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Tuesday, December 26, 2006

Reading List: On Intelligence

Hawkins, Jeff with Sandra Blakeslee. On Intelligence. New York: Times Books, 2004. ISBN 0-8050-7456-2.
Ever since the early days of research into the sub-topic of computer science which styles itself “artificial intelligence”, such work has been criticised by philosophers, biologists, and neuroscientists who argue that while symbolic manipulation, database retrieval, and logical computation may be able to mimic, to some limited extent, the behaviour of an intelligent being, in no case does the computer understand the problem it is solving in the sense a human does. John R. Searle's “Chinese Room” thought experiment is one of the best known and extensively debated of these criticisms, but there are many others just as cogent and difficult to refute.

These days, criticising artificial intelligence verges on hunting cows with a bazooka—unlike the early days in the 1950s when everybody expected the world chess championship to be held by a computer within five or ten years and mathematicians were fretting over what they'd do with their lives once computers learnt to discover and prove theorems thousands of times faster than they, decades of hype, fads, disappointment, and broken promises have instilled some sense of reality into the expectations most technical people have for “AI”, if not into those working in the field and those they bamboozle with the sixth (or is it the sixteenth) generation of AI bafflegab.

AI researchers sometimes defend their field by saying “If it works, it isn't AI”, by which they mean that as soon as a difficult problem once considered within the domain of artificial intelligence—optical character recognition, playing chess at the grandmaster level, recognising faces in a crowd—is solved, it's no longer considered AI but simply another computer application, leaving AI with the remaining unsolved problems. There is certainly some truth in this, but a closer look gives lie to the claim that these problems, solved with enormous effort on the part of numerous researchers, and with the application, in most cases, of computing power undreamed of in the early days of AI, actually represents “intelligence”, or at least what one regards as intelligent behaviour on the part of a living brain.

First of all, in no case did a computer “learn” how to solve these problems in the way a human or other organism does; in every case experts analysed the specific problem domain in great detail, developed special-purpose solutions tailored to the problem, and then implemented them on computing hardware which in no way resembles the human brain. Further, each of these “successes” of AI is useless outside its narrow scope of application: a chess-playing computer cannot read handwriting, a speech recognition program cannot identify faces, and a natural language query program cannot solve mathematical “word problems” which pose no difficulty to fourth graders. And while many of these programs are said to be “trained” by presenting them with collections of stimuli and desired responses, no amount of such training will permit, say, an optical character recognition program to learn to write limericks. Such programs can certainly be useful, but nothing other than the fact that they solve problems which were once considered difficult in an age when computers were much slower and had limited memory resources justifies calling them “intelligent”, and outside the marketing department, few people would remotely consider them so.

The subject of this ambitious book is not “artificial intelligence” but intelligence: the real thing, as manifested in the higher cognitive processes of the mammalian brain, embodied, by all the evidence, in the neocortex. One of the most fascinating things about the neocortex is how much a creature can do without one, for only mammals have them. Reptiles, birds, amphibians, fish, and even insects (which barely have a brain at all) exhibit complex behaviour, perception of and interaction with their environment, and adaptation to an extent which puts to shame the much-vaunted products of “artificial intelligence”, and yet they all do so without a neocortex at all. In this book, the author hypothesises that the neocortex evolved in mammals as an add-on to the old brain (essentially, what computer architects would call a “bag hanging on the side of the old machine”) which implements a multi-level hierarchical associative memory for patterns and a complementary decoder from patterns to detailed low-level behaviour which, wired through the old brain to the sensory inputs and motor controls, dynamically learns spatial and temporal patterns and uses them to make predictions which are fed back to the lower levels of the hierarchy, which in turns signals whether further inputs confirm or deny them. The ability of the high-level cortex to correctly predict inputs is what we call “understanding” and it is something which no computer program is presently capable of doing in the general case.

Much of the recent and present-day work in neuroscience has been devoted to imaging where the brain processes various kinds of information. While fascinating and useful, these investigations may overlook one of the most striking things about the neocortex: that almost every part of it, whether devoted to vision, hearing, touch, speech, or motion appears to have more or less the same structure. This observation, by Vernon B. Mountcastle in 1978, suggests there may be a common cortical algorithm by which all of these seemingly disparate forms of processing are done. Consider: by the time sensory inputs reach the brain, they are all in the form of spikes transmitted by neurons, and all outputs are sent in the same form, regardless of their ultimate effect. Further, evidence of plasticity in the cortex is abundant: in cases of damage, the brain seems to be able to re-wire itself to transfer a function to a different region of the cortex. In a long (70 page) chapter, the author presents a sketchy model of what such a common cortical algorithm might be, and how it may be implemented within the known physiological structure of the cortex.

The author is a founder of Palm Computing and Handspring (which was subsequently acquired by Palm). He subsequently founded the Redwood Neuroscience Institute, which has now become part of the Helen Wills Neuroscience Institute at the University of California, Berkeley, and in March of 2005 founded Numenta, Inc. with the goal of developing computer memory systems based on the model of the neocortex presented in this book.

Some academic scientists may sniff at the pretensions of a (very successful) entrepreneur diving into their speciality and trying to figure out how the brain works at a high level. But, hey, nobody else seems to be doing it—the computer scientists are hacking away at their monster programs and parallel machines, the brain community seems stuck on functional imaging (like trying to reverse-engineer a microprocessor in the nineteenth century by looking at its gross chemical and electrical properties), and the neuron experts are off dissecting squid: none of these seem likely to lead to an understanding (there's that word again!) of what's actually going on inside their own tenured, taxpayer-funded skulls. There is undoubtedly much that is wrong in the author's speculations, but then he admits that from the outset and, admirably, presents an appendix containing eleven testable predictions, each of which can falsify all or part of his theory. I've long suspected that intelligence has more to do with memory than computation, so I'll confess to being predisposed toward the arguments presented here, but I'd be surprised if any reader didn't find themselves thinking about their own thought processes in a different way after reading this book. You won't find the answers to the mysteries of the brain here, but at least you'll discover many of the questions worth pondering, and perhaps an idea or two worth exploring with the vast computing power at the disposal of individuals today and the boundless resources of data in all forms available on the Internet.

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