The Internet Supermind and Beyond

Ben Goertzel & Stephan Vladimir Bugaj

July 2000

Nearly everyone who has seriously thought about the evolution of technology over the next few hundred years has come to the same conclusion: We live at a crucial point in history – an incredibly exciting and frightening point; a point that is stimulating to the point of excess, intellectually, philosophically, physically and emotionally. A number of really big technologies are brewing. Virtual reality, which lets us create synthetic worlds equal in richness to the physical world, thus making the Buddhist maxim “reality is illusion” a palpable technical fact. Biotechnology, allowing us to modify our bodies in various ways, customizing our genes and jacking our brains, organs and sense organs into computers and other devices. Nanotechnology, allowing us to manipulate molecules directly, creating biological, computational, micromechanical, and other kinds of systems that can barely be imagined today. Artificial intelligence, enabling mind, intelligence and reason to emerge out of computer systems – thinking machines built by humans. And advances in unified field theory in physics will in all likelihood join the party, clarifying the physical foundation of life and mind, and giving the nanotechnologists new tricks no one has even speculated about yet.

Even immortality is not completely out of the question. As Eric Drexler argued in Engines of Creation, nano-scale robots could swarm through your body repairing your aging cells. Or, as Hans Moravec depicted in his classic book Mind Children, brain scan technology combined with AI could have us uploading our minds into computers once our bodies wear down. It sounds like science fiction, but it’s entirely scientifically plausible: these would be immense engineering achievements, but wouldn’t violate any known scientific laws. A lot of seemingly impossible things will soon be possible.

Bill Joy, Chief Scientist of Sun Microsystems, one of the leading companies in the current phase of the tech revolution, recently wrote an article in Wired painting this same kind of future, but with a markedly dystopian bent. He believes all this amazing technological development will happen, and he finds it intensely scary. It’s difficult to blame him, actually. The potential for abuse of such technologies is obvious. We have to hope that an ethical evolution comparable to the technological evolution will occur at the same time, beautifully synchronized. This is essentially what Ray Kurzweil foresees in The Age of Spiritual Machines. Kurzweil think its all going to happen in the next 20 years. By comparison, we consider ourselves realists: we think it may take 50 or so, although in 20 years we’ll certainly have moved a long way from where we are now.

The Internet is part of this heady mix. It’s a low-tech virtual reality itself, sucking more and more of our time and attention away from the physical world. It’s both a brain and a perceptual world for artificial intelligence systems. And it’s a tool for accelerating technical progress in every possible direction, enabling unprecedentedly efficient communication and information retrieval.

This evolving network of long-term possibilities is wonderful and amazing to think about, but, on the other hand, it’s almost too big for any one mind or small group of minds to grapple with. Imagine a bunch of pre-linguistic proto-humans trying to comprehend what the advent of language was going to do to them. That’s basically the kind of situation we’re in! Nevertheless, in spite of the difficulty intrinsic in foresight and the impossibility of planning any kind of revolution in advance, least of all the technological and psychocultural kind, there’s something we can do beside sit back and watch as history leads us on. We can focus on particular aspects of the revolution we’re wreaking, understanding these aspects as part of the whole, and also focusing hard on the details, remembering that, historically, some of the subtlest and most profound general humanistic insights have come from dealing with very particular issues.

What the authors of this article have been working on, for the past few years, is what we believe will be the first component of the emerging tech revolution to fall fully into place: artificial intelligence.

Of course, we realize this is a gutsy statement. The field of artificial intelligence isn’t all that fashionable these days; and this is understandable enough. AI’s biggest claim to fame isn’t any of its particular achievements – beating Kasparov at chess, diagnosing diseases better than doctors, mastering integral calculus -- but rather its incredibly persistent habit of over-promising and under-delivering. This is always fatal in the business world, and not very favorable among academic circles either.

But, over the last decade, we’ve studied this history carefully, along with the theoretical foundations of AI, and the practical aspects of implementing intelligence on modern computers. And we believe that AI is finally ready to outgrow its history of big brags and false starts. AI’s time has finally come.

In fact, we’ll venture even further out on our limb. Within the next few years, we believe, there will emerge the first real AI systems. The Webmind system that we’re building at Intelligenesis Corp. will be one of them, but there may well be others. And within the next few decades this technological advance will induce fundamental changes in the human condition, transforming the way we view ourselves as thinking beings, the way we interact with each other through electronic communication networks -- the way we work, the way we feel, the way we live.

Of course, AI is only part of the huge transformation we humans are bringing down on ourselves. Eventually AI will cease to exist as a discipline in itself, becoming diffused in the general matrix of technological innovation. Every new technology will be intelligent in one way or another, and AI will inform nanotech, biotech, virtual reality, unified physics, chemistry, refrigerators, toasters – you name it. But everything has to start somewhere. Life originally started with a few thousand molecules huddling together, perhaps inside a water droplet, developing primitive reproductive and metabolic abilities. Life has gone far beyond that now, growing to encompass things as complex and fantastic as you and me. But the simplest incarnations of life still have something to teach us, because they share so many properties with what has grown from them. Like proto-cells in the primordial soup, the AI programs of the next decade will be the first, primitive, early versions of a whole new order of being. Viewing them with enough imagination, one will be able to get at least a murky, muddled glimpse of what’s awakening within and around us.

The ongoing acceleration of AI development is obvious from technical advances in the Internet industry over the last few years, if one takes the time to study it. Take the case of search engines for example – or in the lingo of computer science insiders, “information retrieval” tools. At the tail end of the technological race for intelligent information retrieval, one has standard search engines like Alta Vista and Yahoo. These have basically no intelligence whatsoever. They rely on pure bulk of information. Then you have moderately intelligent search tools made by firms like LexiQuest and Autonomy, that apply various specialized algorithms to grasp something of the meaning of texts. This is where things have stood for a long time. But then, over the last year, something new has arisen: A dozen or so start-up firms have come to prominence with technology that goes beyond this, and tries to understand text in a more thorough and flexible way, building subtle “semantic maps” describing of the meanings of documents. There are shortcomings in all this work: Talavara, for example, has one of the best AI search systems around, but it focuses too much on the syntactic analysis of documents, and uses a semantic map that isn’t nearly as flexible as the corresponding structures in the human mind. Similarly, WorldFree’s Know-All product does a good job of answering a variety of questions, but it uses an overly rigid “ontology” for representing knowledge – a fixed set of categories, not a flexible one like exists in the human mind.

None of these firms have really come to grips with the problem of reasoning on the semantic maps that their AI systems glean from reading text. But this is the next step. Over the next two years, you’ll likely see firms coming out with sophisticated reasoning engines hooked up to their syntax understanding and semantic mapping engines. Then, a couple years after that, people will realize that reason isn’t enough – that you need intuition as well – and the problem of synthesizing reason and intuition in a single flexible adaptive intelligent system will become paramount. And so on. Our Webmind system provides a way of leapfrogging much of this incremental development, because it’s been designed up-front with a synergetic model of all the mind’s functions, rather than incrementally adding functions based on competitive business needs. However, the main point where the evolution of AI is concerned isn’t the coolness of Webmind or any other particular AI system – it’s that we now have practical commercial problems, such as Web search, that are being solved by AI technologies; and that because of this, AI technologies are improving at a fever pitch. Even the major search engine companies – the technology dinosaurs of the Internet business -- have started to jump on board, with AltaVista releasing an AI-supercharged smart search site last month. It isn’t all pie in the sky anymore. The PR departments of these various firms have a way of avoiding the word “AI”, but that’s exactly what it is. Of course, it isn’t real AI yet – not truly adaptive, flexible, self-aware intelligence -- but the path from current AI products to a real thinking computer program isn’t all that difficult to map out, if you have a feel for the terrain.

What has made this onslaught of AI improvements possible? AI researchers haven’t gotten any more brilliant … and the technical teams of these various AI Internet firms don’t tend have any profound new insights into digital mind. Rather, they’re mostly implementing ideas that have been around in academia for quite a while. What’s sparked the current burst of development is, quite simply, hardware -- the tremendous accelerations in computer hardware that have occurred over the past two decades. There have been vague theories on how to make AI work for a long time, but without hardware adequate for implementing the theories, there was little incentive to make them precise and work out all the details. Now the hardware is there, and what isn’t there will be there in a few years. Computers with gigabytes of RAM cost only a few thousand dollars, and networking technology lets anyone build their own distributed supercomputer. We take for granted that we need to buy a new computer every two years because the old ones so rapidly get useless – but think about how amazing that is! What if the same were true of cars or refrigerators or musical instruments?

This hardware acceleration won’t bring us biotech or nanotech or unified physics; it won’t even bring us virtual reality, which requires much better human sensory interfaces than we have today. But it will bring us artificial intelligence. And AI, once we have it, will help launch the other component technologies of the emerging tech revolution. AI will help accelerate biotech, by helping us to understand how the DNA sequences mapped out in the Human Genome Project actually combine to give rise to self-organizing organisms like you and me. Biotech will allow us to jack computers into our bodies in new ways, enabling truly visceral virtual reality experiences. Eventually maybe AI’s will be pressed into service solving the numerous hard engineering problems required to make nanotech actually work. Everything will fall into place during the next 100 years or so; but we believe that AI will happen first.

This makes thinking carefully about AI a high priority. And the best tool we know for thinking about the nature of AI systems -- and the series of technological and psychocultural effects these systems are fated to unleash -- is the idea of the Metasystem Transition. This notion has a long history in the philosophy of many cultures, but it was most clearly crystallized and formulated in the work of the extraordinary Russian philosopher-scientist Valentin Turchin. A metasystem transition, as Turchin defines it, is a point in the history of evolution of a system where the whole comes to dominate the parts.

According to current physics theories, there was a Metasystem Transition in the early universe, when the primordial miasma of disconnected particles cooled down and settled into atoms. All of a sudden the atom was the thing, and individual stray particles weren’t the key tool to use in modeling the universe. Once particles are inside atoms, the way to understand what particles are doing is to understand the structure of the atom. And then there was another transition from atoms to molecules, leading to the emergence, within the first few instants after the Big Bang, of the Periodic Table of Elements.

There was a metasystem transition on earth around four billion years ago, when the steaming primordial seas caused inorganic chemicals to clump together in groups capable of reproduction and metabolism. Unicellular life emerged, and once chemicals are embedded in life-forms, the way to understand them is not in terms of chemistry alone, but rather, in terms of concept like fitness, evolution, sex, and hunger. Concepts like desire and intention are not far off, even with paramecia: Does the paramecium desire its food? Maybe not … but it comes a lot closer than a rock does to desiring to roll down a hill….

And there was another metasystem transition when multicellular life burst forth – suddenly the cell is no longer an autonomous life form, but rather a component in a life form on a higher level. The Cambrian explosion, immediately following this transition, was the most amazing flowering of new patterns and structures ever seen on Earth – even we humans haven’t equaled it yet. 95% of the species that arose at this time are now extinct, and paleontologists are slowly reconstructing them so we can learn their lessons.

Note that the metasystem transition is not an antireductionist concept, in the strict sense. The idea isn’t that multicellular lifeforms have cosmic emergent properties that can’t be explained from the properties of cells. Of course, if you had enough time and superhuman patience, you could explain what happens in a human body in terms of the component cells. The question is one of naturalness and comprehensibility, or in other words, efficiency of expression. Once you have a multicellular lifeform, it’s much easier to discuss and analyze the properties of this lifeform by reference to the emergent level than by going down to the level of the component cells. In a puddle full of paramecia, on the other hand, the way to explain observed phenomena is usually by reference to the individual cells, rather than the whole population – the population has less wholeness, fewer interesting properties, than the individual cells.

In the domain of mind, there are also a couple levels of metasystem transition. The first one is what we call the emergence of “mind modules.” This is when a huge collection of basic mind components – cells, in a biological brain; “software objects” in a computer mind – all come together in a unified structure to carry out some complex function. The whole is greater than the sum of the parts: the complex functions that the system performs aren’t really implicit in any of the particular parts of the system, rather they come out of the coordination of the parts into a coherent whole. The various parts of the human visual system are wonderful examples of this. Billions of cells firing every which way, all orchestrated together to do one particular thing: map visual output from the retina into a primitive map of lines, shapes and colors, to be analyzed by the rest of the brain. The best current AI systems are also examples of this. In fact, computer systems that haven’t passed this transition I’d be reluctant to call “AI” in any serious sense.

There are some so-called AI systems that haven’t even reached this particular transition – they’re really just collections of rules, and each behavior in the whole system can be traced back to one particular rule. But consider a sophisticated natural language system like LexiQuest – which tries to answer human questions, asked in ordinary language, based on information from databases or extracted from texts. In a system like this, we do have mind module emergence. When the system parses a sentence and tries to figure out what question it represents, it’s using hundreds of different rules for parsing, for finding out what various parts of the sentences mean. The rules are designed to work together, not in isolation. The control parameters of each part of the system are tuned so as to give maximal overall performance. LexiQuest isn’t a mind, but it’s a primitive mind module, with its own, albeit minimal, holistic emergence. The same is true of other current high-quality systems for carrying out language processing, computer vision, industrial robotics, and so forth. For an example completely different from LexiQuest, look at the MIT autonomous robots built under the direction of Rodney Books. These robots seem to exhibit some basic insect-level intelligence, roaming around the room trying to satisfy their goals, displaying behavior patterns that surprise their programmers. They’re action-reaction modules, not minds, but they have holistic structures and dynamics all their own.

On roughly the same level as LexiQuest and Brooks’ robots, we find computational neural networks, which carry out functions like vision or handwriting recognition or robot locomotion using hundreds up to hundreds of thousands of chunks of computer memory emulating biological neurons. As in the brain, the interesting behavior isn’t in any one neuron, it’s in the whole network of neurons, the integrative system. There are dozens of spin-offs from the neural network concepts, such as the Bayesian networks used in products like Autonomy and the Microsoft Help system. Bayesian networks are networks of rules capable of making decisions such as "If the user asks about ‘spreadsheet’, activate the Excel help system". The programmer of such a system never enters a statement where the rule "if the word spreadsheet occurs, activate the help system" appears -- rather this rule emerges from the dynamics of the network. However, the programmer sets up the network in a way that fairly rigidly controls what kinds of rules can emerge. So while the system can discover new patterns of input behavior that seem to indicate what actions should be taken, it is unable to discover new kinds of actions which can be taken – that is, it can only discover new instances of information, not new types of information. It’s not autonomous, not alive.

Each of the modules of our Webmind AI system has roughly the same level of sophistication as one of these bread-and-butter AI programs. Webmind has modules that carry out reasoning, language processing, numerical data analysis, financial prediction, learning, short-term memory, and so forth. Webmind’s modules are all built of the same components, Java software objects called “nodes” and “links” and “wanderers” and “stimuli.” They arrange these components in different ways, so that each module achieves its own emergent behavior, each module realizing a metasystem transition on its own.

But mind modules aren’t real intelligence, not in the sense that we mean it: Intelligence as the ability to carry out complex goals in complex environments. Each mind module only does one kind of thing, requiring inputs of a special type to be fed to it, unable to dynamically adapt to a changing environment. Intelligence itself requires one more metasystem transition: the coordination of a collection of mind modules into a whole mind, each module serving the whole and fully comprehensible only in the context of the whole. This is a domain that AI research has basically not confronted yet – it it’s not mere egotism to assert that the Webmind system is almost unique in this regard. It takes a lot of man-hours, a lot of thinking, and a lot of processing power to build a single mind module, let alone to build a bunch of them – and even more to build a bunch of them in such a way as to support an integrative system passing the next metasystem transition. We’re just barely at the point now, computer-hardware-wise, that we can seriously consider doing such a thing. But even being just barely there is a pretty exciting thing.

Webmind allows the interoperation of these intelligent modules within the context of a shared semantic representation – nodes, links and so forth. Through the shared semantic representation these different intelligent components can interact and thus evolve a dynamical state which is not possible within any one of the modules. Like a human brain, each specialized sub-system is capable of achieving certain complex perceptual (such as reading a page of text) or cognitive (such as inferring causal relations) goals which in themselves seem impressive - but when they are integrated, truly exciting new possibilities emerge. Taken in combination, these intelligent modules embodying systems such as reasoning, learning and natural language processing, etc. undergo a metasystem transition to become a mind capable of achieving complex goals worthy of comparison to human abilities. The resulting mind can not be described merely as a pipeline of AI process modules, rather it has its own dynamical properties which emerge from the interactions of these component parts, creating new and unique patterns which were not present in any of the sub-systems. The Webmind system isn’t complete yet – the first complete version will be launched in mid 2001 – but we’ve created various products based on various simple combinations of the modules: a text categorization application, a market prediction tool, a search engine, etc. Each of these systems exhibits a lower-level of metasystem transition, but each is a building-block in creating the emergent whole of the actual Webmind.

Such a metasystem transition from modules to mind is a truly exciting emergence. A system such as Webmind can autonomously adapt to changes in more complex environments than their single-module predecessors, and can be trained in a manner which is more like training a human than programming a computer. This kind of a system theoretically can be adapted to any task for which it is able to perceive input, and while the initial Webmind system operates an a world of text and numerical files only, integrating it with visual and auditory systems, and perhaps a robot body, would allow it to have some facility to perform in the physical world as well. Applications of even the text and data constrained system are quite varied and exciting, such as autonomous financial analysis, conversational information retrieval, true knowledge extraction from text and data, etc.

While there are other systems that can find some interesting patterns in input data, a mind can determine the presence of previously unknown types of patterns and make judgments that are outside the realm of previous experience. An example of this can be seen in financial market analysis. Previously unknown market forces, such as the Internet, can impact various financial instruments in ways which prevent successful trading using traditional market techniques. A computer mind can detect this new pattern of behavior, and develop a new technique based on inferring how the current situation relates to, but also differs from, from previous experience. The Webmind market predictor already does this, to a limited extent, through the emergence of new behaviors from the integration of only a few intelligent modules. As more modules are integrated the system becomes more intelligent. Currently the Webmind market predictor can create trading strategies in terms of long, short, and hold positions on instruments, detect changes in the market environment (using both numerical indicators and by reading news feeds), and develop new strategies based on these changes.

For another short-term, real-world example of the promise of computational mind, let’s return to the area of information retrieval. What we really want isn’t a search engine – we want a digital assistant, with an understanding of context and conversational give-and-take like a human assistant provides. AskJeeves tries to provide this, but ultimately it’s just a search engine/ chat-bot hybrid. It’s amusing enough, but quite far from the real possibilities in this area. A mind-based conversational search tool, as will be possible using the completed Webmind system, will be qualitatively different. When an ambiguous request is made of a mind, it does not blindly return some information pulled out of a database; a mind asks questions to resolve ambiguous issues, using its knowledge of your mind as well as the subject area to figure out what questions to ask. When you ask a truly intelligent system “find me information about Java”, it will ask back a question such as “do you want information about the island, the coffee, or the computer programming language?” But if it knows you’re a programmer, it should ask instead “Do you want to know about JVM’s or design patterns or what?” Like a human, a machine which has no exposure to the information that there is an island called Java, for example, might only ask about coffee and computers, but the ability to make a decision to resolve the ambiguity in the first place, in a context-appropriate way, is a mark of intelligence. An intelligent system will use its background knowledge and previous experience to include similar information (Java, J++, JVM, etc.), omit misleading information (JavaScript, a totally different programming language from Java), and analyze the quality of the information. Information retrieval segues into information creation, when a program infers new information by combining the information available in the various documents it reads, providing users with this newly created information as well as reiterating what humans have written.

These practical applications are important, but it’s worth remembering that the promise of digital mind goes beyond these simple short-term considerations. Consider, for example, the fact that digital intelligences have the ability to acquire new perception systems during the course of their lives. For instance, an intelligent computer system to be attached to a bubble chamber and given the ability to directly observe elementary particle interactions. Such a system could greatly benefit particle physics research, as the system would be able to think directly about the particle world, without having to resort to metaphorical interpretations of instrument readings as humans must do. Similar advantages are available to computers in terms of understanding financial and economic data, and recognizing trends in vast bodies of text.

This metasystem transition – from mind modules to mind – is the one that the authors have spent most of our time thinking about during the last couple years. But it’s by no means the end of the story. When Turchin formulated the metasystem transition concept, he was actually thinking about something quite different – the concept of the global brain, an emergent system formed from humans and AI systems both, joined together by the Internet and other cutting-edge communication technologies. It’s a scary idea, and a potent one. Communication technology makes the world smaller each day – will it eventually make it so small that the network of people has more intrinsic integrity than any individual person? Shadows of the sci-fi notion of a “hive mind” arise here… images of the Borg Collective from Star Trek. But what Turchin is hoping for is something much more benign: a social structure that permits us our autonomy, but channels our efforts in more productive directions, guided by the good of the whole.

Interestingly, Turchin himself is somewhat pessimistic about the long-term consequences of all this, but not in quite the alarmist vein of Bill Joy -- more in the spirit of a typically Russian ironic doubt in human nature. In other words, Bill Joy believes that high tech may lead us down the road to hell, so we should avoid it; whereas Turchin sees human nature itself as the really dangerous thing, leading us to possible destruction through nuclear, biological, or chemical warfare, or some other physical projection of our intrinsic narrow-mindedness and animal hostility. He hopes that technological advancement will allow us to overcome some of the shortcomings of human nature and thus work toward the survival and true mental health of our race. Through his Principia Cybernetica project, co-developed with Francis Heylighen (of the Free University of Brussels) and Cliff Joslyn (of Los Alamos National Labs in the US), he’s sought to develop a philosophical understanding to go with the coming tech revolution, grounded on the concept of the metasystem transition. As he says, the goal with this is “to develop -- on the basis of the current state of affairs in science and technology -- a complete philosophy to serve as the verbal, conceptual part of a new consciousness.” But this isn’t exactly being done with typical American technological optimism. Rather, as Turchin puts it, “My optimistic scenario is that a major calamity will happen to humanity as a result of the militant individualism; terrible enough to make drastic changes necessary, but, hopefully, still mild enough not to result in a total destruction. Then what we are trying to do will have a chance to become prevalent. But possible solutions must be carefully prepared.”

With this in mind, it’s interesting to note that over the last couple years, Turchin has devoted most of his time to a highly technical but extremely important aspect of the technological revolution: making computer programs run faster. He now lives in New Jersey, and together with his friends Yuri Mostovoy and Andrei Klimov, has started a company Supercompilers LLC, based in New Jersey and Moscow. Admittedly it’s a bit odd for a Russian academic in his 70’s to be masterminding an Internet company, but that’s the wonderful thing about our time: the bizarre quickly becomes routine, accelerating innovation, and opening eyes and minds. Turchin is building a “supercompiler” that will enable Java programs to run 10 to 100 times faster than they normally do now, and use less memory as well. It’s a wonderful piece of technology, that works, in a sense, by recognizing metasystem transitions inside software itself, and using them to improve software performance. It could only have been developed in Russia, where hardware advances were slow and everyone was always using inefficient, obsolete computers -- thus ingenious methods for speeding up programs were highly worthwhile. The importance of this kind of work for the future of AI and the Internet in general cannot be underestimated. Right now the first supercompiler is probably a year from completion; and in the couple years following that, the supercompiler will likely be hybridized with Webmind, yielding an intelligent computer program that continually rewrites its own code -- as if human beings could continually optimize their own DNA in order to improve their own functionality and the nature of their offspring. Oh brave new world that has such programs in it! And, brave new business environment that allows such projects to be funded in the world of commerce, thus accelerating development far beyond what it would be if they had to proceed at the snail’s pace of academic research.

As we see it, the path from the Net that we have today to the global brain that envelops humans and machines in a single overarching superorganism involves not one but several metasystem transitions. The first one is the emergence of the global web mind – the transformation of the Internet into a coherent organism. Currently the best way to explain what happens on the Net is to talk about the various parts of the Net: particular Websites, e-mail viruses, shopping bots, and so forth. But there will come a point when this is no longer the case, when the Net has sufficient high-level dynamics of its own that the way to explain any one part of the Net will be by reference to the whole. This will come about largely through the interactions of AI systems – intelligent programs acting on the behalf of various Websites, Web users, corporations, and governments will interact with each other intensively, forming something halfway between a society of AI’s and an emergent mind whose lobes are various AI agents serving various goals. The traditional economy will be dead, replaced by a chaotically dynamical hypereconomy (a term coined by the late transhumanist theorist Alexander Chislenko) in which there are no intermediaries except for information intermediaries: producers and consumers (individually or in large aggregates created by automatic AI discovery of affinity groups) negotiate directly with each other to establish prices and terms, using information obtained from subtle AI prediction and categorization algorithms. How far off this is we can’t really tell, but it would be cowardly not to give an estimate: we’re betting no more than 10 years.

The advent of this system will be gradual. Initially when only a few AI systems are deployed on the Web, they will be individual systems which are going to be overwhelmed with their local responsibilities. As more agents are added to the Net, there will be more interaction between them. Systems which specialize will refer questions to each other. For example, a system that specialized in (had a lot of background knowledge and evolved and inferred thinking processes about) financial analysis may refer questions about political activities to political analyst systems, and then combine this information with its own knowledge to synthesize information about the effects of political events on market activity. This hypereconomic system of Internet agents will dynamically establish the social and economic value of all information and activities within the system, through interaction amongst all agents in the system. As these interactions become more complex, agent interconnections become more prevalent and more dynamic, and agents become more interdependent the network will become more of a true shared semantic space: a global integrated mind-organism. Individual systems will start to perform activities which have no parallel in the existing natural world. One AI mind will directly transfer knowledge to another by literally sending it a “piece of its mind”; an AI mind will be able to directly sense activities in many geographical locations and carry on multiple context-separated conversations simultaneously; a single global shared-memory will emerge allowing explicit knowledge sharing in a collective consciousness. Across the millions, someday billions, of machines on the Internet, this global Web mind will function as a single collective thought space, allowing individual agents to transcend their individual limitations and share directly in a collective consciousness, extending their capabilities far beyond their individual means.

All this is fabulous enough – collective consciousness among AI systems; the Net as a self-organizing intelligent information space. But yet, it’s after this metasystem transition – from Internet to global hypereconomic Web mind -- that the transition envisioned by Turchin and his colleages at Principia Cybernetica can take place: the effective fusion of the global Web mind and the humans interacting with it. It will be very interesting to see where biotech-enabled virtual reality technology is at this point. At what point will we really be jacking our brains into the global AI matrix, as in Gibson’s novel Neuromancer? At what point will we supercompile and improve our own cognitive functions, or be left behind by our constantly self-reprogramming AI compatriots? But we don’t even need to go that far. Putting these more science-fictional possibilities aside and focusing solely on Internet AI technology, it’s clear that more and more of our interactions will be mediated by the global emergent intelligent Net – every appliance we use will be jacked into the matrix; every word that we say potentially transmitted to anyone else on the planet using wearable cellular telephony or something similar; every thought that we articulate entered into an AI system that automatically elaborates it and connects it with things other humans and AI agents have said and thought elsewhere in the world – or things other humans and AI agents are expected to say based on predictive technology…. The Internet Supermind is not the end of the story – it’s only the initial phase; the seed about which will crystallize a new order of mind, culture and technology. Is this going to be an instrument of fascist control, or factional terrorism? It’s possible, but certainly not inevitable – and the way to avoid this is for as many people as possible to understand what’s happening, what’s likely to happen, and how they can participate in the positive expansion of this technology.

Imagine: human and machine identities joined into the collective mind, creating a complex network of individuals from which emerges the dynamics of a global supermind, with abilities and boundaries far greater than would be possible for any individual mind, human or artificial – or any community consisting of humans or AI’s alone. As Francis Heylighen has said, “Such a global brain will function as a nervous system for the social superorganism, the integrated system formed by the whole of human society.” Through this global human-digital emergent mind, we will obtain a unique perspective on the world, being able to simultaneously sense and think in many geographical locations and potentially across many perceptual media (text, sound, images, and various sensors on satellites, cars, bubble chambers, etc.) The cliché “let’s put our minds together on this problem” will become a reality, allowing people and machines to pool their respective talents directly to solve tough problems in areas ranging from theoretical physics to social system stabilization, and to create interesting new kinds of works in literature and the arts.

Weird? Scary? To be sure. Exciting? Amazing? To be sure, as well. Inevitable? An increasing number of techno-visionaries think so. Some, like Bill Joy, have retreated into neo-Ludditism, believing that technology is a big danger and advocating careful legal control of AI, nanotech, biotech and related things. Turchin is progressing ahead as fast as possible, building the technology needed for the next phase of the revolution, careful to keep an eye on the ethical issues as he innovates, hoping his pessimism about human nature will be proved wrong. As for us, we tend to be optimists. Life isn’t perfect, plants and animals aren’t perfect, humans aren’t perfect, computers aren’t perfect – but yet, the universe has a wonderful way of adapting to its mistakes and turning even ridiculous errors into wonderful new forms.

The dark world of tyranny and fear described in the writings of cyberpunk authors like William Gibson and Bruce Sterling, and in films such as The Matrix and Blade Runner, is certainly a possibility. But there’s also the possibility of less troubling relationships between humans and their machine counterparts, such as we see in the writings of transrealist authors like Rudy Rucker and Stanislaw Lem, and in film characters like Star Trek’s Data and Star Wars’ R2-D2 and C3P0. We believe, through ethical treatment of humans, machines, and information, that a mutually beneficial human-machine union within a global society of mind can be achieved. The ethical and ontological issues of identity, privacy, and selfhood are every bit as interesting and challenging as the engineering issues of AI, and we need to avoid the tendency to set them aside because they’re so difficult to think about. But these things are happening – right now we’re at the beginning, not the end, of this revolution; and the potential rewards are spectacular -- enhanced perception, greater memory, greater cognitive capacity, and the possibility of true cooperation among all intelligent beings on earth.

One might say, flippantly, ”Hold on tight, humanity -- you’ve built this great rollercoaster, and now you’re in for one hell of a ride!” But from an even bigger perspective, we’ve been riding the rollercoaster of universal evolution all along. The metasystem transitions that gave rise to human bodies and human intelligence were important steps along the path, but there will be other steps, improving and incorporating humanity and ultimately going beyond it.