The Global Brain is About to Wake Up

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:

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…

What's After the Real Time Web?

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:

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

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

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

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

Collective Intelligence

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

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

Social Evolution

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

Physical Evolution

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

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

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

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

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

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

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.

Fast Company Interview — "Connective Intelligence"

In this interview with Fast Company, I discuss my concept of "connective intelligence." Intelligence is really in the connections between things, not the things themselves. Twine facilitates smarter connections between content, and between people. This facilitates the emergence of higher levels of collective intelligence.

Interest Networks are at a Tipping Point

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!

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

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

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.

Great Collective Intelligence Book; Includes a Chapter I Wrote

I highly recommend this new book on Collective Intelligence. It features chapters by a Who’s Who of thinkers on Collective Intelligence, including a chapter by me about “Harnessing the Collective Intelligence of the World Wide Web.”

Here is the full-text of my chapter, minus illustrations (the rest of the book is great and I suggest you buy it to have on your shelf. It’s a big volume and worth the read):

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Artificial Stupidity: The Next Big Thing

There has been a lot of hype about artificial intelligence over the years. And recently it seems there has been a resurgence in interest in this topic in the media. But artificial intelligence scares me. And frankly, I don’t need it. My human intelligence is quite good, thank you very much. And as far as trusting computers to make intelligent decisions on my behalf, I’m skeptical to say the least. I don’t need or want artificial intelligence.

No, what I really need is artificial stupidity.

I need software that will automate all the stupid things I presently have to waste far too much of my valuable time on. I need something to do all the stupid tasks — like organizing email, filing documents, organizing folders, remembering things, coordinating schedules, finding things that are of interest, filtering out things that are not of interest, responding to routine messages, re-organizing things, linking things, tracking things, researching prices and deals, and the many other rote information tasks I deal with every day.

The human brain is the result of millions of years of evolution. It’s already the most intelligent thing on this planet. Why are we wasting so much of our brainpower on tasks that don’t require intelligence? The next revolution in software and the Web is not going to be artificial intelligence, it’s going to be creating artificial stupidity: systems that can do a really good job at the stupid stuff, so we have more time to use our intelligence for higher level thinking.

The next wave of software and the Web will be about making software and the Web smarter. But when we say "smarter" we don’t mean smart like a human is smart, we mean "smarter at doing the stupid things that humans aren’t good at." In fact humans are really bad at doing relatively simple, "stupid" things — tasks that don’t require much intelligence at all.

For example, organizing. We are terrible organizers. We are lazy, messy, inconsistent, and we make all kinds of errors by accident. We are terrible at tagging and linking as well, it turns out. We are terrible at coordinating or tracking multiple things at once because we are easily overloaded and we can really only do one thing well at a time. These kinds of tasks are just not what our brains are good at. That’s what computers are for – or should be for at least.

Humans are really good at higher level cognition: complex thinking, decisionmaking, learning, teaching, inventing, expressing, exploring, planning, reasoning, sensemaking, and problem solving — but we are just terrible at managing email, or making sense of the Web. Let’s play to our strengths and use computers to compensate for our weaknesses.

I think it’s time we stop talking about artificial intelligence — which nobody really needs, and fewer will ever trust. Instead we should be working on artificial stupidity. Sometimes the less lofty goals are the ones that turn out to be most useful in the end.

Powerpoint Deck: Making Sense of the Semantic Web, and Twine

Now that I have been asked by several dozen people for the slides from my talk on "Making Sense of the Semantic Web," I guess it’s time to put them online. So here they are, under the Creative Commons Attribution License (you can share it with attribution this site).

You can download the Powerpoint file at the link below:

Download nova_spivack_semantic_web_talk.ppt


Or you can view it right here:

Enjoy! And I look forward to your thoughts and comments.

Quick Video Preview of Twine

The New Scientist just posted a quick video preview of Twine to YouTube. It only shows a tiny bit of the functionality, but it’s a sneak peak.

We’ve been letting early beta testers into Twine and we’re learning a lot from all the great feedback, and also starting to see some cool new uses of Twine. There are around 20,000 people on the wait-list already, and more joining every day. We’re letting testers in slowly, focusing mainly on people who can really help us beta test the software at this early stage, as we go through iterations on the app. We’re getting some very helpful user feedback to make Twine better before we open it up the world.

For now, here’s a quick video preview:

True Knowledge is Cool

The most interesting and exciting new app I’ve seen this month (other than Twine of course!) is a new semantic search engine called True Knowledge. Go to their site and watch their screencast to see what the next generation of search is really going to look like.

True Knowledge is doing something very different from Twine — whereas Twine is about helping individuals, groups and teams manage their private and shared knowledge, True Knowledge is about making a better public knowledgebase on the Web — in a sense they are a better search engine combined with a better Wikipedia. They seem to overlap more with what is being done by natural language search companies like Powerset and companies working on public databases, such as Metaweb and Wikia.

I don’t yet know whether True Knowledge is supporting W3C open-standards for the Semantic Web, but if they do, they will be well-positioned to become a very central service in the next phase of the Web. If they don’t they will just be yet another silo of data — but a very useful one at least. I personally hope they provide SPARQL API access at the very least. Congratulations to the team at True Knowledge! This is a very impressive piece of work.

A Video and an Audio Cast About Twine

Last night I saw that the video of my presentation of Twine at the Web 2.0 Summit is online. My session, "The Semantic Edge," featured Danny Hillis of Metaweb demoing Freebase, Barney Pell demoing Powerset, and myself Demoing Twine, followed by a brief panel discussion with Tim O’Reilly (in that order). It’s a good panel and I recommend the video, however, the folks at Web 2.0 only filmed the presenters; they didn’t capture what we were showing on our screens, so you have to use your imagination as we describe our demos.

An audio cast of one of my presentations about Twine to a reporter was also put online recently, for a more in-depth description.

What a Week!

What a week it has been for Radar Networks. We have worked so hard these last few days to get ready to unveil Twine, and it has been a real thrill to show our work and get such positive feedback and support from the industry, bloggers, the media and potential users.

We really didn’t expect so much excitement and interest. In fact we’ve been totally overwhelmed by the response as thousands upon thousands of people have contacted us in the last 24 hours asking to join our beta, telling us how they would use Twine for their personal information management, their collaboration, their organizations, and their communities. Clearly there is such a strong and growing need out there for the kind of Knowledge Networking capabilities that Twine provides, and it’s been great to hear the stories and make new connections with so many people who want our product. We love hearing about your interest in Twine, what you would use it for, what you want it to do, and why you need it! Keep those stories coming. We read them all and we really listen to them.

Today, in unveiling Twine, over five years of R&D, and contributions from dozens of core contributors, a dedicated group of founders and investors, and hundreds of supporters, advisors, friends and family, all came to fruition. As a company, and a team, we achieved an important milestone and we should all take some time to really appreciate what we have accomplished so far. Twine is a truly ambitious and pardigm-shifting product, that is not only technically profound but visually stunning — There has been so much love and attention to detail in this product.

In the last 6 months, Twine has really matured into a product, a product that solves real and growing needs (for a detailed use-case see this post). And just as our product has matured, so has our organization: As we doubled in size, our corporate culture has become tremendously more interesting, innovative and fun. I could go on and on about the cool things we do as a company and the interesting people who work here. But it’s the passion, dedication and talent of this team that is most inspiring. We are creating a team and a culture that truly has the potential to become a great Silicon Valley company: The kind of company that I’ve always wanted to build.

Although we launched today, this is really just the beginning of the real adventure. There is still much for us to build, learn about, and improve before Twine will really accomplish all the goals we have set out  for it. We have a five-year roadmap. We know this is a marathon, not a sprint and that "slow and steady wins the race." As an organization we also have much learning and growing to do. But this really doesn’t feel like work — it feels like fun — because we all love this product and this company. We all wake up every day totally psyched to work on this.

It’s been an intense, challenging, and rewarding week. Everyone on my team has impressed me and really been at the top of their game. Very few of us got any real sleep, and most of us went far beyond the call of duty. But we did it, and we did it well. As a company we have never cut corners, and we have always preferred to do things the right way, even if the right way is the hard way. But that pays off in the end. That is how great products are built. I really want to thank my co-founders, my team, my investors, advisors, friends, and family, for all their dedication and support.

Today, we showed our smiling new baby to the world, and the world smiled back.

And tonight , we partied!!!

Radar Networks Announces Twine.com

My company, Radar Networks, has just come out of stealth. We’ve announced what we’ve been working on all these years: It’s called Twine.com. We’re going to be showing Twine publicly for the first time at the Web 2.0 Summit tomorrow. There’s lot’s of press coming out where you can read about what we’re doing in more detail. The team is extremely psyched and we’re all working really hard right now so I’ll be brief for now. I’ll write a lot more about this later.

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Gartner is Wrong about Web 3.0

I have a lot of respect for the folks at Gartner, but their recent report in which they support the term "Web 2.0" yet claim that the term "Web 3.0" is just a marketing ploy, is a bit misguided.

In fact, quite the opposite is true.

The term Web 2.0 is in fact just a marketing ploy. It has only come to have something resembling a definition over time. Because it is in fact so ill-defined, I’ve suggested in the past that we just use it to refer to a decade: the second decade of the Web (2000 – 2010). After all there is no actual technology that is called "Web 2.0" — at best there are a whole slew of things which this term seems to label, and many of them are design patterns, not technologies. For example "tagging" is not a technology, it is a design pattern. A tag is a keyword, a string of text — there is not really any new technology there. AJAX is also not a technology in its own right, but rather a combination of technologies and design patterns, most of which existed individually before the onset of what is called Web 2.0.

In contrast, the term Web 3.0 actually does refer to a set of new technologies, and changes they will usher in during the third decade of the Web (2010 – 2020). Chief among these is the Semantic Web. The Semantic Web is actually not one technology, but many. Some of them such as RDF and OWL have been under development for years, even during the Web 2.0 era, and others such as SPARQL and GRDDL are recent emerging standards. But that is just the beginning. As the Semantic Web develops there will be several new technology pieces added to the puzzle for reasoning, developing and sharing open rule definitions, handling issues around trust, agents, machine learning, ontology development and integration, semantic data storage, retrieval and search, and many other subjects.

Essentially, the Semantic Web enables the gradual transformation of the Web into a database. This is a profound structural change that will touch every layer of Web technology eventually. It will transform database technology, CMS, CRM, enterprise middleware, systems integration, development tools, search engines, groupware, supply-chain integration, and all the other topics that Gartner covers.

The Semantic Web will manifest in several ways. In many cases it will improve applications and services we already use. So for example, we will see semantic
social networks, semantic search, semantic groupware, semantic CMS, semantic CRM, semantic
email, and many other semantic versions of apps we use today. For a specific example, take social networking. We are seeing much talk about "opening  up the social graph" so that social networks are more connected and portable. Ultimately to do this right, the social graph should be represented using Semantic Web standards, so that it truly is not only open but also easily extensible and mashable with other data. 

Web 3.0 is not ONLY the Semantic Web however. Other emerging technologies may play a big role as well. Gartner seems to think Virtual Reality will be one of them. Perhaps, but to be fair, VR is actually a Web 1.0 phenomenon. It’s been around for a long time, and it hasn’t really changed that much. In fact the folks at the MIT Media Lab were working on things that are still far ahead of Second Life, even back in the early 1990’s.

So what other technologies can we expect in Web 3.0 that are actually new? I expect that we will have a big rise in "cloud computing" such as open peer-to-peer grid storage and computing capabilities on the Web — giving any application essentially as much storage and computational power as needed for free or a very low cost. In the mobile arena we will see higher bandwidth, more storage and more powerful processors in mobile devices, as well as powerful built-in speech recognition, GPS and motion sensors enabling new uses to emerge. I think we will also see an increase in the power of personalization tools and personal assistant tools that try to help users manage the complexity of their digital lives. In the search arena, we will see search engines get smarter — among other things they will start to not only answer questions, but they will accept commands such as "find me a cheap flight to NYC" and they will learn and improve as they are used. We will also see big improvements in integration and data and account portability between different Web applications. We will also see a fundamental change in the database world as databases move away from the relational model and object model, towards the associative model of data (graph databases and triplestores).

In short, Web 3.0 is about hard-core new technologies and is going to have a much greater impact on enterprise IT managers and IT systems than Web 2.0. But ironically, it may not be until Web 4.0 (2020 – 2030) that Gartner comes to this conclusion!

Knowledge Networking

I’ve been thinking for several years about Knowledge Networking. It’s not a term I invented, it’s been floating around as a meme for at least a decade or two. But recently it has started to resurface in my own work.

So what is a knowledge network? I define a knowledge network as a form of collective intelligence in which a network of people (two or more people connected by social-communication relationships) creates, organizes, and uses a collective body of knowledge. The key here is that a knowledge network is not merely a site where a group of people work on a body of information together (such as the wikipedia), it’s also a social network — there is an explicit representation of a social relationship within it. So it’s more like a social network than for example a discussion forum or a wiki.

I would go so far as to say that knowledge networks are the third-generation of social software. (Note this is based in-part on ideas that emerged in conversations I have had with Peter Rip, so this also his idea):

  • First-generation social apps were about communication (eg.
    messaging such as Email, discussion boards, chat rooms, and IM)
  • Second-generation social apps were about people and content (eg. Social networks, social media sharing, user-generated content)
  • Third-generation social apps are about relationships and knowledge  (eg. Wikis, referral networks, question and answer systems, social recommendation systems, vertical knowledge and expertise portals, social mashup apps, and coming soon, what we’re building at Radar Networks)

Just some thoughts on a Saturday morning…

Web 3.0 — Next-Step for Web?

The Business 2.0 Article on Radar Networks and the Semantic Web just came online. It’s a huge article. In many ways it’s one of the best popular articles written about the Semantic Web in the mainstream press. It also goes into a lot of detail about what Radar Networks is working on.

One point of clarification, just in case anyone is wondering…

Web 3.0 is not just about machines — it’s actually all about humans — it leverages social networks, folksonomies, communities and social filtering AS WELL AS the Semantic Web, data mining, and artificial intelligence. The combination of the two is more powerful than either one on it’s own. Web 3.0 is Web 2.0 + 1. It’s NOT Web 2.0 – people. The "+ 1" is the
addition of software and metadata that help people and other
applications organize and make better sense of the Web. That new layer
of semantics — often called "The Semantic Web" — will add to and
build on the existing value provided by social networks, folksonomies,
and collaborative filtering that are already on the Web.

So at least here at Radar Networks, we are focusing much of our effort on facilitating people to help them help themselves, and to help each other, make sense of the Web. We leverage the amazing intelligence of the human brain, and we augment that using the Semantic Web, data mining, and artificial intelligence. We really believe that the next generation of collective intelligence is about creating systems of experts not expert systems.

Business 2.0 and BusinessWeek Articles About Radar Networks

It’s been an interesting month for news about Radar Networks. Two significant articles came out recently:

Business 2.0 Magazine published a feature article about Radar Networks in their July 2007 issue. This article is perhaps the most comprehensive article to-date about what we are working on at Radar Networks, it’s also one of the better articulations of the value proposition of the Semantic Web in general. It’s a fun read, with gorgeous illustrations, and I highly recommend reading it.

BusinessWeek  posted an article about Radar Networks on the Web. The article covers some of the background that led to my interests in collective intelligence and the creation of the company. It’s a good article and covers some of the bigger issues related to the Semantic Web as a paradigm shift. I would add one or two points of clarification in addition to what was stated in the article: Radar Networks is not relying solely on software to organize the Internet — in fact, the service we will be launching combines human intelligence and machine intelligence to start making sense of information, and helping people search and collaborate around interests more productively. One other minor point related to the article — it mentions the story of EarthWeb, the Internet company that I co-founded in the early 1990’s: EarthWeb’s content business actually was sold after the bubble burst, and the remaining lines of business were taken private under the name Dice.com. Dice is the leading job board for techies and was one of our properties. Dice has been highly profitable all along and recently filed for a $100M IPO.

Listen to this Discussion on the Future of the Web

If you are interested in the future of the Web, you might enjoy listening to this interview with me, moderated by Dr. Paul Miller of Talis. We discuss, in-depth: the Semantic Web, Web 3.0, SPARQL, collective intelligence, knowledge management, the future of search, triplestores, and Radar Networks.

A Bunch of New Press About Radar Networks

We had a bunch of press hits today for my startup, Radar
Networks

PC World  Article on  Web 3.0 and Radar Networks

Entrepreneur Magazine interview

We’re also proud to announce that Jim
Hendler
, one of the founding gurus of the Semantic Web, has joined our technical advisory board.

Metaweb and Radar Networks

This is just a brief post because I am actually slammed with VC meetings right now. But I wanted to congratulate our friends at Metaweb for their pre-launch announcement. My company, Radar Networks, is the only other major venture-funded play working on the Semantic Web for consumers so we are thrilled to see more action in this sector.

Metaweb and Radar Networks are working on two very different applications (fortunately!). Metaweb is essentially making the Wikipedia of the Semantic Web. Here at Radar Networks we are making something else — but equally big — and in a different category. Just as Metaweb is making a semantic analogue to something that exists and is big, so are we: but we’re more focused on the social web — we’re building something that everyone will use. But we are still in stealth so that’s all I can say for now.

This is now an exciting two-horse space. We look forward to others joining the excitement too. Web 3.0 is really taking off this year.

An interesting side note: Danny Hillis (founder of Metaweb), myself (founder of Radar Networks) and Lew Tucker (CTO of Radar Networks) all worked together at Thinking Machines (an early AI massively parallel computer company). It’s fascinating that we’ve all somehow come to think that the only practical way to move machine intelligence forward is by having us humans and applications start to employ real semantics in what we record in the digital world.

Is it Only Wednesday?

Is it only Wednesday? It feels like a whole week already! I’ve been in back-to-back VC meetings, board discussions and strategy meetings since last week. I think this must be related to the heating-up of the "Web 3.0" meme and the semantic sector in general. Perhaps it is also due to the coverage we got in the Guidewire Report and newsletter which went out to everyone who went to DEMO, and also perhaps because of some influential people in the biz have been talking about us. We’ve been very careful not to show our app to anyone because it does some things that are really new. We don’t want to spread that around (yet). Anyway it’s been pretty busy — not just for me, but for the whole team. Everyone is on full afterburners right now.

By the way — I’m really proud or product team (hope you guys are reading this)– the team has made an alpha that is not only a breakthrough on the technical level, but it also looks incredibly good too. Some of the select few who have seen our app so far have said, "the app looks beautiful" and "wow, that’s amazing" etc. We’ve done some cool things with NLP, graph analysis, and statistics under the hood. And the GUI is also very slick. Probably the best team I’ve worked with.

If you are interested in helping to beta-test the consumer Semantic Web, We’re planning on doing invite-only beta trials this summer — sign up at our website to be on our beta invite list.

Breaking the Collective IQ Barrier — Making Groups Smarter

I’ve been thinking since 1994 about how to get past a fundamental barrier to human social progress, which I call “The Collective IQ Barrier.” Most recently I have been approaching this challenge in the products we are developing at my stealth venture, Radar Networks.

In a nutshell, here is how I define this barrier:

The Collective IQ Barrier: The potential collective intelligence of a human group is exponentially proportional to group size, however in practice the actual collective intelligence that is achieved by a group is inversely proportional to group size. There is a huge delta between potential collective intelligence and actual collective intelligence in practice. In other words, when it comes to collective intelligence, the whole has the potential to be smarter than the sum of its parts, but in practice it is usually dumber.

Why does this barrier exist? Why are groups generally so bad at tapping the full potential of their collective intelligence? Why is it that smaller groups are so much better than large groups at innovation, decision-making, learning, problem solving, implementing solutions, and harnessing collective knowledge and intelligence?

I think the problem is technological, not social, at its core. In this article I will discuss the problem in more depth and then I will discuss why I think the Semantic Web may be the critical enabling technology for breaking through the Collective IQ Barrier.

The Effective Size of Groups

For millions of years — in fact since the dawn of humanity — humansocial organizations have been limited in effective size. Groups aremost effective when they are small, but they have less collectiveknowledge at their disposal. Slightly larger groups optimize both effectiveness and access to resources such as knowledge and expertise. In my own experience working on many different kinds of teams, I think that the sweet-spot is between 20and 50 people. Above this size groups rapidly become inefficient andunproductive.

The Invention of Hierarchy

The solution that humans have used to get around this limitation in the effective size of groups is hierarchy.When organizations grow beyond 50 people we start to break them intosub-organizations of less than 50 people. As a result if you look atany large organization, such as a Fortune 100 corporation, you find ahuge complex hierarchy of nested organizations and cross-functionalorganizations. This hierarchy enables the organization to createspecialized “cells” or “organs” of collective cognition aroundparticular domains (like sales, marketing, engineering, HR, strategy,etc.) that remain effective despite the overall size of theorganization.

By leveraging hierarchy an organization of even hundreds ofthousands of members can still achieve some level of collective IQ as awhole. The problem however is that the collective IQ of the wholeorganization is still quite a bit lower than the combined collectiveIQ’s of the sub-organizations that comprise it. Even in well-structured, well-managed hierarchies, the hierarchy is still less thanthe sum of it’s parts. Hierarchy also has limits — the collective IQof an organization is also inversely proportional to the number ofgroups it contains, and the average number of levels of hierarchybetween those groups (Perhaps this could be defined more elegantly asan inverse function of the average network distance between groups inan organization).

The reason that organizations today still have to make suchextensive use of hierarchy is that our technologies for managingcollaboration, community, knowledge and intelligence on a collectivescale are still extremely primitive. Hierarchy is still one of the only and best solutions we have at our disposal. But we’re getting better fast.

Modern organizations are larger and far more complex than ever would have beenpractical in the Middle Ages, for example. They contain more people,distributed more widely around the globe, with more collaboration andspecialization, and more information, making more rapid decisions, thanwas possible even 100 years ago. This is progress.

Enabling Technologies

There have beenseveral key technologies that made modern organizations possible: the printing press,telegraph, telephone, automobile, airplane, typewriter, radio,television, fax machine, and personal computer. These technologies haveenabled information and materials to flow more rapidly, at less cost,across ever more widely distributed organizations. So we can see that technology does make a big difference in organizational productivity. The question is, can technology get us beyond the Collective IQ Barrier?

The advent of the Internet, and in particular the World Wide Webenabled a big leap forward in collective intelligence. These technologies havefurther reduced the cost to distributing and accessing information andinformation products (and even “machines” in the form of software codeand Web services). They have made it possible for collectiveintelligence to function more rapidly, more dynamically, on a wider scale, and at lesscost, than any previous generation of technology.

As a result of evolution of the Web we have seen new organizationalstructures begin to emerge that are less hierarchical, moredistributed, and often more fluid. For example, virtual teams that caninstantly form, collaborate across boundaries, and then dissolve backinto the Webs they come from when their job is finished. Thisprocess is now much easier than it ever was. Numerous hosted Web-basedtools exist to facilitate this: email, groupware, wikis, messageboards, listservers, weblogs, hosted databases, social networks, searchportals, enterprise portals, etc.

But this is still just the cusp of this trend. Even today with thecurrent generation of Web-based tools available to us, we are still notable to effectively tap much more of the potential Collective IQ of ourgroups, teams and communities. How do we get from where we are today(the whole is dumber than the sum of its parts) to where we want to bein the future (the whole is smarter than the sum of its parts)?

The Future of Productivity

The diagram below illustrates how I think about the past, present and future of productivity. In my view, from the advent of PC’s onwards we have seen a rapid growth in individual and group productivity, enabling people to work with larger sets of information, in larger groups. But this will not last — soon as we reach a critical level of information and groups of ever larger size, productivity will start to decline again, unless new technologies and tools emerge to enable us to cope with these increases in scale and complexity. You can read more about this diagram here.

http://novaspivack.typepad.com/nova_spivacks_weblog/2007/02/steps_towards_a.html

In the last 20 years the amount of information that knowledgeworkers (and even consumers) have to deal with on a daily basis has mushroomed by a factor of almost 10orders of magnitude and it will continue like this for several moredecades. But our information tools — and particular our tools forcommunication, collaboration, community, commerce and knowledgemanagement — have not advanced nearly as quickly. As a result thetools that we are using today to manage our information andinteractions are grossly inadequate for the task at hand: They were simply not designed tohandle the tremendous volumes of distributed information, and the rate of change ofinformation, that we are witnessing today.

Case in point: Email. Email was never designed for what it is beingused for today. Email was a simple interpersonal notification andmessaging tool and essentially that is what it is good for. But todaymost of us use our email as a kind of database, search engine,collaboration tool, knowledge management tool, project management tool,community tool, commerce tool, content distribution tool, etc. Emailwasn’t designed for these functions and it really isn’t very productive whenapplied to them.

For groups the email problem is even worse than it is for individuals –not only is everyone’s individual email productivity declining anyway,but collectively as groupsize increases (and thus group information size increases as well),there is a multiplier effect that further reduces everyone’semail productivity in inverse proportion to the size of the group.Email becomes increasingly unproductive as group size and informationsize increase.

This is not just true of email, however, it’s true of almost all theinformation tools we use today: Search engines, wikis, groupware,social networks, etc. They all suffer from this fundamental problem.Productivity breaks down with scale — and the problem is exponentially worse than it is for individuals in groups and organizations. But scale is increasing incessantly — that is a fact — and it will continue to do so for decades at least. Unless something is done about this we will simply be completely buried in our own information within about a decade.

The Semantic Web

I think the Semantic Web is a critical enabling technology that will help us get through this transition. It willenable the next big leap in productivity and collective intelligence.It may even be the technology that enables humans to flip the ratio so thatfor the first time in human history, larger groups of people canfunction more productively and intelligently than smaller groups. Itall comes down to enabling individuals and groups to maintain (andultimately improve) their productivity in theface of the continuing explosion in information and social complexitythat they areexperiencing.

The Semantic Web provides a richer underlying fabric for expressing,sharing, and connecting information. Essentially it provides a betterway to transform information into useful knowledge, and to share andcollaborate with it. It essentially upgrades the medium — in this case the Web and any other data that is connected to the Web — that we use for our information today.

By enriching the medium we can inturn enable new leaps in how applications, people, groups andorganizations can function. This has happened many times before in thehistory of technology.  The printing press is one example. The Web is a more recent one. The Web enriched themedium (documents) with HTML and a new transport mechanism, HTTP, forsharing it. This brought about one of the largest leaps in humancollective cognition and productivity in history. But HTML really onlydescribes formatting and links. XML came next, to start to provide away to enrich the medium with information about structure –the parts of documents. The Semantic Web takes this one step further –it provides a way to enrich the medium with information about the meaning of the structure — what are those parts, what do various links actually mean?

Essentially the Semantic Web provides a means to abstract andexternalize human knowledge about information — previously the meaningof information lived only in our heads, and perhaps in certainspecially-written software applications that were coded to understandcertain types of data. The Semantic Web will disrupt this situation by providingopen-standards for encoding this meaning right into the medium itself.Any application that can speak the open-standards of the Semantic Webcan then begin to correctly interpret the meaning of information, andtreat it accordingly, without having to be specifically coded tounderstand each type of data it might encounter.

This is analogous to the benefit of HTML. Before HTML everyapplication had to be specifically coded to each different documentformat in order to display it. After HTML applications could all juststandardize on a single way to define the formats of differentdocuments. Suddenly a huge new landscape of information becameaccessible both to applications and to the people who used them.The Semantic Web does something similar: It provides a way to makethe data itself “smarter” so that applications don’t have to know somuch to correctly interpret it. Any data structure — a document or adata record of any kind — that can be marked up with HTML to define its formatting, can also be marked up with RDFand OWL (the languages of the Semantic Web) to define its meaning.

Once semantic metadata is added, the document can not only bedisplayed properly by any application (thanks to HTML and XML), but itcan also be correctly understood by that application. For example theapplication can understand what kind of document it is, what it isabout, what the parts are, how the document relates to other things,and what particular data fields and values mean and how they map todata fields and values in other data records around the Web.

The Semantic Web enriches information with knowledge about what thatinformation means, what it is for, and how it relates to other things.With this in hand applications can go far beyond the limitations ofkeyword search, text processing, and brittle tabular data structures.Applications can start to do a much better job of finding, organizing,filtering, integrating, and making sense of ever larger and morecomplex distributed data sets around the Web.

Another great benefit ofthe Semantic Web is that this additional metadata can be added in atotally distributed fashion. The publisher of a document can add theirown metadata and other parties can then annotate that with their ownmetadata. Even HTML doesn’t enable that level of cooperative markup (exceptperhaps in wikis). It takes a distributed solution to keep up with ahighly distributed problem (the Web). The Semantic Web is just such adistributed solution.

The Semantic Web will enrich information and this in turn will enable people, groups and applications to work with information more productively. In particular groups and organizations will benefit the most because that is where the problems of information overload and complexity are the worst. Individuals at least know how they organize their own information so they can do a reasonably good job of managing their own data. But groups are another story — because people don’t necessarily know how others in their group organize their information. Finding what you need in other people’s information is much harder than finding it in your own.

Where the Semantic Web can help with this is by providing a richer fabric for knowledge management. Information can be connected to an underlying ontology that defines not only the types of information available, but also the meaning and relationships between different tags or subject categories, and even the concepts that occur in the information itself. This makes organizing and finding group knowledge easier. In fact, eventually the hope is that people and groups will not have to organize their information manually anymore — it will happen in an almost fully-automatic fashion. The Semantic Web provides the necessary frameworks for making this possible.

But even with the Semantic Web in place and widely adopted, moreinnovation on top of it will be necessary before we can truly breakpast the Collective IQ Barrier such that organizations can in practiceachieve exponential increases in Collective IQ. Human beings are only able to cope with a few chunks ofinformation at a given moment, and our memories and ability to processcomplex data sets are limited. When group size and data size growbeyond certain limits, we simply cannot cope, we become overloaded andjammed, even with rich Semantic Web content at our disposal.

Social Filtering and Social Networking — Collective Cognition

Ultimately, to remain productive in the face of such complexity wewill need help. Often humans in roles that require them to cope with large scales of information, relationships andcomplexity hire assistants, but not all of us can affordto do that, and in some cases even assistants are not able to keep upwith the complexity that has to be managed.

Social networking andsocial filtering are two ways to expand the number of “assistants” weeach have access to, while also reducing the price of harnessing the collective intelligence of those assistants to just about nothing. Essentially these methodologies enable people toleverage the combined intelligence and attention of large communitiesof like-minded people who contribute their knowledge and expertise for free. It’s a collective tit-for-tat form of altruism.

For example, Diggis a community that discovers the most interesting news articles. Itdoes this by enabling thousands of people to submit articles and voteon them. What Digg adds are a few clever algorithms on top of this for rankingarticles such that the most active ones bubble up to the top. It’s notunlike a stock market trader’s terminal, but for a completely differentclass of data. This is a great example of social filtering.

Anothergood example are prediction markets, where groups of people vote onwhat stock or movie or politician is likely to win — in some cases bybuying virtual stock in them — as a means to predict the future. Ithas been shown that prediction markets do a pretty good job of makingaccurate predictions in fact. In addition expertise referral serviceshelp people get answers to questions from communities of experts. Theseservices have been around in one form or another for decades and haverecently come back into vogue with services like Yahoo Answers. Amazonhas also taken a stab at this with their Amazon Mechanical Turk, whichenables “programs” to be constructed in which people perform the work.

I think social networking, social filtering, prediction markets,expertise referral networks, and collective collaboration are extremelyvaluable. By leveraging other people individuals and groups can stayahead of complexity and can also get the benefit of wide-areacollective cognition. These approaches to collective cognition arebeginning to filter into the processes of organizations and othercommunities. For example, there is recent interest in applying socialnetworking to niche communities and even enterprises.

The Semantic Webwill enrich all of these activities — making social networks andsocial filtering more productive. It’s not an either/or choice — thesetechnologies are extremely compatible in fact. By leveraging acommunity to tag, classify and organize content, for example, themeaning of that content can be collectively enriched. This is alreadyhappening in a primitive way in many social media services. TheSemantic Web will simply provide a richer framework for doing this.

The combination of the Semantic Web with emerging social networkingand social filtering will enable something greater than either on it’sown. Together, together these two technologies will enable much smarter groups, social networks, communities and organizations. But this still will not get us all the way past the Collective IQBarrier. It may get us close the threshold though. To cross thethreshold we will need to enable an even more powerful form ofcollective cognition.

The Agent Web

To cope with the enormous future scale andcomplexity of the Web, desktop and enterprise, each individual and group willreally need not just a single assistant, or even a community of humanassistants working on common information (a social filtering communityfor example), they will need thousands or millions of assistants working specificallyfor them. This really only becomes affordable and feasible if we canvirtualize what an “assistant” is.

Human assistants are at the top ofthe intelligence pyramid — they are extremely smart and powerful, and they are expensive — they  should not beused for simple tasks like sorting content, that’s just a waste oftheir capabilities. It would be like using a supercomputer array tospellcheck a document. Instead, we need to free humans up to do thereally high-value information tasks, and find a way to farm out thelow-value, rote tasks to software. Software is cheap or even free and it can be replicated as much asneeded in order to parallelize. A virtual army of intelligent agents is less expensive than a single human assistant, and much more suited to sifting through millions of Web pages every day.

But where will these future intelligent agents get their intelligence? In past attempts at artificial intelligence, researchers tried to buildgigantic expert systems that could reason as well as a small child forexample. These attempts met with varying degrees of success, but theyall had one thing in common: They were monolithic applications.

I believe that that future intelligent agents should be simple. They should not be advanced AI programs or expert systems. They should be capable of a few simple behaviors, the most important of which is to reason against sets of rules and semantic data. The basic logic necessary for reasoning is not enormous and does not require any AI — it’s just the ability to follow logical rules and perhaps do set operations. They should be lightweight and highly mobile. Insteadof vast monolithic AI, I am talking about vast numbers of very simpleagents that working together can do  emergent, intelligent operations en masse.

For example search — you might deploy a thousand agents to search all the sites about Italy for recipes and then assemble those results into a database instantaneously.  Or you might dispatch a thousand or more agents to watch for a job that matches your skills and goals across hundreds of thousands or millions of Websites. They could watch and wait until jobs that matched your criteria appeared, and then they could negotiate amongst themselves to determine which of the possible jobs they found were good enough to show you. Another scenario might be commerce — you could dispatch agents to find you the best deal on a vacation package, and they could even negotiate an optimal itinerary and price for you. All you would have to do is choose between a few finalist vacation packages and make the payment. This could be a big timesaver.

The above examples illustrate how agents might help an individual, but how might they help a group or organization? Well for one thing agents could continuously organize and re-organize information for a group. They could also broker social interactions — for example, by connecting people to other people with matching needs or interests, or by helping people find experts who could answer their questions. One of the biggest obstacles to getting past the Collective IQ Barrier is simply that people cannot keep track of more than a few social relationships and information sources at aany given time — but with an army of agents helping them, individuals might be able to cope with more relationships and data sources at once; the agents would act as their filters, deciding what to let through and how much priority to give it. Agents can also help to make recommendations, and to learn to facilitate and even automate various processes such as finding a time to meet, or polling to make a decision, or escalating an issue up or down the chain of command until it is resolved.

To make intelligent agents useful, they will need access to domain expertise. But the agents themselves will not contain any knowledge or intelligence of their own. The knowledge will exist outside on the Semantic Web, and so will the intelligence. Their intelligence, like their knowledge, will be externalized and virtualized in the form of axioms or rules that will exist out on the Web just like web pages.

For example, a set of axioms about travel could be published to the Web in the form of a document that formally defined them. Any agent that needed to process travel-related content could reference these axioms in order to reason intelligently about travel in the same way that it might reference an ontology about travel in order to interpret travel data structures. The application would not have to be specifically coded to know about travel — it could be a generic simple agent — but whenever it encountered travel-related content it could call up the axioms about travel from the location on the Web where they were hosted, and suddenly it could reason like an expert travel agent. What’s great about this is that simple generic agents would be able to call up domain expertise on an as-needed basis for just about any domain they might encounter. Intelligence — the heuristics, algorithms and axioms that comprise expertise, would be as accessible as knowledge — the data and connections between ideas and information on the Web.

The axioms themselves would be created by human experts in various domains, and in some cases they might even be created or modified by agents as they learned from experience. These axioms might be provided for free as a public service, or as fee-based web-services via API’s that only paying agents could access.

The key is that model is extremely scaleable — millions or billions of axioms could be created, maintained, hosted, accessed, and evolved in a totally decentralized and parallel manner by thousands or even hundreds of thousands of experts all around the Web. Instead of a few monolithic expert systems, the Web as a whole would become a giant distributed system of experts. There might be varying degrees of quality among competing axiom-sets available for any particular domain, and perhaps a ratings system could help to filter them over time. Perhaps a sort of natural selection of axioms might take place as humans and applications rated the end-results of reasoning using particular sets of axioms, and then fed these ratings back to the sources of this expertise, causing them to get more or less attention from other agents in the future. This process would be quite similar to the human-level forces of intellectual natural-selection at work in fields of study where peer-review and competition help to filter and rank ideas and their proponents.

Virtualizing Intelligence

What I have been describing is the virtualization of intelligence — making intelligence and expertise something that can be “published” to the Web and shared just like knowledge, just like an ontology, a document, a database, or a Web page. This is one of the long-term goals of the Semantic Web and it’s already starting now via new languages, such as SWRL, that are being proposed for defining and publishing axioms or rules to the Web. For example, “a non-biologicalparent of a person is their step-parent” is asimple axiom. Another axiom might be, “A child of a sibling of your parent is your cousin.” Using such axioms, an agent could make inferences and do simple reasoning about social relationships for example.

SWRL and other proposed rules languages provide potentialopen-standards for defining rules and publishing them to the Web sothat other applications can use them. By combining these rules withrich semantic data, applications can start to do intelligent things,without actually containing any of the intelligence themselves. The intelligence– the rules and data — can live “out there” on the Web, outside the code of various applications.

All theapplications have to know how to do is find relevant rules, interpret them, and apply them. Even the reasoning that may be necessary can be virtualized into remotely accessible Web services so applications don’t even have to do that part themselves (although many may simply include open-source reasoners in the same way that they include open-source databases or search engines today).

In other words, just as HTML enables any app to process and formatany document on the Web, SWRL + RDF/OWL may someday enable any application to reasonabout what the document discusses. Reasoning is the last frontier. Byvirtualizing reasoning — the axioms that experts use to reason aboutdomains — we can really begin to store the building blocks of humanintelligence and expertise on the Web in a universally-accessibleformat. This to me is when the actual “Intelligent Web” (what I callWeb 4.0) will emerge.

The value of this for groups and organizations is that they can start to distill their intelligence from individuals that comprise them into a more permanent and openly accessible form — axioms that live on the Web and can be accessed by everyone. For example, a technical support team for a product learns many facts and procedures related to their product over time. Currently this learning is stored as knowledge in some kind of tech support knowledgebase. But the expertise for how to find and apply this knowledge still resides mainly in the brains of the people who comprise the team itself.

The Semantic Web provides ways to enrich the knowledgebase as well as to start representing and saving the expertise that the people themselves hold in their heads, in the form of sets of axioms and procedures. By storing not just the knowledge but also the expertise about the product, the humans on the team don’t have to work as hard to solve problems — agents can actually start to reason about problems and suggest solutions based on past learning embodied in the common set of axioms. Of course this is easier said than done — but the technology at least exists in nascent form today. In a decade or more it will start to be practical to apply it.

Group Minds

Someday in the not-too-distant-future groups will be able toleverage hundreds or thousands of simple intelligent agents. Theseagents will work for them 24/7 to scour the Web, the desktop, theenterprise, and other services and social networks they are related to. They will help both the individuals as well as the collectives as-a-whole. They willbe our virtual digital assistants, always alert and looking for thingsthat matter to us, finding patterns, learning on our behalf, reasoningintelligently, organizing our information, and then filtering it,visualizing it, summarizing it, and making recommendations to us sothat we can see the Big Picture, drill in wherever we wish, and makedecisions more productively.

Essentially these agents will give groups something like their own brains. Today the only brains in a group reside in the skulls of the people themselves. But in the future perhaps we will see these technologies enable groups to evolve their own meta-level intelligences: systems of agents reasoning on group expertise and knowledge.

This will be a fundamental leap to a new order of collective intelligence. For the first time groups will literally have minds of their own, minds that transcend the mere sum of the individual human minds that comprise their human, living facets. I call these systems “Group Minds” and I think they are definitely coming. In fact there has been quite a bit of research on the subject of facilitating group collaboration with agents, for example, in government agencies such as DARPA and the military, where finding ways to help groups think more intelligently is often a matter of life and death.

The big win from a future in which individuals and groups canleverage large communities of intelligent agents is that they will bebetter able to keep up with the explosive growth of information complexity andsocial complexity. As the saying goes, “it takes a village.” There is just too much information, and too many relationships, changing too fast and this is only going to get more intense in years to come. The only way to cope with such a distributed problem is a distributed solution.

Perhaps by 2030 it will not be uncommon for Individuals and groups to maintain largenumbers of virtual assistants — agents that will help them keep abreast of themassively distributed, always growing and shifting information and sociallandscapes. When you really think about this, how else could we eversolve this? This is really the only practical long-term solution. But today it is still a bit of a pipedream; we’re not there yet. The key however is that we are closer than we’ve ever been before.

Conclusions

The Semantic Web provides the key enabling technology for all ofthis to happen someday in the future. By enriching the content of theWeb it first paves the way to a generation of smarter applications andmore productive individuals, groups and organizations.

The next majorleap will be when we begin to virtualize reasoning in the form ofaxioms that become part of the Semantic Web. This will enable a newgeneration of applications that can reason across information andservices. This will ultimately lead to intelligent agents that will be able to assist individuals,groups, social networks, communities, organizations and marketplaces sothat they can remain productive in the fact of the astonishinginformation and social network complexity in our future.

By adding more knowledge into our information, the Semantic Webmakes it possible for applications (and people) to use information moreproductively. By adding more intelligence between people,  information,and applications, the Semantic Web will also enable people andapplications to become smarter. In the future, these more-intelligentapps will facilitate higher levels of individual and collectivecognition by functioning as virtual intelligent assistants forindividuals and groups (as well as for online services).

Once we begin to virtualize not just knowledge (semantics) but alsointelligence (axioms) we will start to build Group Minds — groups that have primitive minds of their own. When we reach this point we will finally enable organizations to breakpast the Collective IQ Barrier: Organizations will start to becomesmarter than the sum of their parts. The intelligence of anorganization will not just be from its people, it will also come fromits applications. The number of intelligent applications in anorganization may outnumber the people by 1000 to 1, effectivelyamplifying each individual’s intelligence as well as the collectiveintelligence of the group.

Because software agents work all the time,can self-replicate when necessary, and are extremely fast and precise,they are ideally-suited to sifting in parallel through the millions or billions ofdata records on the Web, day in and day out. Humans and even groups ofhumans will never be able to do this as well. And that’s not what theyshould be doing! They are far too intelligent for that kind of work.Humans should be at the top of the pyramid, making the decisions,innovating, learning, and navigating.

When we finally reach this stage where networks of humans and smartapplications are able to work together intelligently for common goals,I believe we will witness a real change in the way organizations arestructured. In Group Minds, hierarchy will not be as necessary — the maximum effectivesize of a human Group Mind will be perhaps in the thousands or even themillions instead of around 50 people. As a result the shape of organizations in thefuture will be extremely fluid, and most organizations will be flat orcontinually shifting networks. For more on this kind of organization,read about virtual teams and networking, such as these books (by friends of mine who taught me everything I know about network-organization paradigms.)

I would also like to note that I am not proposing “strong AI” — a vision in which we someday makeartificial intelligences that are as or more intelligent thanindividual humans. I don’t think intelligent agents will individually be very intelligent. It will only be in vast communities of agents that intelligence will start to emerge. Agents are analogous to the neurons in the human brain — they really aren’t very powerful on their own.

I’m also not proposing that Group Minds will beas or more intelligent as the individual humans in groups anytime soon. I don’t think thatis likely in our lifetimes. The cognitive capabilities of an adult human are the product of millions of years of evolution. Even in the accelerated medium of the Web where evolution can take place much faster in silico, it may still take decades or even centuries to evolve AI that rivals the human mind (and I doubt such AI will ever be truly conscious, which means that humans, with their inborn natural consciousness, may always play a special and exclusive role in the world to come, but that is the subject of a different essay). But even if they will not be as intelligent as individual humans, Ido think that Group Minds, facilitated by masses of slightly intelligent agents and humans working in concert, can goa long way in helping individuals and groups become more productive.

It’s important to note that the future I am describing is notscience-fiction, but it also will not happen overnight. It will take atleast several decades, if not longer. But with the seeminglyexponential rate of change of innovation, we may make very large stepsin this direction very soon. It is going to be an exciting lifetime forall of us.

Diagram: Beyond Keyword (and Natural Language) Search

Here at Radar Networks we are working on practical ways to bring the Semantic Web to end-users. One of the interesting themes that has come up a lot, both internally, as well as in discussions with VC’s, is the coming plateau in the productivity of keyword search. As the Web gets increasingly large and complex, keyword search becomes less effective as a means for making sense of it. In fact, it will even decline in productivity in the future. Natural language search will be a bit better than keyword search, but ultimately won’t solve the problem either — because like keyword search it cannot really see or make use of the structure of information.

I’ve put together a new diagram showing how the Semantic Web will enable the next step-function in productivity on the Web. It’s still a work in progress and may change frequently for a bit, so if you want to blog it, please link to this post, or at least the .JPG image behind the thumbnail below so that people get the latest image. As always your comments are appreciated. (Click the thumbnail below for a larger version).

Futureofproductivity_2

Today a typical Google search returns up to hundreds of thousands or even millions of results — but we only really look at the first page or two of results. What about the other results we don’t look at? There is a lot of room to improve the productivity of search, and the help people deal with increasingly large collections of information.

Keyword search doesn’t understand the meaning of information, let alone its structure. Natural language search is a little better at understanding the meaning of information — but it still won’t help with the structure of information. To really improve productivity significantly as the Web scales, we will need forms of search that are data-structure-aware — that are able to search within and across data structures, not just unstructured text or semistructured HTML. This is one of the key benefits of the coming Semantic Web: it will enable the Web to be navigated and searched just like a database.

Starting with the "data web" enabled by RDF, OWL, ontologies and SPARQL, structured data is becoming increasingly accessible, searchable and mashable. This in turn sets the stage for a better form of search: semantic search. Semantic search combines the best of keyword, natural language, database and associative search capabilities together.

Without the Semantic Web, productivity will plateau and then gradually decline as the Web, desktop and enterprise continue to grow in size and complexity. I believe that with the appropriate combination of technology and user-experience we can flip this around so that productivity actually increases as the size and complexity of the Web increase.

See Also: A Visual Timeline of the Past, Present and Future of the Web

Envisioning the Whole Digital Person

Another article of note on the subject of our evolving digital lives and what user-experience designers should be thinking about:

Our lives are becoming increasingly digitized—from the ways we
communicate, to our entertainment media, to our e-commerce
transactions, to our online research. As storage becomes cheaper and
data pipes become faster, we are doing more and more online—and in the
process, saving a record of our digital lives, whether we like it or
not.

(snip…)

In the coming years, our ability to interact with the information
we’re so rapidly generating will determine how successfully we can
manage our digital lives. There is a great challenge at our doorsteps—a
shift in the way we live with each other.

As designers of user experiences for digital products
and services, we can make people’s digital lives more meaningful and
less confusing. It is our responsibility to envision not only
techniques for sorting, ordering, and navigating these digital
information spaces, but also to devise methods of helping people feel
comfortable with such interactions. To better understand and ultimately
solve this information management problem, we should take a holistic
view of the digital person. While our data might be scattered, people
need to feel whole.

Intelligence is in the Connections

Google’s Larry Page recently gave a talk to the AAAS about how Google is looking towards a future in which they hope to implement AI on a massive scale. Larry’s idea is that intelligence is a function of massive computation, not of “fancy whiteboard algorithms.” In other words, in his conception the brain doesn’t do anything very sophisticated, it just does a lot of massively parallel number crunching. Each processor and its program is relatively “dumb” but from the combined power of all of them working together “intelligent” behaviors emerge.

Larry’s view is, in my opinion, an oversimplification that will not lead to actual AI. It’s certainly correct that some activities that we call “intelligent” can be reduced to massively parallel simple array operations. Neural networks have shown that this is possible — they excel at low level tasks like pattern learning and pattern recognition for example. But neural networks have not proved capable of higher level cognitive tasks like mathematical logic, planning, or reasoning. Neural nets are theoretically computationally equivalent to Turing Machines, but nobody (to my knowledge) has ever succeeded in building a neural net that can in practice even do what a typical PC can do today — which is still a long way short of true AI!

Somehow our brains are capable of basic computation, pattern detection and learning, simple reasoning, and advanced cognitive processes like innovation and creativity, and more. I don’t think that this richness is reducible to massively parallel supercomputing, or even a vast neural net architecture. The software — the higher level cognitive algorithms and heuristics that the brain “runs” — also matter. Some of these may be hard-coded into the brain itself, while others may evolve by trial-and-error, or be programmed or taught to it socially through the process of education (which takes many years at the least).

Larry’s view is attractive but decades of neuroscience and cognitive science have shown conclusively that the brain is not nearly as simple as we would like it to be. In fact the human brain is far more sophisticated than any computer we know of today, even though we can think of it in simple terms. It’s a highly sophisticated system comprised of simple parts — and actually, the jury is still out on exactly how simple the parts really are — much of the computation in the brain may be sub-neuronal, meaning that the brain may actually a much much more complex system than we think.

Perhaps the Web as a whole is the closest analogue we have today for the brain — with millions of nodes and connections. But today the Web is still quite a bit smaller and simpler than a human brain. The brain is also highly decentralized and it is doubtful than any centralized service could truly match its capabilities. We’re not talking about a few hundred thousand linux boxes — we’re talking about hundreds of billions of parallel distributed computing elements to model all the neurons in a brain, and this number gets into the trillions if we want to model all the connections. The Web is not this big, and neither is Google.

One reader who commented on Larry’s talk made an excellent point on what this missing piece may be: “Intelligence is in the connections, not the bits.”The point is that most of the computation in the brain actually takesplace via the connections between neurons, regions, and perhapsprocesses. This writer also made some good points about quantumcomputation and how the brain may make use of it, a view that forexample Roger Penrose and others have spent a good deal of time on.There is some evidence that brain may make use of microtubules andquantum-level computing. Quantum computing is inherently about fields,correlations and nonlocality. In other words the connections in thebrain may exist on a quantum level, not just a neurological level.

Whether quantum computation is the key or not still remains to bedetermined. But regardless, essentially, Larry’s approach is equivalentto just aiming a massively parallel supercomputer at the Web and hopingthat will do the trick. Larry mentions for example that if allknowledge exists on the Web you should be able to enter a query and geta perfect answer. In his view, intelligence is basically just search ona grand scale. All answers exist on the Web, and the task is just tomatch questions to the right answers. But wait? Is that all thatintelligence does? Is Larry’s view too much of an oversimplification?Intelligence is not just about learning and recall, it’s also aboutreasoning and creativity. Reasoning is not just search. It’s unclearhow Larry’s approach would address that.

In my own opinion, for global-scale AI to really emerge the Web has toBE the computer. The computation has to happen IN the Web, betweensites and along connections — rather than from outside the system. Ithink that is how intelligence will ultimately emerge on a Web-widescale. Instead of some Google Godhead implementing AI from afar for thewhole Web, I think it is more likely that every site, app and person onthe Web will help to implement it. It will be much more of a hybridsystem that combines decentralized human and machine intelligences andtheir interactions along data connections and social relationships. Ithink this may emerge from a future evolution of the Web that providesfor much richer semantics on every piece of data and hyperlink on theWeb, and for decentralized learning, search, and reasoning to takeplace within every node on the Web. I think the Semantic Web is anecessary technology for this to happen, but it’s only the first step.More will need to happen on top of it for this vision to reallymaterialize.

My view is more of an “agent metaphor” for intelligence — perhaps itis similar to Marvin Minsky’s Society of Mind ideas. I think that mindsare more like communities than we presently think. Even in our ownindividual minds for example we experience competing thoughts, multiplethreads, and a kind of internal ecology and natural selection of ideas.These are not low-level processes — they are more like agents — theyare actually each somewhat “intelligent” on their own, they seem to besomewhat autonomous, and they interact in intelligent almost socialways.

Ideas seem to be actors, not just passive data points — they arecompeting for resources and survival in a complex ecology that existsboth within our individual minds and between them in socialrelationships and communities. As the theory of memetics proposes,ideas can even transport themselves through language, culture, andsocial interactions in order to reproduce and evolve from mind to mind.It is an illusion to think that there is some central self or “I” thatcontrols the process (that is just another agent in the community infact, perhaps one with a kind of reporting and selection role).

I’m not sure the complex social dynamics of these communities ofintelligence can really be modeled by a search engine metaphor. Thereis a lot more going on than just search. As well as communication andreasoning between different processes, there may in fact be feedbackacross levels from the top-down as well as the from the bottom-up.Larry is essentially proposing that intelligence is a purely bottom-upemergent process that can be reduced to search in the ideal, simplestcase. I disagree. I think there is so much feedback in every directionthat medium and the content really cannot be separated. The thoughtsthat take place in the brain ultimately feedback down to the neuralwetware itself, changing the states of neurons and connections –computation flows back down from the top, it doesn’t only flow up fromthe bottom. Any computing system that doesn’t include this kind offeedback in its basic architecture will not be able to implement trueAI.

In short, Google is not the right architecture to truly build a globalbrain on. But it could be a useful tool for search andquestions-and-answers in the future, if they can somehow keep up withthe growth and complexity of the Web.