 12 months raising your next round, because that is how long it takes in this economy to get a round done.
4. Reach cash flow breakeven for $5mm or less. If at all possible, design your model to reach breakeven for $2mm. Gone is the day when a tech startup could spend tens of millions over 3 years to reach breakeven. Venture capitalists have no appetite for that kind of model anymore. It’s not that the model is bad — it works, if the funding is available. But it’s not. VC’s are simply not willing to make those kinds of bets anymore because there are fewer opportunities for exits (the IPO market is dead and there are fewer acquirers and acquisitions are at lower prices). Focus on developing a repeatable model and then replicate it. Only start spending big money once you your a repeatable revenue machine actually working.
5. Don’t rely on user-traction to raise funding. You cannot assume that user traction is enough to get your next round done. Even millions of users and exponential growth are not enough. VC’s and their investment committees want to see revenues, and particularly at least breakeven revenues. A large service that isn’t bringing in revenues yet is not a business, it’s an experiment. Perhaps it’s one that someone will buy, but if you can’t find a buyer then what? Don’t assume that VC’s will fund it. They won’t. Venture capital investing has changed dramatically — early stage and late stage deals are the only deals that are getting real funding. Mid-stage companies are simply left to die, unless they are profitable or will soon be profitable.
6. Don’t be the long-term CEO. It seems like being the CEO is the best job, but actually it’s the worst, especially if you raise venture capital. If you are an entrepreneur or founder, do yourself a favor and design a plan that includes bringing in a great operator and management team to run the business for you. As a founder/entrepreneur it is almost certainly the case that you are not the one to operate and grow the company later on. That takes a completely different kind of personality and skillset than you probably have. If you are someone who thinks of great ideas, invents stuff, starts projects, and incubates companies you are almost certainly not the one who should be the long-term CEO as your company gets larger. You need someone who is operational and financially focused, and someone who is not attached to the ideas. It is better for you, and for your team and shareholders to bring in a pro once you have more than 20 people on staff. Take control of this choice and lead the process rather than having others lead it for you. Be the CEO only in the early stages (the first few years, up to first 20 employees, and first revenues) of your business. VC’s who are reading this would also do well to come up with a new model where founders were rewarded for bringing in CEO’s, instead of being penalized for doing so — this transition could be positive, if only people would think outside of the box. The current model is that Founder CEO’s lose their investments and future upside if they bring in professional CEO’s, and this leads to unnecessary friction between venture investors and management teams. It would be easy to fix this however, with some creative incentive plans. VC’s must recognize that founders are the DNA and need to remain involved; they should continue to have strong equity upside even if they bring in operator CEO’s later on.
7. Don’t be afraid to downsize when you have to. It sucks to fire people, but it’s sometimes simply necessary. One of the worst mistakes is to not fire people who should be fired, or to not do layoffs when the business needs require it. You lose credibility as a leader if you don’t act decisively. Often friendships and personal loyalties prevent or delay leaders from firing people that really should be fired. While friendship and loyalty are noble they unfortunately are not always the best thing for the business. It’s better for everyone to take their medicine sooner rather than later. Your team knows who should be fired. Your team knows when layoffs are needed. Ask them. Then do it. If you don’t feel comfortable firing people, or you can’t do it, or you don’t do it when you need to, don’t be the CEO.
8. Develop cheaply, but still pay market salaries. Use offshore development resources, or locate your engineering team outside of the main “tech hub” cities. It is simply too expensive to compete with large public and private tech companies to pay top dollar for engineering talent in places like San Francisco and Silicon Valley.  The cost of top-level engineers is too high in major cities to be affordable and the competition to hire and retain them is intense. If you can get engineers to work for free or for half price then perhaps you can do it, but I believe you get what you pay for. So rather thank skimp on salaries, pay people market salaries, but do it where market salaries are more affordable.
9. Only innovate on one frontier at a time. For example, either innovate by making a new platform, or a new application, or a new business model. Don’t do all of these at once, it’s just too hard. If you want to make a new platform, just focus on that, don’t try to make an application too. If you want to make a new application, use an existing platform rather than also building a platform for it. If you want to make a new business model, use an existing application and platform — they can be ones you have built in the past, but don’t attempt to do it all at once. If you must do all three, do them sequentially, and make sure you can hit cash flow breakeven at each stage, with each one. Otherwise you’re at risk in this economy.
10. Make sure to build a great board of directors. And make sure to fill your outside board seats. A board that is comprised only of investors and management simply cannot think as well as a board that has outside perspectives. But at the same time, make sure to keep your board small, too. And do not have monthly board meetings if you can avoid it — they are a huge distraction to management teams. Meet every 2 months, or quarterly instead. If your investors insist on monthly reporting then send them reports and have brief calls monthly, but don’t do enormous board meetings with lots of prep every month, it takes too much time away from the real work at hand..

These are some key mistakes to avoid, and learn from. I wish I had really taken these to heart years ago, but sometimes you have to learn by doing. I hope that this advice is of some use to entrepreneurs (and VC’s) who are reading this. I’ve personally made all these mistakes myself, so I am speaking from experience. Hopefully I can spare you the trouble of having to learn these lessons the hard way.

What we did Well

I’ve spent considerable time in this article focusing on what didn’t go according to plan, and the mistakes we’ve learned from. But it’s also important to point out what we did right. I’m proud of the fact that Twine accomplished many milestones, including:

• Pioneering the Semantic Web and leading the charge to make it a mainstream topic of conversation.
• Creating the most powerful, developer friendly, platform for the Semantic Web.
• Successfully completing our work on CALO, the largest Semantic Web project in the US.
• Launching the first mainstream consumer application of Semantic Web.
• Having a very successful launch, covered by hundreds of articles.
• Gaining users extremely rapidly — faster than Twitter did in it’s early years.
• Hiring and retaining an incredible team of industry veterans.
• Raising nearly $24mm of venture capital over 2 rounds, because our plan was so promising.
• Developing more than 20 patents, several of which are fundamentally important for the Semantic Web field.
• Surviving two major economic bubbles and the downturns that followed.
• Innovating and most of all, adapting to change rapidly.
• Breaking through to T2 — a truly awesome technological innovation for Web-scale semantic search.
• Selling the company in one of the most difficult economic environments in history.

I am proud of what we accomplished with Twine. It’s been “a long strange trip” but one that has been full of excitement and accomplishments to remember.

Conclusions

If you’ve actually read this far, thank you. This is a big article, but after all, Twine is a big project – One that lasted nearly 5 years (or 9 years if you include our original research phase). I’m still bullish on the Semantic Web, and genuinely very enthusiastic about what Evri will do with Twine.com going forward.

Again I want to thank the hundreds of people who have helped make Twine possible over the years – but in particular the members of our technical and management team who went far beyond the call of duty to get us to the deal we have reached with Evri.

While this is certainly the end of an era, I believe that this story has only just begun. The first chapters are complete and now we are moving into a new era. Much work remains to be done and there are certainly still challenges and unknowns, but progress continues and the Semantic Web is here to stay.

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The metacortex is the Web — our growing global network of information, people, sensors, and computing devices.

The Web is literally a new layer of the human brain that transcends any individual brain. It is a global brain that connects all our brains together. It is intelligent. It is perhaps humanity’s greatest invention.  It collectively senses, reacts, interprets, learns, thinks, and acts in ways that we as individuals can barely comprehend or predict, and this activity comprises an emerging global mind.

Paul Buchheit (creator of Gmail and Friendfeed) calls this “the social brain” — with emphasis on the social networks and collective social interactions that are taking place. I think that while the metacortex includes the social Web, it transcends it — it’s collective knowledge and cognition include all of the activity taking place on the Internet.

Does the metacortex mirror the structure and process of the neocortex? What can we learn about the neocortex from the metacortex and vice versa? What are the functional areas or lobes of the metacortex? I look forward to your comments.

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2. Watch My best Talk: The Global Brain is Coming I’ve posted a link to a video of my best...
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In Part-One of this article on TechCrunch, I covered the emerging paradigm of Virtual Assistants and explored a first look at a new product in this category called Siri. In this article, Part-Two, I interview Tom Gruber, CTO of Siri, about the history, key ideas, and technical foundations of the product:

Nova Spivack: Can you give me a more precise definition of a Virtual Assistant?

Tom Gruber: A virtual personal assistant is a software system that

• Helps the user find or do something (focus on tasks, rather than information)
• Understands the user’s intent (interpreting language) and context (location, schedule, history)
• Works on the user’s behalf, orchestrating multiple services and information sources to help complete the task

In other words, an assistant helps me do things by understanding me and working for me. This may seem quite general, but it is a fundamental shift from the way the Internet works today. Portals, search engines, and web sites are helpful but they don’t do things for me – I have to use them as tools to do something, and I have to adapt to their ways of taking input.

Nova Spivack: Siri is hoping to kick-start the revival of the Virtual Assistant category, for the Web. This is an idea which has a rich history. What are some of the past examples that have influenced your thinking?

Tom Gruber: The idea of interacting with a computer via a conversational interface with an assistant has excited the imagination for some time. Apple’s famous Knowledge Navigator video offered a compelling vision, in which a talking head agent helped a professional deal with schedules and access information on the net. The late Michael Dertouzos, head of MIT’s Computer Science Lab, wrote convincingly about the assistant metaphor as the natural way to interact with computers in his book “The Unfinished Revolution: Human-Centered Computers and What They Can Do For Us”. These accounts of the future say that you should be able to talk to your computer in your own words, saying what you want to do, with the computer talking back to ask clarifying questions and explain results. These are hallmarks of the Siri assistant. Some of the elements of these visions are beyond what Siri does, such as general reasoning about science in the Knowledge Navigator. Or self-awareness a la Singularity. But Siri is the real thing, using real AI technology, just made very practical on a small set of domains. The breakthrough is to bring this vision to a mainstream market, taking maximum advantage of the mobile context and internet service ecosystems.

Nova Spivack: Tell me about the CALO project, that Siri spun out from. (Disclosure: my company, Radar Networks, consulted to SRI in the early days on the CALO project, to provide assistance with Semantic Web development)

Tom Gruber: Siri has its roots in the DARPA CALO project (“Cognitive Agent that Learns and Organizes”) which was led by SRI. The goal of CALO was to develop AI technologies (dialog and natural language understanding,s understanding, machine learning, evidential and probabilistic reasoning, ontology and knowledge representation, planning, reasoning, service delegation) all integrated into a virtual assistant that helps people do things. It pushed the limits on machine learning and speech, and also showed the technical feasibility of a task-focused virtual assistant that uses knowledge of user context and multiple sources to help solve problems.

Siri is integrating, commercializing, scaling, and applying these technologies to a consumer-focused virtual assistant. Siri was under development for several years during and after the CALO project at SRI. It was designed as an independent architecture, tightly integrating the best ideas from CALO but free of the constraints of a national distributed research project. The Siri.com team has been evolving and hardening the technology since January 2008.

Nova Spivack: What are primary aspects of Siri that you would say are “novel”?

Tom Gruber: The demands of the consumer internet focus — instant usability and robust interaction with the evolving web — has driven us to come up with some new innovations:

• A conversational interface that combines the best of speech and semantic language understanding with an interactive dialog that helps guide people toward saying what they want to do and getting it done. The conversational interface allows for much more interactivity that one-shot search style interfaces, which aids usability and improves intent understanding. For example, if Siri didn’t quite hear what you said, or isn’t sure what you meant, it can ask for clarifying information. For example, it can prompt on ambiguity: did you mean pizza restaurants in Chicago or Chicago-style pizza places near you? It can also make reasonable guesses based on context. Walking around with the phone at lunchtime, if the speech interpretation comes back with something garbled about food you probably meant “places to eat near my current location”. If this assumption isn’t right, it is easy to correct in a conversation.
• Semantic auto-complete – a combination of the familiar “autocomplete” interface of search boxes with a semantic and linguistic model of what might be worth saying. The so-called “semantic completion” makes it possible to rapidly state complex requests (Italian restaurants in the SOMA neighborhood of San Francisco that have tables available tonight) with just a few clicks. It’s sort of like the power of faceted search a la Kayak, but packaged in a clever command line style interface that works in small form factor and low bandwidth environments.
• Service delegation – Siri is particularly deep in technology for operationalizing a user’s intent into computational form, dispatching to multiple, heterogeneous services, gathering and integrating results, and presenting them back to the user as a set of solutions to their request. In a restaurant selection task, for instance, Siri combines information from many different sources (local business directories, geospatial databases, restaurant guides, restaurant review sources, online reservation services, and the user’s own favorites) to show a set of candidates that meet the intent expressed in the user’s natural language request.

Nova Spivack: Why do you think Siri will succeed when other AI-inspired projects have failed to meet expectations?

Tom Gruber: In general my answer is that Siri is more focused. We can break this down into three areas of focus:

• Task focus. Siri is very focused on a bounded set of specific human tasks, like finding something to do, going out with friends, and getting around town. This task focus allows it to have a very rich model of its domain of competence, which makes everything more tractable from language understanding to reasoning to service invocation and results presentation
• Structured data focus. The kinds of tasks that Siri is particularly good at involve semistructured data, usually on tasks involving multiple criteria and drawing from multiple sources. For example, to help find a place to eat, user preferences for cuisine, price range, location, or even specific food items come into play. Combining results from multiple sources requires reasoning about domain entity identity and the relative capabilities of different information providers. These are hard problems of semantic information processing and integration that are difficult but feasible today using the latest AI technologies.
• Architecture focus. Siri is built from deep experience in integrating multiple advanced technologies into a platform designed expressly for virtual assistants. Siri co-founder Adam Cheyer was chief architect of the CALO project, and has applied a career of experience to design the platform of the Siri product. Leading the CALO project taught him a lot about what works and doesn’t when applying AI to build a virtual assistant. Adam and I also have rather unique experience in combining AI with intelligent interfaces and web-scale knowledge integration. The result is a “pure play” dedicated architecture for virtual assistants, integrating all the components of intent understanding, service delegation, and dialog flow management. We have avoided the need to solve general AI problems by concentrating on only what is needed for a virtual assistant, and have chosen to begin with a finite set of vertical domains serving mobile use cases.

Nova Spivack: Why did you design Siri primarily for mobile devices, rather than Web browsers in general?

Tom Gruber: Rather than trying to be like a search engine to all the world’s information, Siri is going after mobile use cases where deep models of context (place, time, personal history) and limited form factors magnify the power of an intelligent interface. The smaller the form factor, the more mobile the context, the more limited the bandwidth : the more it is important that the interface make intelligent use of the user’s attention and the resources at hand. In other words, “smaller needs to be smarter.” And the benefits of being offered just the right level of detail or being prompted with just the right questions can make the difference between task completion or failure. When you are on the go, you just don’t have time to wade through pages of links and disjoint interfaces, many of which are not suitable to mobile at all.

Nova Spivack: What language and platform is Siri written in?

Tom Gruber: Java, Javascript, and Objective C (for the iPhone)

Nova Spivack: What about the Semantic Web? Is Siri built with Semantic Web open-standards such as RDF and OWL, Sparql?

Tom Gruber: No, we connect to partners on the web using structured APIs, some of which do use the Semantic Web standards. A site that exposes RDF usually has an API that is easy to deal with, which makes our life easier. For instance, we use geonames.org as one of our geospatial information sources. It is a full-on Semantic Web endpoint, and that makes it easy to deal with. The more the API declares its data model, the more automated we can make our coupling to it.

Nova Spivack: Siri seems smart, at least about the kinds of tasks it was designed for. How is the knowledge represented in Siri – is it an ontology or something else?

Tom Gruber: Siri’s knowledge is represented in a unified modeling system that combines ontologies, inference networks, pattern matching agents, dictionaries, and dialog models. As much as possible we represent things declaratively (i.e., as data in models, not lines of code). This is a tried and true best practice for complex AI systems. This makes the whole system more robust and scalable, and the development process more agile. It also helps with reasoning and learning, since Siri can look at what it knows and think about similarities and generalizations at a semantic level.

Nova Spivack: Will Siri be part of the Semantic Web, or at least the open linked data Web (by making open API’s, sharing of linked data, RDF, available, etc.)?

Tom Gruber: Siri isn’t a source of data, so it doesn’t expose data using Semantic Web standards. In the Semantic Web ecosystem, it is doing something like the vision of a semantic desktop – an intelligent interface that knows about user needs and sources of information to meet those needs, and intermediates. The original Semantic Web article in Scientific American included use cases that an assistant would do (check calendars, look for things based on multiple structured criteria, route planning, etc.). The Semantic Web vision focused on exposing the structured data, but it assumes APIs that can do transactions on the data. For example, if a virtual assistant wants to schedule a dinner it needs more than the information about the free/busy schedules of participants, it needs API access to their calendars with appropriate credentials, ways of communicating with the participants via APIs to their email/sms/phone, and so forth. Siri is building on the ecosystem of APIs, which are better if they declare the meaning of the data in and out via ontologies. That is the original purpose of ontologies-as-specification that I promoted in the 1990s – to help specify how to interact with these agents via knowledge-level APIs.

Siri does, however, benefit greatly from standards for talking about space and time, identity (of people, places, and things), and authentication. As I called for in my Semantic Web talk in 2007, there is no reason we should be string matching on city names, business names, user names, etc.

All players near the user in the ecommerce value chain get better when the information that the users need can be unambiguously identified, compared, and combined. Legitimate service providers on the supply end of the value chain also benefit, because structured data is harder to scam than text. So if some service provider offers a multi-criteria decision making service, say, to help make a product purchase in some domain, it is much easier to do fraud detection when the product instances, features, prices, and transaction availability information are all structured data.

Nova Spivack: Siri appears to be able to handle requests in natural language. How good is the natural language processing (NLP) behind it? How have you made it better than other NLP?

Tom Gruber: Siri’s top line measure of success is task completion (not relevance). A subtask is intent recognition, and subtask of that is NLP. Speech is another element, which couples to NLP and adds its own issues. In this context, Siri’s NLP is “pretty darn good” — if the user is talking about something in Siri’s domains of competence, its intent understanding is right the vast majority of the time, even in the face of noise from speech, single finger typing, and bad habits from too much keywordese. All NLP is tuned for some class of natural language, and Siri’s is tuned for things that people might want to say when talking to a virtual assistant on their phone. We evaluate against a corpus, but I don’t know how it would compare to standard message and news corpuses using by the NLP research community.

Nova Spivack: Did you develop your own speech interface, or are you using third-party system for that? How good is it? Is it battle-tested?

Tom Gruber: We use third party speech systems, and are architected so we can swap them out and experiment. The one we are currently using has millions of users and continuously updates its models based on usage.

Nova Spivack: Will Siri be able to talk back to users at any point?

Tom Gruber: It could use speech synthesis for output, for the appropriate contexts. I have a long standing interest in this, as my early graduate work was in communication prosthesis. In the current mobile internet world, however, iPhone-sized screens and 3G networks make it possible to do so more much than read menu items over the phone. For the blind, embedded appliances, and other applications it would make sense to give Siri voice output.

Nova Spivack: Can you give me more examples of how the NLP in Siri works?

Tom Gruber: Sure, here’s an example, published in the Technology Review, that illustrates what’s going on in a typical dialogue with Siri. (Click link to view the table)

Nova Spivack: How personalized does Siri get – will it recommend different things to me depending on where I am when I ask, and/or what I’ve done in the past? Does it learn?

Tom Gruber: Siri does learn in simple ways today, and it will get more sophisticated with time. As you said, Siri is already personalized based on immediate context, conversational history, and personal information such as where you live. Siri doesn’t forget things from request to request, as do stateless systems like search engines. It always considers the user model along with the domain and task models when coming up with results. The evolution in learning comes as users have a history with Siri, which gives it a chance to make some generalizations about preferences. There is a natural progression with virtual assistants from doing exactly what they are asked, to making recommendations based on assumptions about intent and preference. That is the curve we will explore with experience.

Nova Spivack: How does Siri know what is in various external services – are you mining and doing extraction on their data, or is it all just real-time API calls?

Tom Gruber: For its current domains Siri uses dozens of APIs, and connects to them in both realtime access and batch data synchronization modes. Siri knows about the data because we (humans) explicitly model what is in those sources. With declarative representations of data and API capabilities, Siri can reason about the various capabilities of its sources at run time to figure out which combination would best serve the current user request. For sources that do not have nice APIs or expose data using standards like the Semantic Web, we can draw on a value chain of players that do extract structure by data mining and exposing APIs via scraping.

Nova Spivack: Thank you for the information, Siri might actually make me like the iPhone enough to start using one again.

Tom Gruber: Thank you, Nova, it’s a pleasure to discuss this with someone who really gets the technology and larger issues. I hope Siri does get you to use that iPhone again. But remember, Siri is just starting out and will sometimes say silly things. It’s easy to project intelligence onto an assistant, but Siri isn’t going to pass the Turing Test. It’s just a simpler, smarter way to do what you already want to do. It will be interesting to see how this space evolves, how people will come to understand what to expect from the little personal assistant in their pocket.

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This is Part II of my article “The Global Brain is About to Wake Up” — about the  realtime Web and how it relates to the emerging Global Brain.

Here I focus mainly on thorny philosophical and scientific speculations about the nature of consciousness, the role it plays in the universe, and whether or not the Web can ever be said to be conscious in its own right. Beware — this content may not be of interest to most of my readers. It’s certainly in the “wild speculation” category.

Will the Web Become Conscious?

As the realtime Web gets faster and richer, it will begin to appear to be more cohesive and collectively intelligent. It will begin to appear like an actual, unified Global Brain, rather than just a crowd. Instead of being just a collection of interacting parts we will be able to see it as a functioning whole — a kind of entity in its own right. We will also be able to see this collective “entitiness” emerge for subsets of the whole Web? For example will nations, organizations, markets, industries, enterprises, workgroups and teams start to seem more intelligent? The Web will get smarter and faster, at every level of collective cognition but will it ever actually become conscious?

Yes and no.

It will become collectively more intelligent, and the consciousnesses of individuals around the Web will be more connected and potentially synchronized. But the Web itself won’t actually have it’s own new consciousness, unique from the consciousnesses of the people who participate in it. Still it will seem more conscious than it was before, simply by virtue of the human consciousnesses within it being more connected and focused.

I don’t think the Web will actually develop or have its own meta-level consciousness however. It won’t evolve some new form of Web-scale consciousness that is totally separate from the individual consciousness of the people on the Web.  A Web-scale sentient entity that is unique and separate from the humans minds on the Web will never exist. That will never happen. Instead, the Web as a whole will evolve to better utilize the human consciousness that is already present within it — the consciousness that we human beings already have.

The Irreplaceable Role of Humans in the Web

As conscious entities, we humans play a unique and irreplaceable role in the realtime Web and the Global Brain.  We provide the only consciousness the Web will ever have. Machines may be able to sense and measure what is going on, and even make sense of it for us in ways that transcend the abilities of the individual human brain, but they won’t be able to be conscious of what is going on the way that we humans can be.

We human beings are the consciousness of the Web — that is our special role. No machine or set of machines can replicate consciousness, not even the entire Web as a single machine. However there is a distinction to be made between consciousness and intelligence.

Machines can certainly be made to be intelligent, and that applies even to the entire Web as a machine as well. The Web is getting more intelligent, and as this happens it is becoming our Global Brain. But it’s not becoming more conscious.  Rather, we humans are becoming more conscious of the Web and what is going on within it. Humans are becoming able to be more conscious of the Web, but the Web itself is not becoming conscious at all, let alone more conscious. This is a key point to keep in mind.

Until recently humans have been watching the Web in slow motion. We can only see small glimpses at a time. The individual human brain cannot comprehend the vast patterns that are taking place on the Web, and there are few software tools that can make sense of them for us either. It’s just too big and complex a system, and the patterns which comprise its collective thoughts — the thoughts of the Global Brain — are too spread out in time and space.

We humans are barely able to be lucidly conscious in our little nows — which are really just spans of a few square meters, and a few minutes, at a time — but the collectively intelligent processes and patterns out on the Web cover thousands of miles and can span days, weeks, months or even years. They just don’t fit in our little human nows. The solution is to find a way to visualize them so we can digest them in our little nows. That’s the only practical approach — unless someone figures out how to expand the individual human now.

Fortunately, there are several trends that are going help with this process. As the Web gets faster, processes that used to take too long for us to follow them will become short enough for individuals to watch them play out in reasonable timespans, without getting lost or overwhelmed. The collective thoughts of the Web are starting to happen fast enough for our human minds to see them emerge, change, and interact on our human timescale of minutes and hours. Instead of watching memes develop and spread on the Web as if in slow-motion, we are starting to see and measure them in our timescale, at our speed.

In addition as the Web gets more computationally powerful — computers and software will be able to help us see what is going on beyond the limits of our human nows — larger volumes of data changing over larger spans of time than we can grasp on our own. This too will help to compress and visualize patterns and processes that were previously beyond our comprehension in ways that we can make sense of as individual human observers with our small brains and short nows.

Both of these trends will enable individual human minds to comprehend larger and more complex processes and patterns within the Web. And as individuals become able to be conscious of larger and more complex patterns taking place within the Web, they will be able to react and adapt to those patterns in their own individual behavior. This feedback loop will give rise to increasingly intelligent collective adaptation and behavior. And thus the Web as a whole — the Global Brain that includes humans, machines, software, and all our infrastructure — will appear to become increasingly smart.

Humans drive this process by simply being conscious observers of the Web, and by making intelligent decisions, adding content and taking actions online. But we’re not the only ones. Software will also play a role in this — adding intelligence and content, but not consciousness, to the process.

How Important is Consciousness Anyway?

But how important is human consciousness to the Web, and the Global Brain? One might wonder whether human consciousness really matters in all this, or whether it’s enough just to have non-conscious but intelligent machines?

Would the Global Brain be different without humans there to witness it? If there were no humans in it, but just non-conscious artificially intelligent software that simply follows rules or uses statistics and algorithms — would the Global Brain be more or less conscious or intelligent?

This is actually an absurd question. Without humans there would not be a Web, let alone a Global Brain. But let’s just suspend that for a moment and ask the question in a different way. Suppose that at some time in the distant future, all humans die, but the Web remains. Would the Web still contain any consciousness on it’s own?

I think the answer is no. This ultimately goes back to John Searle’s concept of Qualia. In a nutshell, there is nothing on the Web, apart from humans, that is capable of experiencing qualia — the actual knowing of any experience. So there is nothing on the Web that is capable of being conscious, apart from the humans who participate in it. Without the humans, there could be no consciousness in or on the Web.

There  is a difference between being conscious of the qualia of something, and simply measuring data about something. Qualia is special — as strange and potentially hocus-pocus at that may sound. I just don’t believe qualia is something that can be synthesized in a machine or by any algorithm. Being conscious of the Web is not the same as simply measuring data flows. I believe there is a distinct quality of “knowing” or “being aware” that is the hallmark of actual consciousness and which simply cannot be synthesized in a computer.

From what I can tell, qualia is something unique to being sentient, in other words, aware. And awareness is something special as far as I can tell — I think it might be fundamental like space and time, not something we can create or synthesize, and not something emergent. Again this just my opinion — but I think it’s a defensible one.

The Unexplainableness of Consciousness

I’ve spent decades thinking about the question of consciousness, and whether machines can ever be conscious, and I have never found it plausible to make conscious machines.

Quite the contrary — the more I have examined this question, the more clear it has become to me that consciousness is special — it is something that simply cannot even be described, and literally cannot be found — yet it is undeniably taking place. Ontologically consciousness is similar to space and time — we cannot find space or time, we cannot isolate them or grasp their substance, yet they are undeniably taking place. Consciousness seems to be just like that. Unexplainable, yet undeniable.

I’m something of a mystic with regard to consciousness — but not in a blind way. I’ve come to this view only after really trying to avoid it — through very thorough and painstaking investigation from just about every perspective on it — neuroscience, cognitive science, artificial intelligence, linguistics, philosophy, physics, cosmology, and religion and spirituality.

Consciousness appears finally to be something we just cannot explain, let alone synthesize, and I’d be willing to bet that it’s always going to be beyond our reach. In fact I have made such a bet at the Long Bets project: You can read more about this in my article, “Why Machines Will Never be Conscious.”

Given that I view consciousness as something primordial and beyond physics, from my perspective at least, I don’t think we can manufacture it. I also doubt it will simply magically emerge on the Web, apart from individual human minds.

Consciousness is Neither Emergent Nor Reducible.

But wait. Certainly there is a case to be made that if consciousness can emerge within the human brain, then why not within the Web? The human brain is essentially a more complex Web after all. Why is one kind of Web any more or less qualified to be conscious?

My present answer to this is that I don’t think consciousness ever emerges through some physical process — it’s never created or destroyed, and even when said to be present it’s not “there” like other kinds of things. It doesn’t appear as something, it has no form, shape, color, etc. It cannot be found or grasped at all. It is similar to space in these respects.

Space is never created or destroyed — at least as far as we can tell from within this universe. Similarly, consciousness is never created or destroyed as far as we can tell as conscious observers. That’s just how the universe is — it’s a mystery that is bigger than us. We’ll never be able to comprehend it fully from inside it. Consciousness seems to have the same ontological status as space. The difference is that while space is inert, incapable of observing or knowing, consciousness seems to have a quality of knowing that is quite unique.

My point is actually that the human brain is NOT special. I don’t actually think consciousness comes from the brain or is inside the brain, or running like some kind of software on the hardware of the brain.

If consciousness were merely some physical phenomenon that depended on the brain, then it would be no problem to synthesize it, not just for AI but for the Web as a whole as well. But that’s not the case, in my opinion.

I don’t think consciousness is a material thing, nor is it an emergent phenomena. I think it’s fundamental to the nature of the universe — like space and time — or perhaps even more fundamental than space and time. We can’t create it. We’ll probably never fully understand it. It just is there from the start. It’s the very basis of the entire phenomena of the universe, it’s not merely something that evolves and emerges within the universe. Indeed, I would venture to state that without consciousness — at least in primordial form — no universes would even be possible or would ever arise.

In my view, material things like the physical universe and the human body and brain, emerge from consciousness, rather than consciousness emerging from material things. Consciousness, whatever it is, is primordial and fundamental. Whether or not you reify it as a fundamental first-cause or ultimate thing, or you take the Buddhist view that it is also empty of any entity or nature and therefore not a thing, it is still at least totally primordial.

This isn’t just speculation — it’s something that can both be observed and is entirely logical as well. For example, if you really look closely at what you or anyone can possibly ever observe, it appears this is the only tenable answer we can find. Why? Because we cannot observe anything prior to being conscious ourselves — consciousness is necessary to be an observer. We can’t even ask such questions if we are not conscious in the first place. Consciousness is assumed, and must already be there, as soon as we even start looking for it.

Furthermore, the example of dreams proves that incredibly real virtual worlds, entire universes, can indeed appear and take place within the sphere of an individual dreaming consciousness — and they are indistinguishable (while they occur) from waking experience. Dreaming illustrates the power and scope of consciousness — it shows that it is not absurd to think that the our own so-called waking experience could be appearing like a dream within our own fields of consciousness. Waking experience, like dreaming, happens within the sphere of consciousness. It’s impossible to have waking experience without being conscious.

We have no evidence of there being anything beyond the sphere of consciousness and we cannot possibly observe anything without resorting to consciousness in the process to make the observation. There is no way to logically establish that there exists anything beyond or before the scope of consciousness. Anything we attempt to prove or observer is mediated by our own observing consciousness.

For this reason, as far as I or anyone can ever discern, it is reasonable to posit that each of our unique perspectives — each of our minds — contains the universe from one perspective. It’s similar to a hologram — where each piece of the picture contains the whole picture from a different angle. In the case of consciousness, each individual consciousness is one unique perspective on the universe. And the universe itself cannot be found apart from all these conscious perspectives on it. It’s not “out there” as some separate physical thing that these consciousnesses are simply watching from afar — it is literally a manifestation of these consciousnesses, there is no duality between observer and what is observed at the quantum level.

All the evidence points to consciousness being prior to everything else. There is in fact no evidence that indicates otherwise. As a result I don’t believe consciousness is emergent or reducible. I don’t think it is created or destroyed. And even when present it is not actually findable, because it is basically an axiom of the system we are in. It’s primordial and so we cannot sense it or detect it, other than with consciousness itself. There’s nothing more fundamental to break it down into, or to compare or contrast it against.

Consciousness and the Quantum Substrate

From what I can discern so far, I believe that human consciousness — actual sentience, not simulated sentience — is fundamentally related to the fabric of space-time. It is woven right into the quantum substrate of reality.

At that level of reality there is not clear distinction between mind and matter, it’s some kind of whole that we barely understand. While computers may be able to simulate aspects of this, they do not actually interact directly with the quantum substrate the way that human consciousness does.

This is a big difference between machine minds and human minds: Human consciousness is directly connected to the fundamental quantum nature of the universe, and quite probably plays a role in creating or at least conditioning observed reality. Computer programs — no matter how sophisticated — are not connected to the quantum substrate in the same way — they are not capable of being true quantum observers.

There is at least some evidence for my view of consciousness: On a quantum level, observation and measurement seem to have an impact on what is actually found to occur. The observer affects the experiment. All forms of observation eventually seem to require a human — or equivalently conscious — observer at some point in the process — there’s no escaping that. Without such an observer, the universe remains in an indeterminate quantum state. So it appears that human consciousness — or at least authentic actual consciousness whether human or not — is required to cause the quantum field to actually crystallize into particular events.

On the other hand, there is no evidence that computers can ever be conscious; no evidence that synthetic sentient observers can be created, and even if we created them, there would be no way to prove that their powers of observation were equivalent to our own. Any observations they made of them would ultimately be observed by us humans, and so we would always be the final conscious observers in the chain.

On a quantum level, our observation of our machines, would cascade downwards, causing their observations of reality to have an effect. Without our observing them, machines would not be able to actually affect the quantum level of reality. And indeed it would be difficult to try to prove otherwise, because a human observer is necessary to observe any such proof or system we can devise (and in fact, quantum observer effects have been shown even to propagate backwards in time from a later act of observation to an earlier experiment). So there’s just no way to take human consciousness out of the loop.

We cannot prove that human consciousness isn’t necessary for our universe to appear. We cannot prove that machines can function as independent quantum observers, separate from human observation, and we probably cannot devise any experiment or device which could prove that therefore. There’ s really no evidence to suggest that machines could synthesize this function — all the evidence in fact says otherwise. And this applies by extension to the Web as a whole, and thus to the Global Brain.

As a result, I think human consciousnesses play an absolutely crucial role in the universe, the  Web, and in any eventual Global Brain or form of collective intelligence. Our consciousness is the only actual authentic consciousness in the system. And it plays an important and necessary role at a quantum level in shaping reality through quantum level acts of observation.

By the way, it’s worth noting that consciousness is not exclusively the domain of human beings — animals are also conscious for example. But human beings are at least the most intelligent conscious things that we know of, so I’m limiting this discussion of the Global Brain to humans. In any case, there is no substitute for actual consciousness. It can’t be synthesized. It comes only from humans. At best it can perhaps be aimed, funneled or maybe amplified.

But that doesn’t mean that machine intelligence won’t play a very important enabling and catalyzing role in making the Global Brain smarter. There’s a difference between consciousness and intelligence. In fact, machine intelligence is critical to the Global Brain waking up — because it makes the vast complexity of the Global Brain (in both space and time) comprehensible, digestible, and accessible to the individual human consciousnesses that observe it.

Although humans posses consciousness, our minds are limited in scope — we simply cannot see or make sense of patterns that are above a certain level of scale or complexity in space and time. We need help with that — and that’s where computers enter the story, with their vast abilities to calculate, sort, collate, correlate, and organize masses of data.

Computers essentially increase the scope of human consciousness, by enabling us to observe things and do computations that are beyond the abilities of the individual human brain. It is by making the vast patterns within the complex whole — the entire Web –  more visible and understandable to the observers within it — the human consciousnesses within it — that the Global Brain actually becomes smarter, more reflexively-aware, and more collectively conscious.

By connecting individual human consciousnesses to the vast intelligence and knowledge of the growing global computing network, we will get the best of both: a Global Brain that gets increasingly collectively aware and intelligent.

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This process of of quickening is part of a larger trend which I and others call “Nowism.” You can read more of my thoughts about Nowism here. Nowism is an orientation that is gaining momentum and will help to shape this decade, and in particular, how the Web unfolds. It is the idea that the present-timeframe (“the now”) is getting more important, shorter and also more information-rich. As this happens our civilization is becoming more focused on the now, and less focused on past or the future. Simply keeping up with the present is becoming an all-consuming challenge: Both a threat and an opportunity.

The realtime Web –  what I call “The Stream”  (see “Welcome to the Stream”) — is changing the unit of now. It’s making it shorter. The now is the span of time which we have to be aware of to be effective our work and lives, and it is getting shorter. On a personal level the now is getting shorter and denser — more information and change is packed into shorter spans of time; a single minute on Twitter is overflowing with potentially relevant messages and links. In business as well, the now is getting shorter and denser — it used to be about the size of a fiscal quarter, then it became a month, then a week, then a day, and now it is probably about half a day in span. Soon it will be just a few hours.

To keep up with what is going on we have to check in with the world in at least half-day chunks. Important news breaks about once or twice a day. Trends on Twitter take about a day to develop too. So basically, you can afford to just check  the news and the real-time Web once or twice a day and still get by. But that’s going to change.  As the now gets shorter, we’ll have to check in more frequently to keep abreast of change. As the Stream picks up speed in the middle of this decade, to remain competitive will require near-constant monitoring — we will have to always be connected to, and watching, the real-time Web and our personal streams. Being offline at all will risk missing out on big important trends, threats and opportunities that emerge and develop within minutes or hours. But nobody is capable of tracking the Stream all 24/7 — we must at least take breaks to eat and sleep. And this is a problem.

Big Changes to the Web Coming Soon…

With Nowism comes a faster Web, and this will lead to big changes in how we do various activities on the Web:

• We will spend less time searching. Nowism pushes us to find better alternatives to search, or to eliminate search entirely, because people don’t have time to search anymore. We need tools that do the searching for us and that help with decision support so we don’t have to spend so much of our scarce time doing that. See my article on “Eliminating the Need for Search — Help Engines” for more about that.
• Monitoring (not searching) the real-time stream becomes more important. We need to stay constantly vigilant about what’s happening, what’s trending. We need to be alerted of the important stuff (to us), and we need a way to filter out what’s not important to us. Probably a filter based on influence of people and tweets, and/or time dynamics of memes will be necessary. Monitoring the real-time stream effectively is different from searching it. I see more value in real-time monitoring than realtime search — I haven’t seen any monitoring tools for Twitter that are smart enough to give me just the content I want yet. There’s a real business opportunity there.
• The return of agents. Intelligent agents are going to come back. To monitor the realtime Web effectively each of us will need online intelligent agents that can help us — because we don’t have time, and even if we did, there’s just too much information to sift through.
• Influence becomes more important than relevance. Advertisers and marketers will look for the most influential parties (individuals or groups) on Twitter and other social media to connect with and work through. But to do this there has to be an effective way to measure influence. One service that’s providing a solution for this (which I’ve angel invested in and advise) is Klout.com – they measure influence per person per topic. I think that’s a good start.
• Filtering content by influence. We also will need a way to find the most influential content. Influential content could be the content most RT’d or most RT’d by most influential people. It would be much less noisy to be able to see only the more influential tweets of people I follow. If a tweet gets RT’d a lot, or is RT’d by really influential people, then I want to see it. If not, then only if it’s really important (based on some rule). This will be the only way to cope with the information overload of the real-time Web and keep up with it effectively. I don’t know of anyone providing a service for this yet. It’s a business opportunity.
• Nowness as a measure of value of content. We will need a new form of ranking of results by “nowness” – how timely they are now. So for example, in real-time search engines we shouldn’t rank results merely by how recent they are, but also by how timely, influential, and “hot” they are now. See my article from years ago on “A Physics of Ideas” for more about that. Real-time search companies should think of themselves as real-time monitoring companies — that’s what they are really going to be used for in the end. Only the real-time search ventures that think of themselves this way are going to survive the conceptual paradigm shift that the realtime Web is bringing about. In a realtime context, search is actually too late — once something has happened in the past it really is not that important anymore –what matters is current awareness: discovering the trends NOW. To do that one has to analyze the present, and the very recent past, much more than searching the longer term past. The focus has to be on real-time or near-real-time analytics, statistical analysis, topic and trend detection, prediction, filtering and alerting. Not search.
• New ways to understand and navigate the now. We will need a way to visualize and navigate the now. I’m helping to incubate a stealth startup venture, Live Matrix, that is working on that. It hasn’t launched yet. It’s cool stuff. More on that in the future when they launch.
• New tools for browsing the Stream. New tools will emerge for making the realtime Web more compelling and smarter. I’m working on incubating some new stealth startups in this area as well. They’re very early-stage so can’t say more about them yet.
• The merger of semantics with the realtime Web. We need to make the realtime Web semantic — as well as the rest of the Web — in order to make it easier for software to make sense of it for us. This is the best approach to increasing the signal-to-noise ratio of content we have to look at whether searching or monitoring stuff. The Semantic Web standars of the W3C are key to this. I’ve written a long manifesto on this in “Minding The Planet: The Meaning and Future of the Semantic Web” if you’re really interested in that topic.

Faster Leads to Smarter

As the realtime web unfolds and speeds up, I think it will also have a big impact on what some people call “The Global Brain.” The Global Brain has always existed, but in recent times it has been experiencing a series of major upgrades — particularly around how connected, affordable, accessible and fast it is. First we got phone and faxes, then the Internet, the PC and the Web, and now the real-time Web and the Semantic Web. All of these recent changes are making the Global Brain faster, more richly interconnected. And this makes it smarter. For more about my thoughts on the Global Brain, see these two talks:

• My detailed History and Future of the Global Brain given at the Singularity Summit.
• A talk on the emerging Global Brain and human-machine cybernetic superorganism, with specific focus on what it means for media companies, from the GRID Conference.

What’s most interesting to me is that as the rate of communication and messaging on the Web approaches near-real time, we may see a kind of phase change take place – a much smarter Global Brain will sort of begin to appear out of the chaos. In other words, the speed of collective thinking is as important to the complexity or sophistication of collective thinking, in making the Global Brain significantly more intelligent. In other words, I’m proposing that there is a sort of critical speed of collective thinking, before which the Global Brain seems like just a crowd of actors chaotically flocking around memes, and after which the Global Brain makes big leaps — instead of seeming like a chaotic crowd, it starts to look more like an organized group around certain activitities — it is able to respond to change faster, and optimize and even do things collectively more productively than a random crowd could.

This is kind of like film, or animation. When you watch a movie or animation you are really watching a rapid series of frames. This gives the illusion of there being cohesive, continuous characters, things and worlds in the movie — but really they aren’t there at all, it’s just an illusion — our brains put these scenes together and start to recognize and follow higher order patterns. A certain shape appears to maintain itself and move around relative to other shapes, and we name it with a certain label — but there isn’t really something there, let alone something moving or interacting — there are just frames flicking by rapidly . It turns out that after a critical frame rate (around 20 to 60 frames per second) the human brain stops seeing individual frames and starts seeing a continuous movie. When you start flipping pages fast enough it appears to be a coherent animation and then we start seeing things “moving within the sequence” of frames. In the same way, as the unit of time of (aka the speed) of the real-time Web increases, its behavior will start to seem more continuous and smarter — we won’t see separate chunks of time or messages, we’ll see intelligent continuous collective thinking and adaptation processes.

In other words, as the Web gets faster, we’ll start to see processes emerge within it that appear to be cohesive intelligent collective entities in their own right. There won’t really be any actual entities there that we can isolate, but when we watch the patterns on the Web it will appear as if such entities are there. This is basically what is happening at every level of scale — even in the real world. There really isn’t anything there that we can find — everything is divisible down to the quantum level and probably beyond — but over time our brains seem to recognize and label patterns as discrete “things.” This is what will happen across the Web as well. For example, a certain meme (such as a fad or a movement) may become a “thing” in it’s own right, a kind of entity that seemingly takes on a life of its own and seems to be doing something. Similarly certain groups or social networks or activities they engage in may seem to be intelligent entities in their own rights.

This is an illusion in that there really are no entities there, they are just collections of parts that themselves can be broken down into more parts, and no final entities can be found. However, nonethless, they will seem like intelligent entities when not analyzed in detail. In addition, the behavior of these chaotic systems may resist reduction — they may not even be understandable and their behavior may not be predictable through a purely reductionist approach — it may be that they react to their own internal state and their environments virtually in real-time, making it difficult to take a top-down or bottom-up view of what they are doing. In a realtime world, change happens in every direction.

As the Web gets faster, the patterns that are taking place across it will start to become more animated. Big processes that used to take months or years to happen will happen in minutes or hours. As this comes about we will begin to see larger patterns than before, and they will start to make more sense to us — they will emerge out of the mists of time so to speak, and become visible to us on our human timescale — the timescale of our human-level “now. As a result, we will become more aware of higher order dynamics taking place on the real-time Web, and we will begin to participate in and adapt to those dynamics, making those dynamics in turn even smarter. (For more on my thoughts about how the Global Brain gets smarter, see:  “How to Build the Global Mind.”)

See Part II: “Will The Web Become Conscious?” if you want to dig further into the thorny philosophical and scientific issues that this brings up…

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Instead of finding more ways to get people to search, or ways to make existing search experiences better, I am starting to think about how to reduce or  eliminate the need to search — by replacing it with something better.

People don’t search because they like to. They search because there is something else they are trying to accomplish. So search is in fact really just an inconvenience — a means-to-an-end that we have to struggle through to do in order to get to what we actually really want to accomplish. Search is “in the way” between intention and action. It’s an intermediary stepping stone. And perhaps there’s a better way to get to where we want to go than searching.

Searching is a boring and menial activity. Think about it. We have to cleverly invent and try pseudo-natural-language queries that don’t really express what we mean. We try many different queries until we get results that approximate what we’re looking for. We click on a bunch of results and check them out. Then we search some more. And then some more clicking. Then more searching. And we never know whether we’ve been comprehensive, or have even entered the best query, or looked at all the things we should have looked at to be thorough. It’s extremely hit or miss. And takes up a lot of time and energy. There must be a better way! And there is.

Instead of making search more bloated and more of a focus, the goal should really be get search out of the way.  To minimize the need to search, and to make any search that is necessary as productive as possible. The goal should be to get consumers to what they really want with the least amount of searching and the least amount of effort, with the greatest amount of confidence that the results are accurate and comprehensive. To satisfy these constraints one must NOT simply build a slightly better search engine!

Instead, I think there’s something else we need to be building entirely. I don’t know what to call it yet. It’s not a search engine. So what is it?

Bing’s term “decision engine” is pretty good, pretty close to it. But what they’ve actually released so far still looks and feels a lot like a search engine. But at least it’s pushing the envelope beyond what Google has done with search. And this is good for competition and for consumers. Bing is heading in the right direction by leveraging natural language, semantics, and structured data. But there’s still a long way to go to really move the needle significantly beyond Google to be able to win dominant market share.

For the last decade the search wars have been fought in battles around index size, keyword search relevancy, and ad targeting — But I think the new battle is going to be fought around semantic understanding, intelligent answers, personal assistance, and commerce affiliate fees. What’s coming next after search engines are things that function more like assistants and brokers.

Wolfram Alpha is an example of one approach to this trend. The folks at Wolfram Alpha call their system a “computational knowledge engine” because they use a knowledge base to compute and synthesize answers to various questions. It does a lot of the heavy lifting for you, going through various data, computing and comparing, and then synthesizes a concise answer.

There are also other approaches to getting or generating answers for people — for example, by doing what Aardvark does: referring people to experts who can answer their questions or help them. Expert referral, or expertise search, helps reduce the need for networking and makes networking more efficient. It also reduces the need for searching online — instead of searching for an answer, just ask an expert.

There’s also the semantic search approach — perhaps exemplified by my own Twine “T2″ project — which basically aims to improve the precision of search by helping you get to the right results faster, with less irrelevant noise. Other consumer facing semantic search projects of interest are Goby and Powerset (now part of Bing).

Still another approach is that of Siri, which is making an intelligent “task completion assistant” that helps you search for and accomplish things like “book a romantic dinner and a movie tonight.” In some ways Siri is a “do engine” not a “search engine.” Siri uses artificial intelligence to help you do things more productively. This is quite needed and will potentially be quite useful, especially on mobile devices.

All of these approaches and projects are promising. But I think the next frontier — the thing that is beyond search and removes the need for search is still a bit different — it is going to combine elements of all of the above approaches, with something new.

For a lack of a better term, I call this a “help engine.” A help engine proactively helps you with various kinds of needs, decisions, tasks, or goals you want to accomplish. And it does this by helping with an increasingly common and vexing problem: choice overload.

The biggest problem is that we have too many choices, and the number of choices keeps increasing exponentially. The Web and globalization have increased the number of choices that are within range for all of us, but the result has been overload. To make a good, well-researched, confident choice now requires a lot of investigation, comparisons, and thinking. It’s just becoming too much work.

For example, choosing a location for an event, or planning a trip itinerary, or choosing what medicine to take, deciding what product to buy, who to hire, what company to work for, what stock to invest in, what website to read about some topic. These kinds of activities require a lot of research, evaluations of choices, comparisons, testing, and thinking. A lot of clicking. And they also happen to be some of the most monetizable activities for search engines. Existing search engines like Google that make money from getting you to click on their pages as much as possible have no financial incentive to solve this problem — if they actually worked so well that consumers clicked less they would make less money.

I think the solution to what’s after search — the “next Google” so to speak — will come from outside the traditional search engine companies. Or at least it will be an upstart project within one of them that surprises everyone and doesn’t come from the main search teams within them. It’s really such a new direction from traditional search and will require some real thinking outside of the box.

I’ve been thinking about this a lot over the last month or two. It’s fascinating. What if there was a better way to help consumers with the activities they are trying to accomplish than search? If it existed it could actually replace search. It’s a Google-sized opportunity, and one which I don’t think Google is going to solve.

Search engines cause choice overload. That wasn’t the goal, but it is what has happened over time due to the growth of the Web and the explosion of choices that are visible, available, and accessible to us via the Web.

What we need now is not a search engine — it’s something that solves the problem created by search engines. For this reason, the next Google probably won’t be Google or a search engine at all.

I’m not advocating for artificial intelligence or anything that tries to replicate human reasoning, human understanding, or human knowledge. I’m actually thinking about something simpler. I think that it’s possible to use computers to provide consumers with extremely good, automated decision-support over the Web and the kinds of activities they engage in. Search engines are almost the most primitive form of decision support imaginable. I think we can do a lot better. And we have to.

People use search engines as a form of decision-support, because they don’t have a better alternative. And there are many places where decision support and help are needed: Shopping, travel, health, careers, personal finance, home improvement, and even across entertainment and lifestyle categories.

What if there was a way to provide this kind of personal decision-support — this kind of help — with an entirely different user experience than search engines provide today? I think there is. And I’ve got some specific thoughts about this, but it’s too early to explain them; they’re still forming.

I keep finding myself thinking about this topic, and arriving at big insights in the process. All of the different things I’ve worked on in the past seem to connect to this idea in interesting ways. Perhaps it’s going to be one of the main themes I’ll be working on and thinking about for this coming decade.

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I’m going to test it out and see what happens. I invite you to test it out too. Click here.

What I like about the idea is that it is likely to generate extremely relevant ads — and possibly little or no irrelevant ads. This is because advertisers choose tweeters, and tweeter then get to choose what ads they run to their feeds. Since tweeters are unlikely to select ads that are not relevant to their followers (for fear of spamming their followers) it is a self-policed system. Essentially everyone has an incentive to curate the ads they accept tightly. This means the ads are likely to be quite relevant — maybe even relevant enough to qualify as useful content in some cases.

In any event, there’s definitely going to be advertising in Twitter (they’re in Google, and Facebook – Twitter is next, it’s inevitable) and this system seems to be a very smart approach. At least worth testing.

It’s also an ingeniously viral model that is probably going to spread like wildfire. I wouldn’t be surprised if they dominate advertising on Twitter pretty quickly. After you join via the link above, you’ll see why.

I would love to invest in this deal. It’s quite clever. I bet Twitter will buy this company eventually.

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• The Web on mobile devices like the iPhone. Finally it’s really usable on a phone. Now it goes everywhere with us. Soon we will track our own paths on our phones as we move around, creating a virtual map of our favorite places and routes.
• Location aware applications and services, such as Google Maps Mobile. They link physical places to virtual places on the Web.
• The Web in cars.  Auto avigation units will soon be Web-enabled.
• Next-generation Wi-Fi digital cameras are wifi-enabled, linking directly to camera GPS and to photo sharing and storage services. Will cloud-centric wireless cameras with zero local storage come next?
• Web picture frames such as Ceiva bring the Web into your grandma’s livingroom.
• The Web in restaurants and stores. Your server gets your reservation on the Web from OpenTable. In-store kiosks connect to the Web to help you shop, or to bring up your online account and shopping cart.
• The Web in your garden. GardenGro’s sensor connects your garden to the Web, in order to figure out what to plant and how to cultivate it in your actual location.
• Everything becomes trackable with RFID. Physical objects have virtual locations.
• Sensors are connecting to the Web and popping up everywhere. For example here.
• Plastic Logic’s portable plastic reading device. The pad of paper, version 2.0.
• The beginnings of an Internet of Things — where every thing has an address on the Web.
• The rise of Lifestreaming, in which everything (or much of what) one does is captured to the Web and even broadcast.
• Progress on Augmented Reality — instead of the physical world going into virtual worlds, the virtual world is going to flow into the physical world.

These are just a few data points. There are many many more. The trendline is clear to me.

Things are not going to turn out the way we thought. Instead of everything going digital — a future in which we all live as avatars in cyberspace — The digital world is going to invade the physical world. We already are the avatars and the physical world is becoming cyberspace. The idea that cyberspace is some other place is going to dissolve because everything will be part of the Web. The digital world is going physical.

When this happens — and it will happen soon, perhaps within 20 years or less — the notion of “the Web” will become just a quaint, antique concept from the early days when the Web still lived in a box. Nobody will think about “going on the Web” or “going online” because they will never NOT be on the Web, they will always be online.

Think about that. A world in which every physical object, everything we do, and eventually perhaps our every thought and action is recorded, augmented, and possibly shared. What will the world be like when it’s all connected? When all our bodies and brains are connected together — when even our physical spaces, furniture, products, tools, and even our natural environments, are all online? Beyond just a Global Brain, we are really building a Global Body.

The World is becoming the Web. The “Web Wide World” is coming and is going to be a big theme of the next 20 years.

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Print media publications of all kinds — newspapers and magazines –are dying out, as the Web and online advertising take their place. Increasing amounts of what used to be premium content (via paid wire services and databases for example) is now available for free on the Web.

At the same time the rise of blogs and wikis is giving individuals and groups of people effective ways to publish and distribute content to global audiences. As the major publishing brands decline in audience, upstart online brands are rapidly gaining eyeballs. And now, in the middle of this chaos, social networks like Twitter and Facebook are changing the way content is discovered, further chipping away at the value of the traditional leading media brands.

Major newspapers are closing, journalists, writers and editors are being fired in droves, and there is a sense among those who work in print media that it is the end of an era. Print as a medium is in the process of being superceded by online media. As this happens the content and advertising industries that have formed around print media will undergo radical disruptions and change as well. As we shift to an online-media centric world the economics of content and advertising must and will adapt.

But what will the new model be like? How will the economics of content publishing and distribution be different in the near future of the Web?

In this brief article I will propose the beginnings of a possible new economic framework for Web 3.0 and beyond — one which could revitalize the media business and help it transition to the online world.

I’ll call this new economic model “Content 3.0″ or “C3″ (to coincide with Web 3.0, the third-decade of the Web, when media goes completely online).

In the Content 3.0 (C3) media economy it all begins with pieces of original content. Each piece of content has a corresponding block of “stock” available to be owned by various kinds of investors. The principal classes of stock are:

• Creators Writers, journalists, photographers, artists, designers, editors and their representatives such as agents etc.

• Distributors Publishers or other types of distributors that aggregate audience for content, and who monetize access to that content by their audiences, and their agents if any.

• Participants Audience members or customers who consume the content  (for free or for a fee), rate, annotate, discuss, and share the content. Participants are not just any consumer of the content, they are consumers of the content who choose to invest to earn or purchase shares in the content.

Each piece of content has a certain number of shares of virtual stock, just like a corporation.

When a piece of content is first created 100% of its stock is owned by the Creators. The Creators may then sell some of their shares to Distributors in order to bring it to market.

Distributors bring Participants and revenues to the content, creating a market for it. To attract Participants, Distributors pay to market the content. To attract revenues, Distributors invest in sales and other processes to attract and/or integrate with various monetization partners (such as advertisers, ad networks, affiliate networks, etc.).

Distributors frequently buy and sell shares in content with other Distributors, with some focusing on debut-only content portfolios and others on portfolios of reference and archival material. This aftermarket in content shares is facilitated by various brokers and agents.

Participants may also invest in shares of content, by helping to spread the content (and thereby earning shares) or by buying shares from the other shareholders (Creators and Distributors and any other Participants who hold stock). Participants may also buy and sell shares in content in the same aftermarket that Distributors participate in.

Any profits from monetization of a piece of content are shared as dividends, pro-rata, among the shareholders.

Each piece of content functions like a public company stock in a virtual stock market. This virtual content stock market, like other public markets in securities, is regulated by the SEC or an equivalent regulatory body.

Once a framework like this is in place, complete with the necessary micropayment and legal systems to make it work, the new content economy can really take off. It is a much more loosely coupled and equitable world — one that creates strong entrepreneurial opportunities for professional content Creators, while still providing a solid ROI for content Distributors who team up with them. Participants can also
participate by finding hot content early and investing in order to reap shares in the profits, and to potentially flip their shares to someone else before the price goes down. It works just like the stock market.

The final major element of this picture is that there may not be just one stock market for buying and selling shares of content items. Instead there may be many. Each of these stock markets will be the equivalent of the media empires of today. Various content Creators, Distributors and Participants will participate in these marketplaces in order to transact around the shares of particular pieces of content that are listed in them. It may also be possible for an item of content to list across more than one of these markets at the same time.

While a system like this would face numerous hurdles to actually become real and get official legal status, I believe it could be where we are ultimately headed. It may take 20 or 30 years to fully emerge however. I believe there could be compelling business opportunities to form new business that enable this Content 3.0 ecosystem.

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In the next 10 years, The Stream is going to go through two big phases, focused on two problems, as it evolves:

1. Web Attention Deficit Disorder. The first problem with the real-time Web that is becoming increasingly evident is that it has a bad case of ADD. There is so much information streaming in from so many places at once that it’s simply impossible to focus on anything for very long, and a lot of important things are missed in the chaos. The first generation of tools for the Stream are going to need to address this problem.
2. Web Intention Deficit Disorder. The second problem with the real-time Web will emerge after we have made some real headway in solving Web attention deficit disorder. This second problem is about how to get large numbers of people to focus their intention not just their attention. It’s not just difficult to get people to notice something, it’s even more difficult to get them to do something. Attending to something is simply noticing it. Intending to do something is actually taking action, expending some energy or effort to do something. Intending is a lot more expensive, cognitively speaking, than merely attending. The power of collective intention is literally what changes the world, but we don’t have the tools to direct it yet.

The Stream is not the only big trend taking place right now. In fact, it’s just a strand that is being braided together with several other trends, as part of a larger pattern. Here are some of the other strands I’m tracking:

• Messaging. The real-time Web aka The Stream is really about messaging in essence. It’s a subset of the global trend towards building a better messaging layer for the Web. Multiple forms of messaging are emerging, from the publish-and-subscribe nature of Twitter and RSS, to things like Google Wave, Pubsubhubub, and broadcast style messaging or multicasting via screencast, conferencing and media streaming and events in virtual worlds. The effect of these tools is that the speed and interactivity of the Web are increasing — the Web is getting faster. Information spreads more virally, more rapidly — in other words, “memes” (which we can think of as collective thoughts) are getting more sophisticated and gaining more mobility.
• Semantics. The Web becomes more like a database. The resolution of search, ad targeting, and publishing increases. In other words, it’s a higher-resolution Web. Search will be able to target not just keywords but specific meaning. For example, you will be able to search precisely for products or content that meet certain constraints. Multiple approaches from natural language search to the metadata of the Semantic Web will contribute to increased semantic understanding and representation of the Web.
• Attenuation. As information moves faster, and our networks get broader, information overload gets worse in multiple dimensions. This creates a need for tools to help people filter the firehose. Filtering in its essence is a process of attenuation — a way to focus attention more efficiently on signal versus noise. Broadly speaking there are many forms of filtering from automated filtering, to social filtering, to personalization, but they all come down to helping someone focus their finite attention more efficiently on the things they care about most.
• The WebOS.  As cloud computing resources, mashups, open linked data, and open API’s proliferate, a new level of aggregator is emerging. These aggregators may focus on one of these areas or may cut across them. Ultimately they are the beginning of true cross-service WebOS’s. I predict this is going to be a big trend in the future — for example instead of writing Web apps directly to various data and API’s in dozens of places, just write to a single WebOS aggregator that acts as middleware between your app and all these choices. It’s much less complicated for developers. The winning WebOS is probably not going to come from Google, Microsoft or Amazon — rather it will probably come from someone neutral, with the best interests of developers as the primary goal.
• Decentralization. As the semantics of the Web get richer, and the WebOS really emerges it will finally be possible for applications to leverage federated, Web-scale computing. This is when intelligent agents will actually emerge and be practical. By this time the Web will be far too vast and complex and rapidly changing for any centralized system to index and search it. Only massively federated swarms of intelligent agents, or extremely dynamic distributed computing tools, that can spread around the Web as they work, will be able to keep up with the Web.
• Socialization. Our interactions and activities on the Web are increasingly socially networked, whether individual, group or involving large networks or crowds. Content is both shared and discovered socially through our circles of friends and contacts. In addition, new technologies like Google Social Search enable search results to be filtered by social distance or social relevancy. In other words, things that people you follow like get higher visibility in your search results. Socialization is a trend towards making previously non-social activities more social, and towards making already-social activities more efficient and broader. Ultimately this process leads to wider collaboration and higher levels of collective intelligence.
• Augmentation. Increasingly we will see a trend towards augmenting things with other things. For example, augmenting a Web page or data set with links or notes from another Web page or data set. Or augmenting reality by superimposing video and data onto a live video image on a mobile phone. Or augmenting our bodies with direct connections to computers and the Web.

If these are all strands in a larger pattern, then what is the megatrend they are all contributing to? I think ultimately it’s collective intelligence — not just of humans, but also our computing systems, working in concert.

Collective Intelligence

I think that these trends are all combining, and going real-time. Effectively what we’re seeing is the evolution of a global collective mind, a theme I keep coming back to again and again. This collective mind is not just comprised of humans, but also of software and computers and information, all interlinked into one unimaginably complex system: A system that senses the universe and itself, that thinks, feels, and does things, on a planetary scale. And as humanity spreads out around the solar system and eventually the galaxy, this system will spread as well, and at times splinter and reproduce.

But that’s in the very distant future still. In the nearer term — the next 100 years or so — we’re going to go through some enormous changes. As the world becomes increasingly networked and social the way collective thinking and decision making take place is going to be radically restructured.

Social Evolution

Existing and established social, political and economic structures are going to either evolve or be overturned and replaced. Everything from the way news and entertainment are created and consumed, to how companies, cities and governments are managed will change radically. Top-down beaurocratic control systems are simply not going to be able to keep up or function effectively in this new world of distributed, omnidirectional collective intelligence.

Physical Evolution

As humanity and our Web of information and computatoins begins to function as a single organism, we will evolve literally, into a new species: Whatever is after the homo sapien. The environment we will live in will be a constantly changing sea of collective thought in which nothing and nobody will be isolated. We will be more interdependent than ever before. Interdependence leads to symbiosis, and eventually to the loss of generality and increasing specialization. As each of us is able to draw on the collective mind, the global brain, there may be less pressure on us to do things on our own that used to be solitary. What changes to our bodies, minds and organizations may result from these selective evolutionary pressures? I think we’ll see several, over multi-thousand year timescales, or perhaps faster if we start to genetically engineer ourselves:

• Individual brains will get less good at things like memorization and recall, calculation, reasoning, and long-term planning and action.
• Individual brains will get better at multi-tasking, information filtering, trend detection, and social communication. The parts of the nervous system involved in processing live information will increase disproportionately to other parts.
• Our bodies may actually improve in certain areas. We will become more, not less, mobile, as computation and the Web become increasingly embedded into our surroundings, and into augmented views of our environments. This may cause our bodies to get into better health and shape since we will be less sedentary, less at our desks, less in front of TV’s. We’ll be moving around in the world, connected to everything and everyone no matter where we are. Physical strength will probably decrease overall as we will need to do less manual labor of any kind.

These are just some of the changes that are likely to occur as a result of the things we’re working on today. The Web and the emerging Real-Time Web are just a prelude of things to come.

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With “Beyond Algorithms: Search and the Semantic Web,” we are planning to offer both an overview of the current state of the technology, as well as a careful look at what needs to be
addressed for semantic search to finally reach its potential. We think that semantic search needs to be present, personalized, and precise. What are the catalysts? What are the roadblocks?

At last week’s SES Conference in San Jose, the interactions on and around our “Don’t Call it a Comeback: Semantic Technology and Search” panel showed just how complex these issues are, so we anticipate a lively and wide-ranging discussion for the panel at SXSWi 2010.

We have also proposed a panel on interfacing content streams as real-time interaction becomes the Web’s dominant paradigm.

As we showcased with Twine’s new interface visualization this summer, we feel there are better ways to organize and interact with the stream, and our panel “Islands in the Stream: Interfacing
Real-time Content” will address user experience and interface design for the real-time Web from a variety of perspectives.

We also want to note that Brendan Kessler, the Founder and CEO of ChallengePost, has submitted a panel on “Why Challenge Prizes are the Future of Innovation”.

My $10K challenge to design unblockable, anonymous, and encrypted mobile internet
access is still open, and I will be joining the discussion on the panel, as well.

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Web 1.0, the first decade of the Web (1989 – 1999), was characterized by a distinctly desktop-like search paradigm. The overriding idea was that the Web is a collection of documents, not unlike the folder tree on the desktop, that must be searched and ranked hierarchically. Relevancy was considered to be how closely a document matched a given query string.

Web 2.0, the second decade of the Web (1999 – 2009), ushered in the beginnings of a shift towards social search. In particular blogging tools, social bookmarking tools, social networks, social media sites, and microblogging services began to organize the Web around people and their relationships. This added the beginnings of a primitive “web of trust” to the search repertoire, enabling search engines to begin to take the social value of content (as evidences by discussions, ratings, sharing, linking, referrals, etc.) as an additional measurment in the relevancy equation. Those items which were both most relevant on a keyword level, and most relevant in the social graph (closer and/or more popular in the graph), were considered to be more relevant. Thus results could be ranked according to their social value — how many people in the community liked them and current activity level — as
well as by semantic relevancy measures.

In the coming third decade of the Web, Web 3.0 (2009 – 2019), there will be another shift in the search paradigm. This is a shift to from the past to the present, and from the social to the personal.

Established search engines like Google rank results primarily by keyword (semantic) relevancy. Social search engines rank results primarily by activity and social value (Digg, Twine 1.0, etc.). But the new search engines of the Web 3.0 era will also take into account two additional factors when determining relevancy: timeliness, and personalization.

Google returns the same results for everyone. But why should that be the case? In fact, when two different people search for the same information, they may want to get very different kinds of results. Someone who is a novice in a field may want beginner-level information to rank higher in the results than someone who is an expert. There may be a desire to emphasize things that are novel over things that have been seen before, or that have happened in the past — the more timely something is the more relevant it may be as well.

These two themes — present and personal — will define the next great search experience.

To accomplish this, we need to make progress on a number of fronts.

First of all, search engines need better ways to understand what content is, without having to do extensive computation. The best solution for this is to utilize metadata and the methods of the emerging semantic web.

Metadata reduces the need for computation in order to determine what content is about — it makes that explicit and machine-understandable. To the extent that machine-understandable metadata is added or generated for the Web, it will become more precisely searchable and productive for searchers.

This applies especially to the area of the real-time Web, where for example short “tweets” of content contain very little context to support good natural-language processing. There a little metadata can go a long way. In addition, of course metadata makes a dramatic difference in search of the larger non-real-time Web as well.

In addition to metadata, search engines need to modify their algorithms to be more personalized. Instead of a “one-size fits all” ranking for each query, the ranking may differ for different people depending on their varying interests and search histories.

Finally, to provide better search of the present, search has to become more realtime. To this end, rankings need to be developed that surface not only what just happened now, but what happened recently and is also trending upwards and/or of note. Realtime search has to be more than merely listing search results chronologically. There must be effective ways to filter the noise and surface what’s most important effectively. Social graph analysis is a key tool for doing this, but in
addition, powerful statistical analysis and new visualizations may also be required to make a compelling experience.

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Introduction

Here’s an idea I’ve been thinking about: it’s a concept for a new philosophy, or perhaps just a name for a grassroots philosophy that seems to be emerging on its own. It’s called “Nowism.” The view that now is what’s most important, because now is where one’s life actually happens.

Certainly we have all heard terms like Ram Das’ famous, “Be here now” and we may be familiar with the writings of Eckhart Tolle and his “Power of Now” and others. In addition there was the “Me generation” and the more recent idea of “living in the now.” On the Web there is also now a growing shift towards real-time, what I call the Stream.

These are all examples of the emergence of this trend. But I think these are just the beginnings of this movement — a movement towards a subtle but major shift in the orientation of our civilization’s collective attention. This is a shift towards the now, in every dimension of our lives. Our personal lives, professional lives, in business, in government, in technology, and even in religion and spirituality.

I have a hypothesis that this philosophy — this worldview that the “now” is more important than the past or the future, may come to characterize this new century we are embarking on. If this is true, then it will have profound effects on the direction we go in as a civilization.

It does appear that the world is becoming increasingly now-oriented; more real-time, high-resolution, high-bandwidth. The present moment, the now, is getting increasingly flooded with fast-moving and information-rich streams of content and communication.

As this happens we are increasingly focusing our energy on keeping up with, managing, and making sense of, the now. The now is also effectively getting shorter — in that more happens in less time, making the basic clockrate of the now effectively faster. I’ve written about this elsewhere.

Given that the shift to a civilization that is obsessively focused on the now is occurring, it is not unreasonable to wonder whether this will gradually penetrate into the underlying metaphors and worldviews of coming generations, and how it might manifest as differences from our present-day mindsets.

How might people who live more in the now differ from those who paid more attention to the past, or the future? For example, I would assert that the world in and before the 19th century was focused more on the past than the now or the future. The 20th century was characterized by a shift to focus more on the future than the past or the now. The 21st century will be characterized by a shift in focus onto the now, and away from the past and the future.

How might people who live more in the now think about themselves and the world in coming decades. What are the implications for consumers, marketers, strategists, policymakers, educators?

With this in mind, I’ve attempted to write up what I believe might be the start of a summary of what this emerging worldview of “Nowism” might be like.

It has implications on several levels: social, economic, political, and spiritual.

Nowism Defined

Like Buddhism, Taoism, and other “isms,” Nowism is a view on the nature of reality, with implications for how to live one’s life and how to interpret and relate to the world and other people.

Simply put: Nowism is the philosophy that the span of experience called “now” is fundamental. In other words there is nothing other than now. Life happens in the now. The now is what matters most.

Nowism does not claim to be mutually exclusive with any other religion. It merely claims that all other religions are contained within it’s scope — they, like everything else, take place exclusively within the now, not outside it. In that respect the now, in its actual nature, is fundamentally greater than any other conceivable philosophical or religious system, including even Nowism itself.

Risks of Unawakened Nowism

Nowism is in some ways potentially short-sighted in that there is less emphasis on planning for the future and correspondingly more emphasis on living the present as fully as possible. Instead of making decisions with their effects in the future foremost in mind, the focus is on making the optimal immediate decisions in the context of the present. However, what is optimal in the present may not be optimalover longer spans of time and space.

What may be optimal in the now of a particular individual may not at all be optimal in the nows of other individuals. Nowism can therefore lead to extremely selfish behavior that actually harms others, or it can lead to extremely generous behavior on a scale that far transcends the individual, if one strives to widen their own experience of the now sufficiently.

Very few individuals will ever do the necessary work to develop themselves to the point where their actual experience of now is dramatically wider than average. It is however possible to do this, while quite rare. Such individuals are capable of living exclusively in the now while still always acting with the long-term benefit of both themselves all other beings in mind.

The vast majority of people however will tend towards a more limited and destructive form of Nowism, in which they get lost in deeper forms of consumerism, content and media immersion, hedonism, and conceptualization. Rather than being freed by the now, they will be increasingly imprisoned by it.

This lower form of Nowism — what might be called unawakened Nowism — is characterized by an intense focus on immediate self-gratification, without concern or a sense of responsibility for the consequences of one’s actions on oneself or others in the future. This kind of living in the moment, while potentially extremely fun, tends to end badly for most people. Fortunately most people outgrow this tendency towards extremely unawakened Nowism after graduating college and/or entering the workforce.

Abandoning extremely unawakened Nowist lifestyles doesn’t necessarily result in one realizing any form of awakened Nowism. One might simply remain in a kind of dormant state, sleepwalking through life, not really living fully in the present, not fully experiencing the present in all its potential. To reach this level of higher Nowism, or advanced Nowism, one must either have a direct spontaneous experience of awakening to the deeper qualities of the now, or one must study, practice and work with teachers and friends who can help them to reach such a direct experience of the now.

Benefits of Awakened Nowism: Spiritual and Metaphysical Implications of Nowist Philosophy

In the 21st Century, I believe Nowism may actually become an emerging movement. With it there will come a new conception of the self, and of the divine. The self will be realized to be simultaneously more empty and much vaster than was previously thought. The divine will be understood more directly and with less conceptualization. More people will have spiritual realization this way, because in this more direct approach there is less conceptual material to get caught up in. The experience of now is simply left as it is — as direct and unmediated, unfettered, and unadulterated as possible.

This is a new kind of spirituality perhaps. One in which there is less personification of the divine, and less use of the concept of a personified deity as an excuse or justification for various worldy actions (like wars and laws, for example).

Concepts about the nature of divinity have been used by humans for millenia as tools for various good and bad purposes. But in Nowism, these concepts are completely abandoned. This also means abandoning the notion that there is or is not a divine nature at the core of reality, and each one of us. Nowists do not get caught up in such unresolvable debates. However, at the same time, Nowists do strive for a direct realization of the now — one that is as unmediated and nonconceptual as possible — and that direct realization is considered to BE thedivine nature itself.

Nowism does not assert that nothing exists or that nothing matters. Such views are nihilism not Nowism. Nowism does not assert that what happens is caused or uncaused — such views are those of the materialists and the idealists, not Nowism. Instead Nowism asserts the principles of dependent origination, in which cause and-effect appears to take place, even though it is an illusory process and does not truly exist. On the basis of a relative-level cause-effect process, an ethical system can be founded which seeks to optimize happiness and minimize unhappiness for the greatest number of beings, by adjusting ones actions so as to create causes that lead to increasingly happy effects for oneself and others, increasingly often. Thus the view of Nowism does not lead to hedonism — in fact, anyone who makes a careful study of the now will reach the conclusion that cause and effect operates unfailingly and therefore is a key tool for optimizing happiness in the now.

Advanced Nowists don’t ignore cause-and-effect, in fact quite the contrary: they pay increasingly close attention to cuase-and-effect and their particular actions. The natural result is that they begin to live a life that is both happier and that leads to more happiness for all other beings — at least this is the goal and example of the best-case. The fact that cause-and-effect is in operation, even though it is notfundamentally real, is the root of Nowist ethics. It is precisely the same as the Buddhist conception of the identity of emptiness and dependent-origination.

Numerous principles follow from the core beliefs of Nowism. They include practical guidance for living ones life with a minimum of unnecessary suffering (of oneself as well as others), further principles concerning the nature of reality and the mind, and advanced techniques and principles for reaching greater realizations of the now.

As to the nature of what is taking place right now: from the Nowist perspective, it is beyond concepts, for all concepts, like everything else, appear and disappear like visions or mirages, without ever truly-existing. This corresponds precisely to the Buddhist conception of emptiness.

The scope of the now is unlimited, however for the uninitiated the now is usually considered to be limited to the personal present experience of the individual. Nowist adepts, on the other hand, assert that the scope of the now may be modified (narrowed or widened) through various exercises including meditation, prayer, intense physical activity, art, dance and ritual, drugs, chanting, fasting, etc.

Narrowing the scope of the now is akin to reducing the resolution of present experience. Widening the scope is akin to increasing the resolution. A narrower now is a smaller experience, with less information content. A wider now is a larger experience, with more information content.

Within the context of realizing that now is all there is, one explores carefully and discovers that now does not contain anything findable (such as a self, other, or any entity or fundamental basis for any objective or subjective phenomenon, let alone any nature that could be called “nowness” or the now itself).

In short the now is totally devoid of anything findable whatsoever, although sensory phenomena do continue to appear to arise within it unceasingly. Such phenomena, and the sensory apparatus, body, brain, mind and any conception of self that arises in reaction to them, are all merely illusion-like appearances with no objectively-findable ultimate, fundamental, or independent existence.

This state is not unlike the analogy of a dream in which oneself and all the other places and characters are all equally illusory, or of a completely immersive virtual reality experience that is so convincing one forgets it isn’t real.

Nowism does not assert a divine being or deity, although it also is not mutually exclusive with the existence of one or more such beings. However all such beings are considered to be no more real than any other illusory appearance, such as the appearances of sentient beings, planets, stars, fundamental particles, etc. Any phenomena — whether natural or supernatural — are equally empty of any independent true existince. They are all illusory in nature.

However, Nowists do assert that the nature of the now itself, while completely empty, is in fact the nature of consciousness and what we call life. It cannot be computed, simulated or modeled in an information system, program, machine, or representation of any kind. Any such attempts to represent the now are merely phenomena appearing within the now, not the now itself. The now is fundamentally transcendental in this respect.

The now is not limited to any particular region in space or time, let alone to any individual being’s mind. There is no way to assert there is a single now, or many nows, for no nows are actually findable.

The now is the gap between the past and the future, however, when searched for it cannot really be found, nor can the past or future be found. The past is gone, the future hasn’t happened yet, and the now is infinite, constantly changing, and ungraspable. The entire space-time continuum is in fact within a total all-embracing now, the cosmically extended now that is beyond the limited personalized scope of now we presently think we have. Through practice this can be gradually glimpsed and experienced to greater degrees.

As the now is explored to greater depths, one begins to find that it has astonishing implications. Simultaneously much of the Zen literature — especially the koans — starts to make sense at last.

While Nowism could be said to be a branch of Buddhism, I would actually say it might be the other way arond. Nowism is really the most fundamental, pure, philosophy — stripped of all cultural baggage and historical concepts, and retaining only what is absolutely essential.

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In Part-One of this article on TechCrunch, I covered the emerging paradigm of Virtual Assistants and explored a first look at a new product in this category called Siri. In this article, Part-Two, I interview Tom Gruber, CTO of Siri, about the history, key ideas, and technical foundations of the product:

Nova Spivack: Can you give me a more precise definition of a Virtual Assistant?

Tom Gruber: A virtual personal assistant is a software system that

• Helps the user find or do something (focus on tasks, rather than information)
• Understands the user’s intent (interpreting language) and context (location, schedule, history)
• Works on the user’s behalf, orchestrating multiple services and information sources to help complete the task

In other words, an assistant helps me do things by understanding me and working for me. This may seem quite general, but it is a fundamental shift from the way the Internet works today. Portals, search engines, and web sites are helpful but they don’t do things for me – I have to use them as tools to do something, and I have to adapt to their ways of taking input.

Nova Spivack: Siri is hoping to kick-start the revival of the Virtual Assistant category, for the Web. This is an idea which has a rich history. What are some of the past examples that have influenced your thinking?

Tom Gruber: The idea of interacting with a computer via a conversational interface with an assistant has excited the imagination for some time.  Apple’s famous Knowledge Navigator video offered a compelling vision, in which a talking head agent helped a professional deal with schedules and access information on the net. The late Michael Dertouzos, head of MIT’s Computer Science Lab, wrote convincingly about the assistant metaphor as the natural way to interact with computers in his book “The Unfinished Revolution: Human-Centered Computers and What They Can Do For Us”.  These accounts of the future say that you should be able to talk to your computer in your own words, saying what you want to do, with the computer talking back to ask clarifying questions and explain results.  These are hallmarks of the Siri assistant.  Some of the elements of these visions
are beyond what Siri does, such as general reasoning about science in the Knowledge Navigator.  Or self-awareness a la Singularity.  But Siri is the real thing, using real AI technology, just made very practical on a small set of domains. The breakthrough is to bring this vision to a mainstream market, taking maximum advantage of the mobile context and internet service ecosystems.

Nova Spivack: Tell me about the CALO project, that Siri spun out from. (Disclosure: my company, Radar Networks, consulted to SRI in the early days on the CALO project, to provide assistance with Semantic Web development)

Tom Gruber: Siri has its roots in the DARPA CALO project (“Cognitive Agent that Learns and Organizes”) which was led by SRI. The goal of CALO was to develop AI technologies (dialog and natural language understanding,s understanding, machine learning, evidential and probabilistic reasoning, ontology and knowledge representation, planning, reasoning, service delegation) all integrated into a virtual
assistant that helps people do things.  It pushed the limits on machine learning and speech, and also showed the technical feasibility of a task-focused virtual assistant that uses knowledge of user context and multiple sources to help solve problems.

Siri is integrating, commercializing, scaling, and applying these technologies to a consumer-focused virtual assistant.  Siri was under development for several years during and after the CALO project at SRI. It was designed as an independent architecture, tightly integrating the best ideas from CALO but free of the constraints of a national distributed research project. The Siri.com team has been evolving and hardening the technology since January 2008.

Nova Spivack: What are primary aspects of Siri that you would say are “novel”?

Tom Gruber: The demands of the consumer internet focus — instant usability and robust interaction with the evolving web — has driven us to come up with some new innovations:

• A conversational interface that combines the best of speech and semantic language understanding with an interactive dialog that helps guide
  people toward saying what they want to do and getting it done. The
  conversational interface allows for much more interactivity that one-shot search style interfaces, which aids usability and improves intent understanding.  For example, if Siri didn’t quite hear what you said, or isn’t sure what you meant, it can ask for clarifying information.   For example, it can prompt on ambiguity: did you mean pizza restaurants in Chicago or Chicago-style pizza places near you? It can also make reasonable guesses based on context. Walking around with the phone at lunchtime, if the speech interpretation comes back with something garbled about food you probably meant “places to eat near my current location”. If this assumption isn’t right, it is easy to correct in a conversation.
• Semantic auto-complete – a combination of the familiar “autocomplete” interface of search boxes with a semantic and linguistic model of what might be worth saying. The so-called “semantic completion” makes it possible to rapidly state complex requests (Italian restaurants in the SOMA neighborhood of San Francisco that have tables available tonight) with just a few clicks. It’s sort of like the power of faceted search a la Kayak, but packaged in a clever command line style interface that works in small form factor and low bandwidth environments.
• Service delegation – Siri is particularly deep in technology for operationalizing a user’s intent into computational form, dispatching to multiple, heterogeneous services, gathering and integrating results, and presenting them back to the user as a set of solutions to their request.  In a restaurant selection task, for instance, Siri combines information from many different sources (local business directories, geospatial databases, restaurant guides, restaurant review sources, online reservation services, and the user’s own favorites) to show a set of candidates that meet the intent expressed in the user’s natural language request.

Nova Spivack: Why do you think Siri will succeed when other AI-inspired projects have failed to meet expectations?

Tom Gruber: In general my answer is that Siri is more focused. We can break this down into three areas of focus:

• Task focus. Siri is very focused on a bounded set of specific human tasks, like finding something to do, going out with friends, and getting around town.  This task focus allows it to have a very rich model of its domain of competence, which makes everything more tractable from language understanding to reasoning to service invocation and results presentation
  
• Structured data focus. The kinds of tasks that Siri is particularly good at involve semistructured data, usually on tasks involving multiple criteria and drawing from multiple sources.  For example, to help find a place to eat, user preferences for cuisine, price range, location, or even specific food items come into play.  Combining results from multiple sources requires
  reasoning about domain entity identity and the relative capabilities of different information providers.  These are hard problems of semantic
  information processing and integration that are difficult but feasible
  today using the latest AI technologies.
• Architecture focus. Siri is built from deep experience in integrating multiple advanced technologies into a platform designed expressly for virtual assistants. Siri co-founder Adam Cheyer was chief architect of the CALO project, and has applied a career of experience to design the platform of the Siri product. Leading the CALO project taught him a lot about what works and doesn’t when applying AI to build a virtual assistant. Adam and I also have rather unique experience in combining AI with intelligent interfaces and web-scale knowledge integration. The result is a “pure  play” dedicated architecture for virtual assistants, integrating all the components of intent understanding, service delegation, and dialog flow management. We have avoided the need to solve general AI problems by concentrating on only what is needed for a virtual assistant, and have chosen to begin with a
  finite set of vertical domains serving mobile use cases.

Nova Spivack: Why did you design Siri primarily for mobile devices, rather than Web browsers in general?

Tom Gruber: Rather than trying to be like a search engine to all the world’s information, Siri is going after mobile use cases where deep models of context (place, time, personal history) and limited form factors magnify the power of an intelligent interface.  The smaller the form factor, the more mobile the context,
the more limited the bandwidth : the more it is important that the interface make intelligent use of the user’s attention and the resources at hand.  In other words, “smaller needs to be smarter.”  And the benefits of being offered just the right level of detail or being prompted with just the right questions can make the difference between task completion or failure.  When you are on the go, you just don’t have time to wade through pages of links and disjoint interfaces, many of which are not suitable to mobile at all.

Nova Spivack: What language and platform is Siri written in?

Tom Gruber: Java, Javascript, and Objective C (for the iPhone)

Nova Spivack: What about the Semantic Web? Is Siri built with Semantic Web open-standards such as RDF and OWL, Sparql?

Tom Gruber: No, we connect to partners on the web using structured APIs, some of which do use the Semantic Web standards.  A site that exposes RDF usually has an API that is easy to deal with, which makes our life easier.  For instance, we use geonames.org as one of our geospatial information sources. It is a full-on Semantic
Web endpoint, and that makes it easy to deal with.  The more the API declares its data model, the more automated we can make our coupling to it.

Nova Spivack: Siri seems smart, at least about the kinds of tasks it was designed for. How is the knowledge represented in Siri – is it an ontology or something else?

Tom Gruber: Siri’s knowledge is represented in a unified modeling system that combines ontologies, inference networks, pattern matching agents, dictionaries, and dialog models.  As much as possible we represent things declaratively (i.e., as data in models, not lines of code).  This is a tried and true best practice for complex AI systems.  This makes the whole system more robust and scalable, and the development process more agile.  It also helps with reasoning and learning, since Siri can look at what it knows and think about similarities and generalizations at a semantic level.

Nova Spivack: Will Siri be part of the Semantic Web, or at least the open linked data Web (by making open API’s, sharing of linked data, RDF, available, etc.)?

Tom Gruber: Siri isn’t a source of data, so it doesn’t expose data using Semantic Web standards.  In the Semantic Web ecosystem, it is doing something like the vision of a semantic desktop – an intelligent interface that knows about user needs
and sources of information to meet those needs, and intermediates.  The original Semantic Web article in Scientific American included use cases that an assistant would do (check calendars, look for things based on multiple structured criteria, route planning, etc.).  The Semantic Web vision focused on exposing the structured data, but it assumes APIs that can do transactions on the data.  For example, if a virtual assistant wants to schedule a dinner it needs more than the information
about the free/busy schedules of participants, it needs API access to their calendars with appropriate credentials, ways of communicating with the participants via APIs to their email/sms/phone, and so forth. Siri is building on the ecosystem of APIs, which are better if they declare the meaning of the data in and out via ontologies.  That is the original purpose of ontologies-as-specification that I promoted in the
1990s – to help specify how to interact with these agents via knowledge-level APIs.

Siri does, however, benefit greatly from standards for talking about space and time, identity (of people, places, and things), and authentication.  As I called for in my Semantic Web talk in 2007, there is no reason we should be string matching on city names, business names, user names, etc.

All players near the user in the ecommerce value chain get better when the information that the users need can be unambiguously identified, compared, and combined. Legitimate service providers on the supply end of the value chain also benefit, because structured data is harder to scam than text.  So if some service provider offers a multi-criteria decision making service, say, to help make a product purchase in some domain, it is much easier to do fraud detection when the product instances, features, prices, and transaction availability information are all structured data.

Nova Spivack: Siri appears to be able to handle requests in natural language. How good is the natural language processing (NLP) behind it? How have you made it better than other NLP?

Tom Gruber: Siri’s top line measure of success is task completion (not relevance).  A subtask is intent recognition, and subtask of that is NLP.  Speech is another element, which couples to NLP and adds its own issues.  In this context, Siri’s NLP is “pretty darn good” — if the user is talking about something in Siri’s domains of competence, its intent understanding is right the vast majority of the time, even in the face of noise from speech, single finger typing, and bad habits from too much keywordese.  All NLP is tuned for some class of natural language, and Siri’s is tuned for things that people might want to say when talking to a virtual assistant on their phone. We evaluate against a corpus, but I don’tknow how it would compare to standard message and news corpuses using by the NLP research community.

Nova Spivack: Did you develop your own speech interface, or are you using third-party system for that? How good is it? Is it battle-tested?

Tom Gruber: We use third party speech systems, and are architected so we can swap them out and experiment. The one we are currently using has millions of users and continuously updates its models based on usage.

Nova Spivack: Will Siri be able to talk back to users at any point?

Tom Gruber: It could use speech synthesis for output, for the appropriate contexts.  I have a long standing interest in this, as my early graduate work was in communication prosthesis. In the current mobile internet world, however, iPhone-sized screens and 3G networks make it possible to do so more much than read menu items over the phone.  For the blind, embedded appliances, and other applications it would make sense to give Siri voice output.

Nova Spivack: Can you give me more examples of how the NLP in Siri works?

Tom Gruber: Sure, here’s an example, published in the Technology Review, that illustrates what’s going on in a typical dialogue with Siri. (Click link to view the table)

Nova Spivack: How personalized does Siri get – will it recommend different things to me depending on where I am when I ask, and/or what I’ve done in the past? Does it learn?

Tom Gruber: Siri does learn in simple ways today, and it will get more sophisticated with time.  As you said, Siri is already personalized based on immediate context, conversational history, and personal information such as where you live.  Siri doesn’t forget things from request to request, as do stateless systems like search engines. It always considers the user model along with the domain and task models when coming up with results.  The evolution in learning comes as users have a history with Siri, which gives it achance to make some generalizations about preferences.  There is a natural progression with virtual assistants from doing exactly what they are asked, to making recommendations based on assumptions about intent and preference. That is the curve we will explore with experience.

Nova Spivack: How does Siri know what is in various external services – are you mining and doing extraction on their data, or is it all just real-time API calls?

Tom Gruber: For its current domains Siri uses dozens of APIs, and connects to them in both realtime access and batch data synchronization modes.  Siri knows about the data because we (humans) explicitly model what is in those sources.  With declarative representations of data and API capabilities, Siri can reason about the various capabilities of its sources at run time to figure out which combination would best serve the current user request.  For sources that do not have nice APIs or expose data using standards like the Semantic Web, we can draw on a value chain of players that do extract structure by data mining and exposing APIs via scraping.

Nova Spivack: Thank you for the information, Siri might actually make me like the iPhone enough to start using one again.

Tom Gruber: Thank you, Nova, it’s a pleasure to discuss this with someone who really gets the technology and larger issues. I hope Siri does get you to use that iPhone again. But remember, Siri is just starting out and will sometimes say silly things. It’s easy to project intelligence onto an assistant, but Siri isn’t going to pass the Turing Test. It’s just a simpler, smarter way to do what you already want to do. It will be interesting to see how this space evolves, how people will come to understand what to expect from the little personal assistant in their pocket.

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Welcome to The Stream

The Internet began evolving many decades before the Web emerged. And while today many people think of the Internet and the Web as one and the same, in fact they are different. The Web lives on top of the Internet’s infrastructure much like software and documents live on top of an operating system on a computer.

And just as the Web once emerged on top of the Internet, now something new is emerging on top of the Web: I call this the Stream. The Stream is the next phase of the Internet’s evolution. It’s what comes after, or on top of, the Web we’ve all been building and using.

Perhaps the best and most current example of the Stream is the rise of Twitter, Facebook and other microblogging tools. These services are visibly streamlike, their user-interfaces are literally streams; streams of ideas, thinking and conversation. In reaction to microblogs we are also starting to see the birth of new tools to manage and interact with these streams, and to help understand, search, and follow the trends that are rippling across them. Just as the Web is not any one particular site or service, the Stream is not any one site or service — it’s the collective movement that is taking place across them all.

To meet the challenges and opportunities of the Stream a new ecosystem of services is rapidly emerging: stream publishers, stream syndication tools, stream aggregators, stream readers, stream filters, real-time stream search engines, and stream analytics engines, stream advertising networks, and stream portals are emerging rapidly. All of these new services are the beginning of the era of the Stream.

Web History

The original Tim Berners-Lee proposal that started the Web was in March, 1989. The first two decades of the Web (Web 1.0 from 1989 – 1999, and Web 2.0 from 1999 – 2009) were focused on the development of the Web itself. Web 3.0 (2009 – 2019), the third-decade of the Web, officially began in March of this year and will be focused around the Stream.

• In the 1990’s with the advent of HTTP and HTML, the metaphor of “the Web” was born and concepts of webs and sites captured our imaginations.
• In the early 2000’s the focus shifted to graphs such as social networks and the beginnings of the Semantic Web.
• Now, in the coming third decade, the focus is shifting to the Stream and with it, stream oriented metaphors of flows, currents, and ripples.

The Web has always been a stream. In fact it has been a stream of streams. Each site can be viewed as a stream of pages developing over time. Each page can be viewed as a stream of words, that changes whenever it is edited. Branches of sites can also be viewed as streams of pages developing in various directions.

But with the advent of blogs, feeds, and microblogs, the streamlike nature of the Web is becoming more readily visible, because these newer services are more 1-dimensional and conversational than earlier forms of websites, and they update far more frequently.

Defining the Stream

Just as the Web is formed of sites, pages and links, the Stream is formed of streams.

Streams are rapidly changing sequences of information around a topic. They may be microblogs, hashtags, feeds, multimedia services, or even data streams via APIs.

The key is that streams change often. This change is an important part of the value they provide (unlike static Websites, which do not necessarily need to change in order to provide value). In addition, it is important to note that streams have URI’s — they are addressable entities.

So what defines a stream versus an ordinary website?

1. Change. Change is the key reason why a stream is valuable. That is not always so with a website.  Websites do not have to change at all to be valuable — they could for example just be static but comprehensive reference library collections. But streams on the other hand change very frequently, and it is this constant change that is their main point.
2. Interface Independence.
   Streams are streams of data, and they can be fully accessed and consumed independently of any particular user-interface — via syndication of their data into various tools. Websites on the other hand, are only accessible via their user-interfaces. In the era of the Web the provider controlled the interface. In the new era of the stream, the consumer controls the interface.
3. Conversation is king.
   An interesting and important point is that streams are linked together not by hotlinks, but by acts of conversation — for example, replies, “retweets,” comments and ratings, and “follows.” In the era of the Web the hotlink was king. But in the era of the Stream conversation is king.

In terms of structure, streams are comprised of agents, messages and interactions:

• Agents are people as well as software apps that publish to streams.
• Messages are publications by agents to streams — for example, short posts to their microblogs.
• Interactions are communication acts, such as sending a direct message or a reply, or quoting someone (“retweeting”), that connect and transmit messages between agents.

The Global Mind

If the Internet is our collective nervous system, and the Web is our collective brain, then the Stream is our collective mind. The nervous system and the brain are like the underlying hardware and software, but the mind is what the system is actually thinking in real-time. These three layers are interconnected, yet are distinctly different aspects, of our emerging and increasingly awakened planetary intelligence.

The Stream is what the Web is thinking and doing, right now. It’s our collective stream of consciousness.

The Stream is the dynamic activity of the Web, unfolding over time. It is the conversations, the live streams of audio and video, the changes to Web sites that are happening, the ideas and trends — the memes — that are rippling across millions of Web pages, applications, and human minds.

The Now is Getting Shorter

The Web is changing faster than ever, and as this happens, it’s becoming more fluid. Sites no longer change in weeks or days, but hours, minutes or even seconds. if we are offline even for a few minutes we may risk falling behind, or even missing something absolutely critical. The transition from a slow Web to a fast-moving Stream is happening quickly. And as this happens we are shifting our attention from the past to the present, and our “now” is getting shorter.

The era of the Web was mostly about the past — pages that were published months, weeks, days or at least hours before we looked for them. Search engines indexed the past for us to make it accessible: On the Web we are all used to searching Google and then looking at pages from the recent past and even farther back in the past. But in the era of the Stream, everything is shifting to the present — we can see new
posts as they appear and conversations emerge around them, live, while we watch.

Yet as the pace of the Stream quickens, what we think of as “now” gets shorter. Instead of now being a day, it is an hour, or a few minutes. The unit of change is getting more granular.

For example, if you monitor the public timeline, or even just your friends timeline in Twitter or Facebook you see that things quickly flow out of view, into the past. Our attention is mainly focused on right now: the last few minutes or hours. Anything that was posted before this period of time is “out of sight, out of mind.”

The Stream is a world of even shorter attention spans, online viral sensations, instant fame, sudden trends, and intense volatility. It is also a world of extremly short-term conversations and thinking.

This is the world we may be entering. It is both the great challenge, and the great opportunity of the coming decade of the Web.

How Will We Cope With the Stream?

The Web has always been a stream — it has been happening in real-time since it started, but it was slower — pages changed less frequently, new things were published less often, trends developed less quickly. Today it is getting so much faster, and as this happens its feeding back on itself and we’re feeding into it, amplifying it even more.

Things have also changed qualitatively in recent months. The streamlike aspects of the Web have really moved into the foreground of our mainstream cultural conversation. Everyone is suddenly talking about Facebook and Twitter. Celebrities. Talk show hosts. Parents. Teens.

And suddenly we’re all finding ourselves glued to various activity streams, microblogging manically and squinting to catch fleeting references to things we care about as they rapidly flow by and out of view. The Stream has arrived.

But how can we all keep up with this ever growing onslaught of information effectively? Will we each be knocked over by our own personal firehose, or will tools emerge to help us filter our streams down to managable levels? And if we’re already finding that we have too many streams today, and must jump between them ever more often, how will we ever be able to function with 10X more streams in a few years?

Human attention is a tremendous bottleneck in the world of the Stream. We can only attend to one thing, or at most a few things, at once. As information comes at us from various sources, we have to jump from one item to the next. We cannot absorb it all at once. This fundamental barrier may be overcome with technology in the future, but for the next decade at least it will still be a key obstacle.

We can follow many streams, but only one-item-at-a-time; and this requires rapidly shifting our focus from one article to another and from one stream to another. And there’s no great alternative: Cramming all our separate streams into one merged activity stream quickly gets too noisy and overwhelming to use.

The ability to view different streams for different contexts is very important and enables us to filter and focus our attention effectively. As a result, it’s unlikely there will be a single activity stream — we’ll have many, many streams. And we’ll have to find ways to cope with this reality.

Streams may be unidirectional or bidirectional. Some streams are more like “feeds” that go from content providers to content consumers. Other streams are more like conversations or channels in which anyone can be both a provider and a consumer of content.

As streams become a primary mode of content distribution and communication, they will increasingly be more conversational and less like feeds. And this is important — because to participate in a feed you can be passive, you don’t have to be present synchronously.  But to participate in a conversation you have to be present and synchronous — you have to be there, while it happens, or you may miss out on it entirely.

A Stream of Challenges and Opportunities

We are going to need new kinds of tools for managing and participating in streams, and we are already seeing the emergence of some of them. For example Twitter clients like Tweetdeck, RSS feed readers, and activity stream tracking tools like Facebook and Friendfeed. There are also new tools for filtering our streams around interests, for example Twine.com (* Disclosure: the author of this article is a principal in Twine.com). Real-time search tools are also emerging to provide quick ways to scan the Stream as a whole. And trend discovery tools are helping us to see
what’s hot in real-time.

One of the most difficult challenges will be how to know what to pay attention to in the Stream: Information and conversation flow by so quickly that we can barely keep up with the present, let alone the past. How will know what to focus on, what we just have to read, and what to ignore or perhaps read later?

Recently many sites have emerged that attempt to show what is trending up in real-time, for example by measuring how many retweets various URLs are getting in Twitter. But these services only show the huge and most popular trends. What about all the important stuff that’s not trending up massively? Will people even notice things that are not widely RT’d or “liked”? Does popularity equal importance of content?

Certainly one measure of the value of an item in the Stream is social popularity. Another measure is how relevant it is to a topic, or even more importantly, to our own personal and unique interests. To really cope with the Stream we will need ways to filter that combine both these different approaches. Furthermore as our context shifts throughout the day (for example from work to various projects or clients to shopping to health to entertainment, to family etc) we need tools that can adapt to filter the Stream differently based on what we now care about.

A Stream oriented Internet also offers new opportunities for monetization. For example, new ad distribution networks could form to enable advertisers to buy impressions in near-real time across URLs that are trending up in the Stream, or within various slices of it. For example, an advertiser could distribute their ad across dozens of pages that are getting heavily retweeted right now. As those pages begin to decline in RT’s per minute, the ads might begin to move over to different URLs that are starting to gain.

Ad networks that do a good job of measuring real-time attention trends may be able to capitalize on these trends faster and provide better results to advertisers. For example, an advertiser that is able to detect and immediately jump on the hot new meme of the day, could get their ad in front of the leading influencers they want to reach, almost instantly. And this could translate to sudden gains in awareness and branding.

The emergence of the Stream is an interesting paradigm shift that may turn out to characterize the next evolution of the Web, this coming third-decade of the Web’s development. Even though the underlying data model may be increasingly like a graph, or even a semantic graph, the user experience will be increasingly stream oriented.

Whether Twitter, or some other app, the Web is becoming increasingly streamlike. How will we filter this stream? How will we cope? Whoever can solve these problems first and best is probably going to get rich.

Other Articles on This Topic

http://www.techmeme.com/090517/p6#a090517p6

http://www.techcrunch.com/2009/05/17/jump-into-the-stream/

http://www.techcrunch.com/2009/02/15/mining-the-thought-stream/

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The Problem: Our Communities are Failing

I’ve been thinking about community lately. There is a great need for a new and better model for communities in the world today.

Our present communites are not working and most are breaking down or stagnating. Cities are experiencing urbanization and a host of ensuing  social and economic challenges. Meanwhile the movement towards cities has drained the people — particularly young professionals — away from rural communities, causing them to stagnate and decline.

Local economies have been challenged by national and global economic integratio — from outsourcing of jobs away to other places, to giant retail chains such as Walmart swooping in and driving out local businesses.

From giant megacities and multi-city urban sprawls, to inner city neighborhoods, to suburban bedroom communities, and rural towns and villages, the pain is being felt everywhere and at all levels.

Our current models for community don’t scale, they don’t work anymore, and they don’t fit the kind of world we are living in today. And why should they? After all, they were designed a long time ago for a very different world.

At the same time there are increasing numbers of singles or couples without children, and even families and neighborhoods that are breaking down as cities get larger.

The need for community is growing not declining — especially as existing communities fail and no other alternatives take their place. Loneliness, social isolation, and social fragmentation are huge and growing problems — they lead to crime, suicide, mental illness, lack of productivity, moral decay, civil unrest, and just about every other social and economic problem there is.

The need for an updated and redesigned model for community is increasingly important to all of us.

Intentional Communities

In particular, I am thinking about intentional communities — communities in which people live geographically near one another, and participate in community together, by choice. They may live together or not, dine together or not, work together or not, worship together or not — but at least they need to live within some limit of proximity to one another and participate in community together. These are the minimum requirements.

But is there a model that works? Or is it time to design a new model that fits the time and place in which we live better?

Is this simply a design problem that we can solve by adopting the right model, or is there something about human nature that makes it impossible to succeed no matter what model we apply?

I am an optimist and I don’t think human nature prevents healthy communities from forming and being sustainable. I think it’s a design problem. I think this problem can (and must) be solved with a set of design principles that work better than the ones we’ve come up with so far. This would be a great problem to solve. It could even potentially improve the lives of billions of people.

Models of Intentional Community

Community is extremely valuable and important. We are social beings. And communities enable levels of support and collaboration, economic growth, resiliance, and perhaps personal growth, that individuals or families cannot achieve on their own.

However, do intentional communities work? What examples can we look at and what can we glean from them about what worked and what didn’t?

All of the cities and towns in the world started as intentional communities but today many seem to have lost their way as they got larger or were absorbed into larger communities.

As for smaller intentional communities — recent decades are littered with all kinds of spectacular failures.

The communes and experiemental communities of the 1960’s and 1970’s have mostly fallen apart.

Spiritual communities seem to either tend towards becoming personality cults that are highly prone to tyrranny and corruption, or they too seem to fall apart eventually as well.

There have been so many communities around various gurus, philosophers, or cult-figures, but they have almost all universally become cults or have broken apart.

Human nature is hard to wrangle without strong leadership, yet strong leadership and the power it entails leads inevitably to ego and corruption.

At least some ashrams in India seem to be working well, although their internal dynamics are usually centered around a single guru or leadership group — and while there may be a strong social agreement within these communities, this is not a model of community that will work for everyone. And in fact, only in extremely rare cases, are there any gurus who are actually selfless enough to hold that position without abusing it.

Other kinds of religious communities are equally prone to problems — however perhaps at least some, such as the Quakers, Shakers, and Amish may have solved this — I am not sure however. If they were so successful, why are there so few of them?

Temporary communities are another type of intentional community, for example, Burning Man, seem to work quite well, but only for temporary periods of time — they would have the same problems of all other communities if they became institutionalized or tried to not be temporary.

Educational communities, such as university towns and campuses, do appear to work in many cases. They combine both an ongoing community (tenured faculty, staff and townspeople) and temporary communities (seasonal student and faculty residents).

Economic communes — such as the communes in Soviet-era Russia were prone to corruption, and failed as economic experiments. In Soviet Russia “some were more equal than others” and that ultimately led to corruption and tyranny.

Political-economic communities such as the neighborhood groups in Maoist China only worked because they were firmly, even brutally, controlled from the central government. They were not exactly voluntary intentional communities.

I don’t know enough about the Israeli Kibbutzim experiments, but they at least seem to be continuing, although I am not sure how well they function — I admit my ignorance on that topic.

One type of intentional community that does seem to work are caregiving communities such as assisting living communities, nursing homes, halfway houses, etc — but perhaps they seem to work only because their members don’t remain very long.

Why Aren’t There More Intentional Communities?

So here is my question: Do intentional communities work? And if they work so well, why aren’t there more of them? Or are they flourishing and multiplying under the radar?

Is there a model (or are there models) for intentional community that have proven long-term success? Where are the examples?

Is the fact that there are not more intentional communities emerging and thriving, evidence that intentional communities just don’t work or have stopped replicating or evolving? Or is it evidence that the communities we already live in work well enough, even though they are no longer intentional for most of us?

I don’t think our present-day communities work well enough, nor are they very healthy or rewarding to their participants. I do believe there is the possibility, and even the opportunity, to come up with a better model — one which works so well that it attracts people, grows and self-replicates around the world rapidly. But I don’t yet know what that new model is.

Design Principles

To design the next-evolution of intentional community, perhaps we can start with a set of design principles gleaned from what we have learned from existing communities?

This set of design principles should be selected to be practical for the world we live in today — a world of rapid transit, economic and social mobility, urban sprawls, cultural and ethnic diversity, cheap air travel, declining birth rates, the 24-7 work week, the Internet, and the globally interdependent economy.

In thinking about this further there are a few key “design principles” which seem to be necessary to make a successful, sustainable, healthy community.

This is not an exhaustive list, but it is what we have thought of so far:

Shared intention.
There has to be a common reason for the group of people to be together. The participants each have to share a common intention to form and participate in a community around common themes and purposes together.

Shared contribution . The participants have to each contribute in various ways to the community as part of their membership.

Shared governance.
The participants each have a role to play in the process of decision making, policy formation, dispute resolution, and operations of the community.

Shared boundaries. There are shared, mutually agreed upon and mutually enforced rules.

Freedom to leave. Anyone can leave the community at any time without pressure to remain.

Freedom of choice.
While in the community people are free to make choices about their roles and participation in the community, within the communities boundaries and governance process. This freedom of choice also includes the freedom to opt out of any role or rule, but that might have the consequence of voluntarily recusing oneself from further participation in the community.

Freedom of expression. The ability for community members to freely and fearlessly express their opinions within the community is an essential element of healthy communities. Systems need to be designed to support and channel this activity. If it is restrained it seeks out other channels anyway (subversion, revolution, etc.). By not restraining expression, but instead desiging a community process that authentically engages members in conversation with one another, the
community can be more self-aware and creativity and innovation can flow more freely.

Representative democratic leadership. The leadership is either by consensus and includes everyone equally, or there is a democratic representative process of electing leaders and making decisions.

Community mobility. This is an interesting topic. In the world today, each person may have different sets of interests and purposes, and they are not all compatible. It may be necessary or desirable to be a member of different communities in different places, times of the year, or periods of one’s life. It
should be possible to be able to be in more than one community, or to rotate through communities, or to change communities as one’s interests, goals, needs and priorities shift over time — so long as one participates in each community fully while they are there. The concept of timesharing in various communities, or what one friend calls “colonies,” is interesting. One might be a member of different colonies — one for their religious interests, one for social kinship, one for a hobby, one for recreation and vacation, etc. These might be in different places and have different members and their role and level of participation might be different in each one. Rather than living in only one particular community, perhaps we need a model where there is more mobility.

Size limitations. One thing I would suggest is that communities work better when they are smaller. The reason for this is that once communities reach a size where each member no longer can maintain a personal relationship with each other member, they stop working and begin to fragment into subgroups. So perhaps limiting the size of a community is a good idea. Or alternatively, when a community reaches a certain size it spawns a new separate community where further growth can happen and all new members go there. In fact, you could even see two communities spawning a new “child” community together to absorb their growth.

Proximity. Communities don’t require that people live near each other — they can function non-locally, for example online. However, the kind of intentional communities I am interested in here are ones where people do live together or near one another, at least part of the time. For this kind of community people need to live and/or dine and/or work together on a periodic, if not frequent basis. An eating co-op in a metropolitan area is an example — at least if everyone has to live within a certain distance and eat together a few times a week, and work a few hours in
the co-op per month. A food co-op, such as co-op grocery store is another example.

Shared Economic Participation. For communities to function there needs to be a form of common currency (either created by the community or from a larger economy the community is situated within), and there should be a form of equitable sharing of collective costs and profits among the community members. There are different ways to distribute the wealth — everyone can be equal no matter what, or reward can be proportional to role, or reward can be proportional to level of contribution, etc. What economic works best in the long-term, for both creating sustainability and growth, for maintaining social order and social justice, and for preventing corruption?

Agility. Communities must be designed to change in order to adapt to new environmental, economic and social realities. Communities that are too rigid in structure or process, or even location, are like species of animals that are unable to continue evolving — and that usually leads to extinction. Part of being agile is being open to new ideas and opportunities. Agility is not just the ability to recognize and react to emerging threats, it is the ability to recognize and react to emerging opportunities as well.

Resiliance. Communities must be designed to be resiliant — Challenges and even damages and setbacks are inevitable. They can be minimized and mitigated, but they will still happen to various degrees. Therefore the design should not assume they can be prevented entirely, but rather should plan for the ability to heal and eventually restore the community as effectively as possible when they do.

Diversity. There are many types of diversity: diversity of opinion, ethnic diversity, age group diversity, religious diversity. Not all communities need to support all kinds of diversity, however it is probably safe to say that for a community to be healthy it must at least support diversity of beliefs and opinions among the membership. No matter what selection criteria is used, there must still be freedom
of thought and belief, and expression, within that group. Communities must be designed to support this diversity, and even encourage it. They also must be designed to manage and process the conversations, conflicts, and changes that diversity brings about. Diversity is a key ingredient that powers growth, agility, and resiliance. In biology diversity is essential to species-survival — mutations are key to evolution. Communities must be designed to mutate, and to intelligently filter in or out those mutations that help or harm the community. Processes that encourange and process diversity are essential for this to happen.

Related posts:

1. Communities of Purpose: The Third Type of Community I’ve been thinking about different types of communities recently. Two...
2. Modular by Design Over the next few days, my friend Tristan Louis...
3. Interesting Idea: Start a Magazine that is a Wiki I was reading this article in Wired magazine about wikis,...

What is the universe and where does it come from?

There are two major schools of thought on this question:

1. Science: One is modern-day science, which takes the position that universe is strictly a physical phenomenon and that everything about can be explained by repeatable physical measurements, testable scientific theories, and the rules of math and logic.
2. Religion:
   The other school of thought is religion, which in general, takes the position that the universe comes from something non-physical that is ultimately unexplainable and beyond the reach of science.

In this paper we will take an intellectual adventure into the far fringes of both science and religion, to explore the question of whether or science and religion might be unified. Such a unification is an intellectual “Holy Grail” that could truly change the world. But is it even possible? I think it is, and I’ll propose the core of such a unification here.

The Possibility of Convergence

While there are clearly differences between the approaches and beliefs of the sciences and religions of the world, there are also more similarities than many would like to admit. Beyond that however, at the very deepest levels, they lead to similar logical conclusions and in fact intersect on certain fundamental points, whether their proponents know it or not.

In particular, the question of the origin and nature of the universe is where I believe science and religion converge. Whether one holds the view of science, the view of religion, or both, it turns out that there is a logical necessity for reaching the same final conclusions about the ultimate nature of reality.

Whether one starts from a scientific viewpoint and applies only the methods of science and logic, or one starts from a religious perspective and applies only the methods of religion and logic, either way the conclusion is the same. As long as one regards logic as a valid method of enquiry, the final answer is the same.

The Core Argument

So what is the answer? In short, everything is “nonoriginated.” This has a very specific meaning: the universe (or anything else that we might posit to exist) cannot logically originate from nothingness, from itself, or from some other fundamental thing.

Here’s how this conclusion is reached in a nutshell (I will explain this argument in more depth later in this article, as well as its many implications):

To claim that something originates from nothing is a contradiction.

To claim that something originates from itself is a contradiction.

To claim that something originates from something else leads to an infinite regress unless you claim there is a fundamental first-thing — but claiming there is a fundamental first-thing leads to a contradiction, so it’s not an option. An infinite regress on the other hand, is not really an origin.

Therefore none of the three above ways of originating are logically tenable, yet there is no other possible fourth alternative.

This then leaves only two possible conclusions about the universe (and anything else that is posited to exist):

1. The first option is that the universe is not really happening at all, because there’s no logical way for it to have originated.  But this is immediately contradictory to our experience. It is refuted by obvious, undeniable evidence — right in front of us we can see that something is happening — who knows what it is, but it would be absurd to deny that there is some kind of phenomena taking place.
2. The second option is that the universe is happening, although there is no origin for it (i.e. it is “nonoriginated”). It is not necessary for there to be an ultimate and final origin — no first cause, prime mover, fundamental particle, or first moment of creation. The universe must therefore be infinite in time, space, and levels of scale.

Option (1) is easily refuted. We are left with option (2) – Nonorigination.

But it is a bit strange to imagine a universe that has no beginning, no origin. How can the universe exist if it is truly beginningless? Without a first-cause what could ever have gotten it started? Without a final fundamental particle, what could things actually be made of? In fact, it is precisely because the universe is nonoriginated that it CAN appear at all. This will be explained further in this article.

We can see how this logic applies to the origin of the universe. How about God? Well if God exists then the same logic would apply: God must also be nonoriginated. Anything that is posited to exist must be nonoriginated.

This point of nonorigination is where science and religion intersect. Nonorigination is the ultimate nature of reality. It is not merely a concept — it is the actual nature of all things, and it has many profound implications. It points to a level of reality that is beyond the limits of space and time — and in this respect it is proof of what might be called the Divine, yet it is also completely compatible with the physical world and its laws.

There are several other key dimensions of nonorigination as well. Awareness is one of them. Awareness is the unique capacity of sentient being to make observations. It plays an important role in making the universe happen, and is actually unified with nonorigination. Where there is nonorigination there MUST be awareness and vice-versa.

Likewise the process of cause-and-effect turns out to be a natural corollary to the nonorigination of the universe, and it’s powered by awareness, the act of making observations. If there were no such process, the universe could not work as it does; it would effectively be random.

I will explore these topics in a lot more detail below.

The unification of science and religion is not philosophy, it is logic. But how we interpret it, and what we do with it is a matter of personal preference and personal philosophy. This paper will not attempt to draw conclusions about what scientific or religious belief is best. That is up to you. Use the logical evidence however you see fit.

What Does the Universe Come From?

If one even merely posits the existence of the universe or even just the presence of a fundamental particle — then that immediately leads to further questions such as: Then where does that come from, what is it all really made of, and how could it all be taking place, what is space-time made of or located in, who or what designed this or how did it all happen so perfectly when it is statistically almost impossible?

Some people just can’t imagine that anything as vast as God could be possible, so they simply decide (without any real evidence) that God is impossible. Or they think that there could not be anything greater than or beyond the scope of the physical universe because they feel that the only things that can exist are physical things. To them, there is nothing but the physical, it is all a big machine, this is all there is — and for that reason they can’t believe in some kind of greater being or ultimate reality beyond space and time or the physical laws. But the grounds on which they claim God is not possible can also be used to claim the universe itself is not possible. If they believe in the possibility of the physical universe they also must accept the possibility of God by the same logic.

Here’s why: If the argument against the possibility of God is that it just isn’t possible for there to be something infinite, then that means either space and time are finite or they can’t exist either — the universe would not be possible because space and time are presently thought to be infinite.

Similarly, if the argument against the possibility of God is that there just couldn’t be anything beyond the physical universe, then even the physical universe could not exist — for if there were no possibility of anything greater than or beyond the universe then where is the physical universe taking place? What does it come from? What is it “in?” If it ever ends, what remains? This second argument is a bit of a difficult point so it bears further explanation.

Whenever you posit something, it logically has to either come from nothing, or from itself, or from something else. And at the time it exists it either has to depend on nothing, depend on itself, or depend on something else.

Stating that the universe comes from nothing or depends on nothing is problematic — it is in fact equivalent to saying that the universe comes from or depends on something beyond the universe: some primordial “nothingness.”

Stating that the universe comes from or depends on itself is circular and also a contradiction of sorts — in order for the universe to create itself or depend on itself it must already exist, and so this is impossible and not an option.

Yet stating that the universe comes from something else or depends on something else admits that there must be something beyond it to come from or depend on.

In other words, no matter what position one takes on the universe, it leaves open the possibility – indeed even the logical requirement – that there must be something before it, greater than it, deeper than it, beyond it, after it, etc.

Refuting Ideas that the Universe Comes from Nothingness

There are however some people who are not convinced by the above arguments. They hold tenaciously to the belief that the universe comes from some kind of primordial “nothingness” which they conceptualize as existing somehow on its own, either before or during the existence of the universe.

This belief in some kind of concrete “nothingness” has many problems. First of all to posit “nothingness” is to treat it as some kind of thing in fact — so it is self-contradictory from the start. Secondly, it is impossible to even imagine actual “nothingness” so labelling it, speaking of it, or positing that it exists is simply delluded. To posit it is not actually to posit it. To imagine it is not actually to imagine it. And in fact there is no way to even conceive of nothingness actually existing, for if it were to exist it would not be nothing. Finally, even if we ignore all these logical problems and still cling to the concept of nothingness, how could anything come from nothing? Let’s examine further.

If nothing really is “nothing” it could not contain anything that serve as a cause or origin for anything else, let alone an entire universe. So it could not give rise to anything. In fact it would be a contradiction to assert the co-existence of nothing and something as well — so even if nothingness could somehow give rise to the universe it would have to be destroyed or eliminated at the moment the universe
came into existence — but if that were the case how could it give rise to the universe — it could never overlap with the universe at all so how could it even be said to give rise to it?

For example the universe could not gradually emerge from nothingness since nothingness would be completely eliminated at the very first instant of the process of emergence, and then the process would be over since there would be no more nothingness left for the rest of it to emerge from.

Similary the universe could not emerge all-at-once from nothingness either, because for that to happen there would at least have to be a moment in which nothing and the universe co-existed — the moment in which the universe emerged.

If we don’t allow for at least that one moment of co-existence before the universe replaces nothingness, then causality is not possible to establish: there would be no way to connect the emergence of the universe as coming out of or from a pure state of nothingness that existed before it — and so there would be no point in making this claim at all.

To say that one thing comes from another thing means we have to be able to show how they are connected, and for that to be possible they have to both exist at the same time, or there has to at least be some chain of events we can point to that connects them.

But if nothing and something are truly mutually exclusive then that is simply not possible to establish. All this effort is simply to show finally and totally that nothingness is a flawed concept, and to claim that something can come from nothingness is even more flawed. If you already accept that you don’t have to re-read this paragraph to figure it out, just continue reading below.

Furthermore belief in the concept of nothingness actually refutes belief in the power of science — for nothingness is not measureable, not verifiable in any way, and is therefore impenetrable to science.

So anyone who cites “nothingness” as the origin of the universe is not in fact being scientific, they are abandoning science. To claim that all space and time — and all science — springs from nothingness is akin to claiming that the physical world (and therefore the domain of science) depends upon something beyond the physical world and beyond domain of science, in other words on what is traditionally the domain of religion.

In other words, if we think the universe sprang forth from nothingness that is like saying that science depends on something beyond the realm of science at the fundamental level, and if we say the opposite — that the universe has always existed or there is an infinite series of universes — that is also akin to saying that science depends on something beyond what science can ever explain — for infinity, while not a contradiction at least, is equally impenetrable to science.

Refuting Ideas that the Universe Comes from Itself

If the universe didn’t spring forth magically from nothingness, then perhaps it came from itself. What would this mean? It would mean that the universe already existed before the universe existed, in other words it both existed and did not exist at the same time. That is circular reasoning, and it’s also a logical contradiction. There’s not much more that needs to be said about this. But I’ll say it anyway, just to make it perfectly clear that this is not an option.

Perhaps we might interpret “coming from itself” in a slightly modified manner. For example, the universe today comes from the early universe, and they are quite different. So saying the universe of today comes from the universe of way-back-when is not saying that the universe today comes from itself literally, it is saying it comes from something else: the early universe. That is certainly one way to wiggle out of the fallacy of something coming from itself, but it just leads to an infinite regress: the fallacy of something originating from something else. The next section explores why that isn’t an option either.

Refuting Ideas that the Universe Comes from Something

If the universe doesn’t come from nothingness, or from itself, then what does it come from? If it comes from something else, then what does that thing come from? At some point there has to be a beginning to the process. But if there is a beginning then what is before it? Whatever that is, it is beyond the realm of science.

To state that the universe comes from something else is to say that something else (whatever it is) is the more fundamental level or prior state of the universe. In other words to state that the universe comes from something is really saying the universe comes from the universe, at a deeper level or an earlier time, or a different place, or in a different state or form, or all of the above.

But all such statements are either claims that the universe, taken as a whole (all states of the universe over all time and space) comes from itself, or at worst it is a circular argument that simply pushes the problem down a level: what does that other more fundamental “something” that the universe depends on come from?

On the other hand, if we claim that the universe is beginningless and unoriginated — then what is the eternity in which this “beginninglessness” is taking place? What created eternity? To posit that there is an eternity “beyond” the universe, or that “contains” the universe (including space and time) is already to state that there is
something beyond the realm of science, something outside the universe. That’s acceptable, however, if we then claim that this “eternity” is some kind of more fundamental thing, we just end up in the same infinite regress as before.

Another possibility might be to claim that eternity and the universe are the same thing. This is to say that the universe is infinite in scope — space and time are boundless and contain all there is. This is either equivalent to the claim that the universe comes from nothing, or from itself. Neither of those options is tenable.

If we posit that eternity comes from nothing that is a contradiction. If it is self-originated, that is circular and also a contradiction. If we say it comes from something else, then what — an infinite series of greater eternities, each containing all the lesser ones, like a Russian doll? Or is there a highest level of eternity and if so, what prevents there from being greater levels of eternity — what causes the boundary to exist and if there is a boundary, what is on the other side of it? This leads to either a contradiction or an infinite regress.

If one claims that the universe contains all space and time, then is the container and what is contained finite or infinite in scope? If it is finite there must be some kind of edge, if it is infinite it implies something so inconceivably vast it is frankly mystical in scope.

In short, if we claim the universe comes from something that leads to circular arguments and contradictions, or an infinite regress. If we’re willing to accept circular arugments and logical contradictions or infinite regresses as satisfactory answers then that is not very different than accepting any other self-justified claims taken on faith, such as those made by religions. In fact, it’s just a kind of religious belief disguised as science. If we are willing to think this way — and most scientists are — then why not also believe in God or other religious ideas as well? It would be hypocritical not to.

It’s important to note that the same logic that refutes notions that the universe comes from nothing, itself, or something else, can also be applied to any claims that there is a God. If there is a God, then like the universe, it also cannot originate from nothing, itself, or something else without leading to logical fallacies. To claim that God came from nothingness is again the something-from-nothing argument that we know does not make sense under logical scrutiny. To claim that God comes from God is circular reasoning and contradictory. To claim that God comes from something greater than God contradicts the very notion of God and/or leads to an infinite regress which just pushes the problem down to deeper levels — where does that infinite regress of ever greater Gods come from then?

Both the universe and the concept of God have the same existential status in fact. Neither one of them has an origin that we can actually find or name without ending up in a logical mess of contradictions and infinite regressions. In this respect they are quite similar.

Nonorigination

If neither any possible universe nor any possible God can be said to come from nothing, itself, or something else, then that leaves only two logical conclusions:

1. The first option is that these things are not possible and not happening at all since they can’t have originated — however that option is refuted by the fact that at least in the example of the universe, something is obviously and undeniably happening right now. The presence of the universe refutes the notion that it is impossible for something to exist that does not originate from nothing, itself or something else.
2. The second option is that such things could be possible, but in an “unoriginated” manner. But what does this mean? In short, for something to be “unoriginated” does not mean it is non-existent, it just means that it is not dependent on some initial set of causes and conditions. One way for something to exist in an unoriginated manner is for it to be eternal, or at least beginningless.

Option (1) is refuted by the basic fact that we do observe something happening right now. Option (2) is the only remaining option, and is not refuted in any obvious manner.

But option (2) is mind-bending. How can something beginningless exist? How could it ever have come about if there were never any initial causes or conditions to start it? It’s the primordial chicken-and-the-egg problem.

And this is where things get interesting. Scientific theories claim the universe either has an origin or is unoriginated. Religions also either claim the universe has an origin or is unoriginated.

In the first case, the claim of an origin (such as theories in which the universe started from some physical event before which there was literally nothing, or in which there was nothing and then a Diety appeared and created the universe), we can prove logically that this leads to fallacies (because the origin cannot come from nothing, itself, or something else), so this view is simply wrong, or provisional at best; it’s not a final explanation.

In the second case, the claim of non-orgination, in which the universe is held to be beginningless and possibly endless (for example a never-ending sequence of Big-Bangs and Big-Crunches, or a timelessly existing realm), this begs the question of where did this never-ending sequence come from? How could it have ever started? What is it, what iseternity and what created eternity?

In either case however, whether we use science or religion to approach the problem of the origin of the universe, we end up at the same place in the end. The path we may travel to get there is different, and certainly the language with which we express the conclusions is quite different, but the final result is the same. Logically speaking, the universe must be either unoriginated or created by something unoriginated. It is the only logically tenable conclusion.

In other words whether universe is thought of as purely physical, or originating from God, the only logically tenable conclusion is that it is nonoriginated. And the same goes for God. We may believe that God is greater than the universe, in other words prior to it, and in this case God and the universe are not equivalent, however, upon final analysis, even in this configuration, the only logically tenable conclusion is nonorigination.

For example, if the universe is a physical thing that was created by God, yet God is nonoriginated, then by inference the universe is also ultimately nonoriginated (via God’s nonorigination). Although provisionally we can state that the universe originates from God, since God is in this case nonoriginated, the universe is ultimately nonoriginated, for no final origin can be found or logically established.

In summary, nonorigination is the single fundamental truth of both science and religion. It is where they converge.

Unification

And now, based on the above lines of reasoning, the final capstone on the argument.

If we posit that only the physical universe exists, then we have no other choice but to say the universe itself must be unoriginated, in other words, uncaused and unconditioned — neither coming from nothing or from something else.

There is no escape from this logical conclusion. Nonorigination is always found to be the ultimate nature of whatever is positied to exist. It doesn’t matter how many levels of reality you think there are, as soon as you posit even one, it’s “turtles all the way down,” to quote the famous expression. In other words, if you posit the universe resting on the back of something (for example, a giant turtle) then that something must in turn rest on the back of something else (another giant turtle, for example), and so on, endlessly. The only way to not have an endless pile of turtles resting on still deeper turtles is to posit a final fundamental turtle, but that makes no sense — for that turtle would be in free-fall, meaning the entire stack of turtles would have no foundation and would topple over. What nonorigination really means however is that the stack of turtles can be infinite or finite – it really doesn’t matter and is equivalent — either way the entire stack itself, whether just 1 turtle our countless turtles, is nonoriginated. This is not to say that the stack depends on something else we call nonorigination, it is to say that the stack itself IS nonorigination.

This is very hard to accept conceptually, but it is a logical conclusion. The only way to deal with it intellectually, once you derive it and are convinced there is no way around it, is to simply accept it. The universe really is beyond conception — it really cannot ever be conceived. It’s infinite and its nature is inconceivable.

Now what’s interesting, and unifying, about this conclusion is that nonorigination is a logical and scientific kind of conclusion, and yet there is something about it that is inconceivable and wondrous like what we think of when we speak of something Divine. Nonorigination is unexplainable, inconceivable, prior to all space and time, beyond the limits of the mind, and the nature of all things. This is at once scientific and Divine — it is something infinitely beyond all conceptual limits — it is the point where everything converges.

Nonorigination is also a very subtle truth, because it neither asserts or refutes the universe and/or the Divine. In fact, what appears is free to appear and function — yet if we analyze it we find it is nonoriginated. That doesn’t mean there are no causes and effects in operation, it doesn’t mean the universe is random — in fact quite the contrary will be shown later in this article.

Nonorigination says nothing about the day-to-day “relative level of the world” and how it functions — it is a statement about the ultimate nature of everything: the originlessness and fundamental essencelessness of whatever appears. Thus when speaking of nonorigination, we can make a conceptual distinction between the relative and ultimate levels of truth. They are both true, one does not contradict the other.

Relative truth is truth within limits — specifically a statement that holds true locally but not globally. Ultimate truth applies globally. In this case within the reference frame of the universe alone, we can say that any effect we observe is originated from various causes and conditions, but within the larger frame of the origin of the entire universe, it is nonoriginated. In any case, whether one chooses to accept this modal logic or not is a matter of personal preference.

Beyond Four Logical Extremes

In Buddhism the ultimate nonoriginated, uncaused and unconditioned primordial nature of reality is said to be “unborn.” Since it has no cause it is never actually created or “born” as some thing, yet since it is also not literal nothingness, it is not entirely non-existent, for if it were nothingness it could not be something that we could even apply the labels of nonoriginated, uncaused and unconditioned to.

That which is nonoriginated is entirely free of all logical extremes:

1. Existence
2. Non-existence
3. Both existence and non-existence
4. Neither existence nor non-existence

It doesn’t exist because it is not originated. It doesn’t not-exist because it isn’t literally nothingness. It doesn’t both exist and not-exist because that is a logical contradiction.

The fourth logical extreme is the hardest to overcome and there are a few different arguments to conquer it. First of all the assertion of something neither existing nor not-existing is also a contradiction, via double negatives: if it doesn’t exist then this is equivalet to not-existing, and if it doesn’t not-exist then this is equivalent to existing.

Another way to refute this extreme is by the fact that there is no other alternative to existing or not-existing: to exist is to be something, whereas to not-exist is to not be something. How could there be “something” which is neither something or not-something. If it is “something” that contradicts the prong of claim that it is neither “something” or not-something. Yet if it is “not something” then that contradicts the prong of the claim that it is neither something or “not-something.” In other words, to claim that something is neither something or not-something is contradictory from the very start.

The Nonorigination of Nonorigination

It’s important not to get stuck on conceiving of nonorigination as a special kind of thing. Nonorigination is in fact nonoriginated too. So it can’t be something. It also can’t be nothing. It’s actually free of of four logical extremes of being something or nothing. It’s not any of these four logical possibilities:

• Something
• Nothing
• Something and nothing
• Neither something nor nothing

There are no other logical possibilities than these four. Nonorigination cannot be said to be or not to be.

In fact, if we look for nonorigination we don’t find it. For example, you cannot find the absence of something. The absence of that thing is literally the fact that you cannot find it. Nonorigination is the absence — in any moment of experience — of anything that can be found to exist, not-exist, exist and not-exist, neither exist nor not-exist. It is an absence, not the presence of something else that could be labelled “nonorigination.”

But this absence is not merely a rhetorical or logical point — it really is the actual fundamental nature of reality. In other words, whatever the universe is — whatever appears to us — really does have this nature of nonorigination, this complete absence of existing, not-existing, both, or neither. This means the universe is far more unexplainable than can even be imagined.

The Primordial Nature of Reality

We have found that whatever there is, it must be nonoriginated. There is no other logical alternative. Even nonorigination is nonoriginated. So while there is no final isolated thing we can point to as nonorigination itself, the fact that whatever we can point to is always found to have a nature of being nonoriginated is a fundamental truth. In fact it is perhaps the fundamental truth. It’s the one logical conclusion that we always reach no matter what we analyze.  All roads lead to nonorigination.

If we say that the universe is nonoriginated, then it doesn’t exist the way that most scientists and even most religious thinkers imagine it to. While it’s not nothingness, it’s also not something, or any other alternative. This absence of having an existential status is in fact the way it really is, that’s its primordial and ultimate nature. We can also say that this absence of existential status is the primordial nature of reality.

This means that reality is beyond the limits of existing and non-existing. This may defy common sense, or even feel impossible to imagine, yet it is the only logical option — it is inconceivable yet must be so.

Many great religions all agree on this point at their highest levels of philosophy: Buddhism, Christianity, Judaism, Islam and Hinduism all agree at the purest conception of the Divine is really inconceivable and unameable, and certainly primordial (not created or conditioned by anything else). At it’s very purest essence the universal truth of all religions, and even of science, is that there must be, and is, something uncreated and unconditioned at the root of reality.

Whether the universe is theorized to have sprung out of perfect randomness or nothingness, or it is an eternity, or there are infinite parallel universes, the only logically tenable way that the entire reference frame can exist is if it is nonoriginated. This nonoriginated, uncaused and unconditioned nature, is the primordial nature of reality — of the universe and/or the Divine — regardless of whether one believes in just one, or in both.

So there we have it: the essence of the universe and the essence of the Divine are the same primordial unoriginated reality. We can call that the universe, we can call it God, or we call it Buddha, Christ, Allah, Tao, or something else. It doesn’t matter what we call it really, it is nameless.

Freedom

If something is truly nonoriginated, in other words, uncaused and uncreated, then it is totally free. In particular it is free of all concepts and beliefs about it or anything else. It is free of all limitations. We cannot say that it has a particular name and no other name. We cannot say it can only be reached through one path and not others. We cannot say that it can only be served by obeying particular rules and not others. We cannot say that only some people have access to it while others don’t, or that anyone is closer to it than anyone else.

Who are we to say anything that would limit something that is totally uncaused and unconditioned? Something cannot be partially free. Either it is totally free or it is not free at all. There is no middle ground. If we truly believe in a conception of a “God” that is totally free, then we have to be careful not to impose further concepts onto it or onto ourselves or anyone else. The closer one is to knowing God, the less one can really say about God.

The same goes for science: we eventually must reach similar conclusions about the fabric of reality and the origin of the universe. We may be able to describe and predict all sorts of things about the physical universe, but the deeper or farther we look in space and time, the more it starts to become indescribable. At the smallest scales and the largest scales, and in fact at every scale in between, the origin and nature of the cosmos is and will always be a mystery. The best we can do is categorize it and glean some understandings about how it functions, but we’ll never be able to explain it. The universe, like God, is also beyond conception. It is either uncaused and unconditioned itself — which means it is free — or it depends on something that is uncaused and unconditioned. Either way, it is free.

Think about that for a moment. If the universe is free or depends on something that is free — then either way, what takes place in the universe is ultimately uncaused and unconditioned, meaning the universe is effectively free in both cases. What does “free” actually mean? It means literally that anything can happen. Anything. Any universe is possible. Any set of physical laws are possible. Anything at all is possible — even things which we can’t explain and which perhaps are contradictory to the physical laws (such as anomalies, miracles, etc.).

Observation

But then why do only particular things appear to happen, rather than other alternatives? Why does the universe appear to obey particular physical laws? Why don’t we observe miracles or other anomalies that contradict the physical laws (note: some people do claim they observe these phenomena, so we cannot say with certainty that they don’t happen at all…)? But in any case, why does the universe seem so rational and orderly if indeed absolutely anything is possible?

One school of thought on this question (the Many Worlds interpretation of quantum mechanics) answers that in fact everything does happen, but in parallel universes, all at once. So there’s no real choice being made — all possibilities from those that are consistent with the universe we know to those which are totally outlandish or seemingly impossible do happen, all at once.

Another school of thought claims that somehow the universe makes choices and that these choices come about whenever observations take place, and that they have something to do with probability — the universe is not precisely deterministic, but not entirely non-determinstic either. If that is the case, then the act of observing something essentially causes the universe to choose what actually happens from the set of all the things that could possibly happen.

But if the universe makes quantum mechanical choices at each moment of observation, then what comes first, the act of observation, or what is observed? What creates reality, what causes the choice that selects one possibility versus all the others? Is what appears literally caused by the observer, or is it there before being observed — does it cause the observer to observer it, or does the observer cause it to be observed? It’s unclear, according to quantum mechanics at least; It’s a
chicken-and-the-egg kind of problem. In fact, the situation is better characterized as a kind of feedback loop, or a dance of sorts, that’s been going on forever.

The universe is ultimately free; anything can happen. But anything does not appear to happen, only some things happen. This is currently said to happen because of choices that are made when observations take place, at least on a subatomic level.

But while observation may cause or condition reality on the quantum scale, on the macroscopic level — the level of people and cars and houses and trees, and so forth — the act of observation does not seem to function in the same manner; it doesn’t cause things to happen. Or does it? The classic Zen koan, “If a tree falls in the woods and there is nobody there to hear it, does it make a sound?” addresses this question.

In fact, if there is no observer to hear the sound, how can we say there is a sound? When the tree falls it causes vibrations, but those vibrations only make a sound if they move the eardrum of something that can hear. If there is no observer, but only a recording device in the woods, there is a recording, but not yet a sound. The sound only can be said to exist when the recording device is actually used to play the recorded sound to an observer. Until that happens, the sound is not observed.

Quantum Mechanics

This strange fact is reflected in scientific experiments such as the famous “Double Slit Experiment” and many variations. In that experiment, the act of measuring the path that a photon takes causes it to appear to appear to behave like a particle, while if you don’t measure the path it appears to behave like a wave. In fact, this effect is even stranger — experiments have been done which seem to indicate that this effect can even go backwards in time. Even if you wait to measure the path the photon takes long after it has traveled through the experiment, that observation seems to effectively go backwards in time and cause the photon to retroactively behave one way or another, in the past.

Another famous thought-experiment which illustrates the interaction between observation and reality is the “Schroedinger’s Cat” example, in which a cat in a box is either dead or alive depending on whether a random event happens, but until you actually open the box you can’t know it’s actual status — and on a quantum level in fact, until the cat is observed you cannot really say it is either dead or alive; it exists in a kind of intermediate state. The moment of observation somehow causes the intermediate state to collapse into a particular quantum state. This is very odd stuff. And for a while it was thought to really only apply at very small scales, although more recently there is some evidence that similar logic may apply even at macroscopic scales.

What this all means is that there is something about observation that seems to cause the universe to make choices. Another way of expressing this is that the universe — because it is totally free — has the freedom to make choices, and this happens through the act of observation. This would also imply that the universe is intelligent and creative, because the things that make observations (sentient beings like humans, for example) are intelligent and creative. Perhaps the universe isn’t happening out there on it’s own, perhaps it is in a very real sense, imagining itself through an unfolding process of creatively making observations.

The Improbability of the Universe

If the universe either is something totally free, or depends on something totally free, then either way, the universe is totally free. That is to say there are no limitations on it. Anything can happen. How then is it that we observe particular things and not everything happening? Why don’t each of us experience all possible parallel universes? Why is the universe the way it is, and not even slightly different? Why are things the way they are? We can look at physical things and use scientific knowledge to understand their trajectories and dynamics. That certainly helps us explain a little bit about those physical things. But it doesn’t tell us why the initial conditions were not different, or why the universe is such that the physical laws and physical constants are what they are.

Even a slight change in the structure or unfolding of the universe would have resulted in a vastly different outcome — the physical laws would be different, the physical constants would have different values, and this would result in different kinds of universes. Some would have very different properties than the one we live in. Some would support life, some would not. Some would have led to our planet and human beings, some would not. Some would have stars and galaxies, yet other extreme cases would burn out and collapse into giant black holes almost immediately, while other configurations would have led to the universe breaking into countless separate universes or literally exploding and then dissolving into countless separate black holes. And there are many other possibilities too. These claims may sound wild, but in fact they are predicted using our current scientific model — if we simply change the initial conditions of the early universe slightly.

So why did things turn out the way they did? And why does our universe seem perfectly balanced to support human life — or any life for that matter? There are so many possibilities for how the universe might have unfolded, and most of those possibilities do not result in a universe that could support human life at all. In fact the universe we live in is one of the more statistically improbable outcomes. The odds of our universe happening are infitessimally small. So how did it happen?

Furthermore, at least on a quantum level it appears that until an act of observation takes place we cannot really say the universe makes a choice about what happens. So what about the early universe — before there were any human observers, or any living things at all to make observations? So what was made the first observation? Was there a “prime observer” at the first instant of the universe, and if not, how could it have come into being since on a quantum level without being observed it could not have had a particular state.

Or alternatively was there some other kind of outside observer that made the original observations of every ancient quantum interaction, enabling the universe to make choices, at least until living observers could evolve to make their own observations? Or, has the universe effectively made all those choices retroactively — for example, now that there are observers, has the effect of our present choices gone back in time and caused the universe to make all the necessary past choices to lead to the way things are today (that one is a mind-bender, but on a quantum level it is not unreasonable or impossible to consider — space and time are not obstacles on the quantum level. For more on this, read about the Anthropic Principle in physics and cosmology)

Perhaps only universes that can support life can therefore contain observers, and so only such universes can actually happen because without observers quantum level choices cannot be made — in other words, possible universes that don’t contain observers effectively cancel themselves out and never even happen, leaving only those universes that can and do support observers. This would at least eliminate a lot of possible universes and improve the odds of universes like ours ever happening. But there are still innumerable, literally countless, variations that are possible even within that set of observer-friendly universes. Why did it turn out that exactly one and only one of those possible universes — ours — is what happened?

Here’s another question that we have to consider as well: If observation is required for the universe to make choices and effectively collapse on various states out of the space of possible states it could be in, then either there was a first observer (which leads the contradiction that the first observer could not happen because it was not observed) or there has to be an infinite regression of observers, or we couldn’t have the present universe at all. Once again, we come to the logical problems we encountered earlier when discussing the universe and God. Either we end up in contradictions or regressions.

One possibility is that the universe is an observer of itself. We know that since the universe can contain observers (for example, humans), it is capable of making observations. So why should observations only happen on the human-scale. Perhaps there are larger systems within the universe that can make observations too? But even if we believe this it still doesn’t solve the problem — even if the universe can observe itself, what observes the universe? Alternatively, if we posit some kind of outside observer of the universe, then again, what observes that? In either case, we end up with a logical contradiction or an infinite regression.

Is there any way out?

Yes, there is one, and only one, way out: It all comes down to consciousness.

Conscious Awareness

Just as we found that in order for the universe to exist either it must be nonoriginated, it also must be inherently observed. Without observation, nothing could happen, choices could not be made, at least according to quantum physics.

But if this the case, what made the first observation that started it all? The answer is that there was no first observation. Instead, observation must be inherently unified with nonorigination. There is no other alternative, at least if observation is necessary for the universe to exist, on a quantum mechanical level.

In other words, the universe does not require an outside observer. This MUST be the case, for on a quantum level the early universe — indeed even the Big Bang or whatever we think the universe was like as far back as possible — could not have happened at all without something observing it (on a quantum level). The capacity to make observations must be an inherent property of the universe itself, or at least of what the universe depends on if we think it depends on something else. Either way, the capacity to observe is inherent, it doesn’t come from nothing, itself, or something else — it has no origin. It has to be or we couldn’t have the universe at all, according to current scientific theories about quantum physics.

So what is this mysterious capacity to observe? It seems to be pretty close to what we mean when we use the terms “consciousness” or “awareness” (and of “God” too by the way).

We humans have this capacity to experience our minds and senses — to not only be aware but to be reflexively aware as well — and it appears that animals and other forms of sentient life have this capacity too. We are able to observe and react to stimulus, but also to know it. We don’t just react automatically, like springs bouncing back from being compressed. We experience what we observe — we know — we are. We have a sense of our own being, we are aware that we are aware. We are aware that we are. And that is observation in its most naked form.

The universe supports the evolution of things which are aware of their own being. And that means that the awareness of being either comes from the physical universe or from beyond it. But either way, we have seen in our earlier discussion, that at the end of the day, whether you believe in only the physical universe or you believe in a God beyond the universe, they have the same ultimate nature of
nonorigination.

The characteristics of the universe, and therefore of what we call “God,” are therefore that of being uncaused, unconditioned AND aware (in other words, making observations). There is no other logical, or scientific, alternative.

Consciousness is therefore something deeper than what we might think. It is a reflection of the universe’s and/or God’s inherent capacity to be aware. It literally IS the primordial awareness of the universe. And because consciousness IS primordial awareness — the basic capacity to make observations that observes at least itself and can potentially observe anything or everything else — that means it is coming directly from the most fundamental level of reality — in fact it IS the most fundamental level of reality.

Awareness is uncaused, unconditioned and aware of being. Each of us, and indeed, each sentient being that is aware of anything, is a reflection of the entire universe in a sense, and of whatever we call “God,” if we believe in God. In a very real sense — from a scientific perspective as well as a religious one — there is something divine in every sentient being, and indeed in the entire universe.

This primordial awareness is inconceivable, because it literally IS that which is nonoriginated. Even within our own minds we cannot describe it or limit it in any way. It is the nature of mind, and it is the nature of reality, and of whatever we might call God. The difference between each of our individual human awarenesses and the infinite and inconceivable awareness of the universe and/or God is one of scale, not one of qualities. This also means that each individual’s mind is potentially as totally free as the total freedom of the universe and/or God. This is our true condition, whether we know it or not. Total freedom means the mind is potentially unlimited — truly unlimited. That means it is possible to know or experience or observe anything, for us as individual sentient beings, and for the universe as a whole.

Although anything can happen in theory, sentient beings such as ourselves and others make observations — that is our function in the universe in fact –  and these observations have quantum level repurcussions that actually cause the universe to choose particular outcomes, which in turn feedback to affect the probabilities of our future observations. In a very real sense, observation creates experience.

Whether you believe the universe is an inconceivably vast intelligent and creative being that has free will, or you believe it all depends on a God that is inconceivabley vast, intelligent, creative, and has free will — it’s the same. Take your pick, they lead
to the same conclusion, and the same universe. Awareness — the essence of consciousness — has a very key role in the universe, and/or in whatever we think of as God. It is in fact THE key to it all.

Cause and Effect

From this discussion so far, we have concluded that the universe is nonoriginated. That is to say, the only logical option is that it exists in a nonoriginated manner — it does not arise from nothing, itself, or something else (OR if it arises from something else then that thing must be nonoriginated, or at least something at some point that is causally upstream from it has to be nonoriginated). For example if the universe comes from God, then either God must be nonoriginated, or that which God depends on has to be nonoriginated, and so on. The point is that the series of things and things that create them is finite, not infinite. There is no infinite regress.

This does not deny the operation of cause and effect within the universe, nor does it deny that there can be an infinite series of causes and effects that lead to or stem from any event within the universe. It only denies that there can be an infinite series of causes and effects the lead to the creation of the universe as-a-whole. In
other words, on the relative level, within the universe, cause and effect can operate just as science (or even various religions) might predict. However, the universe as-a-whole is not caused, or eventually depends on something that is not caused.

Therefore the universe as we know it is not contradicted by claiming that it is nonoriginated. Nor is cause and effect contradicted by stating that ultimately the universe as-a-whole, or whatever is that which is nonoriginated, is totally and complely uncaused, unconditioned and therefore free. Furthermore, even though observers — individual sentient beings — within the universe are expressions of that
primordial freedom (by virtue of being aware), they are still subject to the laws of cause and effect within the universe.

For example, a particular observer may make an observation, and in doing so they perturb the universe on a quantum level, which conditions what they end up observing. Observation is a cause. What is observed is partially an effect of the act of observation, and partially an effect of other causes and conditions that relate to it. When an observer makes an observation, together with the appropriate set of causes and conditions, a particular event is observed to take place. Similarly,
that event then acts as a cause or condition for other observations and events to take place for that observer and/or other observers.

In this manner everything that happens within the universe is the result of a complex network of causes and conditions, in which observers play critical roles. Observers actually change the topology of the network (the patterns of linkages between various causes and conditions and observers) whenever they make observations. This ability to rewire the network by making observations is something that is unique to sentient beings — only true observers that are conscious are capable of causing this to happen.

In fact, without observers actively making observations we cannot truly say the network exists in any particular state — it could be in any of an infinite number of possible configurations representing any of an infinite number of possible timelines of universes. The act of observation is what triggers chains of cause and effect to “fire” (almost as if they were patterns of neurons and dendrites in the brain firing sequentially to generate various thoughts). When there is no observation taking place we might say that the universe is frozen in a kind of indeterminate state. Only when observations happen are particular chains of potential cause and effect in time and space activated, and thus particular events they bring about appear to take place.

The process of cause-and-effect changes the probabilities of various events, making them more or less likely to take place, that is, to be observed. And it is the act of observation itself which triggers the chain of cause and effect, which powers it, which makes it happen. This is how the universe works on a quantum level, and also perhaps how it works on other levels too (for example, the law of Karma in Buddhism is effectively this very process of cause and effect, or what is also called dependent-arising, taking place not only in the external physical world and the body, but within all sensory modalities and even within the mind).

But is cause-and-effect required for the universe to function the way it does? Is there an alternative?

Suppose that there were no cause-and-effect within the universe. Instead imagine what it would be like if everything happened randomly. In a totally random universe every event has an equal chance of happening, so either all events would happen at once, or none of them would. We don’t see either of these taking place however. Instead we see very non-random distributions of events taking place.

When you exert a force on an object it is highly likely to exert and equal and opposite reaction on you, and it is quite unlikely that it will do the opposite of that. But in a random universe both events would be equally likely, at least over all time and space and observers and possible universes. So if the all events are equally likely then we could not have the universe we experience, in which that is certainly
not the case.

One might move the problem down a level however by suggesting that perhaps this universe is only one universe in an infinite number of parallel or possible universes, which are all equally likely to happen, and we just got lucky somehow. We happen to be observers within this one, where things fall towards the force of gravity rather than being repelled by it, and so we are able to stand here on the planet and the planet retains its atmosphere, etc.

It’s fine to hold that view, however, even if one does, within this universe at least, it appears to be as if cause and effect is in operation. Whether cause and effect sequences are really happening sequentially over time and are influenced by the free will of observers, or they all happen all at once from the perspective of eternity and thus free will is illusory, what we experience would be the same. Thus these two alternatives are equivalent.

In this universe — which is the only one we observe — it appears to us as if cause and effect processes are unfolding over time, and for all intents and purposes, from our perspectives, whether causality unfolds creatively and non-deterministically over time and in part due to the free will of observers like ourselves influencing what we observe, or it’s all preordained in eternity, its equivalent.

What this means is that for this universe to happen, cause and effect is necessary. There may be other possible universe in the set of all possibilities which may not appear to contain processes that resemble cause and effect, but we are not experiencing any of them right now, nor can we even prove they exist. So from our perspectives it is as if they do not exist. Notably however, we cannot prove they do not exist either.

Now the question is how can a universe that appears to operate by cause and effect, within it, be nonoriginated? How could a universe full of causes and effects not have a cause? How can nonorigination and cause-and-effect be compatible? Isn’t that equivalent to claiming it is an effect (the univeres) that has no cause (nonorigination), and isn’t that therefore a logical contradiction? No. To make such a claim would indeed be a logical contradiction — an effect is the result of a cause and cannot exist without a corresponding cause. The solution is to not claim that the universe is an effect, nor to claim that nonorigination is a cause.

It is contradictory to assert the existence of an effect apart from its cause. Therefore the universe cannot be asserted to be an effect that has no cause. It is simply nonoriginated, it is not the result of anything. For it to be the result of something would contradict nonorigination, which we have already found is the only logical way that the universe can exist at all (because it can’t come from nothing, itself, or something else, so therefore it must either not exist at all, or it must exist in a nonorignated manner, and since it does appear to exist, it must exist in a nonoriginated manner).

Nonorigination requires that the entire universe is not a cause nor an effect. But although the entire universe is not a cause or an effect, it can appear to contain what look like, and function within it as, causes and effects — sequences of events that are causally linked over time and space in complex interdependent networks. This is a real mind-bender and will take some time to explain. Cause-and-effect is a
relative level process — it is provisionally true — but on an ultimate level the process and everything within it is nonoriginated.

For example, we probe further, into any particular event, and we trace back its origins within the universe, and if space and time are infinite, then we may find an infinitely broad and deep network of causes and effects both upstream (leading to it) and downstream (stemming from it) in time. Since these sequences are infinite, they are from a logical perspective infinite regressions. To claim that any effect comes from an infinite series of causes and effects, is logically fallacious — we cannot prove such a claim since we cannot test infinity to see whether or not the series is truly infinite or not, or even what all the causes and effects in the alleged series even are.

Cause and Effect is Nonorigination

Therefore, from a logical level, even though causes and effects may appear within an infinite universe, they too must be nonoriginated — it is the only manner in which they can be said to exist without commiting a fallacy: They must exist in a manner that is free from four logical extremes. In other words, they cannot exist, not-exist, both exist and not-exist, or neither exist or not-exist.

They cannot exist because of infinite regression. They cannot not-exist because that is a logical contradiction and also conflicts with what we observe. Combining existing and not-existing is a logical contradiction. Rejecting both existing and not-existing leads to logical contradiction and also conflicts with what we observe. So while on a relative level the process cause-and-effect appears to operate, on
the ultimate level of analysis, it is equivalent to being unoriginated, from our perspectives at least.

Another way of expressing the same thing is end result is that if the space and time are infinite, then the universe as well as its contents (including all causes, effects, observations, and observers) must be ultimately nonoriginated. And since it’s not possible to have a finite sequence of causes-and-effects (because that would mean that at least one cause or effect would not have a corresponding effect or case, which is not possible (because a cause and an effect are inseperable, it is a contradiction to claim you have one without the other), a finite universe of causes and effects is impossible. Therefore finite universes are impossible, since only universes that contain causes and effects would not be random.

Therefore our universe must be infinite, because we do observe processes of cause and effect, and it also must be nonoriginated (or be equivalent to something that is nonoriginated — for example be being part of an infinite series of causes and effects of universes or by being created by some kind of God’s free will, not by cause and effect (where God is by definition not orignated by anything else). These are the only logical possibilities.

The lines of reasoning in this section, and those above it, prove that lead us to conclude that only infinite universes in which cause and effect appear to operate are possible, and that such universes (and the causes and effects they contain) must be ultimately nonoriginated, and observed, in order to be said to occur.

In other words, cause and effect is nonorigination. Whatever appears to be generated by causes and effects is ultimately nonoriginated.

Nonorigination is Cause and Effect

The same is true in the reverse direction. We cannot say that something is nonoriginated unless there is some relative-level appearance of a thing to make that statement about. The notion that nonorigination could exist on it’s own without some subject or object that is nonoriginated is a contradiction. Nonorigination is a phenomenon that requires a complementary relative-level facet, namely whatever is being asserted to be nonoriginated. To assert nonorigination apart from anything else would be like positing a penny with no sides. A penny must have a heads and tails. It can’t be a penny without them.

Therefore where there is cause and effect is the result of nonorigination and observation, and where there is nonorigination and observation there is some phenomena — some event appearing to take place, and since phenomena do not happen randomly, the only alternative is that some combination causes and effects are at work.

It is the process of observations, causes and effects that makes some possible phenomena more or less likely than others at various locations in space and time. Without such a process all possible phenomena would be equally likely at all possible locations in space and time. That would not result in our universe, or anything like our universe, at least as far as we observers can know from our positions within space and time.

Perhaps one might argue that maybe if we could see eternity we might find that our universe was randomly generated as-a-whole, but that is not possible either — for if all universes were equally likely then they would either all happen at once or none of them would happen at all. The fact that this universe appears refutes the possibility that none of them happen at least. As for the possibility of them all
happening at once, this is a possibility, but we can’t determine this for sure unless we can see eternity ourselves. From our perspective, and as far as we can know, only this one is happening.

Nonorigination is therefore equivalent to cause and effect, and vice-versa. The process of cause-and-effect is not refuted by nonorigination, indeed it is required by nonorigination, and vice-versa. The proof is that this universe is appearing and
functioning the way it does.

Trinity

At each moment of our lives, of each moment of observation no matter how brief or precise — there is something else taking place that is NOT nothingness and NOT exactly whatever appears to us either.

For example when we observe a tree, we see the appearance of the tree visually. That appearance is there, at least as a mere visual image, not unlike an image in a dream. It may be a real image of a real tree, or a dream image of a dream tree — but that doesn’t matter, the two cases are equivalent for in fact we really cannot tell the
difference at the moment of its appearance.

The image of the tree before us is of some thing which we may believe exists “out there” in the “real world” beyond our body and mind, and that it is really just a depiction of the object out there in the visual spectrum, formed by our particular sense organs and their abilities and limitations, and then rendered via the circuitry of our brains onto some kind of internal viewing screen, or to some further set of cognitive processes which then do things like interpret it, label it as a “tree” etc. That’s all fine — whether or not any of that is really what is taking place or not — at the very moment of an appearance appearing that is all hypothetical from our own perspective. All we can know at the moment of an appearance is that it is there in
its own unique way, and that we know it.

The appearance is the object side of a moment of experience. The “we know it” part of the experience is the subject side. There are these two sides to every ordinary moment of experience. This is consciousness, a dualistic interpretation of what is taking place in every moment into having two poles of subject and object that are
somehow two different things. Most people spend their lives experiencing everything — themselves, the outside world, others — in this dualistic mode of cognition. Note that dualism is not inherent, it is a conceptual interpretation of raw experience. Experience itself is not dualistic — there is no actual boundary that we can find between subject and object and we cannot separate them to have one without the other. This dualistic frame of mind is a deep-seated habit and unquestioned belief that is part of our “filter” of the world. It prevents us from knowing experience the way it actually is, and instead splits it like a prism splits a single beam of light, into multiple beams of “subject” and “object” halves of each moment.

It’s key to notice that the dualistic frame of mind — ordinary consciousness — is a kind of artificial division of the moment into two parts. It comes about because a misunderstanding on our own part of what is actually taking place in each moment. What we call the object side of experience is any appearance in any sensory modality or the mind. The subject side of experience is the label we give to the part
of the moment that seems to be witnessing it, or being it.

In fact there are not really two things like this, divided and separate from one another. Instead there is only one thing taking place that has both of these aspects. What is taking place is nonorigination. It has two aspects: awareness and appearance. Actually this triad can be expressed in three formulas:

Nonorigination = awareness + appearance    (N = A + A’)

Appearance = Nonorigination – awareness     (A = N – A’)

Awareness = Nonorigination – appearance     (A = N – A’)

Each moment of experience combines all three of these together into a trinity — they are unified yet still distinct. This might in fact be The Ultimate Trinity of all trinities. Furthermore, if we focus on appearance we will find that it is nonorigination. If we focus on awareness we will find that it too is nonorigination. If we try to focus on nonorigination itself we never find it, instead we always find
moments of awareness plus appearance. Yet if we then try to find the awareness or appearance on their own they dissolve back to nonorigination.

This Trinity is THE most important philosophical point of all. And I cannot take credit for it. Evertying I know about it or have said here is based on what I’ve learned from Buddhism and quantum mechanics. In particular there are thousands of years of highly developed Buddhist logical treatises on precisely this point.

What is Actually Happening

When things happen they don’t just appear out of nothingness.

There isn’t really any nothingness. Nothingness is impossible by virtue of the following proof: Something appears right now. Nothing and something are mutually exclusive.

Furthemore, even IF nothing was possible, it could never generate anything because there is no way to turn nothingness into something other than nothingness.

Instead of nothingness there is a kind of space of knowing or being — what might be called awareness. This space is not inherently personalized — it has no concepts or sense of I or of being an observer, etc. This awareness has the characteristic of being nonoriginated — we cannot find it or call it a concrete, truly-existing, isolated “thing.”

At the same time as there is any knowing or being, appearances spontaneously develop within its scope. For example, this is just like dreaming. In a dream there is the space of the mind and then within this space various appearances (and other sensory experiences, for example of sound, etc.) unfold. We then identify with a particular character or perspective in the dream and the appearance of its body –
and we call that “I” or “self.” That is a habit — there is nothing inherently real about the character we see ourselves as in a dream — it is not really us, not really our body or our actual mind but rather just a dream image of a body and mind. We label it as “I” or “me” out of habit. In fact, our real body is alseep in bed and is not in the dream, and our real mind and self are having the dream they are not really in the dream. Or are they?

When we dream, dreams don’t appear out of nothing, they appear out of awareness.

The same goes for all the experiences (aka appearances in various sensory modalities) that we call a moment of “our universe.” At each moment of experience there is the space of awareness plus at least some appearance. Neither the awareness or the appearances are truly-existing or even separate, they are just two aspects of nonorigination.

Nonorigination — or what in Buddhism is called “emptiness” is not a final fundamental thing that can be grasped or found either — if you find it you find that it dissolves into awareness and appearances and these dissolve back into nonorigination, endlessly.

Time unfolds as the process of this infinite loop — the Trinity of nonorigination, awareness and appearance — iterating. We are always either looking at an appearance, our awareness, or nonorigination. In either case as soon as we make such an observation what we find is that these dissolve into their counterparts. As we keep observing we trigger the process of cause-and-effect which continues to perpetuate appearances and that is what powers the universe so to speak. The
energy we put into it by making observations drives it to “run” this program so to speak, endlessly iterating new moments of experience that then trigger us to make further observations and so on.

On a quantum level, the process of enacting awareness, via simple acts of observation — is literally what causes the universe to make quantum decisions that jolt the quantum field of possibilities to “collapse” onto a single possibility whenever we look for it. This is analogous to being able to cause liquid water to suddenly freeze into ice by just looking at it. When we don’t look, it’s water, but when we do look it instantly freezes into a particular shape.

We can never really see it in its water form, it always freezes just when we look for it. But we can infer the water from the frozen shapes that appear. Even ice has has waterlike qualities — it’s clear, and it melts back into water when heated after all. If we look closely at any observation (any shape made of ice in this analogy), to find its nature, this is analogous to heating the ice we are looking at, which melts it back to liquid form.

Once it melts we can no longer see it (in this analogy) until we make the next observation as we continue to look for it again. Our next observation is conditioned by the previous observation — the network of probabilities for what can appear next are changed by the previous observation — and this causes it to follow from it, statistically, rather than to be completely random — this is the process of cause-and-effect in a nutshell. Therefore our acts of observation crystallize and perpetuate our experience in an ongoing, recursive process.

Each act of observation effectively loads the dice for the next act of observation and so changes the odds of the next possible dicerolls. If the world did not work this way it would be totally random. Since it’s not totally random — it does appear to behave in a non-random fashion, we are able to make various kinds of predictions, there is a certain amount of consistency over time, this is how the universe must
and does work. Cause-and-effect makes the universe non-random and non-randomness of the universe results in cause-and-effect operating.

Metascience: What are the Possible Beliefs We Might Hold?

So far we have explored some very deep questions about the origin and nature of the universe and, if one believes in God, then of God too. We have found that all these questions converge on the same ultimate reality — the reality of nonorigination.

But while they may all converge on that point eventually, there are many different schools of thought within science and religion, and regarding how they relate to one another. So how do we choose what to believe in?

It is necessary to make such choices in order to simply function on a day-to-day level, to resolve difficult moral questions, and to figure out how to live or what to do in the future. Many people just accept the choice that is handed to them by their parents, or by authorities they trust. But if one has the freedom and presence of mind to question this themselves, then on what basis can an intelligent choice be made?

It’s difficult to make sense of the range of belief system choices available, and their biggest differences or main points. One could proceed on an extensive voyage of exploration — surveying every field of science and religion over decades (what I did by default). But the whole task might be a lot faster and more efficient if one had a map to start with.

I propose a field of thinking about what to believe that we might call “Metascience” in which we make maps to help people navigate possible belief systems more intelligently. In this approach we address big philosophical questions from a higher level, starting by enumerating the space of possible beliefs we could hold about them — rather than by starting with a particular choice of belief. (Note: Another word for Metascience might simply be philosophy or metaphysics. But Philosophy and more specifically, metaphysics, have gotten totally lost, irrelevant, and non-objective. It’s time for a refresh.).

So, regarding the choice of beliefs about the relatoinship between God and the universe — Instead of immediately diving into the rathole of arguing the specifics of any one particular belief system or position on the issues, first let’s at least try try to agree on what the set of possible beliefs and positions is, and on a way to enumerate them as elegantly and usefully as possible. Is a universally agreeable
metascience possible? Can we come up with a way to enumerate all the possible belief systems about God and the universe that everyone can agree with?

A Categorization of All Possible Beliefs About The Universe and God

So here is my first attempt at mapping out the exhaustive metascientific enumeration of all possible philosophies regarding God and the Universe.

(A)  Hierarchical Approach: Either the universe or God is more fundamental and/or includes the other

• Theories in which the universe – or all time and space – take place within God’s mind and/or body and is subject to God’s laws and will
• Theories in which God exists as something within the universe, subject to it’s physical laws and conditions

(B) Dualistic Approach: The universe and God are two separate things

• Theories in which God is the first-cause, creator or “blind watchmaker” who started the universe and then detached from it
• Theories in which God is watching the universe from some place outside and separate from it and may or may not intervene
• Theories in which God and the universe are separate things that co-exist within an even higher-order universe and/or pantheon
• Theories in which either God or the universe is more potent or real than the other, and they are separate things

(C) Non-Dualistic Approach: The universe and God are one unified thing

• Theories in which the universe is a vast, intelligent, aware, sentient being of some sort (that we name “God”)
• Theories in which God is just a synonym or label for the universe, or vice-versa.
• The universe and God are a dichotomy; they are neither the same nor different. The universe and God are distinct but connected or merged together as one entity (e.g. God or the universe is considered to be the fundamental aspect and the other is considered to be relative aspect of the same dichotomy, the wave-particle duality, space-time, matter-energy, mind-body, one-many, etc.). Or in other words, theories in which God and the universe are two sides of the same coin so to speak — two distinct sides of the same thing
• Theories in which either God exists and the universe doesn’t, or the universe exists and God doesn’t

(D) Existential Approach: The universe and/or God is a provisionally existing thing

• Theories in which God or the universe has only a provisional kind of existence that when analyzed proves to reduce to a deeper level of existence, or to non-existence.
• Theories in which God or the universe is merely a conceptual construct or label for something that actually has no valid existence of its own (e.g. “the horns of a rabbit”)
• Theories in which God is a conceptual label for something that is impossible (e.g. “this statement is not true)
• Theories in which God is a fictional character in a story (e.g. the character,
  “Aslan” in the Chronicles of Narnia), or is a mental fabrication or projection of someone’s mind
• Theories in which the universe is fictional but taking place – a mere fantasy or dream or a mental fabrication or projection of someone’s mind — it doesn’t exist in reality, it only exists in each of our own perceptions or at least in someone’s mind.
• Theories such as nihilism which posit that there is actually nothing at all (a contradictory, and irrational assertion)

(E) Nonconceptual Approach: The universe and/or God is inconceivable

• Theories in which the universe and/or God is neither posited to exist, not-exist, both exist and not-exist, or neither exist nor not-exist (e.g. the Buddhist theory of “emptiness” or “freedom from four logical extreme views”)
• Theories in which God or the concept of the universe is a conceptualization of something real but inconceivable (e.g. “infinity” or “zero”)
• Theories in which God and/or the universe cannot be conceived of for some axiomatic reason, such as being transcendental, beyond the scope of thought or words, beyond logic, not in the material realm, higher-dimensional, beyond time and space, etc.

There are no other major categories that I can think of regarding the Universe and God. I believe this may be then an exhaustive list. But feel free to add your own thoughts in the comments below.

Are These Questions Worthwhile?

At this point, for the skeptics among us, we should ask whether it is even meaningful and worthwhile to try to unify science and religion.

It is certainly clear that science has value. But what about religion?

Firstly, much of the world’s population believes in some form of religion and these beliefs are at the root of much of what takes place in the world — culturally, politically, economically and more. For that reason, if nothing else, we really should have as deep an understanding of all the various conceptions about God as we can. But that’s just the start. In fact there are sound scientific and philosophical reasons for exploring the topic of God as well. The theory that God originated the universe is just a valid a hypothesis as any other theory — and may even be testable at some point in the future. It’s certainly no more outlandish than some of the more exotic
and hard-to-test cosmological hypotheses put forth in recent decades.

In addition, many people (including even many scientists) have had personal experiences that indicate that there is some greater entity beyond the body, mind or individual self, and perhaps even beyond the physical limits of space and time. While not everyone has had such experiences, and there is no way to validate the experiences of others, the fact that such experiences are so common and so similar, is another data-point that makes this topic worthy of consideration both by those
who claim to have had such experiences, and by those who claim to have not had them. They may be artifacts of the particular architecture of the human body and brain, or they may be pointing to a deeper reality that exists just as objectively as the physical world.

Finally, from a purely scientific perspective, the origin of the universe is a mystery, and therefore the possibility of God is as much an open question as it ever was. Science has been able to learn about how the universe works to some degree, and to map parts of it, and even to form conjectures about how it has developed — but where it comes from, how it started (if it even has a beginning at all), and even
where it is located ultimately are a mystery. If one posits any kind of a beginning — such as a Big Bang — then that immediately begs the question of where did the Beginning come from?

Religion has certainly learned a lot from science over the millennia. But perhaps, ironically, science has as much to learn from religion in coming millennia, at least when it comes to understanding and exploring the farthest possible reaches of cosmology and the mind. The strange relationship between mind and matter may be what the next great scientific revolution will focus on.

Similarities Between Sciences and Religions

While science and religion may disagree on certain points, at the very deepest level, they may actually be more compatible than we might think. In fact, I would go so far as to propose that a grand unification of science and religion may come about in the future as we probe ever deeper into the edges of what we know about cosmology, subatomic physics, and even our understanding of consciousness and the mind.

The strangeness at the boundaries of science already points to a reality that goes beyond a strict division of mind and matter. For example, the simple act of observation seems to have an influence on what is actually measured to take place, according to the field of quantum mechanics. Similarly, at the borders of cosmology, questions still abound on the origin, structure, and fate of the universe. And in particular, given the improbabilty of a universe such as ours, which seems to be precisely balanced to support the emergence of intelligent life, how did this universe happen?

In many cases scientists are very careful to state that they simply don’t know certain things yet. But at the same time, as scienfitic theories come into vogue, they often get out of control. For example the theory of the Big Bang. This particular theory, like most other scientific theories, has gone from being a new and contentious proposal, to a major and mainstream scientific belief, to a term that
even non-scientists embraced as fact, and now today there is new evidence that perhaps the Big Bang theory is flawed and/or totally incorrect.

In the field of the philosophy of science, which studies how scientific paradigms are born, how they develop and compete, and how they are overturned, there are many other examples (the view of the Newtonian universe versus the view of Relativity, for example, or various explanations for the quantum world, and more recently String Theory). As scientific belief systems emerge, their proponents sometimes develop a kind of faith in the veracity of their beliefs that is not yet justified by the evidence, or that can never be justified in some cases — this scientific faith is quite similar to religious faith. It’s a strong belief in an explanation of nature for which there is some evidence but not yet final proof.

In fact, in science, theories can only be falsified, they can never be established as permanent and final. One never knows if and when new evidence may emerge that overturns the received view, or points to a deeper understanding.

It should also be noted that it is not the case that science is rational and religion is not. In fact, most if not all religions claim that that at least some of their beliefs are verifiable by individuals who follow a rational and repeatable process (for example, do certain things and you will get certain results). In addition at least some religions also apply rigorous formal logic to support their viewpoints. Those religions that provide an experimental method (do certain things and anyone will get predictable results) and that also apply rigorous logic to their reasoning, are applying a form of scientific method. It may be a weak form of scientific method, but it is not irrational.

So while science and religion have very different methodologies, at least with regard to their answers to the really Big Questions, such as the origin and ultimate nature of the universe, they both require a certain amount of faith, and they are both rational processes to some degree.

Differences Between Sciences and Religions

However there are also certain key differences between sciences and religions. In particular, many religions are built from axioms (creation myths, dieties, stories, traditions, and rules) which are established tautologically (they are considered to be true because simply they are defined to be true). For example, those religions which found their belief systems on ancient manuscripts that are said to have come directly for God, are building their belief systems from axioms. Such texts are claimed to be axiomatically true and cannot be disputed for they are God’s Word.

Some relgions also make the claim that the only way to test and verify the truth of their beliefs is to first take them on faith as true. In other words, the only way to verify that x is true is to first believe that x is true, and then after you believe it, the evidence will start to emerge. In other words, not having faith — asking questions or having doubts — actually prevents one from discovering the truth. It is the act of having faith that actually opens the door, so to speak.

Putting faith first is the opposite of the scientific method. The scientific method starts with doubt. It invites questioning — nothing is too sacred to examine, and if some theory can’t stand up to scrutiny, or can’t be shown through experiment or logic to be true, then it can’t be said to be scientific fact. In fact, to accept that
something is true without having doubts, but prior to having proof, would be a grave scientific error. This is a key difference between the methodologies of sciences and religions in general.

However, different though it may be from the scientific method, the religious approach seems to work. Billions of people throughout human history who have followed various religions have been able to verify, for themselves at least, the authenticity of their beliefs. Whether or not the stories in a certain religious text are literally true or only metaphorical or allegorical, the fact remains that the religious
process of faith, devotion, prayer and personal growth do lead, in a predictable and repeatable manner, to profound religious experiences and in some cases even to unexplainable “miracles” at times (such as the many documented cases of spontaneous healings, for example). While this is certainly not the scientific method, it appears to work pretty well nonetheless.

It is not my intention to prove that the scientific method of “proof before faith” is better or worse than the religious approach of “faith before proof.” In fact, I think they both have their place, and they both work, for different purposes.

The Boundary Between Science and Religion is Fuzzier Than One Might Think

The boundary between where science ends and religion begins is fuzzy at best. In fact, they are so intimately connected at the deepest levels that perhaps they will oneday turn out to be the same thing.

Already we have found that on the quantum scale there is an intimate and strange connection between conscious observation and what appears to happen. This is not well understood yet, but it is observed experimentally. Yet we don’t have any real understanding of what consciousness is, or how it interacts with what is observed. The sciences have very little understanding of the mind at all. In fact, many scientists don’t even believe there is a mind; they think the brain is a machine and the mind is a kind of illusion. There is no soul, no consciousness, no being at all. Yet others disagree. The jury is still out.

Religions on the other hand have been studying consciousness for millennia, and some are downright scientific about it. For example the ancient Hindu and Buddhist tantric sciences provide extremely detailed and sophisticated technologies for using the breath, posture, visualization, sound, and concentration to bring about extremely unusual states of body and mind (which have recently have been measured in scientific laboratories in a number of studies). Religions are in some ways way ahead of science when it comes to understanding the mind.

The mind is one of the places where science and religion are going to collide and most likely converge. Another is the ultimate nature of the universe — the nature of space and time. The boundary between science and religion becomes fuzzier as one begins to explore the mind, the relationship between mind and matter, and simply as one views the universe at the largest or smallest scales.

There have been many past attempts by scientists at proving and disproving the existence of God. In fact the question of God’s existence was once considered an acceptable topic of enquiry by scientists such as for example, Sir Isaac Newton, and many others. In the past science was concerned with all questions about nature –
including questions about the nature of reality and the mind, and even the possibility of a soul. But in recent times the focus of mainstream science has shifted far away from such topics — which are now seen as almost taboo. But why should they be taboo? They are just as much a subject for enquiry as ever. God has not been proved to exist or not-exist by science, and therefore the jury is still out. The question is whether there is any way to prove that God exists or not? It may in fact be possible to do this, scientifically, eventually.

In any case, just as is the case for the question of God, there are many scientific questions that also have not been answered yet, especially in the fields of cosmology and theoretical physics. Where does the universe come from? What created it? What came before the Big Bang (if there was a Big Bang)? What medium is space-time taking place in right now, or if there is nothing beyond space time then how did it ever happen, what does it come from, how could there be nothing beyond it? Does the universe have any edges and if so what is outside them? If
there are multiple universes, what separates them from each other, or are they connected and if so how? Do all possible states of all possible universes already exist or are they truly unfolding over time? Is everything predetermined by the physical laws, or is it all open to chance, or is there some level of intelligence and creativity taking place in the universe?

Even if science someday were able to describe and define everything there is to know about the physical universe, there would still be something more to know that could not be proved or discribed or defined. Godel’s famous Incompleteness proof established this on a formal logical level — there will always be gaps in our knowledge — of any formal systems we construct. No formal system can be both
consistent and complete at the same time. We will never have perfect scientific knowledge of the universe. And even if we could, it would simply beg the question of what is beyond that — no matter what we say the universe is, the question will always come up: well, then where does it come from and how or why is it happening?

Whether through science or religion, all paths lead to the possibility of something inconcievably beyond what we know. And this is where the boundary between science and religions gets so fuzzy that it dissolves completely.

Making a Choice

Assuming we can all at least agree on the meta-level choices (the set of possible choices), we can then discuss possible criteria for comparing, testing, and even ranking the various possible choices available to us.

At the end of the process of course there may be no final best choice that everyone accepts (in fact, I can guarantee there will not be!), nor any agreement as to what are the best or correct criteria for choosing among them. But at least we can all at least agree on what the choices are and how they compare to one another in various ways.

This could go a long way to promoting and improving tolerance and understanding. Better yet, this kind of process might even lead to useful meta-level or inter-belief-system dialogues that may eventually lead to important discoveries and even grand unifications in the future.

However, for now, regardless of what belief system we prefer, we simply have to accept that the belief system we choose, if any, is a matter of personal choice (some might call that faith, others might call it aesthetic preference, others might call it a hunch or intuition) — at least until such time as someone comes up with a way to objectively prove to everyone else that there is only one correct choice. Until that time, even if we have our own favorite belief system choice, we still have to keep some measure of open-mindedness in the face of the set of other choices available and the fact that we can’t today prove objectively (to everyone) that we made the right choice.

At least however, we should be clear that if we are willing to believe anything about the universe, there are strong reasons why we therefore should keep an open mind with regard to the possibility of God. It is not that huge a leap in fact. If we are willing to accept that something as vast and inconceivable as the universe exists, then why not God too? We really don’t have much solid grounds for holding any beliefs about such things — to do so is really just an act of faith either way. We should not have illusions about that. Believing in scientific explanations of the cosmos is really not that much different than believing in religous ones.

The good news at least is that so long as our conception of God has the properties of being uncaused and unconditioned, we are likely to have made the right choice. This also means that all the great religions, at least at their cores, are in agreement — they are all worshipping the same ultimate God, regardless of what different names they use for it. You really can’t go wrong as long as you believe in an ultimate nature that is uncaused and uncreated. However — where you certainly CAN go wrong is in imposing any further beliefs on it. And many make that mistake.

Nonduality

I have shown in this article that if one believes in the physical universe described by science, then in fact there is a logical requirement that the universe is ultimately nonoriginated.

I have also shown that the same holds for belief in God — God is also logically required to be nonoriginated.

Therefore the universe and God have the same ultimate nature.

In addition I have shown that for the universe to make choices about what happens from the set of all possibilities, observation, and therefore awareness, is required. Furthermore the nature of sentient beings, and of God, is precisely this unique capacity of awareness. Both the universe and what we think of as God are characterized by the same nature of being nonoriginated and aware.

In fact, at this level, the ultimate nature is not very different from the core idea of what God is. On an ultimate level there is not really much of a distinction between the ultimate nature of the universe and the ultimate nature of God — it is just one ultimate reality. The universe and God may be one thing, or they may be two
things, or only one and not the other may exist, but in any and all of these cases, there is still only one ultimate nature: nonoriginated awareness.

There is no escape from this logic. There is no question that somewhere down the line, we must finally accept that there is something greater than the universe — whatever we think the universe is — and the characterstics of that greater thing are in fact the one common theme of the conception of God across all religions. We can name it what we want, and certainly different religions do. We also may have different perspectives on it, and add all sorts of other details. But what all the great religions have in common is an ultimate nature that is essentially transcendental.

In other words, science and religion are two sides of the same coin. You really can’t have one without the other. They are a dichotomy, but not a duality. They are distinct yet unified.

We do however have the freedom to choose our relative level beliefs about science, and our religious tradition. This freedom is an expression of the primordial freedom of the awareness — our ability to choose what to observe — and this in turn is the ultimate nature of reality. Intellectual freedom is therefore not only irrepressible, it is a reflection of the nature of the universe, it is our birthright.

On the ultimate level everything is unified, but on the relative level, there is no one correct science or religion, there will always be different views, and they probably won’t always agree on all points, and this is perfectly in accord with the freedom of the universe, and each individual. So while science and religion may be unified on the ultimate level, they certainly are not unified on the relative level, and in fact even within each indivividual field of science and each religion, there are differing viewpoints and schools of thought. And this is good.

There is a menu of different belief systems in both arenas and various items on the menu are or are not compatible with one another, or with the beliefs of others. It’s really our personal choice to make. However, what should be clear from the above argument is we have to choose both a main course and a desert: science is undeniable, and religion is unavoidable, they are two sides of the same coin.

Science and religion are different on the relative level (though not as different as some might think), but they definitely converge at ultimate level and this convergence is not a matter of faith, it is a matter of logic. Therefore, regardless of whether we prefer science or religion, or any particular sect within either camp, at least we should not err on the side of thinking they are mutually exclusive.

Unifying Physics and Consciousness: The Next Scientific Revolution

If you pursue science to the very edges, you reach nonorigination. Similarly if you become as close as possible to the diety in any religious tradition, you reach nonorigination. Moreover, nonorigination is the nature of appearances and awareness, and vice-verse. They are never separated. It’s a trinity.

The ultimate nature of the universe, and the ultimate nature of God (if you believe in a God) – must logically be precisely the same. This nature unifies the physical world of seemingly “external” sensory experiences and seemingly “internal” mental events, with the unfindable yet undeniable dimension of awareness, and the unfindable yet logically required nature of being nonoriginated.

The beauty of this is that on the ultimate level there really is no question at all about whether or not the universe exists, or whether or not God exists — the appearances of primordially aware nonorigination is the truth — and it is the most amazing miracle of all. It is irrefutable, it is logically required, and it establishes a basis for authentic and universal spirituality. One can logically derive or directly experience this logical trinity through the vehicle of focusing on and logically analyzing any phenomena (the universe, the mind, God, etc.). When this trinity is recognized as the nature of reality, and directly experienced as such, that is the deepest scientific observation or religious experience possible.

The universe including the body and all other physical things in space and time, the conceptual mind and its mental realm of thoughts and emotions, and all possible real or imaginary dieties, all have at their ultimate root, the same primordially nonoriginated awareness.

Proving this once and for all in a non-religiously couched manner — using pure logical reasoning — enables science to progress beyond its present day limitations to finally begin to make sense of the strangeness of the quantum world and of the role and nature of consciousness, and the ultimate nature of space and time.

The next frontier in science will not be simply be a deeper understanding of the physical world — it will be a broader and more integrated understanding that includes both the physical world and the realm of consciousness — the mental realm.

To fully explain and understand the physical world science must find ways to include and measure the crucial role of conscious observers. Each physical event has both sides on a quantum level: the side of the observer and the side of what is observed. Science has so far been focused exclusively on understanding the side of what is observed. But what is observed cannot fully be understood or explained without an equal measure of scientific understanding of the observer and the act of observation.

Similarly, the only way to fully understand consciousness is to include and measure the crucial relationship between consciousness and the process of appearance (namely cause and effect). Both the physical world and consciousness are nonoriginated — they are empty of having an origin, not having an origin, having both, or having neither.

We don’t have the tools for measuring or exploring consciousness yet, but we’re close. Experiments that show the impact of observation on reality are indicators that consciousness is a phenomenon that can affect the observable world. This means that consciousness is indirectly detectable via measurments of the physical world around observers. It may be that consciousness — the act of observing — cannot be directly measured or observed except on its own — by and “within” each individual — but may still me indirectly measured or detected via its affects on the quantum field in the environment when it is present.

By analogy, this is similar to how space is measured, so it is possible to imagine doing this for consciousness. In the case of space, we cannot see it, touch it, or measure it directly. We can only infer things about it by measuring other things — like the way light travels, or the way things move. These indirect measurements lead us to an understanding of space.

Similarly we may be able to triangulate on consciousness by measuring the effects of various physical changes on consciousness (as reported by a conscious observer) and/or by the effects of consciousness (some observer) on physical phenomena (such as the Double Slit experiment). This is definitely an interesting possibility for
further exploration, and perhaps the next scientific revolution is waiting just over the horizon in this direction.

Our civilization has not even scratched the surface of this new frontier — a unified science of physics and consciousness. But we will soon. We have to. It is unavoidable. Our quest for knowledge and understanding will take us there whether we like it or not. Already there are cracks in our present scientific theories, and experiments are showing us gaps and contradictions in our theories that we cannot explain. And the light is spilling through them.

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Twitter is still relatively small in terms of users, and most of the content is still being added by people. But not for long. Two things are beginning to happen that will change Twitter massively:

1. Mainstream Adoption. Tens of millions of new users are going to flood into the service. It is going to fill up with mainstream consumers. Many of them won’t have a clue how to use Twitter.
2. Notifications Galore. Every service on the Web is going to rush to pump notifications and invites into Twitter.

Twitter reminds me of CB radio — and that is a double-edged blessing. In Twitter the “radio frequencies” are people and hashtags. If you post to your Twitter account, or do an @reply to someone else, you are broadcasting to all the followers of that account. Similarly, if you tweet something and add hashtags to it, you are broadcasting that to everyone who follows those hashtags.

This reminds me of something I found out about in New York City a few years back. If you have ever been in a taxi in NYC you may have noticed that your driver was chatting on the radio with other drivers — not the taxi dispatch radio, but a second radio that many of them have in their cabs. It turns out the taxi drivers were tuned into a short range radio frequency for chatting with each other — essentially a pirate CB radio channel.

This channel was full of taxi driver banter in various languages and seemed to be quite active. But there was a problem. Every five minutes or so, the normal taxi chatter would be punctuated by someone shouting insults at all the taxi drivers.

When I asked my driver about this he said, “Yes, that is very annoying. Some guy has a high powered radio somewhere in Manhattan and he sits there all day on this channel and just shouts insults at us.” This is the problem that Twitter may soon face. Open channels are great because they are open. They also can become aweful, because they are open.

The fact that Twitter has open channels for communication is great. But these channels are fragile and are at risk from several kinds of overload:

• Hypertweeting. Some Twitter users tweet legitimately, but far too much. Or the content they tweet is just innane. In doing so they market themselves and dominate everyone’s attention with their presence.
• Hashtag Spam. For example, an advertiser could easily pump out tweets that market their products, and simply attach popular hashtags to them, thus spamming those “channels” with ads. Similarly, clueless users could do the same thing.
• @reply Spam.
  Another way that spammers could create annoyances in Twitter is by doing @replies to people, with ads for products, or simply to make trouble.
• Twitter Chains. It is easy to package a highly contagious meme as a tweet that spreads linearly or exponentially. Some variations of this are highly self-replicating and can quickly spread to millions of people. There are various ways to design such memes to spread exponentially and across multi-level relationships with extreme virality. Multi-level marketers and others could take advantage of this to create havoc, and even potentially flood Twitter with multi-level messages to the point of crashing it.
• Notification Overload. Another issue is the rise of Twitter bots from various services, whether benign in nature or deliberately spammy:
  • It won’t be long before every social network starts pumping updates into Twitter.
  • News and content sites are starting to pump updates into Twitter for every article they publish.
  • Games
    and MMPORG’s are starting to pump notifications for things that take place in their worlds, into Twitter (e.g. player x just defeated player y in a battle)
  • A variety of other desktop, online and mobile apps will be pumping notifications into Twitter

There is soon going to be vastly more content in Twitter, and too much of it will be noise.

The Solution: New Ways to Filter Twitter

The solution to this is filtering. But filtering capabilities are weak at best in existing Twitter apps. And even if app developers start adding them, there are limitations built into Twitter’s messaging system that make it difficult to do sophisticated filtering.

Number of Followers as a Filter. One way to filter would be to use social filtering to infer the value of content. For example, content by people with more followers might have a higher reputation score. But let’s face it, there are people on Twitter who are acquiring followers using all sorts of tricky techniques — like using auto-follow or simply following everyone they can find in the hopes that they will be followed back. Or offering money or prizes to followers — a recent trend. The number of followers someone has does not necessarily reflect reputation.

Re-Tweeting Activity as a Filter. A better measure of reputation might be how many times someone is re-tweeted. RT’s definitely indicate whether someone is adding value to the network. That is worth considering.

Social Network Analysis as a Filter. One might also analyze the social graph to build filters. For example, by looking at who is followed by who. Something similar to Google PageRank might even be possible in Twitter. You could figure out that for certain topics, certain people are more central than others,  by analyzing how many other people who tweet about those topics are following them. Ok good.
Nobody can patent this now.

Metadata for Filtering. But we are going to need more than inferred filtering I believe. We are going to need ways to filter Twitter messages by sender, type of content, size, publisher, trust, popularity, content rating, MIME type, etc. This is going to require metadata in Twitter, ultimately.

Broadly speaking there are two main ways that metadata could be added to Twitter:

1. Metadata Added Outside Twitter. Twitter messages could simply be URLs that point to further resources that in turn carry the actual body and metadata of each message. Thus a message might just be a single URL. Clicking that URL would yield a web page with the content and then XML or RDF metadata about the message. If this were to happen, Twitter messages would be simply URLs created and sent by outside client software — and they would require outside software (special Twitter clients) to unpack and read them.
2. Metadata Added Inside Twitter. Another solution would be for Twitter to extend their message schema so every Twitter message has two parts, a 140 char body and a metadata section with a certain amount of space as well. This would be great. It would be a good move for the people at Twitter to jump the gun by enabling this sooner rather than later. It will help them protect their control over their own franchise.

One thing is certain. In the next 2 years Twitter is going to fill up with so much information, spam and noise that it will become unusable. Just like much of USENET. The solution will be to enable better filtering of Twitter, and this will require metadata about each tweet.

Someone IS going to do this — perhaps it will come from third-party developers who make Twitter clients, or perhaps from the folks who make Twitter itself. It has to happen.

(To followup on this find me at http://twitter.com/novaspivack)

Now read Part II: Best Practices – Proposed Do’s and Don’t’s for Using Twitter

See Also:

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A new article on CNET mentioning this article

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- This article last updated on March 11, 2009.

- For follow-up, connect with me about this on Twitter here.

- See also: for more details, be sure to read the new review by Doug Lenat, creator of Cyc. He just saw the Wolfram Alpha demo and has added many useful insights.

——————————————————————–

Introducing Wolfram Alpha

Stephen Wolfram is building something new — and it is really impressive and significant. In fact it may be as important for the Web (and the world) as Google, but for a different purpose. It’s not a “Google killer” — it does something different. It’s an “answer engine” rather than a search engine.

Stephen was kind enough to spend two hours with me last week to demo his new online service — Wolfram Alpha (scheduled to open in May). In the course of our conversation we took a close look at Wolfram Alpha’s capabilities, discussed where it might go, and what it means for the Web, and even the Semantic Web.

Stephen has not released many details of his project publicly yet, so I will respect that and not give a visual description of exactly what I saw. However, he has revealed it a bit in a recent article, and so below I will give my reactions to what I saw and what I think it means. And from that you should be able to get at least some idea of the power of this new system.

A Computational Knowledge Engine for the Web

In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.

It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example.

Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” or “what would 80/20 vision look like?”

Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.

(Update: in fact, Wolfram Alpha doesn’t merely answer questions, it also helps users to explore knowledge, data and relationships between things. It can even open up new questions — the “answers” it provides include computed data or facts, plus relevant diagrams, graphs, and links to other related questions and sources. It also can be used to ask questions that are new explorations between relationships, data sets or systems of knowledge. It does not just provides textual answers to questions — it helps you explore ideas and create new knowledge as well)

How Does it Work?

Wolfram Alpha is a system for computing the answers to questions. To accomplish this it uses built-in models of fields of knowledge, complete with data and algorithms, that represent real-world knowledge.

For example, it contains formal models of much of what we know about science — massive amounts of data about various physical laws and properties, as well as data about the physical world.

Based on this you can ask it scientific questions and it can compute the answers for you. Even if it has not been programmed explicity to answer each question you might ask it.

But science is just one of the domains it knows about — it also knows about technology, geography, weather, cooking, business, travel, people, music, and more.

Alpha does not answer natural language queries — you have to ask questions in a particular syntax, or various forms of abbreviated notation. This requires a little bit of learning, but it’s quite intuitive and in some cases even resembles natural language or the keywordese we’re used to in Google.

The vision seems to be to create a system wich can do for formal knowledge (all the formally definable systems, heuristics, algorithms, rules, methods, theorems, and facts in the world) what search engines have done for informal knowledge (all the text and documents in various forms of media).

How Does it Differ from Google?

Wolfram Alpha and Google are very different animals. Google is designed to help people find Web pages. It’s a big lookup system basically, a librarian for the Web. Wolfram Alpha on the other hand is not at all oriented towards finding Web pages, it’s for computing factual answers. It’s much more like a giant calculator for computing all sorts of answers to questions that involve or require numbers. Alpha is for calculating, not for finding. So it doesn’t compete with Google’s core business at all. In fact, it is much more comptetive with the Wikipedia than with Google.

On the other hand, while Alpha doesn’t compete with Google, Google may compete with Alpha. Google is increasingly trying to answer factual questions directly — for example unit conversions, questions about the time, the weather, the stock market, geography, etc. But in this area, Alpha has a powerful advantage: it’s built on top of Wolfram’s Mathematica engine, which represents decades of work and is perhaps the most powerful calculation engine ever built.

How Smart is it and Will it Take Over the World?

Wolfram Alpha is like plugging into a vast electronic brain. It provides extremely impressive and thorough answers to a wide range of questions asked in many different ways, and it computes answers, it doesn’t merely look them up in a big database.

In this respect it is vastly smarter than (and different from) Google. Google simply retrieves documents based on keyword searches. Google doesn’t understand the question or the answer, and doesn’t compute answers based on models of various fields of human knowledge.

But as intelligent as it seems, Wolfram Alpha is not HAL 9000, and it wasn’t intended to be. It doesn’t have a sense of self or opinions or feelings. It’s not artificial intelligence in the sense of being a simulation of a human mind. Instead, it is a system that has been engineered to provide really rich knowledge about human knowledge — it’s a very powerful calculator that doesn’t just work for math problems — it works for many other kinds of questions that have unambiguous (computable) answers.

There is no risk of Wolfram Alpha becoming too smart, or taking over the world. It’s good at answering factual questions; it’s a computing machine, a tool — not a mind.

One of the most surprising aspects of this project is that Wolfram has been able to keep it secret for so long. I say this because it is a monumental effort (and achievement) and almost absurdly ambitious. The project involves more than a hundred people working in stealth to create a vast system of reusable, computable knowledge, from terabytes of raw data, statistics, algorithms, data feeds, and expertise. But he appears to have done it, and kept it quiet for a long time while it was being developed.

Computation Versus Lookup

For those who are more scientifically inclined, Stephen showed me many interesting examples — for example, Wolfram Alpha was able to solve novel numeric sequencing problems, calculus problems, and could answer questions about the human genome too. It was also able to compute answers to questions about many other kinds of topics (cooking, people, economics, etc.). Some commenters on this article have mentioned that in some cases Google appears to be able to answer questions, or at least the answers appear at the top of Google’s results. So what is the Big Deal? The Big Deal is that Wolfram Alpha doesn’t merely look up the answers like Google does, it computes them using at least some level of domain understanding and reasoning, plus vast amounts of data about the topic being asked about.

Computation is in many cases a better alternative to lookup. For example, you could solve math problems using lookup — that is what a multiplication table is after all. For a small multiplication table, lookup might even be almost as computationally inexpensive as computing the answers. But imagine trying to create a lookup table of all answers to all possible multiplication problems — an infinite multiplication table. That is a clear case where lookup is no longer a better option compared to computation.

The ability to compute the answer on a case by case basis, only when asked, is clearly more efficient than trying to enumerate and store an infinitely large multiplication table. The computation approach only requires a finite amount of data storage — just enough to store the algorithms for solving general multiplication problems — whereas the lookup table approach requires an infinite amount of storage — it requires actually storing, in advance, the products of all pairs of numbers.

(Note: If we really want to store the products of ALL pairs of numbers, it turns out this is impossible to accomplish, because there are an infinite number of numbers. It would require an infinite amount of time to simply generate the data, and an infinite amount of storage to store it. In fact, just to enumerate and store all themultiplication products of the numbers between 0 and 1 would require an infinite amount of time and storage. This is because the real-numbers are uncountable. There are in fact more real-numbers than integers (see the work of Georg Cantor on this). However, the same problem holds even if we are speaking of integers — it would require an infinite amount of storage to store all their multiplication products, although they at least could be enumerated, given infinite time.)

Using the above analogy, we can see why a computational system like Wolfram Alpha is ultimately a more efficient way to compute the answers to many kinds offactual questions than a lookup system like Google. Even though Google is becoming increasingly comprehensive as more information comes on-line and gets indexed, it will never know EVERYTHING. Google is effectively just a lookup table of everything that has been written and published on the Web, that Google has found. But not everything has been published yet, and furthermore Google’s index is also incomplete, and always will be.

Therefore Google does and always will contain gaps. It cannot possibly index the answer to every question that matters or will matter in the future — it doesn’t contain all the questions or all the answers. If nobody has ever published a particular question-answer pair onto some Web page, then Google will not be able to index it, and won’t be able to help you find the answer to that question — UNLESS Google also is able to compute the answer like Wolfram Alpha does (an area that Google is probably working on, but most likely not to as sophisticated a level as Wolfram’s Mathematica engine enables).

While Google only provide answers that are found on some Web page (or at least in some data set they index), a computational knowledge engine like Wolfram Alpha can provide answers to questions it has never seen before — provided however that it at least knows the necessary algorithms for answering such questions, and it at least has sufficient data to compute the answers using these algorithms. This is a “big if” of course.

Wolfram Alpha substitutes computation for storage. It is simply more compact to store general algorithms for computing the answers to various types of potential factual questions, than to store all possible answers to all possible factual questions. In then end making this tradeoff in favor of computation wins, at least for subject domains where the space of possible factual questions and answers islarge. A computational engine is simply more compact and extensible than a database of all questions and answers.

This tradeoff, as Mills Davis points out in the comments to this article is also referred to as the tradeoff between time and space in computation. For very difficult computations, it may take a long time to compute the answer. If the answer was simply stored in a database already of course that would be faster and more efficient. Therefore, a hybrid approach would be for a system like Wolfram Alpha to store all the answers to any questions that have already been asked of it, so that they can be provided by simple lookup in the future, rather than recalculated each time. There may also already be databases of precomputed answers to very hard problems, such as finding very large prime numbers for example. These should also be stored in the system for simple lookup, rather than having to be recomputed. I think that Wolfram Alpha is probably taking this approach. For many questions it doesn’t make sense to store all the answers in advance, but certainly for some questions it is more efficient to store the answers, when you already know them, and just look them up.

Other Competition

Where Google is a system for FINDING things that we as a civilization collectively publish, Wolfram Alpha is for COMPUTING answers to questions about what we as a civilization collectively know. It’s the next step in the distribution of knowledge and intelligence around the world — a new leap in the intelligence of our collective”Global Brain.” And like any big next-step, Wolfram Alpha works in a new way — it computes answers instead of just looking them up.

Wolfram Alpha, at its heart is quite different from a brute force statistical search engine like Google. And it is not going to replace Google — it is not a general search engine: You would probably not use Wolfram Alpha to shop for a new car, find blog posts about a topic, or to choose a resort for your honeymoon. It is not a system that will understand the nuances of what you consider to be the perfect romanticgetaway, for example — there is still no substitute for manual human-guided search for that. Where it appears to excel is when you want facts about something, or when you need to compute a factual answer to some set of questions about factual data.

I think the folks at Google will be surprised by Wolfram Alpha, and they will probably want to own it, but not because it risks cutting into their core search engine traffic. Instead, it will be because it opens up an entirely new field of potential traffic around questions, answers and computations that you can’t do on Google today.

The services that are probably going to be most threatened by a service like Wolfram Alpha are the Wikipedia, Cyc, Metaweb’s Freebase, True Knowledge, the START Project, and natural language search engines (such as Microsoft’s upcoming search engine, based perhaps in part on Powerset’s technology), and other services that are trying to build comprehensive factual knowledge bases.

As a side-note, my own service, Twine.com, is NOT trying to do what Wolfram Alpha is trying to do, fortunately. Instead, Twine uses the Semantic Web to help people filter the Web, organize knowledge, and track their interests. It’s a very different goal. And I’m glad, because I would not want to be competing withWolfram Alpha. It’s a force to be reckoned with.

Relationship to the Semantic Web

During our discussion, after I tried and failed to poke holes in his natural language parser for a while, we turned to the question of just what this thing is, and how it relates to other approaches like the Semantic Web.

The first question was could (or even should) Wolfram Alpha be built using the Semantic Web in some manner, rather than (or as well as) the Mathematica engine it is currently built on. Is anything missed by not building it with Semantic Web’s languages (RDF, OWL, Sparql, etc.)?

The answer is that there is no reason that one MUST use the Semantic Web stack to build something like Wolfram Alpha. In fact, in my opinion it would be far too difficult to try to explicitly represent everything Wolfram Alpha knows and can compute using OWL ontologies and the reasoning that they enable. It is just too wide a range of human knowledge and giant OWL ontologies are too difficult to build and curate.

It would of course at some point be beneficial to integrate with the Semantic Web so that the knowledge in Wolfram Alpha could be accessed, linked with, and reasoned with, by other semantic applications on the Web, and perhaps to make it easier to pull knowledge in from outside as well. Wolfram Alpha could probably play better with other Web services in the future by providing RDF and OWL representations of it’s knowledge, via a SPARQL query interface — the basic open standards of the Semantic Web. However for the internal knowledge representation and reasoning that takes places in Wolfram Alpah, OWL and RDF are not required and it appears Wolfram has found a more pragmatic and efficient representation of his own.

I don’t think he needs the Semantic Web INSIDE his engine, at least; it seems to be doing just fine without it. This view is in fact not different from the current mainstream approach to the Semantic Web — as one commenter on this article pointed out, “what you do in your database is your business” — the power of the Semantic Web is really for knowledge linking and exchange — for linking data and reasoning across different databases. As Wolfram Alpha connects with the rest ofthe “linked data Web,” Wolfram Alpha could benefit from providing access to its knowledge via OWL, RDF and Sparql. But that’s off in the future.

It is important to note that just like OpenCyc (which has taken decades to build up a very broad knowledge base of common sense knowledge and reasoning heuristics), Wolfram Alpha is also a centrally hand-curated system. Somehow, perhaps just secretly but over a long period of time, or perhaps due to some new formulation or methodology for rapid knowledge-entry, Wolfram and his team have figured out a way to make the process of building up a broad knowledge base about the world practical where all others who have tried this have found it takes far longer than expected. The task is gargantuan — there is just so much diverse knowledge in the world. Representing even a small area of it formally turns out to be extremely difficult and time-consuming.

It has generally not been considered feasible for any one group to hand-curate all knowledge about every subject. The centralized hand-curation of Wolfram Alpha is certainly more controllable, manageable and efficient for a project of this scale and complexity. It avoids problems of data quality and data-consistency. But it’s also apotential bottleneck and most certainly a cost-center. Yet it appears to be a tradeoff that Wolfram can afford to make, and one worth making as well, from what I could see. I don’t yet know how Wolfram has managed to assemble his knowledge base in less than a very long time, or even how much knowledge he and his team have really added, but at first glance it seems to be a large amount. I look forward to learning more about this aspect of the project.

Building Blocks for Knowledge Computing

Wolfram Alpha is almost more of an engineering accomplishment than a scientific one — Wolfram has broken down the set of factual questions we might ask, and the computational models and data necessary for answering them, into basic building blocks — a kind of basic language for knowledge computing if you will. Then, with these building blocks in hand his system is able to compute with them — to break down questions into the basic building blocks and computations necessary to answer them, and then to actually build up computations and compute the answers on the fly.

Wolfram’s team manually entered, and in some cases automatically pulled in, masses of raw factual data about various fields of knowledge, plus models and algorithms for doing computations with the data. By building all of this in a modular fashion on top of the Mathematica engine, they have built a system that is able to actually do computations over vast data sets representing real-world knowledge. More importantly, it enables anyone to easily construct their own computations — simply by asking questions.

The scientific and philosophical underpinnings of Wolfram Alpha are similar to those of the cellular automata systems he describes in his book, “A New Kind of Science” (NKS). Just as with cellular automata (such as the famous “Game of Life” algorithm that many have seen on screensavers), a set of simple rules and data can be used to generate surprisingly diverse, even lifelike patterns. One of the observations of NKS is that incredibly rich, even unpredictable patterns, can be generated from tiny sets of simple rules and data, when they are applied to their own output over and over again.

In fact, cellular automata, by using just a few simple repetitive rules, can compute anything any computer or computer program can compute, in theory at least. But actually using such systems to build real computers or useful programs (such as Web browsers) has never been practical because they are so low-level it would not be efficient (it would be like trying to build a giant computer, starting from theatomic level).

The simplicity and elegance of cellular automata proves that anything that may be computed — and potentially anything that may exist in nature — can be generated from very simple building blocks and rules that interact locally with one another. There is no top-down control, there is no overarching model. Instead, from a bunch of low-level parts that interact only with other nearby parts, complex global behaviors emerge that, for example, can simulate physical systems such as fluid flow, optics, population dynamics in nature, voting behaviors, and perhaps even the very nature of space-time. This is the main point of the NKS book in fact, and Wolfram draws numerous examples from nature and cellular automata to make his case.

But with all its focus on recombining simple bits of information according to simple rules, cellular automata is not a reductionist approach to science — in fact, it is much more focused on synthesizing complex emergent behaviors from simple elements than in reducing complexity back to simple units. The highly synthetic philosophy behind NKS is the paradigm shift at the basis of Wolfram Alpha’s approach too. It is a system that is very much “bottom-up” in orientation. This isnot to say that Wolfram Alpha IS a cellular automaton itself — but rather that it is similarly based on fundamental rules and data that are recombined to form highly sophisticated structures.

Wolfram has created a set of building blocks for working with formal knowledge to generate useful computations, and in turn, by putting these computations together you can answer even more sophisticated questions and so on. It’s a system for synthesizing sophisticated computations from simple computations. Of course anyone who understands computer programming will recognize this as the very essence of good software design. But the key is that instead of forcing users to writeprograms to do this in Mathematica, Wolfram Alpha enables them to simply ask questions in natural language and then automatically assembles the programs to compute the answers they need.

Wolfram Alpha perhaps represents what may be a new approach to creating an “intelligent machine” that does away with much of the manual labor of explicitly building top-down expert systems about fields of knowledge (the traditional AI approach, such as that taken by the Cyc project), while simultaneously avoiding the complexities of trying to do anything reasonable with the messy distributed knowledge on the Web (the open-standards Semantic Web approach). It’s simplerthan top down AI and easier than the original vision of Semantic Web.

Generally if someone had proposed doing this to me, I would have said it was not practical. But Wolfram seems to have figured out a way to do it. The proof is that he’s done it. It works. I’ve seen it myself.

Questions Abound

Of course, questions abound. It remains to be seen just how smartWolfram Alpha really is, or can be. How easily extensible is it? Willit get increasingly hard to add and maintain knowledge as more is addedto it? Will it ever make mistakes? What forms of knowledge will it beable to handle in the future?

I think Wolfram would agree that it is probably never going to be able to give relationship or career advice, for example, because that is “fuzzy” — there is often no single right answer to such questions. And I don’t know how comprehensive it is, or how it will be able to keep up with all the new knowledge in the world (the knowledge in the system is exclusively added by Wolfram’s team right now, which is a labor intensive process). But Wolfram is an ambitious guy. He seems confident that he has figured out how to add new knowledge to the system at a fairly rapid pace, and he seems to be planning to make the system extremely broad.

And there is the question of bias, which we addressed as well. Is there any risk of bias in the answers the system gives because all the knowledge is entered by Wolfram’s team? Those who enter the knowledge and design the formal models in the system are in a position to both define the way the system thinks — both the questions and the answers it can handle. Wolfram believes that by focusing on factual knowledge — things like you might find in the Wikipedia or textbooks or reports — the bias problem can be avoided. At least he is focusing the systemon questions that do have only one answer — not questions for which there might be many different opinions. Everyone generally agrees for example that the closing price of GOOG on a certain data is a particular dollar amount. It is not debatable. These are the kinds of questions the system addresses.

But even for some supposedly factual questions, there are potential biases in the answers one might come up with, depending on the data sources and paradigms used to compute them. Thus the choice of data sources has to be made carefully to try to reflect as non-biased a view as possible. Wolfram’s strategy is to rely on widely accepted data sources like well-known scientific models, public data about factual things like the weather, geography and the stock market published byreputable organizatoins and government agencies, etc. But of course even this is a particular worldview and reflects certain implicit or explicit assumptions about what data sources are authoritative.

This is a system that reflects one perspective — that of Wolfram and his team — which probably is a close approximation of the mainstream consensus scientific worldview of our modern civilization. It is a tool — a tool for answering questions about the world today, based on what we generally agree that we know about it. Still, this is potentially murky philosophical territory, at least for some kinds ofquestions. Consider global warming — not all scientists even agree it is taking place, let alone what it signifies or where the trends are headed. Similarly in economics, based on certain assumptions and measurements we are either experiencing only mild inflation right now, or significant inflation. There is not necessarily one right answer — there are valid alternative perspectives.

I agree with Wolfram, that bias in the data choices will not be a problem, at least for a while. But even scientists don’t always agree on the answers to factual questions, or what models to use to describe the world — and this disagreement is essential to progress in science in fact. If there is only one “right” answer to any question there could never be progress, or even different points of view. Fortunately, Wolfram is desigining his system to link to alternative questions andanswers at least, and even to sources for more information about the answers (such as the Wikipeda for example). In this way he can provide unambiguous factual answers, yet also connect to more information and points of view about them at the same time. This is important.

It is ironic that a system like Wolfram Alpha, which is designed to answer questions factually, will probably bring up a broad range of questions that don’t themselves have unambiguous factual answers — questions about philosophy, perspective, and even public policy in the future (if it becomes very widely used). It is a system that has the potential to touch our lives as deeply as Google. Yet how widely it will be used is an open question too.

The system is beautiful, and the user interface is already quite simple and clean. In addition, answers include computationally generated diagrams and graphs — not just text. It looks really cool. But it is also designed by and for people with IQ’s somewhere in the altitude of Wolfram’s — some work will need to be done dumbing it down a few hundred IQ points so as to not overwhelm the average consumer with answers that are so comprehensive that they require a graduate degree to fully understand.

It also remains to be seen how much the average consumer thirsts for answers to factual questions. I do think all consumers at times have a need for this kind of intelligence once in a while, but perhaps not as often as they need something like Google. But I am sure that academics, researchers, students, government employees, journalists and a broad range of professionals in all fields definitely need a tool like this and will use it every day.

Future Potential

I think there is more potential to this system than Stephen has revealed so far. I think he has bigger ambitions for it in the long-term future. I believe it has the potential to be THE online service for computing factual answers. THE system for factual knowlege on the Web. More than that, it may eventually have the potential to learn and even to make new discoveries. We’ll have to wait and see where Wolfram takes it.

Maybe Wolfram Alpha could even do a better job of retrieving documents than Google, for certain kinds of questions — by first understanding what you really want, then computing the answer, and then giving you links to documents that related to the answer. But even if it is never applied to document retrieval, I think it has the potential to play a leading role in all our daily lives — it could function likea kind of expert assistant, with all the facts and computational power in the world at our fingertips.

I would expect that Wolfram Alpha will open up various API’s in the future and then we’ll begin to see some interesting new, intelligent, applications begin to emerge based on its underlying capabilities and what it knows already.

In May, Wolfram plans to open up what I believe will be a first version of Wolfram Alpha. Anyone interested in a smarter Web will find it quite interesting, I think. Meanwhile, I look forward to learning more about this project as Stephen reveals more in months to come.

One thing is certain, Wolfram Alpha is quite impressive and Stephen Wolfram deserves all the congratulations he is soon going to get.

Appendix: Answer Engines vs. Search Engines

The above article about Wolfram Alpha has created quite a stir on the blogosphere (Note: For those who haven’t used Techmeme before: just move your mouse over the “discussion” links under the Techmeme headline and expand to see references to related responses)

But while the response from most was quite positive and hopeful, some writers jumped to conclusions, went snarky, or entirely missed the point.

For example some articles such as this one by Jon Stokes at Ars Technica, quickly veered into refuting points that I in fact never made (Stokes seems to have not actually read my article in full before blogging his reply perhaps, or maybe he did read it but simply missed my point).

Other articles such as this one by Saul Hansell of the New York Times’ Bits blog,focused on the business questions — again a topic that I did not address in my article. My article was about the technology, not the company or the business opportunity.

The most common misconception in the articles that misesd the point concerns whether Wolfram Alpha is a “Google killer.”

In fact I was very careful in the title of my article, and the content, to make the distinction between Wolfram Alpha and Google. And I tried to make it clear that Wolfram Alpha is not designed to be a “Google killer.” It has a very different purpose: it doesn’t compete with Google for general document retreival, instead it answers factual questions.

Wolfram Alpha is an “answer engine” not a search engine.

Answer engines are different category of tool from search engines. They understand and answer questions — they don’t simply retrieve documents. (Note: in fact, Wolfram Alpha doesn’t merely answer questions, it also helps users to explore knowledge and data visually and can even open up new questions)

Of course Wolfram Alpha is not alone in making a system that can answer questions. This has been a longstanding dream of computer scientists, artificial intelligence theorists, and even a few brave entrepreneurs in the past.

Google has also been working on answering questions that are typed directly into their search box. For example, type a geography question or even “what time is it in Italy” into the Google search box and you will get a direct answer. But the reasoning and computational capabilities of Google’s “answer engine” features are primitivecompared to what Wolfram Alpha does.

For example, the Google search box does not compute answers to calculus problems, or tell you what phase the moon will be in on a certain future date, or tell you the distance from San Francisco to Ulan Bator, Mongolia.

Many questions can or might be answered by Google, using simple database lookup, provided that Google already has the answers in its index or databases. But there are many questions that Google does not yet find or store the answers to efficiently. And there always will be.

Google’s search box provides some answers to common computational questions (perhaps via looking them up in a big database in some cases, or perhaps by computing the answers in other cases). But so far it has limited range. Of course the folks at Google could work more on this. They have the resources if they want to. But they are far behind Wolfram Alpha, and others (for example, the START project, which I recently learned about today, True Knowledge and Cyc project, among many others).

The approach taken by Wolfram Alpha — and others working on “answer engines” is not to build the world’s largest database of answers but rather to build a system that can compute answers to unanticipated questions. Google has built a system that can retrieve any document on the Web. Wolfram Alpha is designed to be a system that can answer any factual question in the world.

Of course, if the Wolfram Alpha people are clever (and they are), they will probably design their system to also leverage databases of known answers whenever they can, and to also store any new answers they compute to save the trouble of re-computing them if asked again in the future. But they are fundamentally not making a database lookup oriented service. They are making a computation oriented service.

Answer engines do not compete with search engines, but some search engines (such as Google) may compete with answer engines. Time will tell if search engine leaders like Google will put enough resources into this area of functionality to dominate it, or whether they will simply team up with the likes of Wolfram and/or others who have put a lot more time into this problem already.

In any case, Wolfram Alpha is not a “Google killer.” It wasn’t designed to be one. It does however answer useful questions — and everyone has questions. There is an opportunity to get a lot of traffic, depending on things that still need some thought (such as branding, for starters). The opportunity is there, although we don’t yet know whether Wolfram Alpha will win it. I think it certainly has all the hallmarks of a strong contender at least.

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As I think about Twitter more deeply, one thing that jumps out to me is that in each wave of messaging technology, the old way is supplanted by a new way that is faster, more interactive, and has less noise. And then noise inevitably comes again and everyone moves to a new tool with less noise. This is the boom and bust cycle of messaging tools on the Web. Twitter is the new “new tool” but inevitably, as Twitter gains broader adoption the noise will come. I see several near-term challenges for Twitter as a service, and for the community of Twitter users:

Spam.
So far I have not encountered much real, deliberate, spam on Twitter. The community does a good job of self-policing, and the spammers haven’t figured out how to co-opt it. Most of what people call spam on Twitter is inadvertent from what I can tell. But the real spammers are coming and that is going to be a serious challenge for Twitter’s relatively simple social networking and messaging model.What is the Twitter community going to do when all the spam and noise inevitably arrives?

Mainstream Users.
Currently Twitter seems a bit like the early Web, and the early blogosphere — it is mostly an elite group of influencers and early adopters who have some sense of connectedness and decorum. But what happens when everyone else joins Twitter? What happens when the mainstream users arrive and fill Twitter up with more voices, and potentially more noise (at least from the perspective of the early users of Twitter) than it contains today.

Keeping Up.
Another challenge that I see as a new user of Twitter is that it is very hard to keep up with what so many people are tweeting effectively and I get the feeling I miss a lot of important things because I simply don’t have time to monitor Twitter at all hours. I need a way to see just the things that are really important, popular or likely to be of interest to me, instead of everything. I’m monitoring a number of
Twitter searches in my Twitter client and this seems to help. I also monitor Twitter searches and certain people’s tweets via RSS. But it’s a lot to keep up with.

Conversation Overload.
Secondly its difficult to manage conversations or to follow many conversations because there is no threading in the Twitter clients I have tried. Without actual threading it is quite hard to follow the flow of conversations, let alone multiple simultaneous conversations. It seems like a great opportunity for visualizaton as well — for example I would love a way to visually see conversations grow and split
into sub-threads in real-time.

Integration Overload.

As an increasing number of external social networks, messaging systems, and publishing engines all start to integrate with Twitter, there will be friction. What are the rules for how services can integrate with Twitter — beyond the API level, I am talking about the user-experience level.

How many messages, of what type, for what purpose can an external service send into Twitter? Are there standards for this that everyone must abide by or is it optional?

The potential for abuse, or for Twitter to just fill up to the point of being totally overloaded with content is huge. It appears inevitable that this will happen. Will a new generation of Twitter clients with more powerful filtering have to be generated to cope with this?

These are certainly opportunities for people making Twitter clients. Whatever Twitter app solves these problems could become very widely used.

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In the world of Twitter things happen in real-time, not Internet-time. It’s even faster than the world of the 1990’s and the early 2000’s.

Here’s an interesting timeline:

1. In the 1980’s the fax machine made snailmail almost obsolete. Faxing was faster.
2. In the 1990’s email made faxing almost obsolete. Email was faster.
3. In the 2000’s social media rose to challenge email’s dominance. The blogosphere became the center of focus.Blogging about something was often a faster way to get attention (to oneself, or to the topic) than emailing people. And you could more easily reach a larger audience.
4. In the 2010’s it looks like Twitter (and other real-time messaging systems) may become more important than email and even blogging. Twitter is simply faster. And you can reach more people in less time, more interactively, in Twitter than via email. Twitter may overcome the asynchronous nature of the Web. Even search may go “real-time.”

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