(from http://www.singinst.org/LOGI/ , definitely worth reading en toto)
What is intelligence? In humans, intelligence is a brain with a hundred billion neurons and a hundred trillion synapses; a brain in which the cerebral cortex alone is organized into 52 cytoarchitecturally distinct areas per hemisphere. Intelligence is not the complex expression of a simple principle; intelligence is the complex expression of a complex set of principles. Intelligence is a supersystem composed of many mutually interdependent subsystems - subsystems specialized not only for particular environmental skills but for particular internal functions. The heart is not a specialized organ that enables us to run down prey; the heart is a specialized organ that supplies oxygen to the body. Remove the heart and the result is not a less efficient human, or a less specialized human; the result is a system that ceases to function.
Why is intelligence? The cause of human intelligence is evolution - the operation of natural selection on a genetic population in which organisms reproduce differentially depending on heritable variation in traits. Intelligence is an evolutionary advantage because it enables us to model, predict, and manipulate reality. Evolutionary problems are not limited to stereotypical ancestral contexts such as fleeing lions or chipping spears; our intelligence includes the ability to model social realities consisting of other humans, and the ability to predict and manipulate the internal reality of the mind. Philosophers of the mind sometimes define "knowledge" as cognitive patterns that map to external reality [Newell80], but a surface mapping has no inherent evolutionary utility. Intelligence requires more than passive correspondence between internal representations and sensory data, or between sensory data and reality. Cognition goes beyond passive denotation; it can predict future sensory data from past experience. Intelligence requires correspondences strong enough for the organism to choose between futures by choosing actions on the basis of their future results. Intelligence in the fully human sense requires the ability to manipulate the world by reasoning backward from a mental image of the desired outcome to create a mental image of the necessary actions. (In Part II, these ascending tests of ability are formalized as sensory, predictive, decisive, and manipulative bindings between a model and a referent.)
Understanding the evolution of the human mind requires more than classical Darwinism; it requires the modern "neo-Darwinian" or "population genetics" understanding of evolution - the Integrated Causal Model set forth by [Tooby92]. One of the most important concepts in the ICM is that of "complex functional adaptation". Evolutionary adaptations are driven by selection pressures acting on genes. A given gene's contribution to fitness is determined by regularities of the total environment, including both the external environment and the genetic environment. Adaptation occurs in response to statistically present genetic complexity, not just statistically present environmental contexts. A new adaptation that requires the presence of a previous adaptation cannot spread unless the prerequisite adaptation is present in the genetic environment with sufficient statistical regularity to make the new adaptation a recurring evolutionary advantage. Evolution uses existing genetic complexity to build new genetic complexity, but evolution exhibits no foresight. Evolution does not construct genetic complexity unless it is an immediate advantage, and this is a fundamental constraint on accounts of the evolution of complex systems.
Complex functional adaptations - adaptations that require multiple genetic features to build a complex interdependent system in the phenotype - are usually, and necessarily, universal within a species. Independent variance in each of the genes making up a complex interdependent system would quickly reduce to insignificance the probability of any phenotype possessing a full functioning system. To give an example in a simplified world, if independent genes for "retina", "lens", "cornea", "iris", and "optic nerve" each had an independent 20% frequency in the genetic population, the random-chance probability of any individual being born with a complete eyeball would be 1 in 3125.
Natural selection, while feeding on variation, uses it up [Sober84]. The bulk of genetic complexity in any single organism consists of a deep pool of panspecies complex functional adaptations, with selection pressures operating on a surface froth of individual variations. The target matter of Artificial Intelligence is not the surface variation that makes one human slightly smarter than another human, but rather the vast store of complexity that separates a human from an amoeba. We must avoid distraction by the surface variations that occupy the whole of our day-to-day social universe. The differences between humans are the points on which we compete and the features we use to recognize our fellows, and thus it is easy to slip into paying them too much attention.
A still greater problem for would-be analysts of panhuman complexity is that the foundations of the mind are not open to introspection. We perceive only the highest levels of organization of the mind. You can remember a birthday party, but you cannot remember your hippocampus encoding the memory.
Is either introspection or evolutionary argument relevant to AI? To what extent can truths about humans be used to predict truths about AIs, and to what extent does knowledge about humans enable us to create AI designs? If the sole purpose of AI as a research field is to test theories about human cognition, then only truths about human cognition are relevant. But while human cognitive science constitutes a legitimate purpose, it is not the sole reason to pursue AI; one may also pursue AI as a goal in its own right, in the belief that AI will be useful and beneficial. From this perspective, what matters is the quality of the resulting intelligence, and not the means through which it is achieved. However, proper use of this egalitarian viewpoint should be distinguished from historical uses of the "bait-and-switch technique" in which "intelligent AI" is redefined away from its intuitive meaning of "AI as recognizable person", simultaneously with the presentation of a AI design which leaves out most of the functional elements of human intelligence and offers no replacement for them. There is a difference between relaxing constraints on the means by which "intelligence" can permissibly be achieved, and lowering the standards by which we judge the results as "intelligence". It is thus permitted to depart from the methods adopted by evolution, but is it wise?
Evolution often finds good ways, but rarely the best ways. Evolution is a useful inspiration but a dangerous template. Evolution is a good teacher, but it's up to us to apply the lessons wisely. Humans are not good examples of minds-in-general; humans are an evolved species with a cognitive and emotional architecture adapted to hunter-gatherer contexts and cognitive processes tuned to run on a substrate of massively parallel 200Hz biological neurons. Humans were created by evolution, an unintelligent process; AI will be created by the intelligent processes that are humans.
Because evolution lacks foresight, complex functions cannot evolve unless their prerequisites are evolutionary advantages for other reasons. The human evolutionary line did not evolve toward general intelligence; rather, the hominid line evolved smarter and more complex systems that lacked general intelligence, until finally the cumulative store of existing complexity contained all the tools and subsystems needed for evolution to stumble across general intelligence. Even this is too anthropocentric; we should say rather that primate evolution stumbled across a fitness gradient whose path includes the subspecies Homo sapiens sapiens, which subspecies exhibits one particular kind of general intelligence.
The human designers of an AI, unlike evolution, will possess the ability to plan ahead for general intelligence. Furthermore, unlike evolution, a human planner can jump sharp fitness gradients by executing multiple simultaneous actions; a human designer can use foresight to plan multiple new system components as part of a coordinated upgrade. A human can take present actions based on anticipated forward compatibility with future plans.
Thus, the ontogeny of an AI need not recapitulate human phylogeny. Because evolution cannot stumble across grand supersystem designs until the subsystems have evolved for other reasons, the phylogeny of the human line is characterized by development from very complex non-general intelligence to very complex general intelligence through the layered accretion of adaptive complexity lying within successive levels of organization. In contrast, a deliberately designed AI is likely to begin as a set of subsystems in a relatively primitive and undeveloped state, but nonetheless already designed to form a functioning supersystem1. Because human intelligence is evolutionarily recent, the vast bulk of the complexity making up a human evolved in the absence of general intelligence; the rest of the system has not yet had time to adapt. Once an AI supersystem possesses any degree of intelligence at all, no matter how primitive, that intelligence becomes a tool which can be used in the construction of further complexity.
Where the human line developed from very complex non-general intelligence into very complex general intelligence, a successful AI project is more likely to develop from a primitive general intelligence into a complex general intelligence. Note that primitive does not mean architecturally simple. The right set of subsystems, even in a primitive and simplified state, may be able to function together as a complete but imbecilic mind which then provides a framework for further development. This does not imply that AI can be reduced to a single algorithm containing the "essence of intelligence". A cognitive supersystem may be "primitive" relative to a human and still require a tremendous amount of functional complexity.
I am admittedly biased against the search for a single essence of intelligence; I believe that the search for a single essence of intelligence lies at the center of AI's previous failures. Simplicity is the grail of physics, not AI. Physicists win Nobel Prizes when they discover a previously unknown underlying layer and explain its behaviors. We already know what the ultimate bottom layer of an Artificial Intelligence looks like; it looks like ones and zeroes. Our job is to build something interesting out of those ones and zeroes. The Turing formalism does not solve this problem any more than quantum electrodynamics tells us how to build a bicycle; knowing the abstract fact that a bicycle is built from atoms doesn't tell you how to build a bicycle out of atoms - which atoms to use and where to put them. Similarly, the abstract knowledge that biological neurons implement human intelligence does not explain human intelligence. The classical hype of early neural networks, that they used "the same parallel architecture as the human brain", should, at most, have been a claim of using the same parallel architecture as an earthworm's brain. (And given the complexity of biological neurons, the claim would still have been wrong.)
"The science of understanding living organization is very different from physics or chemistry, where parsimony makes sense as a theoretical criterion. The study of organisms is more like reverse engineering, where one may be dealing with a large array of very different components whose heterogeneous organization is explained by the way in which they interact to produce a functional outcome. Evolution, the constructor of living organisms, has no privileged tendency to build into designs principles of operation that are simple and general."
-- Leda Cosmides and John Tooby, "The Psychological Foundations of Culture" [Tooby92]
The field of Artificial Intelligence suffers from a heavy, lingering dose of genericity and black-box, blank-slate, tabula-rasa concepts seeping in from the Standard Social Sciences Model (SSSM) identified by [Tooby92]. The general project of liberating AI from the clutches of the SSSM is more work than I wish to undertake in this paper, but one problem that must be dealt with immediately is physics envy. The development of physics over the last few centuries has been characterized by the discovery of unifying equations which neatly underlie many complex phenomena. Most of the past fifty years in AI might be described as the search for a similar unifying principle believed to underlie the complex phenomenon of intelligence.
Physics envy in AI is the search for a single, simple underlying process, with the expectation that this one discovery will lay bare all the secrets of intelligence. The tendency to treat new approaches to AI as if they were new theories of physics may at least partially explain AI's past history of overpromise and oversimplification. Attributing all the vast functionality of human intelligence to some single descriptive facet - that brains are "parallel", or "distributed", or "stochastic"; that minds use "deduction" or "induction" - results in a failure (an overhyped failure) as the project promises that all the functionality of human intelligence will slide out from some simple principle.
The effects of physics envy can be more subtle; they also appear in the lack of interaction between AI projects. Physics envy has given rise to a series of AI projects that could only use one idea, as each new hypothesis for the one true essence of intelligence was tested and discarded. Douglas Lenat's AM and EURISKO programs [Douglas83] - though the results were controversial and may have been mildly exaggerated [Ritchie84] - nonetheless used very intriguing and fundamental design patterns to deliver significant and unprecedented results. Despite this, the design patterns of EURISKO, such as self-modifying decomposable heuristics, have seen almost no reuse in later AIs. Even Lenat's subsequent Cyc project [Lenat86] apparently does not reuse the ideas developed in EURISKO. From the perspective of a modern-day programmer, accustomed to hoarding design patterns and code libraries, the lack of crossfertilization is a surprising anomaly. One would think that self-optimizing heuristics would be useful as an external tool, e.g. for parameter tuning, even if the overall cognitive architecture did not allow for the internal use of such heuristics. The AI field seems to have treated EURISKO as a failed hypothesis, or even a competing hypothesis, rather than an incremental success or a reusable tool.
The most common paradigms of traditional AI - search trees, neural networks, genetic algorithms, evolutionary computation, semantic nets - have in common the property that they can be implemented without requiring a store of preexisting complexity. The processes that have become traditional, that have been reused, are the tools that stand alone and are immediately useful. A semantic network is a "knowledge" representation so simple that it is literally writable on paper. An AI project adding a semantic network need not design a hippocampus-equivalent to form memories, nor build a sensory modality to represent mental imagery. The traditional AI processes accompanying semantic nets - such as theorem proving, case-based reasoning, production systems, and expert systems - are again standalone algorithms. Neural networks and evolutionary computations are not generally intelligent but they are generically intelligent; they can be trained on any problem that has a sufficiently shallow fitness gradient relative to available computing power. (Though EURISKO's self-modifying heuristics probably had generality equalling or exceeding these more typical tools, the source code was not open and the system design was far too complex to build over an afternoon, so the design pattern was not reused - or so I would guess.)
The standalone nature of the traditional processes may make them useful tools for shoring up the initial stages of a general AI supersystem - with the exception of the semantic network; I regard semantic nets as poisonous to AI research for reasons which should shortly become clear. But standalone algorithms are not substitutes for intelligence and they are not complete systems. Genericity is not the same as generality.
"Physics envy" (trying to replace the human cognitive supersystem with a single process or method) should be distinguished from the less ambitious attempt to clean up the human mind design while leaving the essential architecture intact. Cleanup is probably inevitable while human programmers are involved, but it is nonetheless a problem to be approached with extreme caution. Although the population genetics model of evolution admits of many theoretical reasons why the presence of a feature may not imply adaptiveness (much less optimality), in practice the adaptationists usually win. The spandrels of San Marco may not have been built for decorative elegance [Gould79], but they are still holding the roof up. Cleanup should be undertaken, not with pride in the greater simplicity of human design relative to evolutionary design, but with a healthy dose of anxiety that we will leave out something important.
An example: Humans are currently believed to have a modular adaptation for visual face recognition, generally identified with a portion of inferotemporal cortex, though this is a simplification [Rodman99]. At first glance this brainware appears to be an archetypal example of human-specific functionality, an adaptation to an evolutionary context with no obvious analogue for an early-stage AI. However, [Carey92] has suggested from neuropathological evidence (associated deficits) that face recognition brainware is also responsible for the generalized task of acquiring very fine expertise in the visual domain; thus, the dynamics of face recognition may be of general significance for builders of sensory modalities.
Another example is the sensory modalities themselves. As described in greater detail in Part II, the human cognitive supersystem is built to require the use of the sensory modalities which we originally evolved for other purposes. One good reason why the human supersystem uses sensory modalities is that the sensory modalities are there. Sensory modalities are evolutionarily ancient; they would have existed, in primitive or complex form, during the evolution of all higher levels of organization. Neural tissue was already dedicated to sensory modalities, and would go on consuming ATP2 even if inactive, albeit at a lesser rate. Consider the incremental nature of adaptation, so that in the very beginnings of hominid intelligence only a very small amount of de novo complexity would have been involved; consider that evolution has no inherent drive toward design elegance; consider that adaptation is in response to the total environment, which includes both the external environment and the genetic environment - these are all plausible reasons to suspect evolution of offloading the computational burden onto pre-existing neural circuitry, even where a human designer would have chosen to employ a separate subsystem. Thus, it was not inherently absurd for AI's first devotees to try for general intelligence that employed no sensory modalities.
Today we have at least one reason to believe that nonsensory intelligence is a bad approach; we tried it and it didn't work. Of course this is far too general an argument - it applies equally to "we tried non-face-recognizing intelligence and it didn't work" or even "we tried non-bipedal intelligence and it didn't work". The argument's real force derives from specific hypotheses about the functional role of sensory modalities in general intelligence (discussed in Part II). But in retrospect we can identify at least one methodological problem: Rather than identifying the role played by modalities in intelligence, and then attempting to "clean up" the design by substituting a simpler process into the functional role played by modalities3, the first explorers of AI simply assumed that sensory modalities were irrelevant to general intelligence.
Leaving out key design elements, without replacement, on the basis of the mistaken belief that they are not relevant to general intelligence, is an error that displays a terrifying synergy with "physics envy". In extreme cases - and most historical cases have been extreme - the design ignores everything about the human mind except one characteristic (logic, distributed parallelism, fuzziness, etc.), which is held to be "the key to intelligence".
I argue strongly for "supersystems", but I do not believe that "supersystems" are the necessary and sufficient Key to AI. Human general intelligence requires the right supersystem, with the right cognitive subsystems, doing the right things in the right way. Humans are not intelligent by virtue of being "supersystems", but by virtue of being a particular supersystem which implements human intelligence. I emphasize supersystem design because I believe that the field of AI has been crippled by the wrong kind of simplicity - a simplicity which, as a design constraint, rules out workable designs for intelligence; a simplicity which, as a methodology, rules out incremental progress toward an understanding of general intelligence; a simplicity which, as a viewpoint, renders most of the mind invisible except for whichever single aspect is currently promoted as the Key to AI.
If the quest for design simplicity is to be "considered harmful"4, what should replace it? I believe that rather than simplicity, we should pursue sufficiently complex explanations and usefully deep designs. In ordinary programming, there is no reason to assume a priori that the task is enormously large. In AI the rule should be that the problem is always harder and deeper than it looks, even after you take this rule into account. Knowing that the task is large does not enable us to meet the challenge just by making our designs larger or more complicated; certain specific complexity is required, and complexity for the sake of complexity is worse than useless. Nonetheless, the presumption that we are more likely to underdesign than overdesign implies a different attitude towards design, in which victory is never declared, and even after a problem appears to be solved, we go on trying to solve it. If this creed were to be summed up in a single phrase, it would be: "Necessary but not sufficient." In accordance with this creed, it should be emphasized that supersystems thinking is only one part of a larger paradigm, and that an open-ended design process is itself "necessary but not sufficient". These are first steps toward AI, but not the only first steps, and certainly not the last steps.
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1: This does not rule out the possibility of discoveries in cognitive science occurring through less intentional and more evolutionary means. For example, a commercial AI project with a wide range of customers might begin with a shallow central architecture loosely integrating domain-specific functionality across a wide variety of tasks, but later find that their research tends to produce specialized internal functionality hinting at a deeper, more integrated supersystem architecture.
2: Adenine triphosphate, the standard unit of currency in the economy of the human metabolism.
3: I cannot think of any plausible way to do this, and do not advocate such an approach.
4: A phrase due to [Dijkstra68] in "Go To Statement Considered Harmful"; today it indicates that a prevalent practice has more penalties than benefits and should be discarded.
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