[Paleopsych] Meme 32: AI Magazine: What are intelligence? And why?

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Meme 32: AI Magazine: What are intelligence? And why?
sent 4.10.5

[This article doesn't say much about artificial intelligence as such, but 
it's the best piece I've seen on theories of the evolution of human 
intelligence (though the article was written before Geoffrey Miller's idea 
that Darwin's other mechanism, sexual selection, came along. Also 
discussed is intelligence in other animals.]

What are intelligence? And why? - Innovative Applications of AI
AI Magazine 19(1) (1998 Spring): 91 ff.

by Randall Davis

     Relax, there's no mistake in the title. I've indulged a bit of
     British-English that I've always found intriguing: the use of the
     plural verb with collective nouns (as in "Oxford have won the themes

     The selection of verb sense is purposeful and captures one of the main
     themes of the article: I want to consider intelligence as a collective
     noun. I want to see what we in Al have thought of it and review the
     multiple ways in which we've conceived of it. My intention is to make
     explicit the assumptions, metaphors, and models that underlie our
     multiple conceptions.

     I intend to go back to basics here, as a way of reminding us of the
     things that we, individually, and collectively, have taken as given,
     in part because we have taken multiple different, and sometimes
     consistent, things for granted. I believe it will prove useful to
     expose the tacit assumptions, models, and metaphors that we carry
     around, as a way of understanding both what we're about and why we
     sometimes seem to be at odds with one another. That's the first part
     of the article.

     In the second part of the article, I'll ask you to come along on a
     natural history tour -- I'm going to take you away, back to a time
     around 4 million years ago when the first hominids arose and consider
     how intelligence came to be. We'll take an evolutionary view, consider
     intelligence as a natural phenomenon, and ask why it arose. The vague
     answer -- that it provided enhanced survival -- turns out not to be
     very informative; so, we'll ask, why is intelligence, and more
     important, what does that tell us about how we might proceed in AI?

     The third part of the article is concerned with what we might call
     inhuman problem solving; it explores to what degree intelligence is a
     human monopoly. In this part of the article, AI learns about the birds
     and the bees: What kinds of animal intelligence are there, and does
     that, too, inform our search for human intelligence?

     I'll end by considering how we might expand our view, expand our
     exploration of intelligence by exploring aspects of it that have
     received too little attention. AI has been doing some amount of
     consolidation over the past few years, so it may well be time to
     speculate where the next interesting and provocative leaps might be

Fundamental Elements

     If AI is centrally concerned with intelligence, we ought to start by
     considering what behaviors lie at its core. Four behaviors are
     commonly used to distinguish intelligent behavior from instinct and
     stimulus-response associations: (1) prediction, (2) response to
     change, (3) intentional action, and (4) reasoning.

     One core capability is our ability to predict the future, that is, to
     imagine how things might turn out rather than have to try them. The
     essential issue here is imagining, that is, the disconnection of
     thought and action. That disconnection gives us the ability to imagine
     the consequences of an action before, or instead of, experiencing it,
     the ability, as Popper and Raimund (1985) put it, to have our
     hypotheses die in our stead. The second element -- response to change
     -- is an essential characteristic that distinguishes intelligent
     action from inalterable instinct or conditioned reflexes. Intentional
     action refers to having a goal and selecting actions appropriate to
     achieving the goal. Finally, by reasoning, I mean starting with some
     collection of facts and adding to it by any inference method.

Five Views of Reasoning

     AI has of course explored all these in a variety of ways. Yet even if
     we focus in on just one of them -- intelligent reasoning -- it soon
     becomes clear that there have been a multitude of answers explored
     within AI as to what we mean by that, that is, what we mean when we
     say intelligent reasoning. Given the relative youth of our field, the
     answers have often come from work in other fields. Five fields in
     particular -- (1) mathematical logic, (2) psychology, (3) biology, (4)
     statistics, and (5) economics -- have provided the inspiration for
     five distinguishable notions of what constitutes intelligent reasoning
     (table 1).(1)


     One view, historically derived from mathematical logic, makes the
     assumption that intelligent reasoning is some variety of formal
     calculation, typically, deduction; the modern exemplars of this view
     in AI are the logicists. A second view, rooted in work in psychology,
     sees reasoning as a characteristic human behavior and has given rise
     to both the extensive work on human problem solving and the large
     collection of knowledge-based systems. A third approach, loosely
     rooted in biology, takes the view that the key to reasoning is the
     architecture of the machinery that accomplishes it; hence, reasoning
     is a characteristic stimulus-response behavior that emerges from
     parallel interconnection of a large collection of very, simple
     processors. Researchers working on several varieties of connectionism
     are descendants of this line of work; work on artificial life also has
     roots in the biologically inspired view. A fourth approach, derived
     from probability theory, adds to logic the notion of uncertainty,
     yielding a view in which reasoning intelligently means obeying the
     axioms of probability theory. A fifth view, from economics, adds the
     further ingredients of values and preferences, leading to a view of
     intelligent reasoning defined by adherence to the tenets of utility

     Briefly exploring the historical development of the first two of these
     views will illustrate the different conceptions they have of the
     fundamental nature of intelligent reasoning and will demonstrate the
     deep-seated differences in mind set that arise -- even within our own
     field -- as a consequence.

   The Logical View: Reasoning as Formal Calculation Consider first the
     tradition that uses mathematical logic as a view of intelligent
     reasoning. This view has its historical origins in Aristotle's efforts
     to accumulate and catalog the syllogisms, in an attempt to determine
     what should be taken as a convincing argument. (Note that even at the
     outset, there is a hint of the idea that the desired form of reasoning
     might be describable in a set of formal rules.) The line continues
     with Descartes, whose analytic geometry showed that Euclid's work,
     apparently concerned with the stuff of pure thought (lines of zero
     width, perfect circles of the sorts only the gods could make), could
     in fact be married to algebra, a form of calculation, something mere
     mortals could do.

     By the time of Leibnitz, the agenda is quite specific and telling: He
     sought nothing less than a calculus of thought, one that would permit
     the resolution of all human disagreement with the simple invocation
     "let us compute." By this time, there is a clear and concrete belief
     that as Euclid's once godlike and unreachable geometry could be
     captured with algebra, so some (or perhaps any) variety of that
     ephemeral stuff called thought might be captured in calculation,
     specifically logical deduction.

     In the nineteenth century, Boole provided the basis for propositional
     calculus in his Laws or Thought; later work by Frege and Peano
     provided additional foundation for the modern form of predicate
     calculus. Work by Davis, Putnam, and Robinson in the twentieth century
     provided the final steps in mechanizing deduction sufficiently to
     enable the first automated theorem provers. The modern offspring of
     this line of intellectual development include the many efforts that
     use first-order logic as a representation and some variety of
     deduction as the reasoning engine, as well as the large body of work
     with the explicit agenda of making logical reasoning computational,
     exemplified by Prolog.

     Note we have here the underlying premise that reasoning intelligently
     means reasoning logically; anything else is a mistake or an
     aberration. Allied with this is the belief that logically, in turn,
     means first-order logic, typically sound deduction (although other
     models have of course been explored). By simple transitivity, these
     two collapse into one key part of the view of intelligent reasoning
     underlying logic: Reasoning intelligently means reasoning in the
     fashion defined by first-order logic. A second important part of the
     view is the allied belief that intelligent reasoning is a process that
     can be captured in a formal description, particularly a formal
     description that is both precise and concise.

   The Psychological View: Reasoning as Human Behavior
     But very different
     views of the nature of intelligent reasoning are also possible. One
     distinctly different view is embedded in the part of AI influenced by
     the psychological tradition. That tradition, rooted in the work of
     Hebb, Bruner, Miller, and Newell and Simon, broke through the
     stimulus-response view demanded by behaviorism and suggested instead
     that human problem-solving behavior could usefully be viewed in terms
     of goals, plans, and other complex mental structures. Modern
     manifestations include work on SOAR (Rosenbloom, Laird, and Newell
     1993) as a general mechanism for producing intelligent reasoning and
     knowledge-based systems as a means of capturing human expert

     Where the logicist tradition takes intelligent reasoning to be a form
     of calculation, typically deduction in first-order logic, the
     tradition based in psychology takes as the defining characteristic of
     intelligent reasoning that it is a particular variety of human
     behavior. In the logicist view, the object of interest is thus a
     construct definable in formal terms via mathematics, while for those
     influenced by the psychological tradition, it is an empirical
     phenomenon from the natural world.

     There are thus two very different assumptions here about the essential
     nature of the fundamental phenomenon to be captured. One of them makes
     Al a part of mathematics; the other makes it a part of natural

     A second contrast arises in considering the character of the answers
     each seeks. The logicist view has traditionally sought compact and
     precise characterizations of intelligence, looking for the kind of
     characterizations encountered in mathematics (and at times in
     physics). The psychological tradition by contrast suggests that
     intelligence is not only a natural phenomenon, it is an inherently
     complex natural phenomenon: as human anatomy and physiology are
     inherently complex systems resulting from a long process of evolution,
     so perhaps is intelligence. As such, intelligence may be a large and
     fundamentally ad hoc collection of mechanisms and phenomena, one for
     which complete and concise descriptions may not be possible.

     The point here is that there are a number of different views of what
     intelligent reasoning is, even within AI, and it matters which view
     you take because it shapes almost everything, from research
     methodology to your notion of success.

   The Societal View: Reasoning as Emergent Behavior
     AI's view of
     intelligent reasoning has varied in another dimension as well. We
     started out with the straightforward, introspection-driven view that
     intelligence resided in, and resulted from, an individual mind. After
     all, there seems at first glance to be only one mind inside each of

     But this, too, has evolved over time, as AI has considered how
     intelligent reasoning can arise from groups of (more or less)
     intelligent entities, ranging from the simple units that make up
     connectionist networks, to the more complex units in Minsky's (1986)
     society of mind, to the intelligent agents involved in collaborative
     work. Evolutions like this in our concept of intelligence have as
     corollaries a corresponding evolution in our beliefs about where
     sources of power are to be found. One of the things I take Minsky to
     be arguing in his society of mind theory is that power is going to
     arise not from the individual components and their (individual)
     capabilities, but from the principles of organization -- how you put
     things (even relatively simple things) together in ways that will
     cause their interaction to produce intelligence. This leads to the
     view of intelligence as an emergent phenomenon -- something that
     arises (often in a nonobvious fashion) from the interaction of
     individual behaviors. If this is so, we face yet another challenge: If
     intelligence arises in unexpected ways from aggregations, then how
     will we ever engineer intelligent behavior, that is, purposefully
     create any particular variety of it?

     Consider then the wide variety of views we in AI have taken of
     intelligent reasoning: logical and psychological, statistical and
     economic, individual and collaborative. The issue here is not one of
     selecting one of these over another (although we all may have our
     individual reasons for doing so). The issue is instead the
     significance of acknowledging and being aware of the different
     conceptions that are being explored and the fundamentally different
     assumptions they make. AI has been and will continue to be all these
     things; it can embrace all of them simultaneously without fear of

   AI: Exploring the Design Space of Intelligences.
     The temptation
     remains, of course, to try to unify them. I believe this can in fact
     be done, using a view I first heard articulated by Aaron Sloman
     (1994), who suggested conceiving of AI as the exploration of the
     design space of intelligences.

     I believe this is a useful view of what we're about for several
     reasons: First, it's more general than the usual conjunction that
     defines us as a field interested in both human intelligence and
     machine intelligence. Second, the plural -- intelligences --
     emphasizes the multiple possibilities of what intelligence is (or are,
     as my title suggests). Finally, conceiving of it in terms of a design
     space suggests exploring broadly and deeply, thinking about what kinds
     of intelligences there are, for there may be many.

     This view also helps address the at-times debated issue of the
     character of our field: Are we science or engineering, analytic or
     synthetic, empirical or theoretical? The answer of course is, "yes."

     Different niches of our field have different characters. Where we are
     concerned with human intelligence, our work is likely to be more in
     the spirit of scientific, analytical, and empirical undertakings.
     Where the concern is more one of machine intelligence, the work will
     be more engineering, synthetic, and theoretical. But the space is
     roughly continuous, it is large, and all these have their place.

Why Is Intelligence?

     Next I'd like to turn to the question, "Why is intelligence?" That is,
     can we learn from an explicitly evolutionary view? Is there, or could
     there be, a paleocognitive science? If so, what would it tell us?

     We had best begin by recognizing the difficulty of such an
     undertaking. It's challenging for several reasons: First, few of the
     relevant things fossilize. I've checked the ancient bits of amber, and
     sadly, there are no Jurassic ontologies to be found embedded there;
     there are no Paleolithic rule-based systems still available for study;
     and although there is speculation that the cave paintings at Lascaux
     were the earliest implementation of JAVA, this is, of course,

     The examples may be whimsical, but the point is real -- few of the
     elements of our intellectual life from prehistoric times are preserved
     and available for study. There are even those who suggest the entire
     undertaking is doomed from the start. Richard Lewontin (1990), who has
     written extensively on evolution, suggests that "if it were our
     purpose in this chapter to say what is actually known about the
     evolution of human cognition, we would stop at the end of this
     sentence" (p. 229).

     Luckily, he goes on: "That is not to say that a good deal has not been
     written on the subject. Indeed whole books have been devoted to
     discussions of the evolution of human cognition and its social
     manifestations, but these works are nothing more than a mixture of
     pure speculation and inventive stories. Some of these stories might
     even be true, but we do not know, nor is it clear ... how we would go
     about finding out" (p. 229). Hence, we had better be modest in our
     expectations and claims.

     A second difficulty lies in the data that are available. Most attempts
     to date phenomena are good only to something like a factor of two or
     four. The taming of fire, for example, probably occurred around
     100,000 years ago, but it might have been 200,000 or even 400,000.
     Then there is the profusion of theories about why intelligence arose
     (more on those in a moment). Luckily for our purposes, we don't
     actually have to know which, if any, of these many theories are
     correct. I suggest you attend not to the details of each but to the
     overall character of each and what it may tell us about how the mind
     might have arisen.

     Presumably the mind evolved and should as a consequence have some of
     the hallmarks of anything produced by that process. Let's set the
     stage then by asking what's known about the nature of evolution, the
     process that was presumably in charge of, and at the root of, all

The Nature of Evolution

     The first thing to remember about evolution is that it is engaging in
     a pastime that's quite familiar to us: blind search. This is sometimes
     forgotten when we see the remarkable results -- apparently elegant and
     complex systems -- that come from a few million years' worth of
     search. The issue is put well in the title of one article -- "The Good
     Enough Calculi of Evolving Control Systems: Evolution Is Not
     Engineering" (Partridge 1982). The article goes on to contrast
     evolution and engineering problem solving: In engineering, we have a
     defined problem in the form of design requirements and a library of
     design elements available for the solution. But "biology provides no
     definition of a problem until it has been revealed by the advantage of
     a solution. Without a predefined problem, there is no prerequisite
     domain, range, form for a solution, or coordinates for its evaluation,
     except that it provides a statistically improved survival function.
     This filter selects `good enough' new solutions and thereby identifies
     solved problems" (p. R173).

     Consider in particular the claim that "biology provides no definition
     of a problem until it has been revealed by the advantage of a
     solution." The warning here is to be wary of interpreting the results
     of evolution as nature's cleverness in solving a problem. It had no
     problem to solve; it was just trying out variations.

     The consequences of blind search are familiar to us; so, in some ways
     what follows seems obvious, but the consequences are nevertheless
     worth attending to.(2)

     One consequence of random search is that evolution wanders about,
     populating niches wherever it finds them in the design space and the
     environment. Evolution is not a process of ascent or descent; it's a
     branching search space being explored in parallel.

     A second consequence is that nature is sometimes a lousy engineer.
     There are, for example, futile metabolic cycles in our cells --
     apparently circular chemical reactions that go back and forth
     producing and unproducing the same molecules and depleting energy
     stores for no apparent purpose (Katz 1985).

     Third, despite the size of the design space, blind search sometimes
     doubles back on itself, and evolution rediscovers the same mechanisms.
     One widely cited example is the eye of the mammal and the eye of the
     octopus. They are quite similar but for one quite striking fact: The
     human eye is backward compared with the octopus (Katz 1985). In the
     mammalian eye, the photoreceptors are in the retinal layer nearest the
     rear of the eye; as a consequence, light has to go through the retinal
     "back plane" before it encounters the photoreceptors.

     A second striking example arises in the evolution of lungs in mammals
     and birds. Both appear to have arisen from the swim bladders that fish
     use to control buoyancy, but birds' lungs are unidirectionally
     ventilated, unlike the tidal, bidirectional flow in other vertebrates.
     (As a consequence, avian lungs are much more efficient than ours:
     Himalayan geese have been observed not only to fly over human climbers
     struggling with their oxygen tanks to reach the top of Mt. Everest but
     to honk as they do so (Encyclopedia Brittannica 1994-1997); presumably
     this is nature's way of reminding us of our place in the scheme of

     The differences in end results suggest the different Paths that were
     taken to these results, yet the remaining similarities in eyes and
     lungs show that evolution can rediscover the same basic mechanisms
     despite its random search.

     Fourth, there are numerous examples of how nature-is a satisficer, not
     an optimizer. For instance, one of the reasons cuckoos can get away
     with dropping off their eggs in the nests of other birds is that birds
     have only a very crude algorithm for recognizing their eggs and their
     chicks (Calvin 1991). The algorithm is good enough, most of the time,
     but the cuckoo takes advantage of its only adequate (manifestly
     nonoptimal) performance.

     The control of human respiration provides another example. Respiration
     is, for the most part, controlled by the level of [CO.sub.2] in the
     blood. There appear to be a variety of reasons for this (for example,
     controlling [CO.sub.2] is one way to control pH levels in the blood),
     but it's still only an adequate system. Its limits are well known to
     mountain climbers and divers. Mountain climbers know that they have to
     be conscious of the need to breathe at altitude because the thin air
     leaves [CO.sub.2] levels in the blood low, eliminating the normal
     physiological cues to breathe, even through blood-oxygen levels are
     also low.

     Divers need to understand that hyperventilation is dangerous: It can
     drive the [CO.sub.2] level in the blood near zero, but it cannot
     increase blood-oxygen saturation past the blood's normal limits. As a
     result, the [CO.sub.2] level can stay abnormally low past the time
     that oxygen levels have significantly decreased, and the diver will
     feel no need to breathe even though blood-oxygen levels are low enough
     to lead to blackout.

     Fifth, evolution sometimes proceeds by functional conversion, that is,
     the adoption of an organ or system serving one purpose to serve
     another. The premier example here is bird wings: The structures were
     originally developed for thermal regulation (as they are still used in
     insects) and, at some point, were coopted for use in flight.

     Finally, evolution is conservative: It adds new layers of solutions to
     old ones rather than redesigning. This in part accounts for and
     produces vestigal organs and systems, and the result is not
     necessarily pretty from an engineering viewpoint. As one author put
     it, "The human brain is wall-to-wall add-ons, a maze of dinguses and
     gizmos patched into the original pattern of a primitive fish brain. No
     wonder it isn't easy to understand how it works" (Bickerton 1995, p.

     Evolution then is doing random search, and the process is manifest in
     the product. As one author put it,

     In the natural realm, organisms are not
     built by engineers who, with an overall
     plan in mind, use only the most
     appropriate materials, the most effective design,
     and the most reliable construction
     techniques. Instead, organisms are patchworks
     containing appendixes, uvulas, earlobes,
     dewclaws, adenoids, warts, eyebrows,
     underarm hair, wisdom teeth, and
     toe-nails. They are a meld of ancestral parts
     integrated step by step during their
     development through a set of tried and true
     ontogenetic mechanisms. These
     mechanisms ensure matching between disparate
     elements such as nerves and muscles, but
     they have no overall vision. Natural
     ontogenies and natural phylogenies are not
     limited by principles of parsimony, and
     they have no teleology. Possible organisms
     can be overdetermined, unnecessarily
     complex, or inefficiently designed (Katz
     1985, p. 28).

     The important point here for our purposes is that what's manifestly
     true of our anatomy may also be true of our cognitive architecture.
     Natural intelligence is unlikely to have an overall vision and
     unlikely to be limited by principles of parsimony; like our bodies, it
     is likely to be overdetermined, unnecessarily complex, and
     inefficiently designed.

     In the face of that, searching for the minimalism and elegance beloved
     by engineers may be a diversion, for it simply may not be there.
     Somewhat more crudely put: The human mind is a 400,000-year-old legacy
     application. . .and you expected to find structured programming?

     All that in turn gives us all the more reason to explore deeply into
     the design space of intelligence, for the human solution, and its
     sources of power, may be extraordinarily quirky.

The Available Evidence

     If we can't rely on the fossil record for preserved bits of cognition,
     can it supply other useful information? One observation from the
     record of particular relevance is the striking increase in what's
     called the encephalization quotient -- the ratio of brain size to body
     size. Fossil records give clear evidence that the encephalization
     quotient of human ancestors increased by a factor of three to four
     over about four million years (Donald 1991). In evolutionary terms,
     this is an enormous change over a short period of time. Simply put,
     our brains got very big very fast.

     This is interesting in part because brains are metabolically very
     expensive. In the adult, about 20 percent of our metabolism goes into
     maintaining our brains; in children, the brain consumes about 50
     percent of metabolic output (Bickerton 1995). This makes the question
     all the more pressing: Considering how expensive large brains are, why
     do we have them? Why is intelligence? What benefit arose from it?

     A second clear piece of evidence, this time from current studies of
     the brain, is lateralization: The standard examples are language
     (found in the left hemisphere in approximately 93 percent of us) and
     the rapid sequencing of voluntary muscles for things such as throwing
     (found on the left in 89 percent) (Calvin 1983). This is striking in
     part because the human brain has very few anatomical asymmetries; the
     observed asymmetries are almost entirely functional (Eccles 1989). It
     is also striking because the asymmetry arose with the hominids (Homo
     and our ancestors) and appears unique to them; the brains of our
     closest living relatives -- apes and monkeys -- are symmetrical both
     anatomically and functionally (Eccles 1989).

     The interesting question here of course is why, in a world of
     symmetry, is the human brain lateralized, even in part?

     One useful way to set the stage for the various suggested answers is
     to consider the sequence of events that lead to Homo (H.) sapiens.
     Figure 1 gives an overview of the last four million years, indicating
     the evolutionary span of several of our immediate ancestors and their
     average cranial capacity.


     If we zoom in on the last 200,000 years, we see a few additional
     events of note (figure 2). Speech arrives quite recently, around
     200,000 to 400,000 years ago; fire doesn't get tamed until around
     100,000 years ago, which is when more advanced tools also begin to
     appear. The conversion from hunter-gatherers to a settled society
     dependent on the use of agriculture happens roughly 10,000 to 15,000
     years ago, about the same time as the cave paintings at Lascaux.

     One question to ask about all this is, What changed between four
     million years ago and now? Four million years ago, there was
     (presumably) nothing we would recognize as human-level intelligence;
     now there is. What changed in between?

Theories of the Origin of Intelligence

     A variety of theories have been suggested.

   Early Man, the Primal Tool Maker
     One theory is wrapped up in the
     notion that man is a tool maker. The construction of increasingly
     elaborate tools both gave early man a survival advantage and produced
     evolutionary pressure for yet more elaborate tools and the brains to
     build them. Unfortunately, another look at our time scale provides
     some disquieting data. The earliest tools show up around 2.5 million
     years ago and stay largely unchanged until about 300,000 years ago
     (Calvin 1991). Yet during all that time our brains are growing
     quickly. The tool theory thus seems unlikely.

   Early Man and the Killer Frisbee
     A second theory (Calvin 1991, 1983)
     is centered on hunting methods and involves passing a device that is
     sometimes whimsically referred to as the killer frisbee (figure 3).
     It's one of the earliest tools and is more properly called a hand ax
     because it was believed to be a handheld ax. The curious thing about
     it is that if you look closely, you'll see that all its edges are
     sharp -- not a very good idea for something designed to be held in the


     One researcher built replicas of these and discovered that if thrown
     like a discus, it flies like a frisbee at first but soon turns on edge
     and lands with its sharp edge embedded in the earth. Now add to this
     the fact that many of these artifacts have been found in the mud near
     ancient waterholes. This led to the theory that the artifacts were
     thrown by our ancestors at herds of animals gathered at waterholes,
     with the intent of wounding one of them or knocking it down.

     But why should throwing things be interesting -- because throwing
     accurately requires precise time control of motor neurons. For
     example, if you want to throw accurately at a target the size of a
     rabbit that's 30 feet away (figure 4), the motor-control problem is
     substantial: the time window for release of the projectile is less
     than 1 microsecond. But individual neurons are not in general that
     accurate temporally. How do we manage?


     One way to get the needed accuracy is to recruit populations of
     neurons and synchronize them: Enright (1980) shows how precise timing
     can be produced from mutual coupling of heterogeneous, inaccurate
     oscillators (that is, those with differing intrinsic average
     frequencies and that are individually unreliable on a cycle-to-cycle
     basis). With this arrangement, the standard deviation of cycle length
     between successive firings is proportional to


     so quadrupling the number of elements cuts the standard deviation in
     half. This might account for the ability of our brains to control
     muscle action to within fractions of a millisecond, when individual
     neurons are an order of magnitude less precise.

     The theory then is that our brains grew larger because more neurons
     produced an increase in throwing accuracy (or an increase in
     projectile speed with no reduction in accuracy), and that in turn
     offered a major selective advantage: the ability to take advantage of
     a food source -- small mammals -- that was previously untapped by
     hominids. A new food source in turn means a new ecological niche ripe
     for inhabiting. The advantage resulting from even a limited ability to
     make use of a new source of food also provides a stronger and more
     immediate selective pressure than is likely to have arisen from other
     advantages of a slightly enlarged brain (for example, some limited
     protolanguage ability).

     The theory has a number of appealing corollaries. It suggests one
     source of lateralization because throwing is fundamentally asymmetric:
     One-armed throwing is far more accurate and effective than two armed
     for any reasonable-sized projectile (imagine baseball pitchers or
     outfielders propelling the ball overhead with both arms). As a result,
     only the neurons on one side of the brain need be specialized for the
     operation (for why this turns out, in nearly 90 percent of us, to be
     the left side of the brain, see Calvin [1983]).(3) That
     lateralization, which more generally involves precise sequential
     muscle control, may in turn have been a key predecessor to language,
     which also requires fast and accurate control of musculature.

     Thus, the brain may have gotten larger to allow us to hunt better. The
     interesting punchline for our purposes is that thinking may be an
     extra use of all those neurons that evolved for another purpose.

   Early Man and the Killer Climate
     A third theory suggests that climate
     plays a central role (Calvin 1991). The last few hundred thousand
     years of our history have been marked by a series of ice ages. A being
     used to surviving in a temperate climate would face a considerable
     collection of challenges as the weather worsened and winters arrived.
     In order to survive the winter, it would have had to be good enough at
     hunting to accumulate extra food beyond the day-to-day needs (hence
     the related utility of being able to throw accurately), and then it
     would have had to develop both the foresight to put aside some of that
     for the winter and the "technology" for doing so. There is, of course,
     a stiff Darwinian penalty for failure to be that smart.

   Early Man, the Primal Frugivore
     A fourth theory suggests that the
     crucial element was the evolution of early man into a frugivore, or
     fruit eater. Why should this matter -- because you need to be smart to
     be a frugivore. Fruit comes in relatively small pieces, so you need to
     collect a lot of it, and it must be collected within a relatively
     narrow time window. As a consequence, frugivores need good spatial
     maps of their environments (so they know where the sources of fruit
     are) and good temporal maps (so they know when to show up). Perhaps
     this need for good spatial and temporal maps was a force for the
     evolution of larger brains.

     Early Man, the Primal Psychologist Yet another theory suggests that
     our primary use of intelligence is not for making tools, hunting, or
     surviving the winter; it's to get along with one another (Humphrey
     1976; also see Byrne and Whiten [1988]). This theory is sometimes
     called Machiavellian intelligence. In this view, the primary function
     of intelligence is the maintenance of social relationships.

     The evidence for this comes from several sources, among them the
     behavior of monkey troops that have been studied extensively. They are
     seen to spend a good proportion of their time servicing and
     maintaining their relationships within their groups, tending to issues
     of rank and hierarchy and what appear to be allegiances.

     A second source of evidence comes from a study (Dunbar 1992) that
     plotted group size against neocortex ratio (ratio of neocortex size to
     the size of the rest of the brain) for a variety of animals: a nearly
     linear relationship emerged. Perhaps this held true for early man as
     well: As early group size grew, along with the advantages of larger
     groups came increasing demands to be able to understand, predict, and
     perhaps even control the behavior of others. We saw earlier that
     prediction was a key component of intelligent behavior; what more
     complex, fascinating, and useful thing could there be to predict than
     the behavior of then other humans?

   Early Man, the Primal Linguist
     Finally, Bickerton (1995) has suggested
     that language was the crucial driving force behind the evolution of
     our brains. He starts with the interesting observation that if we look
     back at the historical time line, we notice that although brain size
     grows roughly steadily for about three million years, progress in the
     development of modern culture was not nearly so gradual. In fact,
     "instead of a steady ascent ... we find, for 95% of that period, a
     monotonous, almost flat line" (Bickerton 1995, p. 47). Almost nothing
     happens. It is well after the appearance of H. sapiens, and well after
     the leveling off of brain size, that we see the appearance of language
     and all the other elements of what we have come to call civilization.

     Bickerton calls these the two most shocking facts of human evolution:
     (1) our ancestors stagnated so long despite their ever-growing brains
     and (2) human culture grew exponentially only after the brain had
     ceased to grow. It appears that we showed our most obvious evidence of
     intelligence only after our brains stopped growing.

     What was it that happened to produce that evidence? He suggests that
     the crucial event was some sort of reorganization within the brain, a
     reorganization that happened well after size stopped increasing. That
     reorganization made possible two essential things: first, a generative
     syntax, that is, a true language, and second, thought, that is, the
     ability to think about something (like a leopard) without having to
     experience the thing perceptually, and equally important, without
     having to react to it in the way one would on meeting one.

     This leads to what appears to be a crucial distinction between animal
     intelligence and human intelligence. Animal intelligence has a here
     and now character: With animal calls, for example, there is an
     immediate link from the perception to the mind state to the action. If
     a monkey sees a leopard, a certain mind state ensues, and a certain
     behavior (giving the appropriate call) immediately follows.(4)

     Human thought, by contrast, has an unlimited spatiotemporal reference,
     by virtue of several important disconnections. Human thought involves
     the ability to imagine, the ability to think about something in the
     absence of perceptual input, and the ability to imagine without

     In human thought we have the ability, the luxury, of
     "re-presentation." The pun is intentional and probably educational:
     Representations allow us to re-present things to ourselves in the
     absence of the thing, so that we can think about it, not just react to

     Enormous things change when we have both thought and language. Thought
     and its useful disconnection from immediate stimuli and immediate
     action is clearly a great boon -- it's the origin of our ability to
     have our hypotheses die in our stead. But what about language? For our
     purposes, the interesting thing about language is that it makes
     knowledge immortal and makes society, not the individual, the
     accumulator and repository of knowledge. No longer is an individual's
     knowledge limited to what can be experienced and learned in a
     lifetime. Language not only allows us to think, it allows us to share
     and accumulate the fruits of that thought.

     But what then caused our brains to grow over the three million or so
     years during which neither language nor thought (as we know them) was
     present? What was the evolutionary pressure? The theory suggests that
     the life of a successful hunter-gatherer is fact rich and practice
     rich. In order to survive as a hunter-gatherer, you need to know a lot
     of facts about your world and need to know a fair number of skills.
     This then is the hypothesized source of pressure: the increasing
     accumulation of survival-relevant information communicated through a
     form of protolanguage. Early man needed to store "the vast amount of
     lore ... in the collective memories of traditional societies: the uses
     of herbs, the habits of animals, aphorisms about human behavior,
     detailed knowledge of the spatial environment, anecdotes, old wives'
     tales, legends and myths" (Bickerton 1995, p. 63).(5)

     Where does this collection of theories (figure 5) leave us? One
     obvious caution is that they are unlikely to be either independent or
     mutually exclusive. They may be mutually supportive and all true to
     some extent, with each of them contributing some amount of the
     evolutionary pressure toward larger brains and intelligence.

Figure 5. Theories of the Evolution of Intelligence.
     Early man, the primal tool maker
     Early man and the killer frisbee
     Early man and the killer climate
     Early man, the primal frugivore
     Early man, the primal psychologist
     Early man, the protolinguist

     A second point to note is that human intelligence is a natural
     phenomenon, born of evolution, and as suggested earlier, the end
     product likely shows evidence of the process that created it.
     Intelligence is likely to be a layered, multifaceted, and probably
     messy collection of phenomena, much like the other products of

     It also may be rather indirect. Here's Lewontin (1990) again: "There
     may have been no direct natural selection for cognitive ability at
     all. Human cognition may have developed as the purely epiphenomenal
     consequence of the major increase in brain size, which, in turn, may
     have been selected for quite other reasons" (p. 244), for example, any
     of the reasons in figure 5.

     This, too, suggests a certain amount of caution in our approach to
     understanding intelligence, at least of the human variety: The human
     mind is not only a 400,000-year-old legacy application, it may have
     been written for another purpose and adopted for current usage only
     after the fact. In light of that, we should not be too surprised if we
     fail to find elegance and simplicity in the workings of intelligence.

Inhuman Problem Solving

     As we explore the design space of intelligences, it's interesting to
     consider some of the other varieties of intelligence that are out
     there, particularly the animal sort. With that, let me turn to the
     third part of my article, in which it's time for AI to learn about the
     birds and the bees. What do animals know, and (how) do they think?

   Clever Hans and Clever Hands

     Before we get too far into this, it would we wise to consider a couple
     of cautionary tales to ensure the appropriate degree of skepticism
     about this difficult subject. The classic cautionary tale concerns a
     horse named Clever Hans, raised in Germany around 1900, that gave
     every appearance of being able to do arithmetic, tapping out his
     answers with his feet (Boakes 1984) (figure 6). He was able to give
     the correct answers even without his trainer in the room and became a
     focus of a considerable amount of attention and something of a


     In the end, it turned out that Hans was not mathematically gifted; his
     gift was perceptual. The key clue came when he was asked questions to
     which no one in the room knew the answer; in that case, neither did
     he. Hans had been attending carefully to his audience and reacting to
     the slight changes in posture that occurred when he had given the
     correct number of taps.(6)

     The clever hands belong to a chimpanzee named Washoe who had been
     trained in American Sign Language (Gardner et al. 1989). One day
     Washoe, seeing a swan in a pond, gave the sign for water and then
     bird. This seemed quite remarkable, as Washoe seemed to be forming
     compound nouns -- water bird -- that he had not previously known
     (Mithen 1996). But perhaps he had seen the pond and given the sign for
     water, then noticed the swan and given the sign for bird. Had he done
     so in the opposite order -- bird water -- little excitement would have

     The standard caution from both of these tales is always to consider
     the simpler explanation -- trainer effects, wishful interpretation of
     data, and so on -- before being willing to consider that animals are
     indeed capable of thought.

   Narrow Intelligence: Birds and Bees

     Given that, we can proceed to explore some of the varieties of animal
     intelligence that do exist. Several types of rather narrowly defined
     intelligence are supported by strong evidence. Among the birds and the
     bees, for example, bees are well known to "dance" for their hive mates
     to indicate the direction of food sources they have found. Some birds
     have a remarkable ability to construct a spatial map. The Clark's
     nutcracker, as one example, stores away on the order of 30,000 seeds
     in 6,000 sites over the course of the spring and summer and is able to
     find about half of those during the winter (Balda and Kamil 1992).
     This is a narrowly restricted kind of intelligence but, at 6000
     locations, nonetheless impressive.

   Broader Intelligence: Primates

     Broader forms of intelligence are displayed by some primates. One
     particular variety -- the vervet monkey -- has been studied widely in
     the wild and has displayed a range of intelligent-seeming behaviors
     (Cheney and Seyfarth 1990). One of the important elements in the life
     of a monkey group is status -- your place in the dominance hierarchy.
     Vervet monkeys give every sign of understanding and being able to
     reason using relations such as higher-status-than and
     lower-status-than. They can, for example, do simple transitive
     inference to establish the place of others in the hierarchy: If A can
     beat up B, and B can beat up C, there's no need for A and C to fight;
     the result can be inferred (allowing our hypotheses to get battered in
     our stead).

     The monkeys also appear capable of classifying relationships as same
     or different, understanding, for example, that mother-of is a
     different relation from sibling-of. This can matter because if you
     fight with Junior, you had better avoid mother-of(Junior) (who might
     be tempted to retaliate), but sibling-of(Junior) presents no such

     They also seem to have a vocabulary with semantic content -- different
     calls that correspond to the notion of leopard, eagle, and python, the
     three main monkey predators. That the calls are truly referential is
     suggested by the facts that they are given only when appropriate, they
     are learned by trial and error by the young monkeys, and the troop
     takes appropriate action on hearing one of the calls. Hearing the
     eagle call, for instance, all the troop members will look up,
     searching for the eagle, then take cover in the bushes. Note that we
     have referred to this as a vocabulary, not a language, because it
     appears that there is no syntax permitting the construction of

   Lies -- Do Monkeys Cry Leopard?

     There is also some anecdotal evidence that the monkeys lie to one
     another. They have been observed to lie by omission when it concerns
     food: When happening on a modest-sized store of food, a monkey may
     fail to give the standard call ordinarily given when finding food.
     Instead, the lone monkey may simply consume it.

     A more intriguing form of misrepresentation has been observed to occur
     when two neighboring monkey troops get into battles over territory.
     Some of these battles have ended when one of the monkeys gives the
     leopard call -- all the combatants scatter, climbing into trees to
     escape the predator, but there is in fact no leopard to be found. The
     monkeys may be lying to one another as a way of breaking up the fight
     (Cheney and Seyfarth 1991).(7)

   Psittacine Intelligence: Bird Brains No Longer

     One final example of animal intelligence concerns an African Grey
     Parrot named Alex who has been trained for quite a few years by Dr.
     Irene Pepperberg of the University of Arizona. Alex seems capable of
     grasping abstract concepts such as same, different, color, shape, and
     numbers (Pepperberg 1991).

     A videotape of Alex in action (WNET 1995) is particularly compelling;
     even a transcript of the conversation will give you a sense of what's
     been accomplished. Pay particular attention to Alex's ability to deal
     with, and reason about, abstract concepts and relations.

     Narrator. For 17 years, Alex and Dr.
     Irene Pepperberg have been working on
     the mental powers of parrots. Their efforts
     at the University of Arizona have
     produced some remarkable results.

     Dr. Pepperberg. What shape (holding up
     a red square)?

     Alex: Corners.

     Dr. Pepperberg: Yeah, how many
     corners? Say the whole thing.

     Alex: Four ... corners.

     Dr. Pepperberg: That's right, four
     corners. Good birdie.

     Alex: Wanna nut.

     Dr. Pepperberg: You can't have another
     OK, what shape? (holding up a green

     Alex: Three ... corners.

     Dr. Pepperberg: That's right, three
     corners; that's a good boy.
     Now tell me, what color (holding the
     same green triangle)?

     Alex: Green.

     Dr. Pepperberg: Green, ok; here's a nut.
     OK, and what toy (holding up a toy

     Alex: Truck.

     Dr. Pepperberg: Truck; you're a good boy.
     OK, let's see if we can do something
     more difficult
     (holding two keys, one green plastic,
     one red metal; the green is slightly larger).
     Tell, me, how many?

     Alex: Two.

     Dr. Pepperberg: You're right, good parrot.

     Alex: Wanna nut.

     Dr. Pepperberg: Yes, you can have a nut.
     Alright, now look, tell me, what's
     different (same keys)?

     Alex: Color.

     Dr. Pepperberg: Good parrot. You're
     right, different color.
     Alright, now look, tell me, what color
     bigger? What color bigger (same keys)?

     Alex: Green.

     Dr. Pepperberg: Green; good boy. Green
     bigger. Good parrot.
     Oh you're a good boy today. Yes, three
     different questions on the same objects.
     Good parrot.

     Dr. Pepperberg: What we've found out is
     that a bird with a brain that is so different
     from mammals and primates can perform
     at the same level as chimpanzees and
     dolphins on all the tests that we've used and
     performs about at the level of a young,
     say, kindergarten-age child.

     This is an interesting bit of animal intelligence, in part because of
     the careful training and testing that's been done, suggesting that,
     unlike Hans, Alex really does understand certain concepts. This is all
     the more remarkable given the significant differences between bird and
     mammalian brains: Parrot brains are quite primitive by comparison,
     with a far smaller cerebral cortex.


     These varieties of animal intelligence illustrate two important
     points: First, they illuminate for us a number of other
     distinguishable points in the design space of intelligences. The
     narrow intelligences of birds and bees, clearly more limited than our
     own, still offer impressive evidence of understanding and reasoning
     about space. Primate intelligence provides evidence of symbolic
     reasoning that, although primitive, has some of the character of what
     seems central to our own intelligence. Clearly distinguishable from
     our own variety of intelligence, yet impressive on their own terms,
     these phenomena begin to suggest the depth and breadth of the natural
     intelligences that have evolved.

     Second, the fact that even some part of that intelligence appears
     similar to our own suggests the continuity of the design space. Human
     intelligence may be distinct, but it does not sit alone and
     unapproachable in the space. There is a large continuum of
     possibilities in that space; understanding some of our nearest
     neighbors may help us understand our own intelligence. Even admitting
     that there can be near neighbors offers a useful perspective.

   Primate Celebrities

     I can't leave the topic of animal intelligence without paying homage
     to one of the true unsung heroes of early AI research. Everyone in AI
     knows the monkey and bananas problem of course. But what's shocking,
     truly shocking, is that so many of us (myself included) don't know the
     real origins of this problem.

     Thus, for the generations of AI students (and faculty) who have
     struggled with the monkey and bananas problem without knowing its
     origins, I give you, the monkey (figure 7):(8)


     This one is named Rana; he and several other chimps were the subjects
     in an experiment done by gestalt psychologist Wolfgang Kohler (1925)
     in 1918. Kohler was studying the intelligence of animals, with
     particular attention to the phenomenon of insight, and gave his
     subjects a number of problems to solve. Here's Grande, another of the
     chimps, hard at work on the most famous of them (figure 8).


     Thus, there really was a monkey and a stalk of bananas, and it all
     happened back in 1918. just to give you a feeling of how long ago that
     was, in 1918, Herb Simon had not yet won the Nobel Prize.

   Searching Design Space

     In this last segment of the article, I'd like to consider what parts
     of the design space of intelligence we might usefully explore more
     thoroughly. None of these are unpopulated; people are doing some forms
     of the work I'll propose. My suggestion is that there's plenty of room
     for others to join them and good reason to want to.

   Thinking Is Reliving

     One exploration is inspired by looking at alternatives to the usual
     view that thinking is a form of internal verbalization. We also seem
     to be able to visualize internally and do some of our thinking
     visually; we seem to "see" things internally.

     As one common example, if I were to ask whether an adult elephant
     could fit through your bedroom door, you would most likely attempt to
     answer it by reference to some mental image of the doorway and an

     There is more than anecdotal evidence to support the proposition that
     mental imaging is closely related to perception; a variety of
     experimental and clinical data also support the notion. As one
     example, patients who had suffered a loss of their left visual field
     as a consequence of a stroke showed an interesting form of mental
     imagery loss (Bisiach and Luzzatti 1978). These patients were asked to
     imagine themselves standing at the northern end of a town square that
     they knew well and asked to report the buildings that they could "see"
     in their mental image when looking south. Interestingly, they report
     what they would in fact be able to see out of the right half of their
     visual field; that is, they report buildings to the south and west but
     none to the east.

     Even more remarkably, if they are then asked to imagine themselves on
     the south end of the square looking north and asked to report on what
     they "see" in their mental image, they describe the buildings in what
     is now the right half of their visual field (that is, buildings to the
     north and east) and fail completely to report those on the west side
     of the square, even though they had mentioned them only moments

     The process going on in using the mind's eye to "see" is thus
     remarkably similar in some ways to what happens in using the
     anatomical eye to see.

     A second source of support for this view comes from the observation of
     like-modality interference. If I ask you to hold a visual image in
     your mind while you try to detect either a visual or an auditory
     stimulus, the ability to detect the visual stimulus is degraded, but
     detection of the auditory stimulus remains the same (Segal and Fusella

     A third source of evidence comes from experiments done in the 1970s
     that explored the nature of visual thinking. One well-known experiment
     involved showing subjects images that looked like figure 9 and then
     asking whether the two images were two views of the same structure,
     albeit rotated (Shepard and Metzler 1971).


     One interesting result of this work was that people seem to do a form
     of mental rotation on these images. The primary evidence for this is
     that response time is directly proportional to the amount of rotation
     necessary to get the figures in alignment.

     A second experiment in the same vein involved mental folding (Shepard
     and Feng 1972). The task here is to decide whether the two arrows will
     meet when each of the pieces of paper shown in figure 10 is folded
     into a cube.


     If you introspect as you do this task, I think you'll find that you
     are recreating in your mind the sequence of actions you would take
     were you to pick up the paper and fold it by hand.

     What are we to make of these experiments? I suggest two things: First,
     it may be time to take seriously (once again) the notion of visual
     reasoning, that is, reasoning with diagrams as things that we look at,
     whose visual nature is a central part of the representation.

     Second is the suggestion that thinking is a form of reliving. The
     usual interpretation of the data from the rotation and folding
     experiments is that we think visually. But consider some additional
     questions about the experiments: Why does it take time to do the
     rotation, and why does the paper get mentally folded one piece at a
     time? In the rotation experiment, why don't our eyes simply look at
     each block, compute a transform, then do the transformation in one
     step? I speculate that the reason is because our thought processes
     mimic real life: In solving the problem mentally, we're re-acting out
     what we would experience in the physical world.

     That's my second suggestion: Take seriously the notion of thinking as
     a form of reliving our perceptual and motor experiences. That is,
     thinking is not simply the decontextualized manipulation of abstract
     symbols (powerful though that may be). Some significant part of our
     thinking may be the reuse, or simulation, of our experiences in the
     environment. In this sense, vision and language are not simply
     input-output channels into a mind where the thinking gets done; they
     are instead a significant part of the thought process itself. The same
     may be true for our proprioreceptive and motor systems: In mentally
     folding the paper, we simulate the experience as it would be were we
     to have the paper in hand.

     There is, by the way, a plausible evolutionary rationale for this
     speculation that thinking is a form of reliving. It's another instance
     of functional conversion: Machinery developed for perception turns out
     to be useful for thinking. Put differently, visual thinking is the
     offline use of our ability to see. We're making use of machinery that
     happened to be there for another purpose, as has happened many times
     before in evolution.(9)

     One further, ambitious speculation concerns the neural machinery that
     might support such reliving: Ullman (1996) describes counterstreams, a
     pair of complementary, interconnected pathways traveling in opposite
     directions between the high-level and low-level visual areas. Roughly
     speaking, the pathway from the low-level area does data-driven
     processing, but the opposite pathway does model-driven processing. One
     possible mechanism for thinking as reliving is the dominant use of the
     model-driven pathway to recreate the sorts of excitation patterns that
     would result from the actual experience.

     One last speculation I'd like to make concerns the power of visual
     reasoning and diagrams. The suggestion here is that diagrams are
     powerful because they are, among other things, a form of what
     Johnson-Laird (1983) called reasoning in the model. Roughly speaking,
     that's the idea that some of the reasoning we do is not carried out in
     the formal abstract terms of predicate calculus but is instead done by
     creating for ourselves a concrete miniworld where we carry out mental
     actions and then examine the results.

     One familiar example is the use of diagrams when proving theorems in
     geometry. The intent is to get a proof of a perfectly general
     statement, yet it's much easier to do with a concrete, specific model,
     one that we can manipulate and then examine to read off the answers.

     Consider, for example, the hypothesis that any triangle can be shown
     to be the union of two right triangles.

     We might start by drawing a triangle (figure 11a). The proof of course
     calls for any triangle, but we find it much easier with a concrete one
     in front of us.


     We might then play with it a bit and eventually hit on the idea of
     dropping a perpendicular figure 11b).


     Wary of a of, from a single concrete example, we might try a number of
     other triangles and eventually come up with a formal abstract proof.
     But it's often a lot easier to have a concrete example to work with,
     manipulate, and then examine the results of our manipulations.

     What works for something as plainly visual as geometric theorems also
     seems to work for things that are not nearly so visual, such as
     syllogisms. Consider these sentences describing a group of people
     (Johnson-Laird 1983, p. 5):

     Some of the children have balloons.

     Everyone with a balloon has a party hat.

     There's evidence that when asked to determine the logical consequences
     of these statements, people imagine a concrete instance of a room and
     some finite collection of people, then examine it to determine the

     The good news about any concrete example is its concreteness; the bad
     news is its concreteness, that is, its lack of generality -- as many a
     high school geometry student has discovered when he/she drew an
     insufficiently general diagram. For diagrams in particular, the
     problem is compelling: There's no such thing as an approximate
     diagram. Every line drawn has a precise length, every angle a precise
     measure. The good news is that diagrams make everything explicit; the
     bad news is that they can't possibly avoid it.

     Yet there are times when we'd like to marry the virtues of reasoning
     in a concrete diagram with the generality that would allow us to draw
     a line that was about three inches long or long enough to reach this
     other line.

     That's my last speculation: There may be ways to marry the
     concreteness of reasoning in the model with the power and generality
     of abstraction. One early step in this direction is discussed in
     Stahov, Davis, and Shrobe (1996), who discuss how a specific diagram
     can automatically be annotated with constraints that capture the
     appropriate general relationships among its parts, but there is
     plainly much more to be done.


     With that, let me summarize. I want to suggest that intelligence are
     many things, and this is true in several senses. Even within AI, and
     even with the subfield of inference, intelligence has been conceived
     of in a variety of ways, including the logical perspective, which
     considers it a part of mathematical logic, and the psychological
     perspective, which considers it an empirical phenomenon from the
     natural world.

     One way to get a synthesis of these numerous views is to conceive of
     AI as the study of the design space of intelligences. I find this an
     inspiring way to conceive of our field, in part because of its
     inherent plurality of views and in part because it encourages us to
     explore broadly and deeply about all the full range of that space.

     We have also explored how human intelligence is a natural artifact,
     the result of the process of evolution and its parallel, opportunistic
     exploration of niches in the design space. As a result, it is likely
     to bear all the hallmarks of any product of that process -- it is
     likely to be layered, multifaceted, burdened with vestigal components,
     and rather messy. This is a second sense in which intelligence are
     many things -- it is composed of the many elements that have been
     thrown together over evolutionary timescales.

     Because of the origins of intelligence and its resulting character, AI
     as a discipline is likely to have more in common with biology and
     anatomy than it does with mathematics or physics. We may be a long
     time collecting a wide variety of mechanisms rather than coming upon a
     few minimalist principles.

     In exploring inhuman problem solving, we saw that animal intelligence
     seems to fit in some narrowly constrained niches, particularly for the
     birds and bees, but for primates (and perhaps parrots), there are some
     broader varieties of animal intelligence. These other varieties of
     intelligence illustrate a number of other distinguishable points in
     the design space of intelligences, suggesting the depth and breadth of
     the natural intelligences that have evolved and indicating the
     continuity of that design space.

     Finally, I tried to suggest that there are some niches in the design
     space of intelligences that are currently underexplored. There is, for
     example, the speculation that thinking is in part visual, and if so,
     it might prove very useful to develop representations and reasoning
     mechanisms that reason with diagrams (not just about them) and that
     take seriously their visual nature.

     I speculated that thinking may be a form of reliving, that re-acting
     out what we have experienced is one powerful way to think about, and
     solve problems in, the world. And finally, I suggested that it may
     prove useful to marry the concreteness of reasoning in a model with
     the power that arises from reasoning abstractly and generally.


     (1.) Table 1 and some of the text following is from Davis, Shrobe, and
     Szolovits (1993).

     (2.) For a detailed exploration of the consequences and their
     potentially disquieting implications, see Dennett (1995).

     (3.) In brief, he suggests that it arises from the near-universal
     habit of women carrying babies in their left arms, probably because
     the maternal heartbeat is easier for the baby to hear on that side.
     This kept their right arms free for throwing. Hence the first major
     league hunter-pitcher may have been what he calls the throwing madonna
     (not incidentally, the title of his book).

     (4.) That's why the possibility of monkeys "lying" to one another (see
     later discussion) is so intriguing -- precisely because it's a break
     in the perception-action link.

     (5.) Humphrey (1976) also touches on this idea.

     (6.) Oskar Phungst, who determined the real nature of Hans's skill,
     was able to mimic it so successfully that he could pretend to be a
     mentalist, "reading the mind" of someone thinking of a number: Pfungst
     simply tapped until he saw the subtle changes in posture that were
     unconscious to the subject (Rosenthal 1966).

     (7.) For a countervailing view on the question of animal lying, see
     the chapter by Nicholas Mackintosh in Khalfa (1994).

     (8.) A true-life anecdote concerning life in Cambridge: When I went to
     a photographer to have this photo turned into a slide, the man behind
     the counter (probably an underpaid psychology graduate student) looked
     at the old book with some interest, then laughed at the photo I wanted
     reproduced. I pretended to chide him, pointing out that the photo was
     of a famous contributor to psychological theory. "A famous contributor
     to psychology?" he said. "Then I know who it is." "Who?" I asked. "Why
     that's Noam Chimpsky, of course," he replied. Yes, it really happened,
     just that way.

     (9.) There has been significant controversy concerning the exact
     nature and status of mental images; see, for example, Farah (1988),
     who reviews some of the alternative theories as well as
     neuropsychological evidence for the reality of mental images. One of
     the alternative theories suggests that subjects in experiments of the
     mental-rotation sort are mentally simulating their experience of
     seeing rather than actually using their visual pathways. For our
     purposes, that's almost as good: Although literal reuse of the visual
     hardware would be a compelling example of functional conversion, there
     is also something intriguing in the notion that one part of the brain
     can realistically simulate the behavior of other parts.


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     Randall Davis is a professor of computer science at the Massachusetts
     Institute of Technology, where he works on model-based reasoning
     systems for engineering design, problem solving, and troubleshooting.
     He has also been active in the area of intellectual property and
     software, serving on a number of government studies and as an adviser
     to the court in legal cases. He received his undergraduate degree from
     Dartmouth College and his Ph.D. from Stanford University. He serves on
     several editorial boards, including those for Artificial Intelligence
     and AI in Engineering. In 1990, he was named a founding fellow of the
     American Association for Artificial Intelligence and served as
     president of the association from 1995-1997. His e-mail address is
     davis at ai.mit.edu.

[I am sending forth these memes, not because I agree wholeheartedly with 
all of them, but to impregnate females of both sexes. Ponder them and
spread them.]

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