■ This article, derived from the 1996 American Asso-ciation for Artificial Intelligence PresidentialAddress, explores the notion of intelligence from avariety of perspectives and finds that it “are” manythings. It has, for example, been interpreted in avariety of ways even within our own field, rangingfrom the logical view (intelligence as part of math-ematical logic) to the psychological view (intelli-gence as an empirical phenomenon of the naturalworld) to a variety of others. One goal of this arti-cle is to go back to basics, reviewing the things thatwe, individually and collectively, have taken asgiven, in part because we have taken multiple dif-ferent and sometimes inconsistent things forgranted. I believe it will prove useful to expose thetacit assumptions, models, and metaphors that wecarry around as a way of understanding both whatwe’re about and why we sometimes seem to be atodds with one another.
Intelligence are also many things in the sense thatit is a product of evolution. Our physical bodies arein many ways overdetermined, unnecessarily com-plex, and inefficiently designed, that is, the pre-dictable product of the blind search that is evolu-tion. What’s manifestly true of our anatomy is alsolikely true of our cognitive architecture. Naturalintelligence is unlikely to be limited by principlesof parsimony and is likely to be overdetermined,unnecessarily complex, and inefficiently designed.In this sense, intelligence are many things becauseit is composed of the many elements that havebeen thrown together over evolutionary time-scales. I suggest that in the face of that, searchingfor minimalism and elegance may be a diversion,for it simply may not be there. Somewhat morecrudely put: The human mind is a 400,000-year-old legacy application…and you expected to findstructured programming?
I end with a number of speculations, suggestingthat there are some niches in the design space ofintelligences that are currently underexplored.One example is the view that thinking is in partvisual, and hence it might prove useful to developrepresentations and reasoning mechanisms thatreason with diagrams (not just about them) and
that take seriously their visual nature. I speculateas well that thinking may be a form of reliving,that re-acting out what we have experienced is onepowerful way to think about and solve problemsin the world. In this view, thinking is not simplythe decontextualized manipulation of abstractsymbols, powerful though that may be. Instead,some significant part of our thinking may be thereuse or simulation of our experiences in the envi-ronment. In keeping with this, I suggest that itmay prove useful to marry the concreteness of rea-soning in a model with the power that arises fromreasoning abstractly.
Relax, there’s no mistake in the title. I’veindulged a bit of British-English that I’vealways found intriguing: the use of the
plural verb with collective nouns (as in“Oxford have won the Thames regatta”).
The selection of verb sense is purposeful andcaptures one of the main themes of the article:I want to consider intelligence as a collectivenoun. I want to see what we in AI have thoughtof it and review the multiple ways in whichwe’ve conceived of it. My intention is to makeexplicit the assumptions, metaphors, and mod-els that underlie our multiple conceptions.
I intend to go back to basics here, as a way ofreminding us of the things that we, individual-ly and collectively, have taken as given, in partbecause we have taken multiple different, andsometimes inconsistent, things for granted. Ibelieve it will prove useful to expose the tacitassumptions, models, and metaphors that wecarry around, as a way of understanding bothwhat we’re about and why we sometimes seemto be at odds with one another. That’s the firstpart of the article.
In the second part of the article, I’ll ask youto come along on a natural history tour—I’mgoing to take you away, back to a time around
iors lie at its core. Four behaviors are common-ly used to distinguish intelligent behaviorfrom instinct and stimulus-response associa-tions: (1) prediction, (2) response to change,(3) intentional action, and (4) reasoning.
One core capability is our ability to predictthe future, that is, to imagine how thingsmight turn out rather than have to try them.The essential issue here is imagining, that is, thedisconnection of thought and action. That dis-connection gives us the ability to imagine theconsequences of an action before, or insteadof, experiencing it, the ability, as Popper andRaimund (1985) put it, to have our hypothesesdie in our stead. The second element—response to change—is an essential character-istic that distinguishes intelligent action frominalterable instinct or conditioned reflexes.Intentional action refers to having a goal andselecting actions appropriate to achieving thegoal. Finally, by reasoning, I mean starting withsome collection of facts and adding to it byany inference method.
Five Views of ReasoningAI has of course explored all these in a varietyof ways. Yet even if we focus in on just one ofthem—intelligent reasoning—it soon becomesclear that there have been a multitude of
4 million years ago when the first hominidsarose and consider how intelligence came tobe. We’ll take an evolutionary view, considerintelligence as a natural phenomenon, and askwhy it arose. The vague answer—that it provid-ed enhanced survival—turns out not to be veryinformative; so, we’ll ask, why is intelligence,and more important, what does that tell usabout how we might proceed in AI?
The third part of the article is concernedwith what we might call inhuman problem solv-ing; it explores to what degree intelligence is ahuman monopoly. In this part of the article, AIlearns about the birds and the bees: What kindsof animal intelligence are there, and does that,too, inform our search for human intelligence?
I’ll end by considering how we mightexpand our view, expand our exploration ofintelligence by exploring aspects of it thathave received too little attention. AI has beendoing some amount of consolidation over thepast few years, so it may well be time to specu-late where the next interesting and provoca-tive leaps might be made.
Fundamental ElementsIf AI is centrally concerned with intelligence,we ought to start by considering what behav-
Hebb Lashley BayesGoedel Bruner RosenblattPost Miller Ashby Tversky Von NeumannChurch Newell Lettvin Kahneman SimonTuring Simon McCulloch, Pitts RaiffaDavis Heubel, Weisel PutnamRobinson
LOGIC SOAR CONNECTIONIS M Causal networks Rational agentsPROLOG Knowledge-based systems A-life
Frames
Table 1. Views of Intelligent Reasoning and Their Intellectual Origins.
answers explored within AI as to what wemean by that, that is, what we mean when wesay intelligent reasoning. Given the relativeyouth of our field, the answers have oftencome from work in other fields. Five fields inparticular—(1) mathematical logic, (2) psy-chology, (3) biology, (4) statistics, and (5) eco-nomics—have provided the inspiration for fivedistinguishable notions of what constitutesintelligent reasoning (table 1).1
One view, historically derived from mathe-matical logic, makes the assumption that intel-ligent reasoning is some variety of formal calcu-lation, typically, deduction; the modernexemplars of this view in AI are the logicists. Asecond view, rooted in work in psychology, seesreasoning as a characteristic human behaviorand has given rise to both the extensive workon human problem solving and the large collec-tion of knowledge-based systems. A thirdapproach, loosely rooted in biology, takes theview that the key to reasoning is the architec-ture of the machinery that accomplishes it;hence, reasoning is a characteristic stimulus-response behavior that emerges from parallelinterconnection of a large collection of verysimple processors. Researchers working on sev-eral varieties of connectionism are descendantsof this line of work; work on artificial life alsohas roots in the biologically inspired view. Afourth approach, derived from probability the-ory, adds to logic the notion of uncertainty,yielding a view in which reasoning intelligentlymeans obeying the axioms of probability theo-ry. A fifth view, from economics, adds the fur-ther ingredients of values and preferences, lead-ing to a view of intelligent reasoning defined byadherence to the tenets of utility theory.
Briefly exploring the historical developmentof the first two of these views will illustrate thedifferent conceptions they have of the funda-mental nature of intelligent reasoning and willdemonstrate the deep-seated differences inmind set that arise—even within our ownfield—as a consequence.
The Logical View: Reasoning as FormalCalculation Consider first the traditionthat uses mathematical logic as a view of intel-ligent reasoning. This view has its historicalorigins in Aristotle’s efforts to accumulate andcatalog the syllogisms, in an attempt to deter-mine what should be taken as a convincingargument. (Note that even at the outset, thereis a hint of the idea that the desired form ofreasoning might be describable in a set of for-mal rules.) The line continues with Descartes,whose analytic geometry showed that Euclid’swork, apparently concerned with the stuff ofpure thought (lines of zero width, perfect cir-
cles of the sorts only the gods could make),could in fact be married to algebra, a form ofcalculation, something mere mortals could do.
By the time of Leibnitz, the agenda is quitespecific and telling: He sought nothing lessthan a calculus of thought, one that would per-mit the resolution of all human disagreementwith the simple invocation “let us compute.”By this time, there is a clear and concrete beliefthat as Euclid’s once godlike and unreachablegeometry could be captured with algebra, sosome (or perhaps any) variety of that ephemer-al stuff called thought might be captured incalculation, specifically logical deduction.
In the nineteenth century, Boole providedthe basis for propositional calculus in his Lawsof Thought; later work by Frege and Peano pro-vided additional foundation for the modernform of predicate calculus. Work by Davis, Put-nam, and Robinson in the twentieth centuryprovided the final steps in mechanizing deduc-tion sufficiently to enable the first automatedtheorem provers. The modern offspring of thisline of intellectual development include themany efforts that use first-order logic as a rep-resentation and some variety of deduction asthe reasoning engine, as well as the large bodyof work with the explicit agenda of makinglogical reasoning computational, exemplifiedby Prolog.
Note we have here the underlying premisethat reasoning intelligently means reasoninglogically; anything else is a mistake or an aber-ration. Allied with this is the belief that logical-ly, in turn, means first-order logic, typicallysound deduction (although other models haveof course been explored). By simple transitivi-ty, these two collapse into one key part of theview of intelligent reasoning underlying logic:Reasoning intelligently means reasoning in thefashion defined by first-order logic. A secondimportant part of the view is the allied beliefthat intelligent reasoning is a process that canbe captured in a formal description, particular-ly a formal description that is both precise andconcise.
The Psychological View: Reasoning asHuman Behavior But very different viewsof the nature of intelligent reasoning are alsopossible. One distinctly different view isembedded in the part of AI influenced by thepsychological tradition. That tradition, rootedin the work of Hebb, Bruner, Miller, andNewell and Simon, broke through the stimu-lus-response view demanded by behaviorismand suggested instead that human problem-solving behavior could usefully be viewed interms of goals, plans, and other complex men-tal structures. Modern manifestations include
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laborative work. Evolutions like this in ourconcept of intelligence have as corollaries acorresponding evolution in our beliefs aboutwhere sources of power are to be found. One ofthe things I take Minsky to be arguing in hissociety of mind theory is that power is going toarise not from the individual components andtheir (individual) capabilities, but from theprinciples of organization—how you putthings (even relatively simple things) togetherin ways that will cause their interaction to pro-duce intelligence. This leads to the view ofintelligence as an emergent phenomenon—something that arises (often in a nonobviousfashion) from the interaction of individualbehaviors. If this is so, we face yet anotherchallenge: If intelligence arises in unexpectedways from aggregations, then how will we everengineer intelligent behavior, that is, purpose-fully create any particular variety of it?
Consider then the wide variety of views wein AI have taken of intelligent reasoning: logi-cal and psychological, statistical and econom-ic, individual and collaborative. The issue hereis not one of selecting one of these over anoth-er (although we all may have our individualreasons for doing so). The issue is instead thesignificance of acknowledging and beingaware of the different conceptions that arebeing explored and the fundamentally differ-ent assumptions they make. AI has been andwill continue to be all these things; it canembrace all of them simultaneously withoutfear of contradiction.
AI: Exploring the Design Space of Intel-ligences. The temptation remains, of course,to try to unify them. I believe this can in factbe done, using a view I first heard articulatedby Aaron Sloman (1994), who suggested con-ceiving of AI as the exploration of the designspace of intelligences.
I believe this is a useful view of what we’reabout for several reasons: First, it’s more gener-al than the usual conjunction that defines usas a field interested in both human intelligenceand machine intelligence. Second, the plur-al—intelligences—emphasizes the multiplepossibilities of what intelligence is (or are, asmy title suggests). Finally, conceiving of it interms of a design space suggests exploringbroadly and deeply, thinking about what kindsof intelligences there are, for there may bemany.
This view also helps address the at-timesdebated issue of the character of our field: Arewe science or engineering, analytic or synthet-ic, empirical or theoretical? The answer ofcourse is, “yes.”
Different niches of our field have different
work on SOAR (Rosenbloom, Laird, and Newell1993) as a general mechanism for producingintelligent reasoning and knowledge-basedsystems as a means of capturing human expertreasoning.
Where the logicist tradition takes intelligentreasoning to be a form of calculation, typicallydeduction in first-order logic, the traditionbased in psychology takes as the defining char-acteristic of intelligent reasoning that it is aparticular variety of human behavior. In thelogicist view, the object of interest is thus aconstruct definable in formal terms via math-ematics, while for those influenced by the psy-chological tradition, it is an empirical phe-nomenon from the natural world.
There are thus two very different assump-tions here about the essential nature of thefundamental phenomenon to be captured.One of them makes AI a part of mathematics;the other makes it a part of natural science.
A second contrast arises in considering thecharacter of the answers each seeks. The logi-cist view has traditionally sought compact andprecise characterizations of intelligence, look-ing for the kind of characterizations encoun-tered in mathematics (and at times in physics).The psychological tradition by contrast sug-gests that intelligence is not only a naturalphenomenon, it is an inherently complex nat-ural phenomenon: as human anatomy andphysiology are inherently complex systemsresulting from a long process of evolution, soperhaps is intelligence. As such, intelligencemay be a large and fundamentally ad hoc col-lection of mechanisms and phenomena, onefor which complete and concise descriptionsmay not be possible.
The point here is that there are a number ofdifferent views of what intelligent reasoningis, even within AI, and it matters which viewyou take because it shapes almost everything,from research methodology to your notion ofsuccess.
The Societal View: Reasoning as Emer-gent Behavior AI’s view of intelligent rea-soning has varied in another dimension aswell. We started out with the straightforward,introspection-driven view that intelligenceresided in, and resulted from, an individualmind. After all, there seems at first glance to beonly one mind inside each of us.
But this, too, has evolved over time, as AIhas considered how intelligent reasoning canarise from groups of (more or less) intelligententities, ranging from the simple units thatmake up connectionist networks, to the morecomplex units in Minsky’s (1986) society ofmind, to the intelligent agents involved in col-
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characters. Where we are concerned withhuman intelligence, our work is likely to bemore in the spirit of scientific, analytical, andempirical undertakings. Where the concern ismore one of machine intelligence, the workwill be more engineering, synthetic, and theo-retical. But the space is roughly continuous, itis large, and all these have their place.
Why Is Intelligence?Next I’d like to turn to the question, “Why isintelligence?” That is, can we learn from anexplicitly evolutionary view? Is there, or couldthere be, a paleocognitive science? If so, whatwould it tell us?
We had best begin by recognizing the diffi-culty of such an undertaking. It’s challengingfor several reasons: First, few of the relevantthings fossilize. I’ve checked the ancient bitsof amber, and sadly, there are no Jurassicontologies to be found embedded there; thereare no Paleolithic rule-based systems stillavailable for study; and although there is spec-ulation that the cave paintings at Lascauxwere the earliest implementation of JAVA, thisis, of course, speculation.
The examples may be whimsical, but thepoint is real—few of the elements of our intel-lectual life from prehistoric times are preservedand available for study. There are even thosewho suggest the entire undertaking is doomedfrom the start. Richard Lewontin (1990), whohas written extensively on evolution, suggeststhat “if it were our purpose in this chapter tosay what is actually known about the evolu-tion of human cognition, we would stop at theend of this sentence” (p. 229).
Luckily, he goes on: “That is not to say thata good deal has not been written on the sub-ject. Indeed whole books have been devoted todiscussions of the evolution of human cogni-tion and its social manifestations, but theseworks are nothing more than a mixture of purespeculation and inventive stories. Some ofthese stories might even be true, but we do notknow, nor is it clear…how we would go aboutfinding out” (p. 229). Hence, we had better bemodest in our expectations and claims.
A second difficulty lies in the data that areavailable. Most attempts to date phenomenaare good only to something like a factor of twoor four. The taming of fire, for example, prob-ably occurred around 100,000 years ago, but itmight have been 200,000 or even 400,000.Then there is the profusion of theories aboutwhy intelligence arose (more on those in amoment). Luckily for our purposes, we don’tactually have to know which, if any, of these
many theories are correct. I suggest you attendnot to the details of each but to the overallcharacter of each and what it may tell us abouthow the mind might have arisen.
Presumably the mind evolved and should asa consequence have some of the hallmarks ofanything produced by that process. Let’s setthe stage then by asking what’s known aboutthe nature of evolution, the process that waspresumably in charge of, and at the root of, allthis.
The Nature of Evolution The first thing to remember about evolution isthat it is engaging in a pastime that’s quitefamiliar to us: blind search. This is sometimesforgotten when we see the remarkableresults—apparently elegant and complex sys-tems—that come from a few million years’worth of search. The issue is put well in thetitle of one article—“The Good Enough Calculiof Evolving Control Systems: Evolution Is NotEngineering” (Partridge 1982). The article goeson to contrast evolution and engineeringproblem solving: In engineering, we have adefined problem in the form of design require-ments and a library of design elements avail-able for the solution. But “biology provides nodefinition of a problem until it has beenrevealed by the advantage of a solution. With-out a predefined problem, there is no prerequi-site domain, range, form for a solution, orcoordinates for its evaluation, except that itprovides a statistically improved survival func-tion. This filter selects ‘good enough’ new solu-tions and thereby identifies solved problems”(p. R173).
Consider in particular the claim that “biolo-gy provides no definition of a problem until ithas been revealed by the advantage of a solu-tion.” The warning here is to be wary of inter-preting the results of evolution as nature’s clev-erness in solving a problem. It had no problemto solve; it was just trying out variations.
The consequences of blind search are famil-iar to us; so, in some ways what follows seemsobvious, but the consequences are neverthe-less worth attending to.2
One consequence of random search is thatevolution wanders about, populating nicheswherever it finds them in the design space andthe environment. Evolution is not a process ofascent or descent; it’s a branching search spacebeing explored in parallel.
A second consequence is that nature issometimes a lousy engineer. There are, forexample, futile metabolic cycles in ourcells—apparently circular chemical reactionsthat go back and forth producing and unpro-
… can welearn from an explicitlyevolutionaryview? Is there, or could there be, apaleocognitivescience? If so, whatwould it tell us?
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in the blood near zero, but it cannot increaseblood-oxygen saturation past the blood’s nor-mal limits. As a result, the CO2 level can stayabnormally low past the time that oxygen lev-els have significantly decreased, and the diverwill feel no need to breathe even thoughblood-oxygen levels are low enough to lead toblackout.
Fifth, evolution sometimes proceeds byfunctional conversion, that is, the adoption ofan organ or system serving one purpose toserve another. The premier example here isbird wings: The structures were originallydeveloped for thermal regulation (as they arestill used in insects) and, at some point, werecoopted for use in flight.
Finally, evolution is conservative: It addsnew layers of solutions to old ones rather thanredesigning. This in part accounts for and pro-duces vestigal organs and systems, and theresult is not necessarily pretty from an engi-neering viewpoint. As one author put it, “Thehuman brain is wall-to-wall add-ons, a maze ofdinguses and gizmos patched into the originalpattern of a primitive fish brain. No wonder itisn’t easy to understand how it works” (Bicker-ton 1995, p. 36).
Evolution then is doing random search, andthe process is manifest in the product. As oneauthor put it,
In the natural realm, organisms are notbuilt by engineers who, with an overallplan in mind, use only the most appropri-ate materials, the most effective design,and the most reliable construction tech-niques. Instead, organisms are patchworkscontaining appendixes, uvulas, earlobes,dewclaws, adenoids, warts, eyebrows,underarm hair, wisdom teeth, and toe-nails. They are a meld of ancestral partsintegrated step by step during their devel-opment through a set of tried and trueontogenetic mechanisms. These mecha-nisms ensure matching between disparateelements such as nerves and muscles, butthey have no overall vision. Natural onto-genies and natural phylogenies are notlimited by principles of parsimony, andthey have no teleology. Possible organismscan be overdetermined, unnecessarilycomplex, or inefficiently designed (Katz1985, p. 28).
The important point here for our purposesis that what’s manifestly true of our anatomymay also be true of our cognitive architec-ture. Natural intelligence is unlikely to havean overall vision and unlikely to be limitedby principles of parsimony; like our bodies,it is likely to be overdetermined, unnecessar-
ducing the same molecules and depleting ener-gy 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 mecha-nisms. One widely cited example is the eye ofthe mammal and the eye of the octopus. Theyare quite similar but for one quite striking fact:The human eye is backward compared with theoctopus (Katz 1985). In the mammalian eye,the photoreceptors are in the retinal layer near-est the rear of the eye; as a consequence, lighthas to go through the retinal “back plane”before it encounters the photoreceptors.
A second striking example arises in the evo-lution of lungs in mammals and birds. Bothappear to have arisen from the swim bladdersthat fish use to control buoyancy, but birds’lungs are unidirectionally ventilated, unlike thetidal, bidirectional flow in other vertebrates. (Asa consequence, avian lungs are much more effi-cient than ours: Himalayan geese have beenobserved not only to fly over human climbersstruggling with their oxygen tanks to reach thetop of Mt. Everest but to honk as they do so(Encyclopedia Brittannica 1994–1997); presum-ably this is nature’s way of reminding us of ourplace in the scheme of things.)
The differences in end results suggest thedifferent paths that were taken to these results,yet the remaining similarities in eyes and lungsshow that evolution can rediscover the samebasic mechanisms despite its random search.
Fourth, there are numerous examples ofhow nature is a satisficer, not an optimizer. Forinstance, one of the reasons cuckoos can getaway with dropping off their eggs in the nestsof other birds is that birds have only a verycrude algorithm for recognizing their eggs andtheir chicks (Calvin 1991). The algorithm isgood enough, most of the time, but the cuckootakes advantage of its only adequate (manifest-ly nonoptimal) performance.
The control of human respiration providesanother example. Respiration is, for the mostpart, controlled by the level of CO2 in theblood. There appear to be a variety of reasonsfor this (for example, controlling CO2 is oneway to control pH levels in the blood), but it’sstill only an adequate system. Its limits are wellknown to mountain climbers and divers.Mountain climbers know that they have to beconscious of the need to breathe at altitudebecause the thin air leaves CO2 levels in theblood low, eliminating the normal physiologi-cal cues to breathe, even through blood-oxy-gen levels are also low.
Divers need to understand that hyperventi-lation is dangerous: It can drive the CO2 level
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ily complex, and inefficiently designed. In the face of that, searching for the mini-
malism and elegance beloved by engineersmay be a diversion, for it simply may not bethere. Somewhat more crudely put: Thehuman mind is a 400,000-year-old legacyapplication…and you expected to find struc-tured programming?
All that in turn gives us all the more reasonto explore deeply into the design space ofintelligence, for the human solution, and itssources of power, may be extraordinarilyquirky.
The Available Evidence If we can’t rely on the fossil record for pre-served bits of cognition, can it supply otheruseful information? One observation from therecord of particular relevance is the strikingincrease in what’s called the encephalizationquotient—the ratio of brain size to body size.Fossil records give clear evidence that theencephalization quotient of human ancestorsincreased by a factor of three to four overabout four million years (Donald 1991). Inevolutionary 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 aremetabolically very expensive. In the adult,about 20 percent of our metabolism goes intomaintaining our brains; in children, the brainconsumes about 50 percent of metabolic output(Bickerton 1995). This makes the question allthe more pressing: Considering how expensivelarge brains are, why do we have them? Why isintelligence? What benefit arose from it?
A second clear piece of evidence, this timefrom current studies of the brain, is lateraliza-tion: The standard examples are language(found in the left hemisphere in approximate-ly 93 percent of us) and the rapid sequencingof voluntary muscles for things such as throw-ing (found on the left in 89 percent) (Calvin1983). This is striking in part because thehuman brain has very few anatomical asym-metries; the observed asymmetries are almostentirely functional (Eccles 1989). It is alsostriking because the asymmetry arose with thehominids (Homo and our ancestors) andappears unique to them; the brains of our clos-est living relatives—apes and monkeys—are
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H. erectusH. sapiens
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Figure 1. The Fossil Record (derived from data in Donald [1991], Eccles [1989], Mithen [1996], and Hyland [1993]).Note: The averages shown in this chart do not make evident the apparent discontinuities in the size increases. As Mitherton (1996) dis-cusses, there were apparently two bursts of brain enlargement, one about two million years ago, at the transition from A. africanus to H.habilis, and another about 500,000 years ago, with the transition from H. erectus. And yes, the brains of H. neanderthalensis were on averagelarger than those of modern man, though so, too, was its body. Finally, note that the data in this field change more rapidly than one mightexpect: This chart was accurate when drawn in August 1996, but by December 1996 new evidence (Swisher et al. 1996) was reported sug-gesting that H. erectus did not in fact die off 250,000 years ago and may have lived contemporaneously with H. sapiens and the Neanderthals.
Theories of the Origin of IntelligenceA variety of theories have been suggested.
Early Man, the Primal Tool Maker Onetheory is wrapped up in the notion that manis a tool maker. The construction of increasing-ly elaborate tools both gave early man a sur-vival advantage and produced evolutionarypressure for yet more elaborate tools and thebrains to build them. Unfortunately, anotherlook at our time scale provides some disquiet-ing data. The earliest tools show up around 2.5million years ago and stay largely unchangeduntil about 300,000 years ago (Calvin 1991).Yet during all that time our brains are growingquickly. The tool theory thus seems unlikely.
Early Man and the Killer Frisbee A sec-ond theory (Calvin 1991, 1983) is centered onhunting methods and involves passing adevice that is sometimes whimsically referredto as the killer frisbee (figure 3). It’s one of theearliest tools and is more properly called ahand ax because it was believed to be a hand-held ax. The curious thing about it is that ifyou look closely, you’ll see that all its edges aresharp—not a very good idea for somethingdesigned to be held in the hand.
One researcher built replicas of these anddiscovered that if thrown like a discus, it flies
symmetrical both anatomically and function-ally (Eccles 1989).
The interesting question here of course iswhy, in a world of symmetry, is the humanbrain lateralized, even in part?
One useful way to set the stage for the vari-ous suggested answers is to consider thesequence of events that lead to Homo (H.) sapi-ens. Figure 1 gives an overview of the last fourmillion years, indicating the evolutionary spanof several of our immediate ancestors and theiraverage cranial capacity.
If we zoom in on the last 200,000 years, wesee a few additional events of note (figure 2).Speech arrives quite recently, around 200,000to 400,000 years ago; fire doesn’t get tameduntil around 100,000 years ago, which is whenmore advanced tools also begin to appear. Theconversion from hunter-gatherers to a settledsociety dependent on the use of agriculturehappens roughly 10,000 to 15,000 years ago,about the same time as the cave paintings atLascaux.
One question to ask about all this is, Whatchanged between four million years ago andnow? Four million years ago, there was (pre-sumably) nothing we would recognize ashuman-level intelligence; now there is. Whatchanged in between?
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Figure 2. A More Detailed Look at the Fossil Record.
like a frisbee at first but soon turns on edge andlands with its sharp edge embedded in theearth. Now add to this the fact that many ofthese artifacts have been found in the mudnear ancient waterholes. This led to the theorythat the artifacts were thrown by our ancestorsat herds of animals gathered at waterholes,with the intent of wounding one of them orknocking it down.
But why should throwing things be interest-ing—because throwing accurately requires pre-cise time control of motor neurons. For exam-ple, if you want to throw accurately at a targetthe size of a rabbit that’s 30 feet away (figure4), the motor-control problem is substantial:the time window for release of the projectile isless than 1 microsecond. But individual neu-rons are not in general that accurate temporal-ly. How do we manage?
One way to get the needed accuracy is torecruit populations of neurons and synchro-nize them: Enright (1980) shows how precisetiming can be produced from mutual couplingof heterogeneous, inaccurate oscillators (that
is, those with differing intrinsic average fre-quencies and that are individually unreliableon a cycle-to-cycle basis). With this arrange-ment, the standard deviation of cycle lengthbetween successive firings is proportional to
so quadrupling the number of elements cutsthe standard deviation in half. This mightaccount for the ability of our brains to controlmuscle action to within fractions of a millisec-ond, when individual neurons are an order ofmagnitude less precise.
The theory then is that our brains grew larg-er because more neurons produced an increasein throwing accuracy (or an increase in projec-tile speed with no reduction in accuracy), andthat in turn offered a major selective advan-tage: the ability to take advantage of a foodsource—small mammals—that was previouslyuntapped by hominids. A new food source inturn means a new ecological niche ripe forinhabiting. The advantage resulting from evena limited ability to make use of a new source offood also provides a stronger and more imme-
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Figure 3. An Early Tool: Top and Side Views.Reproduced with permission from Calvin (1991).
to be good enough at hunting to accumulateextra food beyond the day-to-day needs (hencethe related utility of being able to throw accu-rately), and then it would have had to developboth the foresight to put aside some of that forthe winter and the “technology” for doing so.There is, of course, a stiff Darwinian penaltyfor failure to be that smart.
Early Man, the Primal Frugivore Afourth theory suggests that the crucial elementwas 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 youneed to collect a lot of it, and it must be col-lected within a relatively narrow time window.As a consequence, frugivores need good spatialmaps of their environments (so they knowwhere the sources of fruit are) and good tem-poral maps (so they know when to show up).Perhaps this need for good spatial and tempo-ral maps was a force for the evolution of largerbrains.
Early Man, the Primal Psychologist Yetanother theory suggests that our primary useof intelligence is not for making tools, hunt-ing, or surviving the winter; it’s to get alongwith one another (Humphrey 1976; also seeByrne and Whiten [1988]). This theory issometimes called Machiavellian intelligence. Inthis view, the primary function of intelligenceis the maintenance of social relationships.
The evidence for this comes from severalsources, among them the behavior of monkey
diate selective pressure than is likely to havearisen from other advantages of a slightlyenlarged brain (for example, some limited pro-tolanguage ability).
The theory has a number of appealing corol-laries. It suggests one source of lateralizationbecause throwing is fundamentally asymmet-ric: One-armed throwing is far more accurateand effective than two armed for any reason-able-sized projectile (imagine baseball pitchersor outfielders propelling the ball overheadwith both arms). As a result, only the neuronson one side of the brain need be specialized forthe operation (for why this turns out, in nearly90 percent of us, to be the left side of the brain,see Calvin [1983]).3 That lateralization, whichmore generally involves precise sequentialmuscle control, may in turn have been a keypredecessor to language, which also requiresfast and accurate control of musculature.
Thus, the brain may have gotten larger toallow us to hunt better. The interesting punch-line for our purposes is that thinking may bean extra use of all those neurons that evolvedfor another purpose.
Early Man and the Killer Climate Athird theory suggests that climate plays a cen-tral role (Calvin 1991). The last few hundredthousand years of our history have beenmarked by a series of ice ages. A being used tosurviving in a temperate climate would face aconsiderable collection of challenges as theweather worsened and winters arrived. Inorder to survive the winter, it would have had
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Figure 4. Throwing Stones.A. At 4 meters, the launch window is 11 microseconds; at 8 meters, it narrows to 1.4 microseconds. Reproducedwith permission from Calvin (1991).
troops that have been studied extensively.They are seen to spend a good proportion oftheir time servicing and maintaining theirrelationships within their groups, tending toissues of rank and hierarchy and what appearto be allegiances.
A second source of evidence comes from astudy (Dunbar 1992) that plotted group sizeagainst neocortex ratio (ratio of neocortex sizeto the size of the rest of the brain) for a varietyof animals: a nearly linear relationshipemerged. Perhaps this held true for early manas well: As early group size grew, along with theadvantages of larger groups came increasingdemands to be able to understand, predict, andperhaps even control the behavior of others.We saw earlier that prediction was a key com-ponent of intelligent behavior; what morecomplex, fascinating, and useful thing couldthere be to predict than the behavior of thenother humans?
Early Man, the Primal Linguist Finally,Bickerton (1995) has suggested that languagewas the crucial driving force behind the evolu-tion of our brains. He starts with the interest-ing observation that if we look back at the his-torical time line, we notice that although brainsize grows roughly steadily for about three mil-lion years, progress in the development ofmodern culture was not nearly so gradual. Infact, “instead of a steady ascent . . . we find, for95% of that period, a monotonous, almost flatline” (Bickerton 1995, p. 47). Almost nothinghappens. It is well after the appearance of H.sapiens, and well after the leveling off of brainsize, that we see the appearance of languageand all the other elements of what we havecome to call civilization.
Bickerton calls these the two most shockingfacts of human evolution: (1) our ancestorsstagnated so long despite their ever-growingbrains and (2) human culture grew exponen-tially only after the brain had ceased to grow.It appears that we showed our most obviousevidence of intelligence only after our brainsstopped growing.
What was it that happened to produce thatevidence? He suggests that the crucial eventwas some sort of reorganization within thebrain, a reorganization that happened wellafter size stopped increasing. That reorganiza-tion made possible two essential things: first, agenerative syntax, that is, a true language, andsecond, thought, that is, the ability to thinkabout something (like a leopard) without hav-ing to experience the thing perceptually, andequally important, without having to react toit in the way one would on meeting one.
This leads to what appears to be a crucial dis-
tinction between animal intelligence andhuman intelligence. Animal intelligence has ahere and now character: With animal calls, forexample, there is an immediate link from theperception to the mind state to the action. If amonkey sees a leopard, a certain mind stateensues, and a certain behavior (giving theappropriate call) immediately follows.4
Human thought, by contrast, has an unlim-ited spatiotemporal reference, by virtue of sev-eral important disconnections. Human thoughtinvolves the ability to imagine, the ability tothink about something in the absence of per-ceptual input, and the ability to imagine with-out reacting.
In human thought we have the ability, theluxury, of “re-presentation.” The pun is inten-tional and probably educational: Representa-tions allow us to re-present things to ourselvesin the absence of the thing, so that we canthink about it, not just react to it.
Enormous things change when we haveboth thought and language. Thought and itsuseful disconnection from immediate stimuliand immediate action is clearly a greatboon—it’s the origin of our ability to have ourhypotheses die in our stead. But what aboutlanguage? For our purposes, the interestingthing about language is that it makes knowl-edge immortal and makes society, not the indi-vidual, the accumulator and repository ofknowledge. No longer is an individual’s knowl-edge limited to what can be experienced andlearned in a lifetime. Language not only allowsus to think, it allows us to share and accumu-late the fruits of that thought.
But what then caused our brains to growover the three million or so years during whichneither language nor thought (as we knowthem) was present? What was the evolutionarypressure? The theory suggests that the life of asuccessful hunter-gatherer is fact rich and prac-tice rich. In order to survive as a hunter-gath-erer, you need to know a lot of facts about yourworld and need to know a fair number of skills.This then is the hypothesized source of pres-sure: the increasing accumulation of survival-relevant information communicated througha form of protolanguage. Early man needed tostore “the vast amount of lore . . . in the collec-tive memories of traditional societies: the usesof herbs, the habits of animals, aphorismsabout human behavior, detailed knowledge ofthe spatial environment, anecdotes, old wives’tales, legends and myths” (Bickerton 1995, p.63).5
Where does this collection of theories (fig-ure 5) leave us? One obvious caution is thatthey are unlikely to be either independent or
… anothertheory suggests thatour primaryuse of intelligence is not formaking tools, hunting, orsurviving the winter; it’s to getalong withone another.
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it’s time for AI to learn about the birds and thebees. What do animals know, and (how) dothey think?
Clever Hans and Clever HandsBefore we get too far into this, it would we wiseto consider a couple of cautionary tales toensure the appropriate degree of skepticismabout this difficult subject. The classic caution-ary tale concerns a horse named Clever Hans,raised in Germany around 1900, that gaveevery appearance of being able to do arith-metic, tapping out his answers with his feet(Boakes 1984) (figure 6). He was able to givethe correct answers even without his trainer inthe room and became a focus of a considerableamount of attention and something of acelebrity.
In the end, it turned out that Hans was notmathematically gifted; his gift was perceptual.The key clue came when he was asked ques-tions to which no one in the room knew theanswer; in that case, neither did he. Hans hadbeen attending carefully to his audience andreacting to the slight changes in posture thatoccurred when he had given the correct num-ber of taps.6
The clever hands belong to a chimpanzeenamed Washoe who had been trained inAmerican Sign Language (Gardner et al. 1989).One day Washoe, seeing a swan in a pond,gave the sign for water and then bird. Thisseemed quite remarkable, as Washoe seemed tobe forming compound nouns—water bird—that he had not previously known (Mithen1996). But perhaps he had seen the pond andgiven the sign for water, then noticed the swanand given the sign for bird. Had he done so inthe opposite order—bird water—little excite-ment would have followed.
The standard caution from both of thesetales is always to consider the simpler explana-tion—trainer effects, wishful interpretation ofdata, and so on—before being willing to con-sider that animals are indeed capable ofthought.
Narrow Intelligence: Birds and Bees Given that, we can proceed to explore some ofthe varieties of animal intelligence that doexist. Several types of rather narrowly definedintelligence are supported by strong evidence.Among the birds and the bees, for example,bees are well known to “dance” for their hivemates to indicate the direction of food sourcesthey have found. Some birds have a remark-able ability to construct a spatial map. TheClark’s nutcracker, as one example, stores awayon the order of 30,000 seeds in 6,000 sites over
mutually exclusive. They may be mutuallysupportive and all true to some extent, witheach of them contributing some amount of theevolutionary pressure toward larger brains andintelligence.
A second point to note is that human intel-ligence is a natural phenomenon, born of evo-lution, and as suggested earlier, the end prod-uct likely shows evidence of the process thatcreated it. Intelligence is likely to be a layered,multifaceted, and probably messy collection ofphenomena, much like the other products ofevolution.
It also may be rather indirect. Here’s Lewon-tin (1990) again: “There may have been nodirect natural selection for cognitive ability atall. Human cognition may have developed asthe purely epiphenomenal consequence of themajor increase in brain size, which, in turn,may have been selected for quite other rea-sons” (p. 244), for example, any of the reasonsin figure 5.
This, too, suggests a certain amount of cau-tion in our approach to understanding intelli-gence, at least of the human variety: Thehuman mind is not only a 400,000-year-oldlegacy application, it may have been writtenfor another purpose and adopted for currentusage only after the fact. In light of that, weshould not be too surprised if we fail to findelegance and simplicity in the workings ofintelligence.
Inhuman Problem SolvingAs we explore the design space of intelligences,it’s interesting to consider some of the othervarieties of intelligence that are out there, par-ticularly the animal sort. With that, let meturn to the third part of my article, in which
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Early man, the primal tool makerEarly man and the killer frisbeeEarly man and the killer climateEarly man, the primal frugivore
Early man, the primal psychologistEarly man, the protolinguist
Figure 5. Theories of the Evolution of Intelligence.
the course of the spring and summer and isable to find about half of those during the win-ter (Balda and Kamil 1992). This is a narrowlyrestricted kind of intelligence but, at 6000locations, nonetheless impressive.
Broader Intelligence: PrimatesBroader forms of intelligence are displayed bysome primates. One particular variety—thevervet monkey—has been studied widely inthe wild and has displayed a range of intelli-gent-seeming behaviors (Cheney and Seyfarth1990). One of the important elements in thelife of a monkey group is status—your place inthe dominance hierarchy. Vervet monkeysgive every sign of understanding and beingable to reason using relations such as higher-status-than and lower-status-than. They can,for example, do simple transitive inference toestablish the place of others in the hierarchy: IfA can beat up B, and B can beat up C, there’sno need for A and C to fight; the result can beinferred (allowing our hypotheses to get bat-tered in our stead).
The monkeys also appear capable of classify-ing relationships as same or different, under-standing, for example, that mother-of is a dif-ferent relation from sibling-of. This can matterbecause if you fight with Junior, you had betteravoid mother-of(Junior) (who might be tempt-ed to retaliate), but sibling-of(Junior) presentsno such threat.
They also seem to have a vocabulary withsemantic content—different calls that corre-spond to the notion of leopard, eagle, andpython, the three main monkey predators. Thatthe calls are truly referential is suggested by thefacts that they are given only when appropri-ate, they are learned by trial and error by theyoung monkeys, and the troop takes appropri-ate action on hearing one of the calls. Hearingthe eagle call, for instance, all the troop mem-bers will look up, searching for the eagle, thentake cover in the bushes. Note that we havereferred to this as a vocabulary, not a language,because it appears that there is no syntax per-mitting the construction of phrases.
Lies—Do Monkeys Cry Leopard?There is also some anecdotal evidence that themonkeys lie to one another. They have beenobserved to lie by omission when it concernsfood: When happening on a modest-sizedstore of food, a monkey may fail to give thestandard call ordinarily given when findingfood. Instead, the lone monkey may simplyconsume it.
A more intriguing form of misrepresenta-tion has been observed to occur when twoneighboring monkey troops get into battlesover territory. Some of these battles have end-ed when one of the monkeys gives the leopardcall—all the combatants scatter, climbing intotrees to escape the predator, but there is in fact
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Figure 6. Clever Hans, the Mathematical Horse.His owner and trainer is rightmost of the group at the rear. Reproduced with permission from Boakes (1984).
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 differentfrom mammals and primates can performat the same level as chimpanzees and dol-phins on all the tests that we’ve used andperforms about at the level of a young,say, kindergarten-age child.
This is an interesting bit of animal intelli-gence, in part because of the careful trainingand testing that’s been done, suggesting that,unlike Hans, Alex really does understand cer-tain concepts. This is all the more remarkablegiven the significant differences between birdand mammalian brains: Parrot brains are quiteprimitive by comparison, with a far smallercerebral cortex.
ConsequencesThese varieties of animal intelligence illustratetwo important points: First, they illuminate forus a number of other distinguishable points inthe design space of intelligences. The narrowintelligences of birds and bees, clearly morelimited than our own, still offer impressive evi-dence of understanding and reasoning aboutspace. Primate intelligence provides evidenceof symbolic reasoning that, although primi-tive, has some of the character of what seemscentral to our own intelligence. Clearly distin-guishable from our own variety of intelligence,yet impressive on their own terms, these phe-nomena begin to suggest the depth andbreadth of the natural intelligences that haveevolved.
Second, the fact that even some part of thatintelligence appears similar to our own suggeststhe continuity of the design space. Humanintelligence may be distinct, but it does not sitalone and unapproachable in the space. Thereis a large continuum of possibilities in thatspace; understanding some of our nearestneighbors may help us understand our ownintelligence. Even admitting that there can benear neighbors offers a useful perspective.
Primate CelebritiesI can’t leave the topic of animal intelligencewithout paying homage to one of the true
no leopard to be found. The monkeys may belying to one another as a way of breaking upthe fight (Cheney and Seyfarth 1991).7
Psittacine Intelligence: Bird Brains NoLongerOne final example of animal intelligence con-cerns an African Grey Parrot named Alex, whohas been trained for quite a few years by Dr.Irene Pepperberg of the University of Arizona.Alex seems capable of grasping abstract con-cepts such as same, different, color, shape, andnumbers (Pepperberg 1991).
A videotape of Alex in action (WNET 1995)is particularly compelling; even a transcript ofthe conversation will give you a sense of what’sbeen accomplished. Pay particular attention toAlex’s ability to deal with, and reason about,abstract concepts and relations.
Narrator: For 17 years, Alex and Dr.Irene Pepperberg have been working onthe mental powers of parrots. Their effortsat the University of Arizona have pro-duced some remarkable results.
Dr. Pepperberg: What shape (holding upa red square)?
Alex: Corners. Dr. Pepperberg: Yeah, how many cor-
ners? Say the whole thing. Alex: Four…corners. Dr. Pepperberg: That’s right, four cor-
ners. Good birdie. Alex: Wanna nut. Dr. Pepperberg: You can’t have another
nut. OK, what shape? (holding up a green
triangle). Alex: Three…corners. Dr. Pepperberg: That’s right, three cor-
ners; 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
truck)?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 differ-
ent (same keys)?
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unsung heroes of early AI research. Everyonein AI knows the monkey and bananas problemof course. But what’s shocking, truly shocking,is that so many of us (myself included) don’tknow the real origins of this problem.
Thus, for the generations of AI students (andfaculty) who have struggled with the monkeyand bananas problem without knowing its ori-gins, I give you, the monkey (figure 7):8
This one is named Rana; he and several oth-er chimps were the subjects in an experimentdone by gestalt psychologist Wolfgang Kohler(1925) in 1918. Kohler was studying the intel-ligence of animals, with particular attention tothe phenomenon of insight, and gave his sub-jects a number of problems to solve. Here’sGrande, another of the chimps, hard at workon the most famous of them (figure 8).
Thus, there really was a monkey and a stalkof bananas, and it all happened back in 1918.Just to give you a feeling of how long ago thatwas, in 1918, Herb Simon had not yet won theNobel Prize.
Searching Design SpaceIn this last segment of the article, I’d like toconsider what parts of the design space ofintelligence we might usefully explore morethoroughly. None of these are unpopulated;people are doing some forms of the work I’llpropose. My suggestion is that there’s plenty ofroom for others to join them and good reasonto want to.
Thinking Is RelivingOne exploration is inspired by looking at alter-natives to the usual view that thinking is aform of internal verbalization. We also seem tobe able to visualize internally and do some ofour thinking visually; we seem to “see” thingsinternally.
As one common example, if I were to askwhether an adult elephant could fit throughyour bedroom door, you would most likelyattempt to answer it by reference to some men-tal image of the doorway and an elephant.
There is more than anecdotal evidence tosupport the proposition that mental imaging isclosely related to perception; a variety ofexperimental and clinical data also support thenotion. As one example, patients who had suf-fered a loss of their left visual field as a conse-quence of a stroke showed an interesting formof mental imagery loss (Bisiach and Luzzatti1978). These patients were asked to imaginethemselves standing at the northern end of atown square that they knew well and asked toreport the buildings that they could “see” in
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Figure 7. Rana, Star of an Early AI Problem.Reproduced with permission from Kohler (1969).
Figure 8. Grande Going for the Gold(en) Bananas.Reproduced with permission from Kohler (1925).
people seem to do a form of mental rotationon these images. The primary evidence for thisis that response time is directly proportional tothe amount of rotation necessary to get the fig-ures in alignment.
A second experiment in the same veininvolved mental folding (Shepard and Feng1972). The task here is to decide whether thetwo arrows will meet when each of the piecesof paper shown in figure 10 is folded into acube.
If you introspect as you do this task, I thinkyou’ll find that you are recreating in yourmind the sequence of actions you would takewere you to pick up the paper and fold it byhand.
What are we to make of these experiments?I suggest two things: First, it may be time totake seriously (once again) the notion of visualreasoning, that is, reasoning with diagrams asthings that we look at, whose visual nature is acentral part of the representation.
Second is the suggestion that thinking is aform of reliving. The usual interpretation ofthe data from the rotation and folding experi-ments is that we think visually. But considersome additional questions about the experi-ments: Why does it take time to do the rota-tion, and why does the paper get mentallyfolded one piece at a time? In the rotationexperiment, why don’t our eyes simply look ateach block, compute a transform, then do thetransformation in one step? I speculate thatthe reason is because our thought processesmimic real life: In solving the problem mental-ly, we’re re-acting out what we would experi-
their mental image when looking south. Inter-estingly, they report what they would in factbe able to see out of the right half of their visu-al field; that is, they report buildings to thesouth and west but none to the east.
Even more remarkably, if they are thenasked to imagine themselves on the south endof the square looking north and asked toreport on what they “see” in their mentalimage, they describe the buildings in what isnow the right half of their visual field (that is,buildings to the north and east) and fail com-pletely to report those on the west side of thesquare, even though they had mentionedthem only moments earlier.
The process going on in using the mind’seye to “see” is thus remarkably similar in someways to what happens in using the anatomicaleye to see.
A second source of support for this viewcomes from the observation of like-modalityinterference. If I ask you to hold a visual imagein your mind while you try to detect either avisual or an auditory stimulus, the ability todetect the visual stimulus is degraded, butdetection of the auditory stimulus remains thesame (Segal and Fusella 1970).
A third source of evidence comes fromexperiments done in the 1970s that exploredthe nature of visual thinking. One well-knownexperiment involved showing subjects imagesthat looked like figure 9 and then askingwhether the two images were two views of thesame structure, albeit rotated (Shepard andMetzler 1971).
One interesting result of this work was that
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Bloci A Block B Block B Block C
Figure 9. Are A and B the Same Object; Are B and C?Reprinted with permission from Shepard, R. N., and Metzler, J., Mental Rotation of Three-Dimensional Objects, Science 171:701–703, copy-right 1971, American Association for the Advancement of Science.
ence in the physical world.That’s my second suggestion: Take seriously
the notion of thinking as a form of reliving ourperceptual and motor experiences. That is,thinking is not simply the decontextualizedmanipulation of abstract symbols (powerfulthough that may be). Some significant part ofour thinking may be the reuse, or simulation,of our experiences in the environment. In thissense, vision and language are not simplyinput-output channels into a mind where thethinking gets done; they are instead a signifi-cant part of the thought process itself. Thesame may be true for our proprioreceptive andmotor systems: In mentally folding the paper,we simulate the experience as it would be werewe to have the paper in hand.
There is, by the way, a plausible evolution-ary rationale for this speculation that thinkingis a form of reliving. It’s another instance offunctional conversion: Machinery developedfor perception turns out to be useful for think-ing. Put differently, visual thinking is theoffline use of our ability to see. We’re makinguse of machinery that happened to be there foranother purpose, as has happened many timesbefore in evolution.9
One further, ambitious speculation con-cerns the neural machinery that might supportsuch reliving: Ullman (1996) describes counter-streams, a pair of complementary, intercon-nected pathways traveling in opposite direc-
tions between the high-level and low-levelvisual areas. Roughly speaking, the pathwayfrom the low-level area does data-driven pro-cessing, but the opposite pathway does model-driven processing. One possible mechanismfor thinking as reliving is the dominant use ofthe model-driven pathway to recreate the sortsof excitation patterns that would result fromthe actual experience.
One last speculation I’d like to make con-cerns the power of visual reasoning and dia-grams. The suggestion here is that diagrams arepowerful because they are, among otherthings, a form of what Johnson-Laird (1983)called reasoning in the model. Roughly speaking,that’s the idea that some of the reasoning wedo is not carried out in the formal abstractterms of predicate calculus but is instead doneby creating for ourselves a concrete miniworldwhere we carry out mental actions and thenexamine the results.
One familiar example is the use of diagramswhen proving theorems in geometry. Theintent is to get a proof of a perfectly generalstatement, yet it’s much easier to do with aconcrete, specific model, one that we canmanipulate and then examine to read off theanswers.
Consider, for example, the hypothesis thatany triangle can be shown to be the union oftwo right triangles.
We might start by drawing a triangle (figure
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Figure 10. Do the Arrows Meet When the Paper Is Folded into a Cube?Reprinted with permission from Shepard, R. N., and Feng, C., A Chronometric Study of Mental Paper Folding,Cognitive Psychology 3:228–243, copyright 1972, American Association for the Advancement of Science.
draw a line that was about three inches long orlong enough to reach this other line.
That’s my last speculation: There may beways to marry the concreteness of reasoning inthe model with the power and generality ofabstraction. One early step in this direction isdiscussed in Stahov, Davis, and Shrobe (1996),who discuss how a specific diagram can auto-matically be annotated with constraints thatcapture the appropriate general relationshipsamong its parts, but there is plainly muchmore to be done.
Summary With that, let me summarize. I want to suggestthat intelligence are many things, and this istrue in several senses. Even within AI, andeven with the subfield of inference, intelli-gence has been conceived of in a variety ofways, including the logical perspective, whichconsiders it a part of mathematical logic, andthe psychological perspective, which considersit an empirical phenomenon from the naturalworld.
One way to get a synthesis of these numer-ous views is to conceive of AI as the study ofthe design space of intelligences. I find this aninspiring way to conceive of our field, in partbecause of its inherent plurality of views andin part because it encourages us to explorebroadly and deeply about all the full range ofthat space.
We have also explored how human intelli-gence is a natural artifact, the result of theprocess of evolution and its parallel, oppor-tunistic exploration of niches in the designspace. As a result, it is likely to bear all the hall-marks of any product of that process—it is like-ly to be layered, multifaceted, burdened with
11a). The proof of course calls for any triangle,but we find it much easier with a concrete onein front of us.
We might then play with it a bit and even-tually hit on the idea of dropping a perpendic-ular (figure 11b).
Wary of a proof from a single concreteexample, we might try a number of other tri-angles and eventually come up with a formalabstract proof. But it’s often a lot easier to havea concrete example to work with, manipulate,and then examine the results of our manipula-tions.
What works for something as plainly visualas geometric theorems also seems to work forthings that are not nearly so visual, such as syl-logisms. Consider these sentences describing agroup 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 deter-mine the logical consequences of these state-ments, people imagine a concrete instance of aroom and some finite collection of people,then examine it to determine the answer.
The good news about any concrete exampleis its concreteness; the bad news is its concrete-ness, that is, its lack of generality—as many ahigh school geometry student has discoveredwhen he/she drew an insufficiently general dia-gram. For diagrams in particular, the problem iscompelling: There’s no such thing as an approx-imate diagram. Every line drawn has a preciselength, every angle a precise measure. The goodnews 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 marrythe virtues of reasoning in a concrete diagramwith the generality that would allow us to
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a b
Figure 11. Triangles.A. A random triangle. B. A random triangle with a perpendicular.
vestigal components, and rather messy. This isa second sense in which intelligence are manythings—it is composed of the many elementsthat have been thrown together over evolu-tionary timescales.
Because of the origins of intelligence and itsresulting character, AI as a discipline is likely tohave more in common with biology andanatomy than it does with mathematics orphysics. We may be a long time collecting awide variety of mechanisms rather than com-ing upon a few minimalist principles.
In exploring inhuman problem solving, wesaw that animal intelligence seems to fit insome narrowly constrained niches, particular-ly for the birds and bees, but for primates (andperhaps parrots), there are some broader vari-eties of animal intelligence. These other vari-eties of intelligence illustrate a number of oth-er distinguishable points in the design space ofintelligences, suggesting the depth andbreadth of the natural intelligences that haveevolved and indicating the continuity of thatdesign space.
Finally, I tried to suggest that there are someniches in the design space of intelligences thatare currently underexplored. There is, for exam-ple, the speculation that thinking is in part visu-al, and if so, it might prove very useful to devel-op representations and reasoning mechanismsthat reason with diagrams (not just about them)and that take seriously their visual nature.
I speculated that thinking may be a form ofreliving, that re-acting out what we have expe-rienced is one powerful way to think about,and solve problems in, the world. And finally,I suggested that it may prove useful to marrythe concreteness of reasoning in a model withthe power that arises from reasoning abstractlyand generally.
Notes1. Table 1 and some of the text following is fromDavis, Shrobe, and Szolovits (1993).
2. For a detailed exploration of the consequencesand their potentially disquieting implications, seeDennett (1995).
3. In brief, he suggests that it arises from the near-universal habit of women carrying babies in theirleft arms, probably because the maternal heartbeat iseasier for the baby to hear on that side. This kepttheir right arms free for throwing. Hence the firstmajor league hunter-pitcher may have been what hecalls the throwing madonna (not incidentally, the titleof his book).
4. That’s why the possibility of monkeys “lying” toone another (see later discussion) is sointriguing—precisely because it’s a break in the per-ception-action link.
5. Humphrey (1976) also touches on this idea.
6. Oskar Phungst, who determined the real nature ofHans’s skill, was able to mimic it so successfully thathe could pretend to be a mentalist, “reading themind” of someone thinking of a number: Pfungstsimply tapped until he saw the subtle changes inposture that were unconscious to the subject (Rosen-thal 1966).
7. For a countervailing view on the question of ani-mal lying, see the chapter by Nicholas Mackintoshin Khalfa (1994).
8. A true-life anecdote concerning life in Cambridge:When I went to a photographer to have this phototurned into a slide, the man behind the counter(probably an underpaid psychology graduate stu-dent) looked at the old book with some interest,then laughed at the photo I wanted reproduced. Ipretended to chide him, pointing out that the photowas of a famous contributor to psychological theory.“A famous contributor to psychology?” he said.“Then I know who it is.” “Who?” I asked. “Whythat’s Noam Chimpsky, of course,” he replied. Yes, itreally happened, just that way.
9. There has been significant controversy concerningthe exact nature and status of mental images; see, forexample, Farah (1988), who reviews some of thealternative theories as well as neuropsychologicalevidence for the reality of mental images. One of thealternative theories suggests that subjects in experi-ments of the mental-rotation sort are mentally sim-ulating their experience of seeing rather than actual-ly using their visual pathways. For our purposes,that’s almost as good: Although literal reuse of thevisual hardware would be a compelling example offunctional conversion, there is also somethingintriguing in the notion that one part of the braincan realistically simulate the behavior of other parts.
ReferencesBalda, R. P., and Kamil, A. C. 1992. Long-Term Spa-tial Memory in Clark’s Nutcrackers. Animal Behav-iour 44:761–769.
Bickerton, D. 1995. Language and Human Behavior.Seattle, Wash.: University of Washington Press.
Bisiach, E., and Luzzatti, C. 1995. Unilateral Neglectof Representational Space. Cortex 14:129–133.
Boakes, R. 1984. From Darwin to Behaviorism. NewYork: Cambridge University Press.
Byrne, R. W., and Whiten A., eds. 1988. Machiavel-lian Intelligence: Social Expertise and the Evolution ofIntellect in Monkeys, Apes, and Humans. Oxford, U.K.:Clarendon.
Calvin, W. H. 1983. The Throwing Madonna. NewYork: McGraw-Hill.
Calvin, W. H. 1991. The Ascent of Mind: Ice Age Cli-mates and the Evolution of Intelligence. New York: Ban-tam.
Cheney, D. L., and Seyfarth, R. M. 1991. Truth andDeception in Animal Communication. In CognitiveEthology, ed. C. Ristau, 127–151. Hillsdale, N.J.:Lawrence Erlbaum.
Cheney, D. L., and Seyfarth, R. M. 1990. How Mon-keys See the World: Inside the Mind of Another Species.
Presidential Address
SPRING 1998 109
Pepperberg, I. R. 1991. A Communicative Approachto Animal Cognition: A Study of Conceptual Abili-ties of an African Grey Parrot. In Cognitive Ethology,ed. C. Ristau, 153–186. Hillsdale, N.J.: Lawrence Erl-baum.
Popper, K. R. 1985. Evolutionary Epistemology. InPopper Selections, ed. D. Miller, 78–86. Princeton, N.J.:Princeton University Press.
Rosenbloom, P.; Laird, J.; Newell, A., eds. 1993. TheSOAR Papers: Research on Integrated Intelligence. Cam-bridge, Mass.: MIT Press.
Rosenthal, R. 1966. Experimenter Effects in BehavioralResearch. New York: Meredith.
Segal S., and Fusella, V. 1970. Influence of ImagedPictures and Sounds on Detection of Visual andAuditory Signals. Journal of Experimental Psychology83:458–464.
Shepard, R. N., and Feng, C. 1972. A ChronometricStudy of Mental Paper Folding. Cognitive Psychology3:228–243.
Shepard, R. N., and Metzler, J. 1971. Mental Rotationof Three-Dimensional Objects. Science 171:701–703.
Sloman, A. 1994. Explorations in Design Space. InProceedings of the Eleventh European Conferenceon Artificial Intelligence, ed. A. G. Cohn, 578–582.New York: Wiley.
Stahovich T.; Davis, R.; and Shrobe, H. 1996. Gener-ating Multiple New Designs from a Sketch. In Pro-ceedings of the Thirteenth National Conference onArtificial Intelligence, 1022–1029. Menlo Park,Calif.: American Association for Artificial Intelli-gence.
Swisher, C. C., III; Rink, W. J.; Antón, S. C.; Schwarcz,H. P.; Curtis, G. H.; Suprijo, A.; and Widiasmoro. Lat-est Homo Erectus of JAVA: Potential Contemporaneitywith Homo Sapiens in Southeast Asia. Science274(5294): 1870.
Ullman, S. 1996. High-Level Vision. Cambridge,Mass.: MIT Press.
WNET. 1995. Parrots: Look Who’s Talking. New York:Nature Video Library. Videocassette.
Randall Davis is a professor ofcomputer science at the Massa-chusetts Institute of Technology,where he works on model-basedreasoning systems for engineeringdesign, problem solving, and trou-bleshooting. He has also beenactive in the area of intellectualproperty and software, serving on
a number of government studies and as an adviser tothe court in legal cases. He received his undergradu-ate degree from Dartmouth College and his Ph.D.from Stanford University. He serves on several edito-rial boards, including those for Artificial Intelligenceand AI in Engineering. In 1990, he was named afounding fellow of the American Association forArtificial Intelligence and served as president of theassociation from 1995–1997. His e-mail address [email protected].
Chicago: University of Chicago Press.
Davis, R.; Shrobe, H.; and Szolovits, P. 1993. What Isa Knowledge Representation? AI Magazine 14(1):17–33.
Dennett, D. C. 1995. Darwin’s Dangerous Idea. NewYork: Simon and Schuster.
Diamond, J. 1992. The Third Chimpanzee. New York:Harper Collins.
Donald, M. 1991. Origins of the Modern Mind. Cam-bridge, Mass.: Harvard University Press.
Dunbar, R. I. 1992. Neocortex Size as a Constraint onGroup Size in Primates. Journal of Human Evolution20:469–493.
Eccles, J. C. 1989. Evolution of the Brain. New York:Routledge.
Encyclopedia Brittanica. 1994–1997. Respiration andRespiratory Systems: General Feature of the Respira-tory Process: Dynamics of Vertebrate RespiratoryMechanisms: Birds. In Encyclopedia Brittanica. Elec-tronic version. Available at www.eb.com:180/cgi-bin/g?DocF=macro/5005/50/11.html.
Enright, J. T. 1980. Temporal Precision in CircadianSystems: A Reliable Neuronal Clock from UnreliableComponents? Science 209:1542–1545.
Farah M. 1980. Is Visual Imagery Really Visual? Over-looked Evidence from Neuropsychology. Psychologi-cal Review 95(3): 307–317.
Gardner, R. A.; Gardner, B. T.; and vanCantfort, T.1989. Teaching Sign Language to Chimpanzees. NewYork: State University of New York Press.
Humphrey, N . K. 1976. The Social Function of Intel-lect. In Growing Points in Ethology, eds. P. Bateson andR. Hinde, 303–317. New York: Cambridge UniversityPress.
Hyland, M. E. 1993. Size of Human Groups duringthe Paleolithic and the Evolutionary Significance ofIncreased Group Size. Behavioral and Brain Sciences16(4): 709–710.
Johnson-Laird, P. N. 1983. Mental Models. Cam-bridge, Mass.: Harvard University Press.
Khalfa, J., ed. 1994. What Is Intelligence? New York:Cambridge University Press.
Katz, M. J. 1985. On the Wings of Angels: An Explo-ration of the Limits of the Biological Enterprise. Har-vard Magazine 88(1): 25–32.
Kohler, W. 1969. The Task of Gestalt Psychology.Princeton, N.J.: Princeton University Press.
Kohler, W. 1925. The Mentality of Apes. New York:Harcourt, Brace.
Lewontin, R. 1990. The Evolution of Cognition. InAn Invitation to Cognitive Science, Volume 3, eds. D.Osherson and E. Smith, 229–245. Cambridge, Mass.:MIT Press.
Minsky, M. 1986. The Society of Mind. New York:Simon and Schuster.
Mithen, S. 1996. The Prehistory of the Mind. London:Thames and Hudson.
Partridge, L. D. 1982. The Good Enuf Calculi ofEvolving Control Systems: Evolution Is Not Engi-neering. American Journal of Physiology242:R173–R177.
