This Blog has Moved to NovaSpivack.com

Republished from March 11: Twine CEO Comments about Evri Acquisition

Please see this article -- my comments on the Evri/Twine deal, as CEO of Twine. This provides more details about the history of Twine and what led to the acquisition.

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 areincreasing -- 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 activites 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.

Sneak Peak - Siri -- Interview with Tom Gruber

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Watch My best Talk: The Global Brain is Coming

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

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!!!

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:

| View | Upload your own

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

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.

Enriching the Connections of the Web -- Making the Web Smarter

Web 3.0 -- aka The Semantic Web -- is about enriching the connections of the Web. By enriching the connections within the Web, the entire Web may become smarter.

I  believe that collective intelligence primarily comes from connections -- this is certainly the case in the brain where the number of connections between neurons far outnumbers the number of neurons; certainly there is more "intelligence" encoded in the brain's connections than in the neurons alone. There are several kinds of connections on the Web:

1. Connections between information (such as links)
2. Connections between people (such as opt-in social relationships, buddy lists, etc.)
3. Connections between applications (web services, mashups, client server sessions, etc.)
4. Connections between information and people (personal data collections, blogs, social bookmarking, search results, etc.)
5. Connections between information and applications (databases and data sets stored or accessible by particular apps)
6. Connections between people and applications (user accounts, preferences, cookies, etc.)

Are there other kinds of connections that I haven't listed -- please let me know!

I believe that the Semantic Web can actually enrich all of these types of connections, adding more semantics not only to the things being connected (such as representations of information or people or apps) but also to the connections themselves.

In the Semantic Web approach, connections are represented with statements of the form (subject, predicate, object) where the elements have URIs that connect them to various ontologies where their precise intended meaning can be defined. These simple statements are sometimes called "triples" because they have three elements. In fact, many of us are working with statements that have more than three elements ("tuples"), so that we can represent not only subject, predicate, object of statements, but also things like provenance (where did the data for the statement come from?), timestamp (when was the statement made), and other attributes. There really is no limit to what kind of metadata can be stored in these statements. It's a very simple, yet very flexible and extensible data model that can represent any kind of data structure.

The important point for this article however is that in this data model rather than there being just a single type of connection (as is the case on the present Web which basically just provides the HREF hotlink, which simply means "A and B are linked" and may carry minimal metadata in some cases), the Semantic Web enables an infinite range of arbitrarily defined connections to be used.  The meaning of these connections can be very specific or very general.

For example one might define a type of connection called "friend of" or a type of connection called "employee of" -- these have very different meanings (different semantics) which can be made explicit and also machine-readable using OWL. By linking a page about a person with the "employee of" link to another page about a different person, we can express that one of them employs the other. That is a statement that any application which can read OWL is able to see and correctly interpret, by referencing the underlying definition of "employee of" which is defined in some ontology and might for example specify that an "employee of" relation connects a person to a person or organization who is their employer. In other words, rather than just linking things with the generic "hotlink" we are all used to, they can now be linked with specific kinds of links that have very particular and unambiguous meaning and logical implications.

This has the potential at least to dramatically enrich the information-carrying capacity of connections (links) on the Web. It means that connections can carry more meaning, on their own. It's a new place to put meaning in fact -- you can put meaning between things to express their relationships. And since connections (links) far outnumber objects (information, people or applications) on the Web, this means we can radically improve the semantics of the structure of the Web as a whole -- the Web can become more meaningful, literally. This makes a difference, even if all we do is just enrich connections between gross-level objects (in other words, connections between Web pages or data records, as opposed to connections between concepts expressed within them, such as for example, people and companies mentioned within a single document).

Even if the granularity of this improvement in connection technology is relatively gross level it could still be a major improvement to the Web. The long-term implications of this have hardly been imagined let alone understood -- it is analogous to upgrading the dendrites in the human brain; it could be a catalyst for new levels of computation and intelligence to emerge.

It is important to note that, as illustrated above, there are many types of connections that involve people. In other words the Semantic Web, and Web 3.0, are just as much about people as they are about other things. Rather than excluding people, they actually enrich their relationships to other things. The Semantic Web, should, among other things, enable dramatically better social networking and collaboration to take place on the Web. It is not only about enriching content.

Now where will all these rich semantic connections come from? That's the billion dollar question. Personally I think they will come from many places: from end-users as they find things, author content, bookmark content, share content and comment on content (just as hotlinks come from people today), as well as from applications which mine the Web and automatically create them. Note that even when Mining the Web a lot of the data actually still comes from people -- for example, mining the Wikipedia, or a social network yields lots of great data that was ultimately extracted from user-contributions. So mining and artificial intelligence does not always imply "replacing people" -- far from it! In fact, mining is often best applied as a means to effectively leverage the collective intelligence of millions of people.

These are subtle points that are very hard for non-specialists to see -- without actually working with the underlying technologies such as RDF and OWL they are basically impossible to see right now. But soon there will be a range of Semantically-powered end-user-facing apps that will demonstrate this quite obviously. Stay tuned!

Of course these are just my opinions from years of hands-on experience with this stuff, but you are free to disagree or add to what I'm saying. I think there is something big happening though. Upgrading the connections of the Web is bound to have a significant effect on how the Web functions. It may take a while for all this to unfold however. I think we need to think in decades about big changes of this nature.

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.

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.

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.

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.

Must-Know Terms for the 21st Century Intellectual

Read this fun article that lists and defines some of the key concepts that every post-singularity transhumanist meta-intellectual should know! (via Kurzweil)

Web 3.0 Redefined

KurzweilAI.net has published an article I wrote redefining the meaning of Web 3.0. Basically, I am proposing that Web 3.0 include a set of emerging technologies that are all reaching new levels of maturity at the same time.

What is the Semantic Web, Actually?

I've read several blog posts reacting to John Markoff's article today. There seem to be some misconceptions in those posts about what the Semantic Web is and is not. Here I will try to  succinctly correct a few of the larger misconceptions I've run into:

• The Semantic Web is not just a single Web. There won't be one Semantic Web, there will be thousands or even millions of them, each in their own area. They will all be part of one Semantic Web in that they will use the same open-standard languages and their data will be universally accessible, but they won't all be run by any single company. They will connect together over time, forming a tapestry. But nobody will own this or run this as a single service. It will be just as decentralized as the Web already is.
• The Semantic Web is not separate from the existing Web. The Semantic Web won't be a new Web apart from the Web we already have. It simply adds new metadata and data to the existing Web. It merges right into the existing HTML Web just like XML does, except this new metadata is in RDF (since RDF can in fact be expressed in XML).
  
• The Semantic Web is not just about unstructured data. In fact, the Semantic Web is really about structured data: it provides a means (RDF) to turn any content or data into structured data that other software can make use of. This is really what RDF enables.
• The Semantic Web does not require complex ontologies. Even without making use of OWL and more sophisticated ontologies, powerful data-sharing and data-integration can be enabled on the existing Web using even just RDF alone.
• The Semantic Web does not only exist on Web pages. RDF works inside of applications and databases, not just on Web pages. Calling it a "Web" is a misnomer of sorts -- it's not just about the Web, it's about all information, data and applications.
• The Semantic Web is not only about AI, and doesn't require it. There are huge benefits from the Semantic Web without ever using a single line of artificial intelligence code. While the next-generation of AI will certainly be enabled by richer semantics, AI is not the only benefit of RDF. Making data available in RDF makes it more accessible, integratable, and reusable -- regardless of any AI. The long-term future of the Semantic Web is AI for sure -- but to get immediate benefits from RDF no AI is necessary.
• The Semantic Web is not only about mining, search engines and spidering. Application developers and content providers, and end-users, can benefit from using the Semantic Web (RDF) within their own services, regardless of whether they expose that RDF metadata to outside parties. RDF is useful without doing any data-mining -- it can be baked right into content within authoring tools and created transparently when information is published. RDF makes content more manageable and frees developers and content providers from having to look at relational data models. It also gives end-users better ways to collect and manage content they find.
• The Semantic Web is not just research. It's already in use and starting to reach the market. The government uses it of course. But also so do companies like Adobe, and more recently Yahoo (Yahoo Food has started to use some Semantic Web technologies now). And one flavor of RSS is defined with RDF. Oracle has released native RDF support in their products. The list goes on...

Learning more:

• To learn about the technology of RDF, I recommend this terrific presentation by my friend Ian Davis.
• This is my long-term outlook for the Web and the Semantic Web.
• PlanetRDF is a good feed to read if you want to keep up with what's going on in this field.
• My own company, Radar Networks, is working on bringing the Semantic Web to the masses. You can sign up for our launch list at our site to be notified of future announcements.

New York Times Article About the Emerging Semantic Web

A New York Times article came out today about the Semantic Web -- in which I was quoted, speaking about my company Radar Networks. Here's an excerpt:

Referred to as Web 3.0, the effort is in its infancy, and the very idea has given rise to skeptics who have called it an unobtainable vision. But the underlying technologies are rapidly gaining adherents, at big companies like I.B.M. and  Google as well as small ones. Their projects often center on simple, practical uses, from producing vacation recommendations to predicting the next hit song.

But in the future, more powerful systems could act as personal advisers in areas as diverse as financial planning, with an intelligent system mapping out a retirement plan for a couple, for instance, or educational consulting, with the Web helping a high school student identify the right college.

The projects aimed at creating Web 3.0 all take advantage of increasingly powerful computers that can quickly and completely scour the Web.

“I call it the World Wide Database,” said Nova Spivack, the founder of a start-up firm whose technology detects relationships between nuggets of information mining the World Wide Web. “We are going from a Web of connected documents to a Web of connected data.”

Web 2.0, which describes the ability to seamlessly connect applications (like geographical mapping) and services (like photo-sharing) over the Internet, has in recent months become the focus of dot-com-style hype in Silicon Valley. But commercial interest in Web 3.0 — or the “semantic Web,” for the idea of adding meaning — is only now emerging.

Minding The Planet -- The Meaning and Future of the Semantic Web

NOTES

• Master Copy can be found at this URL or http://tinyurl.com/yynb93
• Last Update: Tuesday, November 7, 2006, 10:17AM PST
  
• License -- This article is distributed under the Creative Commons Deed.  If you would like to distribute a version of this article, please link back to http://www.mindingtheplanet.net from your version, thanks.
• Printable version -- Click here to download the printable PDF version of this article
• Illustrated Version -- See the version by the Lifeboat Foundation

 

Prelude

Many years ago, in the late 1980s, while I was still a college student, I visited my late grandfather, Peter F. Drucker, at his home in Claremont, California. He lived near the campus of Claremont College where he was a professor emeritus. On that particular day, I handed him a manuscript of a book I was trying to write, entitled, "Minding the Planet" about how the Internet would enable the evolution of higher forms of collective intelligence.

My grandfather read my manuscript and later that afternoon we sat together on the outside back porch and he said to me, "One thing is certain: Someday, you will write this book." We both knew that the manuscript I had handed him was not that book, a fact that was later verified when I tried to get it published. I gave up for a while and focused on college, where I was studying philosophy with a focus on artificial intelligence. And soon I started working in the fields of artificial intelligence and supercomputing at companies like Kurzweil, Thinking Machines, and Individual.

A few years later, I co-founded one of the early Web companies, EarthWeb, where among other things we built many of the first large commercial Websites and later helped to pioneer Java by creating several large knowledge-sharing communities for software developers. Along the way I continued to think about collective intelligence. EarthWeb and the first wave of the Web came and went. But this interest and vision continued to grow. In 2000 I started researching the necessary technologies to begin building a more intelligent Web. And eventually that led me to start my present company, Radar Networks, where we are now focused on enabling the next-generation of collective intelligence on the Web, using the new technologies of the Semantic Web. 

But ever since that day on the porch with my grandfather, I remembered what he said: "Someday, you will write this book." I've tried many times since then to write it. But it never came out the way I had hoped. So I tried again. Eventually I let go of the book form and created this weblog instead. And as many of my readers know, I've continued to write here about my observations and evolving understanding of this idea over the years. This article is my latest installment, and I think it's the first one that meets my own standards for what I really wanted to communicate. And so I dedicate this article to my grandfather, who inspired me to keep writing this, and who gave me his prediction that I would one day complete it.

This is an article about a new generation of technology that is sometimes called the Semantic Web, and which could also be called the Intelligent Web, or the global mind. But what is the Semantic Web, and why does it matter, and how does it enable collective intelligence? And where is this all headed? And what is the long-term far future going to be like? Is the global mind just science-fiction? Will a world that has a global mind be good place to live in, or will it be some kind of technological nightmare?

Radar Networks is Seeking Search Engineers for Large-Scale Web Mining Initiative

My company, Radar Networks, is building a very large dataset by crawling and mining the Web. We then apply a range of new algorithms to the data (part of our secret sauce) to generate some very interesting and useful new information about the Web. We are looking for a few experienced search engineers to join our team -- specifically people with hands-on experience designing and building large-scale, high-performance Web crawling and text-mining systems. If you are interested, or you know anyone who is interested or might be qualified for this, please send them our way. This is your chance to help architect and build a really large and potentially important new system. You can read more specifics abour our open jobs here.

I'm Going to Start Blogging About Radar Networks Here

I haven't blogged very much about my stealth startup, Radar Networks, yet. At the most, I've made a few cryptic posts and announcements in the past, but we've been keeping things pretty quiet. That's been a conscious  decision because we have been working intensively on R&D  and we just weren't ready to say much yet.

Unlike some companies which have done massive and deliberate hype about unreleased vapor software, we really felt it would be better to just focus on our work and let it speak for itself when we release it.

The fact is we have been working quietly for several years on something really big, and really hard. It hasn't always been easy -- there have been some  technical challenges that took a long time to overcome. And it took us a long time to find VC's daring enough to back us.

The thing is, what we are making is not a typical Web 2.0 "build it and flip it in 6 months" kind of project. It's deep technology that has long-term infrastructure-level implications for the Web and the future of content. And until recently we really didn't even have a good way to describe it to non-techies. So we just focused on our work and figured we would talk about it someday in the future.

But perhaps I've erred on the side of caution -- being so averse to gratuitous hype that I have literally said almost nothing publicly about the company. We didn't even issue a press release about our Series A round (which happened last April -- I'll be adding one to our new corporate site, which launches on Sunday night however, for historical purposes), and until today, our site at Radar has been  just a one-page placeholder with no info at all about what we are doing.

But something happened that changed my mind about this recently. I had lunch with my friend Munjal Shah, the CEO of Riya. Listening to Munjal tell his stories about how he has blogged so openly about Riya's growth, even from way before their launch, and how that has provided him and his team with amazingly valuable community feedback, support, critiques, and new ideas, really got me thinking. Maybe it's time Radar Networks started telling a little more of its story? It seems like the team at Riya really benefitted from being so open. So although, we're still in stealth-mode and there are limits to what we can say at this point, I do think there are some aspects we can start to talk about, even before we've launched. And besides that our story itself is interesting -- it's the story of what it's like to build and work in a deep-technology play in today's venture economy.

So that's what I'm going to start doing here -- I'm going to start telling our story on this blog, Minding the Planet. I already have around 500 regular readers, and most of them are scientists and hard-core techies and entrepreneurs. I've been writing mainly about emerging technologies that are interesting enough to inspire me to post about them, and once in a while about ideas I have been thinking about. These are also subjects that are of interest to the people who read this blog. But now I'm also going to start blogging more about Radar Networks and what we are doing and how it's going. I'll post about our progress, the questions we have, the achievements on our team, and of course news about our launch plans. And I hope to hear from people out there who are interested in joining us when we do our private invite-only beta tests.

We're still quite a ways from a public launch, but we do have something working in the lab and it's very exciting. Our VC's want us to launch it now, but it's still an early alpha and we think it needs a lot more work (and testing) before our baby is ready to step out into the big world out there. But it looks promising. I do think, all modesty aside for a moment, that it has the potential to really advance the Web on a broad scale. And it's exciting to work on.

This post is already long enough, so I'll finish here for the moment. In my upcoming posts I will start to talk a little bit more about the new category that Radar Networks is going to define, and some of the technologies we're using, and challenges we've overcome along the way. And I'll share some insights, and stories, and successes we've had.

But I'm getting ahead of myself, and besides that, my dinner's ready. More later.

What is Radar Networks up to?

Shel Israel and I just finished up working together for 10 days. I needed Shel's perspective on what we are working on at Radar Networks. Shel lived up to his reviews as a brilliant thinker on strategic messaging, branding and positioning. So what are the 15 people at Radar Networks working on? It's still a secret, but yes, it's related to the Semantic Web, and yes, Shel has hinted on his blog at some of it. But it's probably not what you think. And, no, it's not semantic video blogging either. More hints later on. For now, if you are a blogger and you have a wish-list for what wikis or blogs could do next, feel free to submit your list in the comments on this post: I'm doing some informal market research...

[Corrected due to typo.]

Microsoft Photosynth is Incredible

Check out this video demo of Microsoft Photosynth -- an experimental technology that combines multiple photos of the same thing into a 3-D model that can then be navigated and explored -- it's beautiful, visionary and well... just awesome.

Cool Collective Intelligence Group Drawing Game

Check out The Broth -- it's a "global mosaic" in which you can move tiles around in real time with other people to create emergent artworks. It's really cool to watch images grow and morph from the combined imagination of people around the Net. Beautiful.

Harnessing The Collective Mind

Today I read an interesting article in the New York Times about a company called Rite-Solutions which is using a home-grown stock market for ideas to catalyze bottom-up innovation across all levels of personnel in their organization. This is a way to very effectively harness and focus the collective creativity and energy in an organization around the best ideas that the organization generates.

Using virtual stock market systems to measure community sentiment is not a new concept but it is a new frontier. I don't think we've even scratched the surface of what this paradigm can accomplish. For lots of detailed links to resources on this topic see the wikipedia entry on prediction markets. This prediction markets portal also has collected interesting links on the topic. Here is an informative blog post about recent prediction market attempts. Here is a scathing critique of some prediction markets.

There are many interesting examples of prediction markets on the Web:

• Google uses a similar kind of system -- their own version of a prediction market -- to enable staff members to collaboratively predict the likelihood that various internal projects and events will occur on-schedule.
• Yahoo also has a prediction market called BuzzGame that enables visitors to help predict technology trends. 
• Newsfutures Exchange is a prediction market about the news, which is powered by a commercial prediction market engine sold by a company called Newsfutures.
• BlogShares, a fantasy stock market for Weblogs in which players invest virtual money in the blogs they think will gain the most audience share.
• Intrade is another exchange for trading on idea futures.
• The Iowa Political Futures Exchange is a prediction market that focuses on political change.
• Tradesports is a prediction market around sports topics.
• The Hollywood Stock Exchange is a prediction market around movies.
• The Foresight Exchange is another prediction market for predicting future events.

Here are some interesting, more detailed discussions of prediction market ideas and potential features.

• http://hanson.gmu.edu/ideafutures.html
• http://hanson.gmu.edu/ifwired.html
• http://ideastockexchange.com/about.htm

Another area that is related, but highly underleveraged today, are ways to enable communities to help establish whether various ideas are correct using argumentation. By enabling masses of people to provide reasons to agree or disagree with ideas, and with those reasons as well, we can automatically rate what ideas are most agreed with or disagreed with. One very interesting example of this is TruthMapping.com. Some further concepts related to this approach are discussed in this thread.

Collective Intelligence 2.0

Introduction:

This article proposes the creation of a new open, nonprofit service on the Web that will provide something akin to “collective self-awareness” back to the Web. This service is like a "Google Zeitgeist" on steroids, but with a lot more real-time, interactive, participatory data, technology and features in it. The goal is to measure and visualize the state of the collective mind of humanity, and provide this back to humanity in as close to real-time as is possible, from as many data sources as we can handle -- as a web service.

By providing this service, we will enable higher levels of collective intelligence to emerge and self-organize on the Web. The key to collective intelligence (or any intelligence in fact) is self-awareness. Self-awareness is, in essence, a feedback loop in which a system measures its own internal state and the state of its environment, then builds a representation of that state, and then reasons about and reacts to that representation in order to generate future behavior. This feedback loop can be provided to any intelligent system -- even the Web, even humanity as-a-whole. If we can provide the Web with such a service, then the Web can begin to “see itself” and react to its own state for the first time. And this is the first step to enabling the Web, and humanity as-a-whole, to become more collectively intelligent.

It should be noted that by "self-awareness" I don’t mean consciousness or sentience – I think that the consciousness comes from humans at this point and we are not trying to synthesize it (we don't need to; it's already there). Instead, by "self-awareness" I mean a specific type of feedback loop -- a specific Web service -- that provides a mirror of the state of the whole back to its parts. The parts are the conscious elements of the system – whether humans and/or machines – and can then look at this meta-mirror to understand the whole as well as their place in it. By simply providing this meta-level mirror, along with ways that the individual parts of the system can report their state to it, and get the state of the whole back from it, we can enable a richer feedback loop between the parts and the whole. And as soon as this loop exists the entire system suddenly can and will become much more collectively intelligent.

What I am proposing is something quite common in artificial intelligence. For example, in the field of robotics, such as when building an autonomous robot. Until a robot is provided with a means by which it can sense its own internal state and the state of its nearby environment, it cannot behave intelligently or very autonomously. But once this self-representation and feedback loop is provided, it can then react to it’s own state and environment and suddenly can behave far more intelligently. All cybernetic systems rely on this basic design pattern. I’m simply proposing we implement something like this for the entire Web and the mass of humanity that is connected to it. It's just a larger application of an existing pattern. Currently people get their views of “the whole” from the news media and the government – but these views suffer from bias, narrowness, lack of granularity, lack of real-time data, and the fact that they are one-way, top-down services with no feedback loop capabilities. Our global collective self-awareness -- in order to be truly useful and legitimate really must be two-way, inclusive, comprehensive, real-time and democratic. In the global collective awareness, unlike traditional media, the view of the whole is created in a bottom-up, emergent fashion from the sum of the reports from all the parts (instead of just a small pool of reporters or publishers, etc.).

The system I envision would visualize the state of the global mind on a number of key dimensions, in real-time, based on what people and software and organizations that comprise its “neurons” and “regions” report to it (or what it can figure out by mining artifacts they create). For example, this system would discover and rank the current most timely and active topics, current events, people, places, organizations, events, products, articles, websites, in the world right now. From these topics it would link to related resources, discussions, opinions, etc. It would also provide a real-time mass opinion polling system, where people could start polls, vote on them, and see the results in real-time. And it would provide real-time statistics about the Web, the economy, the environment, and other key indicators.

The idea is to try to visualize the global mind – to make it concrete and real for people, to enable them to see what it is thinking, what is going on, and where they fit in it – and to enable them to start adapting and guiding their own behavior to it. By giving the parts of the system more visibility into the state of the whole, they can begin to self-organize collectively which in turn makes the whole system function more intelligently

Essentially I am proposing the creation of the largest and most sophisticated mirror ever built – a mirror that can reflect the state of the collective mind of humanity back to itself. This will enable an evolutionary process which eventually will result in humanity becoming more collectively self-aware and intelligent as-a-whole (instead of what it is today -- just a set of separeate interacting intelligent parts). By providing such a service, we can catalyze the evolution of higher-order meta-intelligence on this planet -- the next step in human evolution. Creating this system is a grand cultural project of profound social value to all people on earth, now and in the future.

This proposal calls for creating a nonprofit orgnaization to build and host this service as a major open-source initiative on the Web, like the Wikipedia, but with a very different user-experience and focus. It also calls for implementing the system with a hybrid central and distributed architecture. Although this vision is big, the specific technologies, design patterns, and features that are necessary to implement it are quite specific and already exist. They just have to be integrated, wrapped and rolled out. This will require an extraordinary and multidisciplanary team. If you're interested in getting involved and think you can contribute resources that this project will need, let me know (see below for details).

Further Thoughts

Today I re-read this beautiful, visionary article by Kevin Kelley, about the birth of the global mind, in which he states:

The planet-sized "Web" computer is already more complex than a human brain and has surpassed the 20-petahertz threshold for potential intelligence as calculated by Ray Kurzweil. In 10 years, it will be ubiquitous. So will superintelligence emerge on the Web, not a supercomputer?

Kevin's article got me thinking once again about an idea that has been on my mind for over a decade. I have often thought that the Web is growing into the collective nervous system of our species. This will in turn enable the human species to function increasingly as an intelligent superorganism, for example, like a beehive, or an ant colony -- but perhaps even more intelligent. But the key to bringing this process about is self-awareness. In short, the planetary supermind cannot become truly intelligent until it evolves a form of collective self-awareness. Self-awareness is the most critical component of human intelligence -- the sophistication of human self-awareness is what makes humans different from dumb machines, and from less intelligent species.

The Big Idea that I have been thinking about for over a decade is that if we can build something that functions like a collective self-awareness, then this could catalyze a huge leap in collective intelligence that would essentially "wake up" the global supermind and usher in a massive evolution in its intelligence and behavior. As the planetary supermind becomes more aware of its environment, its own state, and its own actions and plans, it will then naturally evolve higher levels of collective intelligence around this core. This evolutionary leap is of unimaginable importance to the future of our species.

In order for the collective mind to think and act more intelligently it must be able to sense itself and its world, and reason about them, with more precision -- it must have a form of self-awareness. The essence of self-awareness is self-representation -- the ability to sense, map,  reason about, and react to, one's own internal state and the state of one's nearby environment. In other words, self-awareness is a feedback loop by which a system measures and reacts to its own self-representations. Just as is the case with the evolution of individual human intelligence, the evolution of more sophisticated collective human intelligence will depend on the emergence of better collective feedback loops and self-representations. By enabling a feedback loop in which information can flow in both directions between the self-representations of individuals and a meta-level self-representation for the set of all individuals, the dynamics of the parts and the whole become more closely coupled. And when this happens, the system can truly start to adapt to itself intelligently, as a single collective intelligence instead of a collection of single intelligences.

In summary, in order to achieve higher levels of collective intelligence and behavior, the global mind will first need something that functions as its collective self-awareness -- something that enables the parts to better sense and react to the state of the whole, and the whole to better sense and react to the state of its parts. What is needed essentially is something that functions as a collective analogue to a self -- a global collective self.

Think of the global self as a vast mirror, reflecting the state of the global supermind back to itself. Mirrors are interesting things. At first they merely reflect, but soon they begin to guide decisionmaking. By simply providing humanity with a giant virtual mirror of what is going on across the minds of billions of individuals, and millions of groups and organizations, the collective mind will crystallize, see itself for the first time, and then it will begin to react to its own image. And this is the beginning of true collective cognition. When the parts can see themselves as a whole and react in real-time, then they begin to function as a whole instead of just a collection of separate parts. As this shift transpires the state of the whole begins to feedback into the behavior of the parts, and the state of the parts in turns feeds back to the state of the whole. This cycle of bidirectional feedback between the parts and whole is the essence of cognition in all intelligent systems, whether individual brains, artificial intelligences, or entire worlds.

I believe that the time has come for this collective self to emerge on our planet. Like a vast virtual mirror, it will function as the planetary analogue to our own individual self-representations -- that capacity of our individual minds which represents us back to ourselves. It will be comprised of maps that combine real-time periodic data updates, and historical data, from perhaps trillions of data sources (one for each person, group, organization and software agent on the grid). The resulting visualizations will be something like a vast fluid flow, or a many particle simulation. It will require a massive computing capability to render it -- perhaps a distributed supercomputer comprised of the nodes on the Web themselves, each hosting a part of the process. It will require new thinking about how to visualize trends in such vast amounts of data and dimensions. This is a great unexplored frontier in data visualization and knowledge discovery.

How It Might Work

I envision the planetary self functioning as a sort of portal -- a Web service that aggregates and distributes all kinds of current real-time and historical data about the state of the whole, as well as its past states and future projected states. This portal would collect opinions, trends, and statistics about the human global mind, the environment, the economy, society, geopolitical events, and other indicators, and would map them graphically in time, geography, demography, and subject space -- enabling everyone to see and explore the state of the global mind from different perspectives, with various overlays, and at arbitrary levels of magnification.

I think this system should provide an open data model, and open API for adding and growing data sets, querying, remixing, visualizing, and subscribing to the data. All services that provide data sets, analysis or visualizations (or other interpretations) of potential value to understanding the state of the whole would be able to post data into our service for anyone to find and use. Search engines could post in the top search query terms. Sites that create tag clouds could post in tags and tag statistics. Sites that analyze the blogosphere could post in statistics about blogs, bloggers, and blog posts. Organizations that do public opinion polling, market and industry research, trend analysis, social research, or economic research could post in statistics they are generating. Academic researchers could post in statistics generated by projects they are doing to analyze trends on the Web, or within our data-set itself.

As data is pushed to us, or pulled by us, we would grow the largest central data repository about the state of the whole. Others could then write programs to analyze and remix our data, and then post their results back into the system for others to use as well. We would make use of our data for our own analysis, but anyone else could also do research and share their analysis through our system. End users and others could also subscribe to particular data, reports, or visualizations from our service, and could post in their own individual opinions, attention data feeds, or other inputs. We would serve as a central hub for search, analysis, and distribution of collective self-awareness.

The collective self would provide a sense of collective identity: who are we, how do we appear, what are we thinking about, what do we think about what we are thinking about, what are we doing, how well are we doing it, where are we now, where have we been, where are we going next. Perhaps it could be segmented by nation, or by age group, or by other dimensions as well to view various perspectives on these questions within it. It could gather its data by mining for it, as well as through direct push contributions from various data-sources. Individuals could even report on their own opinions, state, and activities to it if they wanted to, and these votes and data points would be reflected back in the whole in real time. Think of it as a giant emergent conversation comprised of trillions of participants, all helping to make sense of the same subject -- our global self identity -- together. It could even have real-time views that are animated and alive -- like a functional brain image scan -- so that people could see the virtual neurons and pathways in the global brain firing as they watch.

If this global self-representation existed, I would want to subscribe to it as a data feed on my desktop. I would want to run it in a dashboard in the upper right corner of my monitor -- that I could expand at any time to explore further. It would provide me with alerts when events transpired that matched my particular interests, causes, or relationships. It would solicit my opinions and votes on issues of importance and interest to me. It would simultaneously function as my window to the world, and the world's window to me. It would be my way of participating in the meta-level whole, whenever I wanted to. I could tell it my opinions about key issues, current events, problems, people, organizations, or even legislative proposals. I could tell it about the quality of life from my perspective, where I am living, in my industry and demographic niche. I could tell it about my hopes and fears for the future. I could tell it what I think is cool, or not cool, interesting or not interesting, good or bad, etc. I could tell it what news I was reading and what I think is noteworthy or important. And it would listen and learn, and take my contributions into account democratically along with those of billions of other people just like me all around the world. From this would emerge global visualizations and reports about what we are all thinking and doing, in aggregate, that I could track and respond to. Linked from these flows I could then find relevant news, conversations, organizations, people, products, services, events, and knowledge. And from all of this would emerge something greater than anything I can yet imagine -- a thought process too big for any one human mind to contain.

I want to build this. I want to build the planetary Self. I am not suggesting that we build the entire global mind, I am just suggesting that we build the part of the system that functions as its collective self-awareness. The rest of the global mind is already there, as raw potential at least, and doesn't have to be built. The Web, human minds, software agents, and organizations already exist. Their collective state just needs to be reflected in a single virtual mirror. As soon as this mirror exists they can begin to collectively self-organize and behave more intelligently, simply because they will have, for the first time, a way of measuring their collective state and behavior. Once there is a central collective self-awareness loop, the intelligence of the global mind will emerge and self-organize naturally over time. This collective self-awareness infrastructure is the central enabling technology that has to be there first for the next-leap in intelligence of the global mind to evolve.

Project Structure

I think this should be created as a non-profit open-source project. In fact, that is the only way that it can have legitimacy -- it must be independent of any government, cultural or commercial perspective. It must be by and for the people, as purely and cleanly as possible. My guess is that to build this properly we would need to create a distributed grid computing system to collect, compute, visualize and distribute the data -- it could be similar to SETI@Home; everyone could help host it. At the center of this grid, or perhaps in a set of supernodes, would be a vast supercomputing array that would manage the grid, do focused computations and data fusion operations. There would also need to be some serious money behind this project as well -- perhaps from major foundations and donors. This system would be a global resource of potential incalculable value to the future of human evolution. It would be a project worth funding.

My Past Writing On This Topic

A Physics of Ideas: Measuring the Physical Properties of Memes
Towards a Worldwide Database
The Metaweb: A Graph of the Future
From Semantic Web to Global Mind
The Birth of the Metaweb
Are Organizations Organisms?
From Application-Centric to Data-Centric Computing
The Human Menome Project

Other Noteworthy Projects

Principia Cybernetica -- the Global Mind Group
The Global Consciousness Project
W3C - The Semantic Web Working Group
Amazon's Mechanical Turk
CHI -- Harnessing Networks of Humans

New Text-Mining Project Aims to Help Scientists

A new project applies text-mining to help scientists in the UK discover knowledge in large collections of research articles and data (Found in: KurzweilAI):

Julie Nightingale
Tuesday   January   10, 2006
The Guardian
 
 Scientific research is being added to at an alarming rate: the Human Genome Project alone is generating enough documentation to "sink battleships". So it's not surprising that academics seeking data to support a new hypothesis are getting swamped with information overload. As data banks build up worldwide, and access gets easier through technology, it has become easier to overlook vital facts and figures that could bring about groundbreaking discoveries.

The government's response has been to set up the National Centre for Text Mining, the world's first centre devoted to developing tools that can systematically analyse multiple research papers, abstracts and other documents, and then swiftly determine what they contain.

The article above also cites some recent discoveries that have been enabled by text-mining approaches:

The more breathtaking results have included the discovery of new therapeutic uses for the drug Thalidomide to treat conditions such as chronic hepatitis C and acute pancreatitis and that chlorpromazine may reduce cardiac hypertrophy - enlargement of the heart leading to heart failure.

Big Thinkers' Most Dangerous Ideas

The Edge has published mini-essays by 119 "big thinkers" on their "most dangerous ideas" -- fun reading.

The history of science is replete with discoveries that were considered socially, morally, or emotionally dangerous in their time; the Copernican and Darwinian revolutions are the most obvious. What is your dangerous idea? An idea you think about (not necessarily one you originated) that is dangerous not because it is assumed to be false, but because it might be true?

 

Amazon Launches new Service that Harnesses Networks of Human Minds to Do Tasks

Amazon has launched a new service that seeks to create a marketplace for human intelligence on the Net. The idea is to utilize humans like one might utilize intelligent agents, to help complete tasks that humans do better than computers -- for example like image adjustments, formatting, tagging and marking up content, adding metatdata to documents, filing and filtering, etc. The idea is that people can sign up to do these tasks and make money. People who need tasks can farm them out to the marketplace. It's like a big army of "human agents" who can use "human intelligence" to do stuff for you.

The name of the service is "Amazon Mechanical Turk" -- quite bizarre. But OK. It's a cool idea. I think the combination of human and machine intelligence is ultimately going to be smarter than either form of intelligence on its own. This system is at least a start -- it harnesses groups of human intelligence to help do things.

But think about where this could go: For example, the system could actually be built right into applications --  for example, imagine if in Photoshop there was a new menu command for "fix this image" that charged you a dollar and farmed the image out to 2 or 3 humans who each attempted to improve the image. It would function just like a filter, but instead of software doing the work it would be humans. For you, the end-user, it would be functionally equivalent. You would get 3 versions of your adjusted image back in a few minutes and could choose the best one or use them all.

The idea of building in menu options into software and services that actually trigger behaviors among networks of humans is very interesting.

But to do this well you really need and API that all applications can use to harness "human intelligence" and "human functions" in their apps. One the best proposals for how to do this more  is here.  And an update about that is here.

Turing's Cathedral

George Dyson wrote a nice piece on his impressions from a visit to Google, and some speculations about the future of AI on the Net.

Beautiful Idea: How to Program the Global Mind

I just read this really cool idea about how to design a programming language for the global brain -- think of it as grid computing, but where some of the agents in the grid are humans and others are computers, working together to solve problems. I've had similar ideas to this over the years, for example the use of collaborative networks to mark up and tag content on the Semantic Web, as well as various forms of expertise referral networks. What I like about this new proposal is that it suggests an actual language for writing global mind programs. That's a new angle. Brilliant.

A Machine That Sees The Future?

This article provides an overview of the Global Consciousness Project at Princeton, which has found that the behavior of a network of specially shielded random number generators deviates from stasticial randomness prior to major world events. I have been following this project for several years and have made various suggestions for further experiments to test the system. It is very intriguing.

A Stock Market for Ideas

Media Mammon  is a new stock market for memes. You can invest play money in words and phrases that are spreading through the media. May the best meme win! See also: A Physics of Ideas.

Brain Study Reveals Differences Between Semantic and Episodic Memory

This interesting new brain study reveals processing differences between Semantic Memory and Episodic Memory in human brains. Nature performs these functions differently, and there is probably a good reason why that is so. On the Web we don't really have an equivalent of Episodic Memory or Semantic Memory yet... but we're working on it!

New Technique for Analyzing Sentiment in Texts

Researchers at Cornell have come up with a clever new way to determine the sentiment expressed in textual data. Their method relies of separating objective statements from subjective statements, and then just measuring the subjective ones. This results in more accurate measures of sentiment.

Use of Role Classes to Define Predicate Semantics: Proposal for Semantic Web Best-Practice

This article proposes a design pattern for ontologies and the Semantic Web based on the concept of formally defined Roles as a means to richly express the semantics of relationships among entities in ontologies. Roles are special types of n-ary relations, and thus the use of Roles is a subset of the Semantic Web best-practices recommendation for N-Ary Relations.

The Semantic Web relies on ontologies - formal definitions of the meaning of various concepts. For example, an ontology could define the formal meaning of the term "Person" -- specifically, that a "Person" is a "Human" that has a "First Name" and a "Last Name" and has "Legal Status," "Friends" and a "Gender" and many other attributes. Each of these attributes could be further defined specifically -- for example, "a Friend" is a different Person who is "Socially-related" to the former Person and "Has Met" that Person at least once, and "is Liked by" and "Trusted by" that Person. Each of these predicates, such as "Socially-related," "Trusted by," and "Has Met" may or may not be further defined, depending on the structure of particular ontologies.

Most simple ontologies use binary relations to express predicates that connect things together. More complex and sophisticated ontologies, such as the ones I have developed for the Radar Platform and my work with SRI and DARPA and the University of Texas Clib ontology project, instead only cast the most basic building-block predicates with object and data type relations (in OWL). Instead, most relations (including even those that could be expressed with simple object properties) are defined using special classes called Roles. This moves much of the weight of expressing how classes interconnect from properties to Role classes.

While using Roles instead of simple object properties introduces certain minor complexities -- such as the requirement to model N-ary relations, and thus Roles, such that they can be used in place of object properties to connect instances of classes -- it results in even more important benefits. In particular, a major benefit is that the use of classes to represent Role relations enables far more expressive ontologies to be developed. This method is even more expressive than the potential use of additional facets on properties. While adding special additional facets to properties is certainly one way to augment the semantics of predicates, it still is not as richly expressive as simply using Role classes instead of properties for most relations. The use of Role classes enables ontolology designers to create rich ontologies of relations, such that every relation that is modeled by a Role can be formally defined as a concept with respect to other entities and relations in the ontology. In other words, it enables a much richer semantics to be defined for the domain.

I propose that the use of Role classes to define the semantics of various types of relations among entities (including among relations themselves) should be a Semantic Web Best Practice and should be adopted in all but the most simplistic ontologies. The rest of this article explains why I believe this in more detail.

Video Explains Semantic Web + A Comment

Ben Hammersly has come out with a video of his talk explaining the Semantic Web for beginners. It's a great resource to explain what the Semantic Web is all about for people who are new to the subject or simply interested in the underlying technology of RDF, the concept of triples, etc. It is also unique because Ben's video appears in the upper left corner of his presentation, synched to his slides -- now that is really cool! What a great idea!

Now my reaction to this presentation is that Ben makes many good points, but he says the only remaining big problems of the Semantic Web are "getting people to make data" and the "user-interface" for search and what he calls "compound queries" -- what I call "semantic queries" (and what have been called "multifaceted navigation" by library scientists for a long-time). However, I think there is another unsolved problem -- one that is equally important to adoption of the Semantic Web, what I call "The Ontology Problem." See my next post for more on this...

My "A Physics of Ideas" Manifesto has been Published!

Change This, a project that helps to promote interesting new ideas so that they get noticed above the noise level of our culture has published my article on "A Physics of Ideas" as one of their featured Manifestos. They use an innovative PDF layout for easier reading, and they also provide a means for readers to provide feedback and even measure the popularity of various Manifestos. I'm happy this paper is getting noticed finally -- I do think the ideas within it have potential. Take a look.

Great Article on Psychohistory and Sociophysics -- Can We Predict Behavior?

Great find from Rob Usey at Psydex Corporation: This article is a survey of the emerging field of "sociophysics" which attempts to apply statistical mechanics to predict human social behavior. It's very cool stuff if you're interested in social networks, memes, sociology and prediction science. The article discusses recent progress towards Isaac Asimov's vision for a science of Psychohistory as proposed in his Foundation stories. This relates in many ways to my previous article on "A Physics of Ideas" in which I proposed some elementary ways to measure the trajectories of memes as if they were moving particles in a Newtonian system.

Minding the Planet: From Semantic Web to Global Mind

Draft 1.1 for Review (integrates some fixes from readers)
Nova Spivack (www.mindingtheplanet.net)

INTRODUCTION

This article presents some thoughts about the future of intelligence on Earth. In particular, I discuss the similarities between the Internet and the brain, and how I believe the emerging Semantic Web will make this similarity even greater.

DISTRIBUTED INTELLIGENCE

The Semantic Web enables the formal communication of a higher level of language -- metalanguage. Metalanguage is language about language -- language that encodes knowledge about how to interpret and use information. Metalanguages – particularly semantic metalanguages for encoding relationships between information and systems of concepts – enable a new layer of communication and processing. The combination of computing networks with semantic metalanguages represents a major leap in the history of communication and intelligence.

The invention of written language long ago changed the economics of communication by making it possible for information to be represented and shared independently of human minds. This made it less costly to develop and spread ideas widely across populations in space and time. Similarly, the emergence of software based on semantic metalanguages will dramatically change the economics not only of information distribution, but of intelligence -- the act of processing and using information.

Semantic metalanguages provide a way to formally express, distribute and share the knowledge necessary to interpret and use information, independently of the human mind. In other words, they make it possible not just to write down and share information, but also to encode and share the background necessary for intelligently making use of that information. Prior to the invention of such a means to share this background knowledge about information, although information could be written and shared, the recipients of such information had to be intelligent and appropriately knowledgeable in advance in order to understand it. Semantic metalanguages remove this restriction by making it possible to distill the knowledge necessary to understand information into a form that can be shared just as easily as the information itself.

The recipients of information – whether humans or software – no longer have to know in advance (or attempt to deduce) how to interpret and use the information; this knowledge is explicitly coded in the metalanguage about the information. This is important for artificial intelligence because it means that expertise for specific domains does not have to be hard-coded into programs anymore -- instead programs simply need to know how to interpret the metalanguage. By adding semantic metalanguage statements to information data becomes “smarter,” and programs can therefore become “thinner.” Once programs can speak this metalanguage they can easily import and use knowledge about any particular domain, if and when needed, so long as that knowledge is expressed in the metalanguage.

In other words, whereas basic written languages simply make raw information portable, semantic metalanguages make knowledge (conceptual systems) and even intelligence (procedures for processing knowledge) about information portable. They make it possible for knowledge and intelligence to be formally expressed, stored digitally, and shared independently of any particular minds or programs. This radically changes the economics of communicating knowledge and of accessing and training intelligence. It makes it possible for intelligence to be more quickly, easily and broadly distributed across time, space and populations of not only humans but also of software programs.

The emergence of standards for sharing semantic metalanguage statements that encode the meaning of information will catalyze a new era of distributed knowledge and intelligence on the Internet. This will effectively “make the Internet smarter.” Not just monolithic expert systems and complex neural networks, but even simple desktop programs and online software agents will begin to have access to a vast decentralized reserve of knowledge and intelligence.

The externalization, standardization and sharing of knowledge and intelligence in this manner, will make it possible for communities of humans and software agents to collaborate on cognition, not just on information. As this happens and becomes increasingly linked into our daily lives and tools, the "network effect" will deliver increasing returns. While today most of the intelligence on Earth still resides within human brains, In the near future, perhaps even within our lifetimes, the vast majority of intelligence will exist outside of human brains on the Semantic Web.

THE INTERNET IS A BRAIN AND THE WEB IS ITS MIND

Anyone familiar with the architecture and dynamics of the human nervous system cannot help but notice the striking similarity between the brain and the Internet. But is this similarity more than a coincidence - is the Internet really a brain in its own right - the brain of our planet? And is its collective behavior intelligent - does it constitute a global mind? How might this collective form of intelligence compare to that of an individual human mind, or a group of human minds?

I believe that the Internet (the hardware) is already evolving into a distributed global brain, and its ongoing activity (the software, humans and data) represents the cognitive process of an increasingly intelligent global mind. This global mind is not centrally organized or controlled, rather it is a bottom-up, emergent, self-organizing phenomenon formed from flows of trillions of information-processing events comprised of billions of independent information processors.

As with other types of emergent computing systems, for example John Conway’s familiar cellular automaton “The Game of Life,” on the Internet large scale homeostatic systems and seemingly intentional or guided information processes naturally emerge and interact within it. The emergence of sophisticated information systems does not require top-down design or control, it can happen in an evolutionary bottom-up manner as well.

Like a human brain, the Internet is a vast distributed computing network comprised of billions of interacting parallel processors. These processors include individual human beings as well as software programs, and systems of them such as organizations, which can all be referred to as "agents" in this system. Just as the computational power of the human brain as a whole is vastly greater than that of any of the individual neurons or systems within it, the computational power of the Internet is vastly beyond any of the individual agents it contains. Just as the human brain is not merely the sum of its parts, the Internet is more than the sum of its parts - like other types of distributed emergent computing systems, it benefits from the network effect. The power of the system grows exponentially as agents and connections between them are added.

The human brain is enabled by an infrastructure comprised of networks of organic neurons, dendrites, synapses and protocols for processing chemical and electrical messages. The Internet is enabled by an infrastructure of synthetic computers, communications networks, interfaces, and protocols for processing digital information structures. The Internet also interfaces with organic components however – the human beings who are connected to it. In that sense the Internet is not merely an inorganic system – it could not function without help from humans, for the moment at least. The Internet may not be organized in exactly the same form as the human brain, but it is at least safe to say it is an extension of it.

The brain provides a memory system for storing, locating and recalling information. The Internet also provides shared address spaces and protocols for using them. This enables agents to participate in collaborative cognition in a completely decentralized manner. It also provides a standardized shared environment in which information may be stored, addressed and retrieved by any agent of the system. This shared information space functions as the collective memory of the global mind.

Just as no individual neuron in the human brain could be said to have the same form or degree of intelligence as the brain as-a-whole - we individual humans cannot possibly comprehend the distributed intelligence that is evolving on the Internet. But we are part of it nonetheless, whether we know it or not.  The global mind is emerging all around us, and via us, is our creation but it is already becoming independent of us - truly it represents the evolution of a new form of meta-level intelligence that has never before existed on our planet.

Although we created it, the Internet is already far beyond our control or comprehension - it surrounds us and penetrates our world - it is inside our buildings, our tools, our vehicles, and it connects us together and modulates our interactions. As this process continues and the human body and biology begins to be networked into this system we will literally become part of this network - it will become an extension of our nervous systems and eventually, via brain-computer interfaces, it will be an extension of our senses and our minds. Eventually the distinction between humans and machines, and the individual and the collective, will gradually start to dissolve, along with the distinction between human and artificial forms of intelligence.