Nova Spivack - Minding the Planet » Web/Tech

Web 3.0 Documentary by Kate Ray – I'm interviewed

Nova — Sat, 08 May 2010 23:41:44 +0000

Kate Ray has done a terrific job illustrating and explaining Web 3.0 and the Semantic Web in her new documentary. She interviews, Tim Berners-Lee, Clay Shirky, me, and many others. If you’re interested in where the Web is headed, and the challenges and opportunities ahead, then you should watch this, and share it too!

Is Live Content More Valuable than On-Demand Content?

Nova — Fri, 07 May 2010 06:32:25 +0000

I have started blogging about a new concept that I call The Scheduled Web. The Scheduled Web is the next evolution of the Real-Time Web, in which it will become possible to actually navigate the time dimension of the Web more productively.

There is a popular misconception that on-demand content, such as archived video, is more valuable than live content. But in fact, this may not be the case.

Live content has built-in perishability that makes it potentially more valuable than on-demand content – if relevant audiences can find it while it is live. If a piece of high-demand content is only live for a short period of time it can attract more traffic in less time, provided that people who would want to participate interactively (or even transactively) in it are notified beforehand.

More demand in less time translates to higher advertising revenues, or higher prices in time-based sales like auctions. A series of high-demand live events could actually earn more revenues than a series of on-demand content releases in any given unit of time.

A live event is only live for some limited period of time, after which even though it may later be available in archived form, the event is finished, it is no longer a live event. If you want to get the live experience and be able to actually participate in a live event, you have to be there. It isn’t the same to watch it after the fact. And in some cases, for example auctions, sales, games, contests and chats, if you miss the event you can’t participate and may not even be able to access an archived version (if you even wanted to).

Live events are the best of both worlds for several reasons:

1. They have extra perishability because they are live, giving people a stronger incentive to participate synchronously when they are actually happening. Furthermore, if a live event is also interactive in some way, it is even more valuable to those who are present. A good example of this is American Idol, where for instance, the audience can participate in the voting process that selects finalists. Interactivity makes the show more engaging and gives viewers a sense of ownership and personal investment in the content.

2. Live events can also be archived and made available on-demand, as well. The key to getting this double-layer of value out of live events is to schedule them so that they can be found before or while they are actually live. This amplifies the initial demand and attendance to the event, and also provides any archived version that follows an added social virality.

At Live Matrix we believe it is incorrect to assume that the television model carries over directly to the Web. The Web is an entirely different medium because it is two-way, interactive, both synchronous and asynchronous, and distribution is open to anyone and portable across any device. Television over the Web is going to be different than TV on cable and satellite networks. The fact that consumers can consume Web video content asynchronously is a plus, but it doesn’t obviate the need or opportunity for live synchronous content on the Web. In fact, for any event that requires or even wants to leverage interactivity, live synchronous attendance by audience members is a key part of the experience.

There are many use-cases where live synchronous content consumption cannot be replaced by asynchronous content consumption — for example a live chat, or a time-limited sale or auction, or a multiplayer live game. Even in the case of video and audio there are many cases where live synchronous content is more valuable than asynchronous on-demand content. For example who wants to watch the Superbowl months after the game is over? Who really wants to watch a major presidential address or a press conference weeks later? Who wants to watch video of election coverage months after it’s decided? These kinds of “timely” events are live by their nature, and part of the value of consuming the content is the act of doing it in a timely manner.

The value of live interactive content begins to become even more clear as on-demand content that is originally streamed live has the ability to generate more revenues over its lifetime than simply recorded, on-demand content alone. The Scheduled Web will thus even improve traffic and revenues for on-demand content, if that content can be initiated as live events, or at least paired with them in some way.

The value of the Scheduled Web will be realized as not simply a schedule of video content, but of all scheduled events of any type that take place on the Internet. While much of this content is valuable both when it initially goes live and on an ongoing basis as on-demand content after the fact, there is also a lot of content in Live Matrix that will be inherently and necessarily more valuable when it is live, such as sales and auctions or games.

In addition there is a new category of “exclusively live” online events that we may see emerge in 2011. These events will be one-time events, with no archived copies after they finish. They may be high-profile events where attendance requires paid admission for example. They will be marketed as special experiences – where not only do you have to be there to experience them, but where being there has special advantages, like being able to interact with others who are there and perhaps with the performers or celebrities involved as well. Some events may also offer backstage passes, or special break-out sessions as well.

For events like these — where the only value created is during the event’s live run — discovery must happen prior to or during the event for participation to take place. For these, the Scheduled Web is absolutely essential.

The Birth of the Scheduled Web

Nova — Fri, 07 May 2010 01:04:09 +0000

If 2010 was the year of the Real-Time Web, then 2011 is going to be the year that it evolves into the Scheduled Web.

The Real-Time Web happens in the now: it is spontaneous, overwhelming, and disorganized. Things just happen unpredictably and nobody really knows what to expect or what will happen when.

The Real-Time Web is something of a misnomer, however, because usually it’s not real-time at all – it’s after-the-fact. Most people find out about things that happened on the Real-Time Web after they happen, or, if they are lucky, when they happen. There is no way to know what is going to happen before it happens; there is no way to prepare or ensure that you will be online when something happens on the Real-Time Web. It’s entirely hit-or-miss.

If we are going to truly realize the Real-Time Web vision, then “time” needs to be the primary focus. So far, the Real-Time Web has mainly just been about simultaneity and speed – for example how quickly people on Twitter can respond to an event in the real world such as the Haiti Earthquake or the Oscars.

This obsession with the present is a sign of the times, but it is also a form of collective myopia — the Real-Time Web really doesn’t include the past or the future – it exists in a kind of perpetual now. To put the “time” into Real-Time, we need to provide a way to see the past, present and the future Real-Time Web at once. For example, we need a way to search and browse the past, present, and the future of a stream – what happened, what is happening, and what is scheduled to happen in the future. And this is where what I am calling The Scheduled Web comes in. It’s the next step for the Real-Time Web.

Defining the Scheduled Web

With the Scheduled Web things will start to make sense again. There will be a return of some semblance of order thanks to schedule metadata that enables people (and software) to find out about upcoming things on the Web that matter to them, before they happen, and to find out about past things that matter, after they happen.

The Scheduled Web is a Web that has a schedule, or many schedules, which exist in some commonly accessible, open format. These schedules should be searchable, linkable, shareable, interactive, collaborative, and discoverable. And they should be able to apply to anything — not just video, but any kind of content or activity online.

Why is this needed? Well consider this example. Imagine if there was no TV Guide on digital television. How would you navigate the constantly changing programming of more than 1000 digital TV channels without an interactive program guide (IPG)? It would be extremely difficult to find shows in a timely manner. According to clickstream data from television set-top boxes, about 10% of all time spent watching TV is spent in the IPG environment. And that is not even counting additional time-spent in on-demand guidance interfaces on DVRs. The point here is that guidance is key when you have lots of streams of content happening over time.

Now extend this same problem to the Web where there are literally millions of things happening every minute. These streams of content are not just limited to video. There are myriad types of real-time streams, everything from sales, auctions, and chats, to product launches, games, and audio, to streams of RSS feeds, Web pages appearing on Web sites, photos appearing on photo sites, software releases, announcements, etc.

Without some kind of guidance it is simply impossible to navigate the firehose of live online content streams on the Web efficiently. This firehose is too much to cope with in the present moment, let alone the past, or the future. This is what the Scheduled Web will solve.

By giving people a way to see into the past, present and future of the Real-Time Web, the Scheduled Web will enable the REAL Real-Time Web to be truly actualized. People will be able to know and plan in advance to actually be online when live events they care about take place.

Instead of missing that cool live Web concert or that auction for your favorite brand of shoes, simply because you didn’t know about it beforehand, you will be able to discover it in advance, RSVP, and get reminded before it starts — so you can be there and participate in the experience, right as it happens.

We are just beginning to see the emergence of the Scheduled Web. Two new examples of startups that are at work in the space are Clicker and Live Matrix.

Clicker, a site that mainly provides on-demand video clips of past TV episodes, this week launched a schedule for live video streams on the Web.

Live Matrix (my new startup), is soon to launch a schedule for all types of online events, not just video streams.

Some people have compared Live Matrix to Clicker, however this is not a wholly accurate comparison. We have very different, although intersecting, goals.

While Clicker is an interesting play to compete with TV Guide and companies like Hulu, Live Matrix is creating a broader index of all the events taking place across the Scheduled Web, not just video/TV content events.

The insight behind Live Matrix is that there is much more to the Scheduled Web than video and TV content. The Web is not just about TV or video – it is about many different kinds of content.

Applying a TV metaphor to the Web is like trying to apply a print metaphor to tablet computing. While print has many positive qualities, tablet devices should not be limited just to text should they? Likewise, while the TV metaphor has advantages, it doesn’t make sense to limit the experience of time or scheduled content on the Web just to video.

With this in mind, while Live Matrix includes scheduled live video streams, we view video and TV type content as just one of many different types of scheduled Web content that matter.

For example, Live Matrix also includes online shopping events like sales and auctions, which comprise an enormous segment of the Scheduled Web. As an illustration eBay alone lists around 10 million scheduled auctions and sales each day! Live Matrix also includes scheduling metadata for many other kinds of content — online games, online chats, online audio, and more.

Live Matrix is building something quite a bit broader than current narrow conceptions of the Real-Time Web, or the narrow metaphor of TV on the Web. We are creating a way to navigate and search the full time dimension of the Web, we are building the schedule of the Web.

This will become a valuable, even essential, layer of metadata that just about every application, service and Internet surfer will make use of every day. Because after all, life happens in time and so does the Web. By adding metadata about time to the Web, Live Matrix will help make the Web – and particularly the Real-Time Web – easier to navigate.

Online vs. Offline Events

One of the key rules of Live Matrix is that, to be included in our schedule, an event must be consumable on-line. This means that it must be possible to access and participate in an event on an Internet-connected device.

Live Matrix is not a schedule of offline events or events that cannot be consumed or participated in using Internet-connected devices.

We made this rule because we believe that in the near-future almost everything interesting will, in fact, be consumable online, even if it has an offline component to it. We want to focus attention on those events which can be consumed on Internet-connected devices, so that if you have a connected device you can know that everything in Live Matrix can be accessed directly on your device. You don’t have to get in your car and drive to some physical venue, you don’t have to leave the Internet and go to some other device and network (like a TV and cable network).

Note the shift in emphasis here: We believe that the center of an increasing number of events is going to be online, and the offline world is going to increasingly become more peripheral.

For example, if a retail sale generates more revenues from online purchases than physical in-store purchases, the center of the sale is really on-line and the physical store becomes peripheral. Similarly, if a live concert has 30,000 audience members in a physical stadium but 10,000,000 people attending it online, the bulk of the concert is in fact online. This is already starting to happen.

For example, the recent Youtube concert featuring U2 had 10 million live streams – that’s up to 10 million live people in the audience at one time, making it possibly the largest online concert in history; it’s certainly a lot more people than any physical stadium could accommodate. Similarly, online venues like Second Life and World of Warcraft can accommodate thousands of players interacting in the same virtual spaces – not only do these spaces not even have a physical analogue (they exist only in virtual space), but there are no physical spaces that could accommodate such large games. These are examples of how online events may start to eclipse offline events.

I’m not saying this trend is good or bad; I’m simply stating a fact of our changing participatory culture. The world is going increasingly online and with this shift the center of our lives is going increasingly online, as well. It is this insight that gave my co-founder, Sanjay Reddy, and I, the inspiration to start Live Matrix, and to begin building what we hope will be the backbone of the Scheduled Web.

The Digital Generation Gap

Nova — Mon, 05 Apr 2010 02:28:12 +0000

We exist in a epoch of great technological change. Within the space of just a few generations we have gone from horse drawn carriages to exploring the outer reaches of our solar system, from building with wood, stone and metals to nanoscale construction with individual atoms, and from manual printing presses and physical libraries, to desktop publishing and the World Wide Web. The increasing pace of technological evolution brings with it many gifts, but also poses challenges never-before-faced by humanity. One of these challenges is the digital generation gap.

The digital generation gap is the result of the extremely rapid rise of personal computing, the Internet, mobile applications, and coming next, biotechnology. Never before in the history of our species have we been faced with a situation where each living generation is focused around a different technology platform.

The tools and practices that the elders of our civilization use are still based on the pre-digital analog era. Their children — the Baby Boomers — use entirely different tools and practices based around the PC. And the youth of today — the Boomers’ children, exist in yet another domain: the world of mobile devices.

The digital generation gap presents a major challenge to our civilization. In particular because of the effect this has on education — both informal education that takes place at home and in communities, and formal education that takes place in school settings. The tools that teachers grew up with and now teach with (PC’s) are not the same tools that the students of today are using today to learn and communicate with (mobile devices).

Baby Boomers grew up before the advent of any of these technologies — they lived in an analog world in which daily life took place primarily on the physical, face-to-face human scale, with physical materials and physical information media like printed books and newspapers. This world was similar to the world of their parents and grandparents — even though it was increasingly automated and industrialized during their lives. As children and during their young adult years the Boomers grew up amidst the fruition of the industrial revolution: mass-produced physical and synthetic goods of all kinds. Among the defining shifts of this period was the transition from a world of manual labor to one of increasing automation. The pinnacle of this transition was the adoption of the first generations of computers.

The Boomer’s children — people in their 30’s and 40’s today — arrived to usher in the transition from an automated analog world, to the new digital world. They were born into a civilization where monolithic computers had already taking hold in government and industry, and they witnessed the birth of waves of increasingly powerful, inexpensive and portable personal computers, the Internet, and the Web. This generation built the bridges from the industrial world of the Boomers to the digital world we live in today. They integrated systems, connected devices, and brought the whole world together as one global social and economic network.

Now their children – the children and youth of today — are growing up in a world that is primarily focused around mobile devices and mobile applications. They have always lived with ubiquitous mobile access and social media. No longer concerned with building bridges to the legacy industrial world of their parents and grandparents, they are plunging headlong into an increasingly digital culture. One in which dating, shopping, business, education — almost everything we do as humans — is taking place online, and via mobile devices.

Each generation is out of touch with the means of production and consumption of the other generations. The result is an increasing communications gap between the generations: They use different platforms. And not surprisingly the inter-generational transmission of knowledge, traditions, cultural norms and standards is not operating like it used to. In fact it may be breaking down entirely.

Many of the cultural and social stresses making headline news are related to the digital generation gap. For example, the increasing growth of cyberbullying is the result of parents and teachers being totally out of touch with the mobile world that kids live in today.

Parents and teachers are so out of the loop technologically, compared to kids today, that they are literally unable to see what is going on between them, let alone do anything about it.

It’s no wonder that kids are running wild online, “sexting,” cyberbullying, and cheating in school. There are few adults, and little to no adult-supervision, where they spend their time online keeping order.

There is no period in recent history when this has ever been the case. It used to be that schoolkids took recess breaks in the schoolyard under the watchful eyes their teachers. There was a certain level of adult supervision in school, and also at home. Not today. Teachers and parents can’t see what their kids are up to online and have no control over what they do with their mobile devices. We have a generation of kids who are growing up with less adult oversight and supervision than ever before.

And the newest generation — the babies of today — what will their experience be? Will the pace of technological progress finally start to plateau for them? Will their world be more like the world of their parents?

Instead of a sudden shift to yet a smaller level of scale or a more powerful technology platform, will they and many generations to come, live on a more stable and shared technology platform? If the pace does slow down for a while, we may see inter-generational gaps decrease. Perhaps this will serve to standardize and solidify our emerging global digital culture. A new set of digital norms and traditions will have time to form and be handed down across generations.

Alternatively, what if in fact the pace of change continues to quicken instead? What if the babies of today grow up in a world of augmented reality and industrial-scale genetic engineering? And what if their children (the grandchildren of people in their 40’s today) grow up in a world of direct brain-machine interfaces and personal genetic engineering? Those of us today who think of ourselves as being on the cutting edge will be the elders of tomorrow, and we will be hopelessly out of touch.

Evri Ties the Knot with Twine — Twine CEO Comments and Analysis

Nova — Thu, 11 Mar 2010 16:52:41 +0000

Today I am pleased to announce that my company, Radar Networks, and its flagship product, Twine, have been acquired by Evri. TechCrunch broke the story here.

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.

(Note: You may also be interested in viewing this presentation, “A Yarn About Twine” which covers the full history of the project with lots of pictures of various iterations of our work from the early semantic desktop app to Twine, to T2.)

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

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), 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. 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 multiple development teams instead of one was a challenging and costly endeavor. 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:

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).
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.
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.
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.
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.
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.
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.
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.
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.
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.

How HiSiri Works – Interview with Tom Gruber, CTO of SIRI

Nova — Wed, 27 Jan 2010 07:11:56 +0000

Sneak Preview of Siri – The Virtual Assistant that will Make Everyone Love the iPhone, Part 2: The Technical Stuff

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.

Will the Web Become Conscious?

Nova — Tue, 19 Jan 2010 09:44:05 +0000

“All reality is virtual” — Terrence McKenna

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.

Consciousness vs. Intelligence

The Global Brain is About to Wake Up

Nova — Mon, 18 Jan 2010 23:43:25 +0000

The emerging realtime Web is not only going to speed up the Web and our lives, it is going to bring about a kind of awakening of our collective Global Brain. It’s going to change how many things happen on online, but it’s also going to change how we see and understand what the Web is doing. By speeding up the Web, it will cause processes that used to take weeks or months to unfold online, to happen in days or even minutes. And this will bring these processes to the human-scale — to the scale of our human “now” — making it possible for us to be aware of larger collective processes than before. We have until now been watching the Web in slow motion. As it speeds up, we will begin to see and understand what’s taking place on the Web in a whole new way.

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…

Eliminating the Need for Search – Help Engines

Nova — Sat, 16 Jan 2010 19:42:31 +0000

We are so focused on how to improve present-day search engines. But that is a kind of mental myopia. In fact, a more interesting and fruitful question is why do people search at all? What are they trying to accomplish? And is there a better way to help them accomplish that than search?

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.

Twine "T2" – Latest Demo Screenshots (Internal Alpha)

Nova — Tue, 22 Dec 2009 04:52:41 +0000

This is a series of screenshots that demo the latest build of the consumer experience and developer tools for Twine.com’s “T2″ semantic search product. This is still in internal alpha — not released to public yet.

Twine T2 Demo & Dev Tools (Screenshots Series)

View more presentations from Twine.com.

The Road to Semantic Search — The Twine.com Story

Nova — Tue, 22 Dec 2009 04:24:24 +0000

This is the story of Twine.com — our early research (with never before seen screenshots of our early semantic desktop work), and our evolution from Twine 1.0 towards Twine 2.0 (“T2″) which is focused on semantic search.

A Yarn About Twine — ISWC 2009 Keynote — Nova Spivack

View more presentations from Twine.com.

The Evolution of the Web: Past, Present, Future

Nova — Tue, 22 Dec 2009 04:02:30 +0000

This is a talk I have given many times, on the past, present and future evolution of the Web, and particularly the Semantic Web.

Web Evolution Nova Spivack Twine

View more presentations from Twine.com.

The Web Wide World — The Web Spreads Into the Physical World

Nova — Wed, 04 Nov 2009 20:32:34 +0000

I have noticed an interesting and important trend of late. The Web is starting to spread outside of what we think of as “the Web” and into “the World.” This trend is exemplified by many data points. For example:

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.

What's After the Real Time Web?

Nova — Wed, 28 Oct 2009 04:08:01 +0000

In typical Web-industry style we’re all focused minutely on the leading trend-of-the-year, the real-time Web. But in this obsession we have become a bit myopic. The real-time Web, or what some of us call “The Stream,” is not an end in itself, it’s a means to an end. So what will it enable, where is it headed, and what’s it going to look like when we look back at this trend in 10 or 20 years?

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

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.
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.

The Future of the Web: BBC Interview

Nova — Sat, 23 May 2009 07:31:18 +0000

The BBC World Service’s Business Daily show interviewed the CTO of Xerox and me, about the future of the Web, printing, newspapers, search, personalization, the real-time Web. Listen to the audio stream here. I hear this will only be online at this location for 6 more days. If anyone finds it again after that let me know and I’ll update the link here.

The Next Generation of Web Search — Search 3.0

Nova — Sat, 23 May 2009 06:26:15 +0000

The next generation of Web search is coming sooner than expected. And with it we will see several shifts in the way people search, and the way major search engines provide search functionality to consumers.

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.

Sneak Peak – Siri — Interview with Tom Gruber

Nova — Sat, 16 May 2009 05:08:19 +0000

Sneak Preview of Siri – The Virtual Assistant that will Make Everyone Love the iPhone, Part 2: The Technical Stuff

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

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.

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.

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.

Welcome to the Stream – Next Phase of the Web

Nova — Fri, 08 May 2009 11:00:33 +0000

May 8, 2009

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?

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.
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.
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/

How Social Media Changes Content Distribution from Web Sites to People to Software

Nova — Tue, 10 Mar 2009 21:15:58 +0000

I’ve written a new article about how content distribution has evolved, and where it is heading. It’s published here: http://www.siliconangle.com/social-media/content-distribution-is-changing-again/.

Wolfram Alpha is Coming — And It Could be as Important as Google

Nova — Sun, 08 Mar 2009 06:20:33 +0000

Notes:

- 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.

Video: My Talk on The Future of Libraries — "Library 3.0"

Nova — Sat, 14 Feb 2009 07:42:28 +0000

If you are interested in semantics, taxonomies, education, information overload and how libraries are evolving, you may enjoy this video of my talk on the Semantic Web and the Future of Libraries at the OCLC Symposium at the American Library Association Midwinter 2009 Conference. This event focused around a dialogue between David Weinberger and myself, moderated by Roy Tennant. We were forutnate to have an audience of about 500 very vocal library directors in the audience and it was an intensive day of thinking together. Thanks to the folks at OCLC for a terrific and really engaging event!

Video: My Talk on the Evolution of the Global Brain at the Singularity Summit

Nova — Sat, 14 Feb 2009 07:32:41 +0000

If you are interested in collective intelligence, consciousness, the global brain and the evolution of artificial intelligence and superhuman intelligence, you may want to see my talk at the 2008 Singularity Summit. The videos from the Summit have just come online.

(Many thanks to Hrafn Thorisson who worked with me as my research assistant for this talk).

Twine's Explosive Growth

Nova — Fri, 06 Feb 2009 19:05:38 +0000

Twine has been growing at 50% per month since launch in October. We've been keeping that quiet while we wait to see if it holds. VentureBeat just noticed and did an article about it. It turns out our January numbers are higher than Compete.com estimates and February is looking strong too. We have a slew of cool viral features coming out in the next few months too as we start to integrate with other social networks. Should be an interesting season.

How to Build the Global Mind

Nova — Mon, 27 Oct 2008 18:12:16 +0000

Kevin Kelly recently wrote another fascinating article about evidence of a global superorganism. It’s another useful contribution to the ongoing evolution of this meme.

I tend to agree that we are at what Kevin calls, Stage III. However, an important distinction in my own thinking is that the superorganism is not comprised just of machines, but it is also comprised of people.

(Note: I propose that we abbreviate the One Machine, as “the OM.” It’s easier to write and it sounds cool.)

Today, humans still make up the majority of processors in the OM. Each human nervous system comprises billions of processors, and there are billions of humans. That’s a lot of processors.

However, Ray Kurzweil posits that the balance of processors is rapidly movingtowards favoring machines — and that sometime in the latter half of this century, machine processors will outnumber or at least outcompute all the human processors combined, perhaps many times over.

While agree with Ray’s point that machine intelligence will soon outnumber human intelligence, I’m skeptical of Kurzweil’s timeline, especially in light of recent research that shows evidence of quantum level computation within microtubules inside nuerons. If in fact the brain computes at the tubulin level then it may have many orders of magnitude more processors than currently estimated. This remains to be determined. Those who argue against this claim that the brain can be modelled on a Classical level and that quantum computing need not be invoked. To be clear, I am not claiming that the brain is a quantum computer, I am claiming that there seems to be evidence that computation in the brain takes place at the quantum level, or near it. Whether quantum effects have any measurable effect on what the brain does is not the question, the question is simply whether microtubules are the lowest level processing elements of the brain. If they are,then there are a whole lot more processors in the brain than previously thought.

Another point worth considering is that much of the brain’s computation is not taking place within the neurons but rather in the gaps between synapses, and this computation happens chemically rather than electrically. There are vastly more synapses than neurons, and computation within the synapses happens at a much faster and more granular level than neuronal firings. It is definitely the case thatchemical-level computations take place with elements that are many orders of magnitude smaller than neurons. This is another case for the brain computing at a much lower level than is currently thought.

In other words the resolution of computation in the human brain is still unknown. We have several competing approximations but no final answer on this. I do think however that evidence points to computation being much more granular than we currently think.

In any case, I do agree with Kurzweil that at least it is definitely the case that artificial computers will outnumber naturally occurring human computers on this planet — it’s just a question of when. In my view it will take a little longer than he thinks: it is likely to happen after 100 to 200 years at the most.

There is another aspect of my thinking on this subject which I think may throw a wrench in the works. I don’t think that what we call “consciousness” is something that can be synthesized. Humans appear to be conscious, but we have no idea what that means yet. It is undeniable that we all have an experience of being conscious, and this experience is mysterious. It is also the case that at least so far, nobody hasbuilt a software program or hardware device that seems to be having this experience. We don’t even know how to test for consciousness in fact. For example, the much touted Turing Test does not test consciousness, it tests humanlike intelligence. There really isn’t a test for consciousness yet. Devising one is an interesting an important goal that we should perhaps be working on.

In my own view, consciousness is probably fundamental to the substrate of the universe, like space, time and energy. We don’t know what space, time and energy actually are. We cannot actually measure them directly either. All our measurements of space, time and energy are indirect — we measure other things that imply that space, time and energy exist. Space, time and energy are inferred by effects we observe on material things that we can measure. I think the same may be true of consciousness. So the question is, what are the measureable effects ofconsciousness? Well one candidate seems to be the Double Slit experiment, which shows that the act of observation causes the quantum wave function to collapse. Are there other effects we can cite as evidence of consciousness?

I have recently been wondering how connected consciousness is to the substrate of the universe we are in. If consciousness is a property of the substrate, then it may be impossible to synthesize. For example, we never synthesize space, time or energy — no matter what we do, we are simply using the space, time and energy of the substrate that is this universe.

If this is the case, then creating consciousness is impossible. The best we can do is somehow channel the consciousness that is already there in the substrate of the universe. In fact, that may be what the human nervous system does: it channels consciousness, much in the way that an electrical circuit channels electricity. The reason that software programs will probably not become conscious is that they aretoo many levels removed from the substrate. There is little or no feedback between the high-level representations of cognition in AI programs and the quantum-level computation (and possibly consciousness) of the physical substrate of the universe. That is not the case in the human nervous system — in the human nervous system the basic computing elements and all the cognitive activity are directly tied to thephysical substrate of the universe. There is at least the potential for two-way feedback to take place between the human mind (the software), the human brain (a sort of virtual machine), and the quantum field (the actual hardware).

So the question I have been asking myself lately is how connected is consciousness to the physical substrate? And furthermore, how important is consciousness to what we consider intelligence to be? If consciousness is important to intelligence, then artificial intelligence may not be achievable through software alone — it mayrequire consciousness, which may in turn require a different kind of computing system, one which is more connected (through bidirectional feedback) to the physical quantum substrate of the universe.

What all this means to me is that human beings may form an important and potentially irreplaceable part of the OM — the One Machine — the emerging global superorganism. In particular today the humans are still the most intelligent parts. But in the future when machine intelligence may exceed human intelligence a billionfold, humans may still be the only or at least most conscious parts of the system. Because of the uniquely human capacity for consciousness (actually, animals and insects are conscious too), I think we have an important role to playin the emerging superorganism. We are it’s awareness. We are who watches, feels, and knows what it is thinking and doing ultimately.

Because humans are the actual witnesses and knowers of what the OM does and thinks, the function of the OM will very likely be to serve and amplify humans, rather than to replace them. It will be a system that is comprised of humans and machines working together, for human benefit, not for machine benefit. This is a very different future outlook than that of people who predict a kind of “Terminator-esque” future in which machines get smart enough to exterminate the human race. It won’t happen that way. Machines will very likely not get that smart for a long time, if ever, because they are not going to be conscious. I think we should be much more afraid of humans exterminating humanity than of machines doing it.

So to get to Kevin Kelly’s Level IV, what he calls “An Intelligent Conscious Superorganism” we simply have to include humans in the system. Machines alone are not, and will not ever be, enough to get us there. I don’t believe consciousness can be sythesized or that it will suddenly appear in a suitably complex computer program. I think it is a property of the substrate, and computer programs are just too many levels removed from the substrate. Now, it is possible that we mightdevise a new kind of computer architecture — one which is much more connected to the quantum field. Perhaps in such a system, consciousness, like electricity, could be embodied. That’s a possibility. It is likely that such a system would be more biological in nature, but that’s just a guess. It’s an interesting direction forresearch.

In any case, if we are willing to include humans in the global superorganism — the OM, the One Machine — then we are already at Kevin Kelly’s Level IV. If we are not willing to include them, then I don’t think will reach Level IV anytime soon, or perhaps ever.

It is also important to note that consciousness has many levels, just like intelligence. There is basic raw consciousness which simply perceives the qualia of what takes place. But there are also forms of consciousness which are more powerful — for example, consciousness that is aware of itself, and consciousness which is so highly tuned that it has much higher resolution, and consciousness which is aware of the physical substrate and its qualities of being spacelike and empty of any kind of fundamental existence. These are in fact the qualities of the quantum substrate we live in. Interestingly, they are also the qualities of reality that Buddhists masters also point out to be the ultimate nature of reality and of the mind (they do not consider reality and mind to be two different things ultimately). Consciousness may or may not be aware of these qualities of consciousness and ofreality itself — consciousness can be dull, or low-grade, or simply not awake. The level to which consciousness is aware of the substrate is a way to measure the grade of consciousness taking place. We might call this dimension of consciousness, “resolution.” The higher the resolution of consciousness is, the more acutely aware it is of the actual nature of phenomena, the substrate. At the highest resolutionit can directly percieve the space-like, mind-like, quantum nature of what it observes. At the highest level of resolution, there is no perception of duality between observer and observed — consciousness perceives everything to be essentially consciousness appearing in different forms and behaving in a quantum fashion.

Another dimension of consciousness that is important to consider is what we could call “unity.” On the lowest level of the unity scale, there is no sense of unity, but rather a sense of extreme isolation or individuality. At the highest level of the scale there is a sense of total unification of everything within one field of consciousness. That highest-level corresponds to what we could call “omniscience.” TheBuddhist concept of spiritual enlightenment is essentially consciousness that has evolved to BOTH the highest level of resolution and the highest level of unity.

The global superorganism is already conscious, in my opinion, but it has not achieved very high resolution or unity. This is because most humans, and most human groups and organizations, have only been able to achive the most basic levels of consciousness themselves. Since humans, and groups of humans, comprise the consciousness of the global superorganism, our individual and collective conscious evolution is directly related to the conscious evolution of the superorganism as a whole. This is why it is important for individuals and groups to work on their own consciousnesses. Consciousness is “there” as a basic property of the physical substrate, but like mass or energy, it can be channelled and accumulated and shaped. Currently the consciousness that is present in us as individuals, and in groups of us, is at best, nascent and underdeveloped.

In our young, dualistic, materialistic, and externally-obsessed civilization, we have made very little progress on working with consciousness. Instead we have focused most or all of our energy on working with certain other more material-seeming aspects of the substrate — space, time and energy. In my opinion a civilizationbecomes fully mature when it spends equal if not more time on the concsiousness dimension of the substrate. That is something we are just beginning to work on, thanks to the strangeness of quantum mechanics breaking our classical physical paradims and forcing us to admit that consciousness might play a role in our reality.

But there are ways to speed up the evolution of individual and collective consciousness, and in doing so we can advance our civilization as a whole. I have lately been writing and speaking about this in more detail.

On an individual level one way to rapidly develop our own consciousness is the path of meditation and spirituality — this is most important and effective. There may also be technological improvements, such as augmented reality, or sensory augmentation, that can improve how we perceive, and what we perceive. In the not too distant future we will probably have the opportunity to dramatically improve the range and resolution of our sense organs using computers or biological means. We may even develop new senses that we cannot imagine yet. In addition, using the Internet for example, we will be able to be aware of more things at once than ever before. But ultimately, the scope of our individual consciousness has to develop on an internal level in order to truly reach higher levels of resolution and unity.Machine augmentation can help perhaps, but it is not a substitute for actually increasing the capacity of our consciousnesses. For example, if we use machines to get access to vastly more data, but our consciousnesses remain at a relatively low-capacity level, we may not be able to integrate or make use of all that new data anyway.

It is a well known fact that the brain filters out most of the information we actually percieve. Furthermore when taking a a hallucinogenic drug, the filter opens up a little wider, and people become aware of things which were there all along but which they previously filtered out. Widening the scope of consciousness — increasing the resolution and unity of consciousness, is akin to what happens when taking such a drug, except that it is not a temporary effect and it is more controllable and functional on a day-to-day basis. Many great Tibetan lamas I know seem to have accomplished this — the scope of their consciousness is quite vast, and the resolution is quite precise. They literally can and do see every detail of eventhe smallest things, and at the same time they have very little or no sense of individuality. The lack of individuality seems to remove certain barriers which in turn enable them to perceive things that happen beyond the scope of what would normally be considered their own minds — for example they may be able to perceive the thoughts of others, or see what is happening in other places or times. This seems to take place because they have increased the resolution and unity oftheir consciousnesses.

On a collective level, there are also things we can do to make groups, organizations and communities more conscious. In particular, we can build systems that do for groups what the “self construct” does for individuals.

The self is an illusion. And that’s good news. If it wasn’t an illusion we could never see through it and so for one thing spiritual enlightenment would not be possible to achieve. Furthermore, if it wasn’t an illusion we could never hope to synthesize it for machines, or for large collectives. The fact that “self” is an illusion is something that Buddhist, neuroscientists, and cognitive scientists all seem to agree on. The self is an illusion, a mere mental construct. But it’s a very useful one, when applied in the right way. Without some concept of self we humans would find it difficult to communicate or even navigate down the street. Similarly, without some concept of self groups, organizations and communities also cannot function very productively.

The self construct provides an entity with a model of itself, and its environment. This model includes what is taking place “inside” and what is taking place “outside” what is considered to be self or “me.” By creating this artificial boundary, and modelling what is taking place on both sides of the boundary, the self construct is able to measure and plan behavior, and to enable a system to adjust and adaptto “itself” and the external environment. Entities that have a self construct are able to behave far more intelligently than those which do not. For example, consider the difference between the intelligence of a dog and that of a human. Much of this is really a difference in the sophistication of the self-constructs of these two different species. Human selves are far more self-aware, introspective, and sophisticatedthan that of dogs. They are equally conscious, but humans have more developed self-constructs. This applies to simple AI programs as well, and to collective intelligences such as workgroups, enterprises, and online communities. The more sophisticated the self-construct, the smarter the system can be.

The key to appropriate and effective application of the self-construct is to develop a healthy self, rather than to eliminate the self entirely. Eradication of the self is form of nihilism that leads to an inability to function in the world. That is not somethingthat Buddhist or neuroscientists advocate. So what is a healthy self? In an individual, a healthy self is a construct that accurately represents past, present and projected future internal and external state, and that is highly self-aware, rational but not overly so, adaptable, respectful of external systems and other beings, and open to learning and changing to fit new situations. The same is true for a healthy collective self. However, most individuals today do not have healthy selves — they have highly delluded, unhealthy self-constructs. This in turn is reflected in the higher-order self-constructs of the groups, organizations and communities we build.

One of the most important things we can work on now is creating systems that provide collectives — groups, organizations and communities — with sophisticated, healthy, virtual selves. These virtual selves provide collectives with a mirror of themselves. Having a mirror enables the members of those systems to see the whole, and how they fit in. Once they can see this they can then begin to adjust their own behavior to fit what the whole is trying to do. This simplemirroring function can catalyze dramatic new levels of self-organization and synchrony in what would otherwise be a totally chaotic “crowd” of individual entities.

In fact, I think that collectives move through three levels of development:

Level 1: Crowds. Crowds are collectives in which the individuals are not aware of the whole and in which there is no unified sense of identity or purpose. Nevertheless crowds do intelligent things. Consider for example, schools of fish, or flocks of birds. There is no single leader, yet the individuals, by adapting to what their nearby neighbors are doing, behave collectively as a single entity of sorts. Crowds are amoebic entities that ooze around in a bloblike fashion. They are not that different from physical models of gasses.
Level 2: Groups. Groups are the next step up from crowds. Groups have some form of structure, which usually includes a system for command and control. They are more organized. Groups are capable of much more directed and intelligent behaviors. Families, cities, workgroups, sports teams, armies, universities, corporations, and nations are examples of groups. Most groups have intelligences that are roughly similar to that of simple animals. Theymay have a primitive sense of identity and self, and on the basis of that, they are capable of planning and acting in a more coordinated fashion.

Level 3: Meta-Individuals. The highest level of collective intelligence is the meta-individual. This emerges when what was once a crowd of separate individuals, evolves to become a new individual in its own right, and is faciliated by the formation of a sophisticated meta-level self-construct for the collective. This evolutionary leap is called a metasystem transition — the parts join together to form a new higher-order whole that is made of the parts themselves. This new whole resembles the parts, but transcends theirabilities. To evolve a collective to the level of being a true individual, it has to have a well-designed nervous system, it has to have a collective brain and mind, and most importantly it has to achieve a high-level of collective consciousness. High level collective consciousness requires a sophisticated collective self construct to serve as a catalyst. Fortunately, this is something we can actually build, because as has been asserted previously, self is an illusion, a consturct, and therefore selves can be built, even for large collectives comprised of millions or billions of members.

The global superorganism has been called The Global Brain for over a century by a stream of forward looking thinkers. Today we may start calling it the One Machine, or the OM, or something else. But in any event, I think the most important work that we can can do to make it smarter is to provide it with a more developed and accurate sense of collective self. To do this we might start by working on ways toprovide smaller collectives with better selves — for example, groups, teams, enterprises and online communities. Can we provide them with dashboards and systems which catalyze greater collective awareness and self-organization? I really believe this is possible, and I am certain there are technological advances that can support this goal. That is what I’m working on with my own project, Twine.com. But this is just the beginning.

Interest Networks are at a Tipping Point

Nova — Mon, 20 Oct 2008 22:01:45 +0000

UPDATE: There’s already a lot of good discussion going on around this post in my public twine.

I’ve been writing about a new trend that I call “interest networking” for a while now. But I wanted to take the opportunity before the public launch of Twine on Tuesday (tomorrow) to reflect on the state of this new category of applications, which I think is quickly reaching its tipping point. The concept is starting to catch on as people reach for more depth around their online interactions.

In fact – that’s the ultimate value proposition of interest networks – they move us beyond the super poke and towards something more meaningful. In the long-term view, interest networks are about building a global knowledge commons. But in the short term, the difference between social networks and interest networks is a lot like the difference between fast food and a home-cooked meal – interest networks are all about substance.

At a time when social media fatigue is setting in, the news cycle is growing shorter and shorter, and the world is delivered to us in soundbytes and catchphrases, we crave substance. We go to great lengths in pursuit of substance. Interest networks solve this problem – they deliver substance.t

So, what is an interest network?

In short, if a social network is about who you are interested in, an interest network is about what you are interested in. It’s the logical next step.

Twine for example, is an interest network that helps you share information with friends, family, colleagues and groups, based on mutual interests. Individual “twines” are created for content around specific subjects. This content might include bookmarks, videos, photos, articles, e-mails, notes or even documents. Twines may be public or private and can serve individuals, small groups or even very large groups of members.

I have also written quite a bit about the Semantic Web and the Semantic Graph, and Tim Berners-Lee has recently started talking about what he calls the GGG (Giant Global Graph). Tim and I are in agreement that social networks merely articulate the relationships between people. Social networks do not surface the equally, if not more important, relationships between people and places, places and organizations, places and other places, organization and other organizations, organization and events, documents and documents, and so on.

This is where interest networks come in. It’s still early days to be clear, but interest networks are operating on the premise of tapping into a multi–dimensional graph that manifests the complexity and substance of our world, and delivers the best of that world to you, every day.

We’re seeing more and more companies think about how to capitalize on this trend. There are suddenly (it seems, but this category has been building for many months) lots of different services that can be viewed as interest networks in one way or another, and here are some examples:

What all of these interest networks have in common is some sort of a bottom-up, user-driven crawl of the Web, which is the way that I’ve described Twine when we get the question about how we propose to index the entire Web (the answer: we don’t.

We let our users tell us what they’re most interested in, and we follow their lead).

Most interest networks exhibit the following characteristics as well:

They have some sort of bookmarking/submission/markup function to store and map data (often using existing metaphors, even if what’s under the hood is new)
They also have some sort of social sharing function to provide the network benefit (this isn’t exclusive to interest networks, obviously, but it is characteristic)
And in most cases, interest networks look to add some sort of “smarts” or “recommendations” capability to the mix (that is, you get more out than you put in)

This last bullet point is where I see next-generation interest networks really providing the most benefit over social bookmarking tools, wikis, collaboration suites and pure social networks of one kind or another.

To that end, we think that Twine is the first of a new breed of intelligent applications that really get to know you better and better over time – and that the more you use Twine, the more useful it will become. Adding your content to Twine is an investment in the future of your data, and in the future of your interests.

At first Twine begins to enrich your data with semantic tags and links to related content via our recommendations engine that learns over time. Twine also crawls any links it sees in your content and gathers related content for you automatically – adding it to your personal or group search engine for you, and further fleshing out the semantic graph of your interests which in turn results in even more relevant recommendations.

The point here is that adding content to Twine, or other next-generation interest networks, should result in increasing returns. That’s a key characteristic, in fact, of the interest networks of the future – the idea that the ratio of work (input) to utility (output) has no established ceiling.

Another key characteristic of interest networks may be in how they monetize. Instead of being advertising-driven, I think they will focus more on a marketing paradigm. They will be to marketing what search engines were to advertising. For example, Twine will be monetizing our rich model of individual and group interests, using our recommendation engine. When we roll this capability out in 2009, we will deliver extremely relevant, useful content, products and offers directly to users who have demonstrated they are really interested in such information, according to their established and ongoing preferences.

6 months ago, you could not really prove that “interest networking” was a trend, and certainly it wasn’t a clearly defined space. It was just an idea, and a goal. But like I said, I think that we’re at a tipping point, where the technology is getting to a point at which we can deliver greater substance to the user, and where the culture is starting to crave exactly this kind of service as a way of making the Web meaningful again.

I think that interest networks are a huge market opportunity for many startups thinking about what the future of the Web will be like, and I think that we’ll start to see the term used more and more widely. We may even start to see some attention from analysts — Carla, Jeremiah, and others, are you listening?

Now, I obviously think that Twine is THE interest network of choice. After all we helped to define the category, and we’re using the Semantic Web to do it. There’s a lot of potential in our engine and our application, and the growing community of passionate users we’ve attracted.

Our 1.0 release really focuses on UE/usability, which was a huge goal for us based on user feedback from our private beta, which began in March of this year. I’ll do another post soon talking about what’s new in Twine. But our TOS (time on site) at 6 minutes/user (all time) and 12 minutes/user (over the last month) is something that the team here is most proud of – it tells us that Twine is sticky, and that “the dogs are eating the dog food.”

Now that anyone can join, it will be fun and gratifying to watch Twine grow.

Still, there is a lot more to come, and in 2009 our focus is going to shift back to extending our Semantic Web platform and turning on more of the next-generation intelligence that we’ve been building along the way. We’re going to take interest networking to a whole new level.

Stay tuned!

Watch My best Talk: The Global Brain is Coming

Nova — Thu, 02 Oct 2008 19:56:57 +0000

I’ve posted a link to a video of my best talk — given at the GRID ‘08 Conference in Stockholm this summer. It’s about the growth of collective intelligence and the Semantic Web, and the future and role the media. Read more and get the video here. Enjoy!

The World is the Web

Nova — Fri, 19 Sep 2008 04:16:37 +0000

I’ve posted a new article in my public twine about how we are moving from the World Wide Web to the Web Wide World. It’s about how the Web is spreading into the physical world, and what this means.

New Video: Leading Minds from Google, Yahoo, and Microsoft talk about their Visions for Future of The Web

Nova — Fri, 12 Sep 2008 20:29:32 +0000

Video from my panel at DEMO Fall ‘08 on the Future of the Web is now available.

I moderated the panel, and our panelists were:

Howard Bloom, Author, The Evolution of Mass Mind from the Big Bang to the 21st Century

Peter Norvig, Director of Research, Google Inc.

Jon Udell, Evangelist, Microsoft Corporation

Prabhakar Raghavan, PhD, Head of Research and Search Strategy, Yahoo! Inc.

The panel was excellent, with many DEMO attendees saying it was the best panel they had ever seen at DEMO.

Many new and revealing insights were provided by our excellent panelists. I was particularly interested in the different ways that Google and Yahoo describe what they are working on. They covered lots of new and interesting information about their thinking. Howard Bloom added fascinating comments about the big picture and John Udell helped to speak about Microsoft’s longer-term views as well.

Enjoy!!!

The Future of the Desktop

Nova — Sun, 27 Jul 2008 01:14:49 +0000

This is an older version of this article. The most recent version is located here:

http://www.readwriteweb.com/archives/future_of_the_desktop.php

—————

I have spent the last year really thinking about the future of the Web. But lately I have been thinking more about the future of the desktop. In particular, here are some questions I am thinking about and some answers I’ve come up so far.

(Author’s Note: This is a raw, first-draft of what I think it will be like. Please forgive any typos — I am still working on this and editing it…)

What Will Happen to the Desktop?

As we enter the third decade of the Web we are seeing an increasing shift from local desktop applications towards Web-hosted software-as-a-service (SaaS). The full range of standard desktop office tools (word processors, spreadsheets, presentation tools, databases, project management, drawing tools, and more) can now be accessed as Web-hosted apps within the browser. The same is true for an increasing range of enterprise applications. This process seems to be accelerating.

As more kinds of applications become available in Web-based form, the Web browser is becoming the primary framework in which end-users work and interact. But what will happen to the desktop? Will it too eventually become a Web-hosted application? Will the Web browser swallow up the desktop? Where is the desktop headed?

Is the desktop of the future going to just be a web-hosted version of the same old-fashioned desktop metaphors we have today?

No. There have already been several attempts at doing this — and they never catch on. People don’t want to manage all their information on the Web in the same interface they use to manage data and apps on their local PC.

Partly this is due to the difference in user experience between using files and folders on a local machine and doing that in “simulated” fashion via some Flash-based or HTML-based imitation of a desktop. Imitations desktops to-date have simply been clunky and slow imitations of the real-thing at best. Others have been overly slick. But one thing they all have in common: None of them have nailed it. The desktop of the future – what some have called “the Webtop” – still has yet to be invented.

It’s going to be a hosted web service

Is the desktop even going to exist anymore as the Web becomes increasingly important? Yes, there will have to be some kind of interface that we consider to be our personal “home” and “workspace” — but ultimately it will have to be a unified space that all our devices connect to and share. This requires that it be a hosted online service.

Currently we have different information spaces on different devices (laptop, mobile device, PC). These will merge. Native local clients could be created for various devices, but ultimately the simplest and therefore most likely choice is to just use the browser as the client. This coming “Webtop” will provide an interface to your local devices, applications and information, as well as to your online life and information.

Today we think of our Web browser running inside our desktop as an applicaiton. But actually it will be the other way around in the future: Our desktop will run inside our browser as an application.

Instead of the browser running inside, or being launched from, some kind of next-generation desktop web interface technology, it’s will be the other way around: The browser will be the shell and the desktop application will run within it either as a browser add-in, or as a web-based application.

The Web 3.0 desktop is going to be completely merged with the Web — it is going to be part of the Web. In fact there may eventually be no distinction between the desktop and the Web anymore.

The focus shifts from information to attention

As our digital lives shift from being focused on the old fashioned desktop to the Web environment we will see a shift from organizing information spatially (directories, folders, desktops, etc.) to organizing information temporally (feeds, lifestreams, microblogs, timelines, etc.).

Instead of being just a directory, the desktop of the future is going to be more like a feed reader or social news site. The focus will be on keeping up with all the stuff flowing in and out of the user’s environment. The interface will be tuned to help the user understand what the trends are, rather than just on how things are organized.

The focus will be on helping the user to manage their attention rather than just their information. This is a leap to the meta-level: A second-order desktop. Instead of just being about the information (the first-order), it is going to be about what is happening with the information (the second-order).

Users are going to shift from acting as librarians to acting as daytraders.

Our digital roles are already shifting from acting as librarians to becoming more like daytraders. In the PC era we were all focused on trying to manage the stuff on our computers — in other words, we were acting as librarians. But this is going to shift. Librarians organize stuff, but daytraders are focused on discovering and keeping track of trends. It’s a very different focus and activity, and it’s what we are all moving towards.

We are already spending more of our time keeping up with change and detecting trends, than on organizing information. In the coming decade the shelf-life of information is going to become vanishingly short and the focus will shift from storage and recall to real-time filtering, trend detection and prediction.

The Webtop will be more social and will leverage and integrate collective intelligence

The Webtop is going to be more socially oriented than desktops of today — it will have built-in messaging and social networking, as well as social-media sharing, collaborative filtering, discussions, and other community features.

The social dimension of our lives is becoming perhaps our most important source of information. We get information via email from friends, family and colleagues. We get information via social networks and social media sharing services. We co-create information with others in communities.

The social dimension is also starting to play a more important role in our information management and discovery activities. Instead of those activities remaining as solitary, they are becoming more communal. For example many social bookmarking and social news sites use community sentiment and collaborative filtering to help to highlight what is most interesting, useful or important.

It’s going to have powerful semantic search and social search capabilities built-in

The Webtop is going to have more powerful search built-in. This search will combine both social and semantic search features. Users will be able to search their information and rank it by social sentiment (for example, “find documents about x and rank them by how many of my friends liked them.”)

Semantic search will enable highly granular search and navigation of information along a potentially open-ended range of properties and relationships.

For example you will be able to search in a highly structured way — for example, search for products you once bookmarked that have a price of $10.95 and are on-sale this week. Or search for documents you read which were authored by Sue and related to project X, in the last month.

The semantics of the future desktop will be open-ended. That is to say that users as well as other application and information providers will be able to extend it with custom schemas, new data types, and custom fields to any piece of information.

Interactive shared spaces instead of folders

Forget about shared folders — that is an outmoded paradigm. Instead, the new metaphor will be interactive shared spaces.

The need for shared community space is currently being provided for online by forums, blogs, social network profile pages, wikis, and new community sites. But as we move into Web 3.0 these will be replaced by something that combines their best features into one. These next-generation shared spaces will be like blogs, wikis, communities, social networks, databases, workspaces and search engines in one.

Any group of two or more individuals will be able to participate in a shared space that connects their desktops for a particular purpose. These new shared spaces will not only provide richer semantics in the underlying data, social network, and search, but they will also enable groups to seamlessly and collectively add, organize, track, manage, discuss, distribute, and search for information of mutual interest.

The personal cloud

The future desktop will function like a “personal cloud” for users. It will connect all their identities, data, relationships, services and activities in one virtual integrated space. All incoming and outgoing activity will flow through this space. All applications and services that a user makes use of will connect to it.

The personal cloud may not have a center, but rather may be comprised of many separate sub-spaces, federated around the Web and hosted by different service-providers. Yet from an end-user perspective it will function as a seamlessly integrated service. Users will be able to see and navigate all their information and applications, as if they were in one connected space, regardless of where they are actually hosted. Users will be able to search their personal cloud from any point within it.

Open data, linked data and open-standards based semantics

The underlying data in the future desktop, and in all associated services it connects, will be represented using open-standard data formats. Not only will the data be open, but the semantics of the data – the schema – will also be defined in an open way. The emerigng Semantic Web provides a good infrastructure for enabling this to happen.

The value of open linked-data and open semantics is that data will not be held prisoner anywhere and can easily be integrated with other data.

Users will be able to seamlessly move and integrate their data, or parts of their data, in different services. This means that your Webtop might even be portable to a different competing Webtop provider someday. If and when that becomes possible, how will Webtop providers compete to add value?

It’s going to be smart

One of the most important aspects of the coming desktop is that it’s going to be smart. It’s going to learn and help users to be more productive. Artificial intelligence is one of the key ways that competing Webtop providers will differentiate their offerings.

As you use it, it’s going to learn about your interests, relationships, current activities, information and preferences. It will adaptively self-organize to help you focus your attention on what is most important to whatever context you are in.

When reading something while you are taking a trip to Milan it may organize itself to be more contextually relevant to that time, place and context. When you later return home to San Francisco it will automatically adapt and shift to your home context. When you do a lot of searches about a certain product it will realize your context and intent has to do with that product and will adapt to help you with that activity for a while, until your behavior changes.

Your desktop will actually be a semantic knowledge base on the back-end. It will encode a rich semantic graph of your information, relationships, interests, behavior and preferences. You will be able to permit other applications to access part or all of your graph to datamine it and provide you with value-added views and even automated intelligent assistance.

For example, you might allow an agent that cross-links things to see all your data: it would go and add cross links to relevant things onto all the things you have created or collected. Another agent that makes personalized buying recommendations might only get to see your shopping history across all shopping sites you use.

Your desktop may also function as a simple personal assistant at times. You will be able to converse with your desktop eventually — through a conversational agent interface. While on the road you will be able to email or SMS in questions to it and get back immediate intelligent answers. You will even be able to do this via a voice interface.

For example, you might ask, “where is my next meeting?” or “what Japanese restaurants do I like in LA?” or “What is Sue’s Smith’s phone number?” and you would get back answers. You could also command it to do things for you — like reminding you to do something, or helping you keep track of an interest, or monitoring for something and alerting you when it happens.

Because your future desktop will connect all the relationships in your digital life — relationships connecting people, information, behavior, prefences and applications — it will be the ultimate place to learn about your interests and preferences.

Federated, open policies and permissions

This rich graph of meta-data that comprises your future desktop will enable the next-generation of smart services to learn about you and help you in an incredibly personalized manner. It will also of course be rife with potential for abuse and privacy will be a major function and concern.

One of the biggest enabling technologies that will be necessary is a federated model for sharing meta-data about policies and permissions on data. Information that is considered to be personal and private in Web site X should be recognized and treated as such by other applications and websites you choose to share that information with. This will require a way for sharing meta-data about your policies and permissions between different accounts and applicaitons you use.

The semantic web provides a good infrastructure for building and deploying a decentralized framework for policy and privacy integration, but it has yet to be developed, let alone adopted. For the full vision of the future desktop to emerge a universally accepted standard for exchanging policy and permission data will be a necessary enabling technology.

Who is most likely to own the future desktop?

When I think about what the future desktop is going to look like it seems to be a convergence of several different kinds of services that we currently view as separate.

It will be hosted on the cloud and accessible across all devices. It will place more emphasis on social interaction, social filtering, and collective intelligence. It will provide a very powerful and extensible data model with support for both unstructured and arbitrarily structured information. It will enable almost peer-to-peer like search federation, yet still have a unified home page and user-experience. It will be smart and personalized. It will be highly decentralized yet will manage identity, policies and permissions in an integrated cohesive and transparent manner across services.

By cobbling together a number of different services that exist today you could build something like this in a decentralized fashion. Is that how the desktop of the future will come about? Or will it be a new application provided by one player with a lot of centralized market power? Or could an upstart suddently emerge with the key enabling technologies to make this possible? It’s hard to predict, but one thing is certain: It will be an interesting process to watch.

Watch my Panel with Tim Berners-Lee today Live on the Web

Nova — Wed, 11 Jun 2008 15:10:08 +0000

Tim Berners-Lee is giving a talk, and then we’re on a panel, live, today, discussing the Semantic Web, Net Neturality and Web Science. Watch the live Webcast and submit your questions to the panel interactively. Details and times are here.