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Foresight Update 5 (page 3)

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Table of Contents - Foresight Update 5

AI Directions

by K. Eric Drexler

Artificial intelligence, like nanotechnology, will reshape our future. Nanotechnology means thorough, inexpensive control of the structure of matter, and early assemblers will enable us to build better assemblers: this will make it a powerful and self-applicable technology. Artificial intelligence (that is, genuine, general-purpose artificial intelligence) will eventually bring millionfold-faster problem solving ability, and, like nanotechnology, it will be self-applicable: early AI systems will help solve the problem of building better, faster AI systems.

AI differs from nanotechnology in that its basic principles are not yet well understood. Although we have the example of human brains to show that physical systems can be (at least somewhat) intelligent, we don't understand how brains work or how their principles might be generalized. In contrast, we do understand how machines and molecules work and how to design many kinds of molecular machines. In nanotechnology, the chief challenge is developing tools so that we can build things; in AI, the chief challenge is knowing what to build with the tools we have.

To get some sense of the possible future of AI--where research may go, and how fast--one needs a broad view of where AI research is today. This article gives a cursory survey of some major areas of activity, giving a rough picture of the nature of the ideas being explored and of what has been accomplished. It will inevitably be superficial and fragmentary. For descriptive purposes, most current work can be clumped into three broad areas: classical AI, evolutionary AI, and neural networks.

Table of Contents - Foresight Update 5

Classical AI

Since its inception, mainstream artificial intelligence work has tried to model thought as symbol manipulation based on programmed rules. This field has a huge literature; good sources of information include a textbook (Artificial Intelligence by Patrick Winston, Addison-Wesley, 1984) and two compilations of papers (Readings in Artificial Intelligence, Bonnie Lynn Webber, Nils J. Nilsson, eds., Morgan Kaufmann, 1981, and Readings in Knowledge Representation, Ronald J. Brachman, Hector J. Levesque, eds., Morgan Kaufmann, 1985).

The standard criticism of AI systems of this sort is that they are brittle, rather than flexible. One would like a system that can generalize from its knowledge, know its limits, and learn from experience. Existing systems lack this flexibility: they break down when confronted with problems outside a narrow domain, and they must be programmed in painful detail. Work continues on alternative ways to represent knowledge and action, seeking systems with greater flexibility and a measure of common sense. (A learning program called Soar [ also this link ], developed by Allen Newell of Carnegie Mellon University in collaboration with John Laird and Paul Rosenbloom, is prominent in this regard.) In the meantime, systems have been built that can provide expert-level advice (diagnosis, etc.) within certain narrow domains. Though not general and flexible, they represent achievements of real value. Many of these so-called "expert systems" are in commercial use, and many more are under construction.

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Evolutionary AI

When one reads "artificial intelligence" in the media, the term typically refers to expert systems. If this were the whole of AI, it would still be important, but not potentially revolutionary. The great potential of AI lies in systems that can learn, going beyond the knowledge spoon-fed to them by human experts.

The most flexible and promising learning schemes are based on evolutionary processes, on the variation and selection of patterns. Doug Lenat's EURISKO program used this principle, applying heuristics (rules of thumb) to solve problems and to vary and select heuristics. It achieved significant successes, but Lenat concluded that it lacked sufficient initial knowledge. He has since turned to a different project, CYC, which aims to encode the contents of a single-volume encyclopedia--along with the commonsense knowledge needed to make sense of it--in representations of the sort used in classical AI work.

Another approach to evolutionary AI, pioneered by John Holland, involves classifier systems modified by genetic algorithms. A classifier system uses a large collection of rules, each defined by a sequence of ones, zeroes, and don't-care symbols. A rule "fires" (produces an output sequence) when its sensor-sequence matches the output of a previous rule; a collection of rules can support complex behavior. Rules can be made to evolve through genetic algorithms, which make use of mutation and recombination (like chromosome crossover in biology) to generate new rules from old. This work, together with a broad theoretical framework, is described in the book Induction: Processes of Inference, Learning, and Discovery (by John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard, MIT Press, 1986). So far as I know, these systems are still limited to research use.

Mark S. Miller and I have proposed an agoric approach to evolving software, including AI software. If one views complex, active systems as being composed of a network of active parts, the problem of obtaining intelligent behavior from the system can be recast as the problem of coordinating and guiding the evolution of those parts. The agoric approach views this as analogous to the problem of coordinating economic activity and rewarding valuable innovation; accordingly, it proposes the thorough application of market mechanisms to computation. The broader agoric open systems approach would invite and reward human involvement in these computational markets, which distinguishes it from the "look Ma--no hands!" approach to machine intelligence. These ideas are described in three papers ("Comparative Ecology: A Computational Perspective," "Markets and Computation: Agoric Open Systems," and "Incentive Engineering for Computational Resource Management") included in a book on the broader issues of open computational systems (The Ecology of Computation, B. A. Huberman, ed., in Studies in Computer Science and Artificial Intelligence, North-Holland, 1988).

Ted Kaehler of Apple Computer has used agoric concepts in an experimental learning system initially intended to predict future characters in a stream of text (including written dates, arithmetic problems, and the like). Called "Derby," in part because it incorporates a parimutuel betting system, this system also makes use of neural network principles.

Table of Contents - Foresight Update 5

Neural nets

Classical AI systems work with symbols and cannot solve problems unless they have been reduced to symbols. This can be a serious limitation.

For a machine to perceive things in the real world, it must interpret messy information streams--taking information representing a sequence of sounds and finding words, taking information representing a pattern of light and color and finding objects, and so forth. To do this, it must work at a pre-symbolic or sub-symbolic level; vision systems, for example, start their work by seeking edges and textures in patterns of dots of light that individually symbolize nothing.

The computations required for such tasks typically require a huge mass of simple, repetitive operations before patterns can be seen in the input data. Conventional computers simply do one operation at a time, but these operations can be done by many simpler devices operating simultaneously. Indeed, these operations can be done as they are in the brain--by neurons (or neuron-like devices), each responding in a simple way to inputs from many neighbors, and providing outputs in turn.

Recent years have seen a boom in neural network research. Different projects follow diverse approaches, but all share a "connectionist" style in which significant patterns and actions stem not from symbols and rules, but from the cooperative behavior of large numbers of simple, interconnected units. These units roughly resemble neurons, though they are typically simulated on conventional computers, and the resemblance in behavior is often very rough indeed. Neural networks have shown many brain-like properties, performing pattern recognition, recovering complete memories from fragmentary hints, tolerating noisy signals or internal damage, and learning--all within limits, and subject to qualification. A variety of neural network models are described in the two volumes of Parallel Distributed Processing: Explorations in the Microstructure of Cognition (edited by David E. Rummelhart and James L. McClelland, MIT Press, 1986). Neural network systems are beginning to enter commercial use. Some characteristics of neural networks have been captured in more conventional computer programs (Efficient Algorithms with Neural Network Behavior, by Stephen M. Omohundro, Report UIUCDCS-R-87-1331, Department of Computer Science, University of Illinois at Urbana-Champaign, 1987).

Webmaster's Note: Some WWW sources of information on neural nets:
A major strength of the neural-network approach is that it is patterned on something known to work--the brain. From this perspective a major weakness of most current systems is that they don't very closely resemble real neuronal networks. Computational models inspired by brain research are described in a broad, readable book on AI, philosophy, and the neurosciences (Neurophilosophy, by Patrica Smith Churchland, MIT Press, 1986) and in a more difficult work presenting a specific theory (Neural Darwinism, by Gerald Edelman, Basic Books, 1987). A bundle of insights based on AI and the neurosciences appears in The Society of Mind (by Marvin Minsky, Simon and Schuster, 1986).

Table of Contents - Foresight Update 5

Some observations

For all its promise and successes, AI has hardly revolutionized the world. Machines have done surprising things, but they still don't think in a flexible, open-ended way. Why has success been so limited?

One reason is elementary: as robotics researcher Hans Moravec of Carnegie-Mellon University has noted, for most of its history, AI research has attempted to embody human-like intelligence in computers with no more raw computational power than the brain of an insect. Knowing as little as we do about the requirements for intelligence, it makes sense to try to embody it in novel and efficient ways. But if one fails to make an insect's worth of computer behave with human intelligence--well, it's certainly no surprise.

Machine capacity has increased exponentially for several decades, and if trends continue, it will match the human brain (in terms of raw capacity, not necessarily of intelligence!) in a few more decades. Meanwhile, researchers work with machines that are typically in the sub-microbrain range. What are the prospects for getting intelligent behavior from near-term machines?

If machine intelligence should require slavish imitation of brain activity at the neural level, then machine intelligence will be a long time coming. Since brains are the only known systems with general intelligence, this is the proper conservative assumption, which I made for the sake of argument at one point in Engines of Creation. Nonetheless, just as assemblers will enable construction of many materials and devices that biological evolution never stumbled across, so human programmers may be able to build novel kinds of intelligent systems. Here we cannot be so sure as in nanotechnology, since here we do not know what to build, yet novel systems seem plausible. It is, I believe, reasonable to speculate that there exist forms of spontaneous order in neural-style systems that were never tested by evolution--indeed, that may make little biological sense--and that some of these are orders of magnitude better (in speed of learning, efficiency of computation, or similar measures) than today's biological systems. Stepping outside the neural realm for a moment, Steve Omohundro (see above) has found algorithms that outperform conventional neural networks in certain learning and mapping tasks by factors of millions or trillions.

Algorithms of neural style may exist that were never tested by evolution

Thus, although there is good reason to explore brain-like neural networks, there is also good reason to explore novel systems. Indeed, some of the greater successes in current neural network research involve multi-level versions of "back-propagation" learning schemes that seem rather nonbiological (and Omohundro's algorithms seem entirely nonbiological).

In summary, AI research is rich in diverse, promising approaches. Our ignorance of our degree of ignorance precludes any accurate estimate of how long it will take to develop genuine, flexible artificial intelligence (of the sort that could build better AI systems and design novel computers and nanomechanisms). If genuine AI requires understanding the brain and developing computers a million times more powerful than today's, then it is likely to take a long time. If genuine AI can emerge through the discovery of more efficient spontaneous-order processes (or through the synergistic coupling of those already being studied separately) then it might emerge next month, and shake the world to its foundations the month after.

In this, as in so many areas of the future, it will not do to form a single expectation and pretend that it is likely ("We will certainly have genuine AI in about 20 years"--poppycock!). Rather, we must recognize our uncertainty and keep in mind a range of expectations, a range of scenarios for how the future may unfold. Genuine AI may come very soon, or very late; it is more likely to come sometime in between. Since we don't know what we're doing, it's hard to guess the rate of advance. Sound foresight in this area means planning for multiple contingencies.

Table of Contents - Foresight Update 5

Foresight thanks Dave Kilbridge for converting Update 5 to html for this web page.

From Foresight Update 5, originally published 1 March 1989.
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