Presidential Address

What Are Intelligence?
And Why?
1996 AAAI Presidential Address
Randall Davis■ This article, derived from the 1996 American Association for Artificial Intelligence Presidential
Address, 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, ranging
from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural
world) to a variety of others. One goal of this article is to go back to basics, reviewing the things that
we, individually and collectively, have taken as
given, in part because we have taken multiple different and sometimes inconsistent things for
granted. I believe it will prove useful to expose thetacit 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 atodds with one another.
Intelligence are also many things in the sense that
it is a product of evolution. Our physical bodies arein many ways overdetermined, unnecessarily complex, and inefficiently designed, that is, the predictable product of the blind search that is evolution. What’s manifestly true of our anatomy is also
likely true of our cognitive architecture. Natural
intelligence is unlikely to be limited by principles
of parsimony and is likely to be overdetermined,
unnecessarily complex, and inefficiently designed.
In this sense, intelligence are many things because
it is composed of the many elements that havebeen thrown together over evolutionary timescales. I suggest that in the face of that, searchingfor minimalism and elegance may be a diversion,for it simply may not be there. Somewhat more
crudely put: The human mind is a 400,000-yearold 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 develop
representations and reasoning mechanisms thatreason with diagrams (not just about them) andthat take seriously their visual nature. I speculate
as well 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. In this view, thinking is not simply
the decontextualized manipulation of abstract
symbols, powerful though that may be. Instead,some significant part of our thinking may be thereuse or simulation of our experiences in the environment. In keeping with this, I suggest that it
may prove useful to marry the concreteness of reasoning in a model with the power that arises fromreasoning abstractly.

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 theplural 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 collective
noun. I want to see what we in AI have thoughtof it and review the multiple ways in which
we’ve conceived of it. My intention is to makeexplicit 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, andsometimes inconsistent, 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 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 you
to come along on a natural history tour—I’m
going to take you away, back to a time around

Copyright © 1998, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1998 / $2.00

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Mathematical
Logic

Psychology

Biology

Statistics

Economics

Laplace

Bentham, Pareto

Aristotle
Descartes
Boole

James

Frege
Peano

BernoulliFriedman

Hebb
BrunerMillerNewellSimon

Lashley
Rosenblatt
Ashby
Lettvin
McCulloch, Pitts
Heubel, Weisel

Bayes
Tversky
Kahneman

Von Neumann
Simon
Raiffa

LOGIC

SOAR

CONNECTIONIS M

Causal networks

Rational agents

PROLOG

Knowledge-based systems
FramesA-lifeGoedelPost
ChurchTuringDavis
PutnamRobinson

Table 1. Views of Intelligent Reasoning and Their Intellectual Origins.

4 million years ago when the first hominids
arose and consider how intelligence came tobe. 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 concernedwith what we might call inhuman problem solving; it explores to what degree intelligence is ahuman monopoly. In this part of the article, AI
learns 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 might
expand our view, expand our exploration of
intelligence by exploring aspects of it thathave 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 made.

iors 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, thedisconnection of thought and action. That disconnection 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 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 andselecting actions appropriate to achieving thegoal. Finally, by reasoning, I mean starting withsome collection of facts and adding to it by
any inference method.

Fundamental Elements

AI has of course explored all these in a variety
of ways. Yet even if we focus in on just one ofthem—intelligent reasoning—it soon becomes
clear that there have been a multitude of

If AI is centrally concerned with intelligence,
we ought to start by considering what behav-

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Presidential Address

answers explored within AI as to what wemean 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 inparticular—(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, seesreasoning as a characteristic human behavior
and has given rise to both the extensive workon human problem solving and the large collection of knowledge-based systems. A third
approach, loosely rooted in biology, takes theview that the key to reasoning is the architecture of the machinery that accomplishes it;hence, reasoning is a characteristic stimulusresponse behavior that emerges from parallelinterconnection of a large collection of very
simple processors. Researchers working on several varieties of connectionism are descendantsof 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 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 fundamental nature of intelligent reasoning and will
demonstrate the deep-seated differences inmind 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 historicalorigins in Aristotle’s efforts to accumulate andcatalog 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 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 of
calculation, something mere mortals could do.
By the time of Leibnitz, the agenda is quite
specific and telling: He sought nothing lessthan a calculus of thought, one that would permit the resolution of all human disagreementwith 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, sosome (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
of 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 automatedtheorem provers. The modern offspring of thisline of intellectual development include themany efforts that use first-order logic as a representation and some variety of deduction as
the reasoning engine, as well as the large bodyof work with the explicit agenda of making
logical reasoning computational, exemplified
by Prolog.
Note we have here the underlying premisethat 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, typicallysound deduction (although other models haveof course been explored). By simple transitivity, these two collapse into one key part of theview 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 canbe captured in a formal description, particularly a formal description that is both precise andconcise.

The Psychological View: Reasoning as
Human Behavior But very different viewsof the nature of intelligent reasoning are also
possible. One distinctly different view isembedded in the part of AI influenced by thepsychological tradition. That tradition, rooted
in the work of Hebb, Bruner, Miller, andNewell and Simon, broke through the stimulus-response view demanded by behaviorism
and suggested instead that human problemsolving behavior could usefully be viewed interms of goals, plans, and other complex mental structures. Modern manifestations include

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work on SOAR (Rosenbloom, Laird, and Newell1993) as a general mechanism for producing
intelligent reasoning and knowledge-based
systems as a means of capturing human expert
reasoning.
Where the logicist tradition takes intelligent
reasoning to be a form of calculation, typicallydeduction in first-order logic, the tradition
based in psychology takes as the defining characteristic of intelligent reasoning that it is aparticular variety of human behavior. In the
logicist view, the object of interest is thus aconstruct 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 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 logicist view has traditionally sought compact andprecise 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 andphysiology 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 ofdifferent 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 us.
But this, too, has evolved over time, as AI
has considered how intelligent reasoning canarise from groups of (more or less) intelligent
entities, ranging from the simple units thatmake up connectionist networks, to the more
complex units in Minsky’s (1986) society ofmind, to the intelligent agents involved in col-94

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laborative work. Evolutions like this in ourconcept of intelligence have as corollaries acorresponding 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 toarise not from the individual components andtheir (individual) capabilities, but from theprinciples 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 ofintelligence 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 anotherchallenge: If intelligence arises in unexpected
ways from aggregations, then how will we everengineer intelligent behavior, that is, purposefully create any particular variety of it?
Consider then the wide variety of views wein 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 thesignificance of acknowledging and being
aware of the different conceptions that are
being explored and the fundamentally different assumptions they make. AI has been andwill continue to be all these things; it canembrace all of them simultaneously without
fear of contradiction.

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 intelligenceand machine intelligence. Second, the plural—intelligences—emphasizes the multiple
possibilities of what intelligence is (or are, as
my title suggests). Finally, conceiving of it interms of a design space suggests exploring
broadly and deeply, thinking about what kindsof 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 ofcourse is, “yes.”
Different niches of our field have different

Presidential Address

characters. Where we are concerned withhuman intelligence, our work is likely to be
more in the spirit of scientific, analytical, andempirical undertakings. Where the concern ismore 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 isintelligence?” 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 bitsof amber, and sadly, there are no Jurassic
ontologies to be found embedded there; there
are no Paleolithic rule-based systems stillavailable for study; and although there is speculation that the cave paintings at Lascaux
were the earliest implementation of JAVA, thisis, of course, speculation.
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 doomedfrom the start. Richard Lewontin (1990), who
has written extensively on evolution, suggeststhat “if it were our purpose in this chapter tosay what is actually known about the evolution 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 subject. Indeed whole books have been devoted to
discussions of the evolution of human cognition 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 not
know, 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 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 thesemany theories are correct. I suggest you attend
not to the details of each but to the overallcharacter 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 ofanything produced by that process. Let’s set
the stage then by asking what’s known about
the nature of evolution, the process that waspresumably in charge of, and at the root of, all
this.

The Nature of Evolution
The first thing to remember about evolution isthat 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 thetitle of one article—“The Good Enough Calculi
of Evolving Control Systems: Evolution Is Not
Engineering” (Partridge 1982). The article goeson to contrast evolution and engineering
problem solving: In engineering, we have adefined problem in the form of design requirements and a library of design elements available for the solution. But “biology provides nodefinition 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 itprovides 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 thatevolution wanders about, populating niches
wherever it finds them in the design space andthe environment. Evolution is not a process ofascent or descent; it’s a branching search space
being explored in parallel.
A second consequence is that nature issometimes a lousy engineer. There are, forexample, futile metabolic cycles in ourcells—apparently circular chemical reactions
that go back and forth producing and unpro-… can welearn froman explicitly
evolutionaryview?
Is there,or could
there be, apaleocognitive
science?
If so, what
would ittell us?

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ducing 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 ofthe mammal and the eye of the octopus. They
are 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 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. Bothappear to have arisen from the swim bladders
that 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 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 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 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 getaway with dropping off their eggs in the nestsof other birds is that birds have only a very
crude algorithm for recognizing their eggs andtheir chicks (Calvin 1991). The algorithm isgood 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 mostpart, controlled by the level of CO2 in the
blood. There appear to be a variety of reasonsfor this (for example, controlling CO2 is one
way to control pH levels in the blood), but it’s
still only an adequate system. Its limits are wellknown 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 CO2 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 CO2 level

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in the blood near zero, but it cannot increase
blood-oxygen saturation past the blood’s normal limits. As a result, the CO2 level can stayabnormally low past the time that oxygen levels 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, were
coopted for use in flight.
Finally, evolution is conservative: It adds
new layers of solutions to old ones rather thanredesigning. This in part accounts for and produces vestigal organs and systems, and theresult is not necessarily pretty from an engineering viewpoint. As one author put it, “Thehuman 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. 36).
Evolution then is doing random search, andthe process is manifest in the product. As one
author put it,
In the natural realm, organisms are notbuilt by engineers who, with an overallplan 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 toenails. 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, butthey have no overall vision. Natural ontogenies and natural phylogenies are notlimited by principles of parsimony, andthey have no teleology. Possible organisms
can be overdetermined, unnecessarily
complex, or inefficiently designed (Katz
1985, p. 28).
The important point here for our purposesis that what’s manifestly true of our anatomy
may also be true of our cognitive architecture. Natural intelligence is unlikely to havean overall vision and unlikely to be limited
by principles of parsimony; like our bodies,
it is likely to be overdetermined, unnecessar-

Presidential Address

crancial
capacity1600

H. neanderthalensis
H. sapiens

1200

H. erectus

800

H. habilus

400

A. africanus

- 4M

-3M

-2M

-1M

0

Time (millions of years)
A. = Australopithecus
H.= Homo

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) discusses, 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 might
expect: This chart was accurate when drawn in August 1996, but by December 1996 new evidence (Swisher et al. 1996) was reported suggesting that H. erectus did not in fact die off 250,000 years ago and may have lived contemporaneously with H. sapiens and the Neanderthals.

ily 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 ofintelligence, 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 otheruseful information? One observation from therecord 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 theencephalization quotient of human ancestors
increased by a factor of three to four overabout four million years (Donald 1991). In
evolutionary terms, this is an enormouschange 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 isintelligence? What benefit arose from it?
A second clear piece of evidence, this timefrom 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) (Calvin1983). This is striking in part because thehuman brain has very few anatomical asymmetries; the observed asymmetries are almost
entirely functional (Eccles 1989). It is alsostriking because the asymmetry arose with thehominids (Homo and our ancestors) andappears unique to them; the brains of our closest living relatives—apes and monkeys—are

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1600

H. neanderthalensis
H. sapiens

1200

H. erectus

800

H. habilus

400

A. africanus

-4M

-3M

-200K

-100K

speechfire
adv. tools

AAAI-96
agriculture
Lascaux

Figure 2. A More Detailed Look at the Fossil Record.

symmetrical both anatomically and functionally (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 various suggested answers is to consider thesequence of events that lead to Homo (H.) sapiens. Figure 1 gives an overview of the last four
million years, indicating the evolutionary spanof several of our immediate ancestors and theiraverage 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 whenmore 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, Whatchanged between four million years ago andnow? Four million years ago, there was (presumably) nothing we would recognize as
human-level intelligence; now there is. Whatchanged in between?

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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 evolutionarypressure for yet more elaborate tools and thebrains to build them. Unfortunately, anotherlook 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 adevice that is sometimes whimsically referred
to 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 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 hand.
One researcher built replicas of these anddiscovered that if thrown like a discus, it flies

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Figure 3. An Early Tool: Top and Side Views.
Reproduced with permission from Calvin (1991).

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 theory
that the artifacts were thrown by our ancestors
at herds of animals gathered at waterholes,
with the intent of wounding one of them orknocking 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 (figure4), the motor-control problem is substantial:
the time window for release of the projectile isless 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 torecruit populations of neurons and synchronize them: Enright (1980) shows how precise
timing can be produced from mutual couplingof heterogeneous, inaccurate oscillators (thatis, 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 to1N

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 ofmagnitude less precise.
The theory then is that our brains grew larger because more neurons produced an increasein throwing accuracy (or an increase in projectile speed with no reduction in accuracy), andthat in turn offered a major selective advantage: the ability to take advantage of a foodsource—small mammals—that was previously
untapped 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 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).

diate selective pressure than is likely to havearisen 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 pitchersor outfielders propelling the ball overhead
with both arms). As a result, only the neurons
on one side of the brain need be specialized forthe 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, whichmore 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 toallow 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 ClimateA
third theory suggests that climate plays a central role (Calvin 1991). The last few hundred
thousand 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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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 FrugivoreA
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 useof intelligence is not for making tools, hunting, or surviving the winter; it’s to get alongwith one another (Humphrey 1976; also see
Byrne and Whiten [1988]). This theory issometimes called Machiavellian intelligence. In
this view, the primary function of intelligenceis the maintenance of social relationships.
The evidence for this comes from several
sources, among them the behavior of monkey

Presidential Address

troops that have been studied extensively.
They are seen to spend a good proportion of
their time servicing and maintaining theirrelationships within their groups, tending toissues of rank and hierarchy and what appear
to be allegiances.
A second source of evidence comes from astudy (Dunbar 1992) that plotted group size
against neocortex ratio (ratio of neocortex size
to the size of the rest of the brain) for a varietyof animals: a nearly linear relationshipemerged. Perhaps this held true for early man
as well: As early group size grew, along with theadvantages of larger groups came increasing
demands to be able to understand, predict, andperhaps 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 thenother 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 ofmodern culture was not nearly so gradual. In
fact, “instead of a steady ascent . . . we find, for
95% of that period, a monotonous, almost flatline” (Bickerton 1995, p. 47). Almost nothing
happens. It is well after the appearance of H.
sapiens, and well after the leveling off of brainsize, that we see the appearance of language
and all the other elements of what we havecome 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 thebrain, a reorganization that happened wellafter size stopped increasing. That reorganization made possible two essential things: first, agenerative syntax, that is, a true language, andsecond, thought, that is, the ability to think
about something (like a leopard) without having 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 a
monkey sees a leopard, a certain mind stateensues, and a certain behavior (giving theappropriate 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 tothink about something in the absence of perceptual input, and the ability to imagine without reacting.
In human thought we have the ability, theluxury, of “re-presentation.” The pun is intentional and probably educational: Representations 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 its
useful disconnection from immediate stimuli
and immediate action is clearly a great
boon—it’s the origin of our ability to have ourhypotheses 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 ofknowledge. No longer is an individual’s knowledge limited to what can be experienced andlearned 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 growover the three million or so years during which
neither language nor thought (as we knowthem) was present? What was the evolutionarypressure? 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 yourworld and need to know a fair number of skills.
This then is the hypothesized source of pressure: the increasing accumulation of survivalrelevant information communicated througha form of protolanguage. Early man needed tostore “the vast amount of lore . . . in the collective memories of traditional societies: the usesof herbs, the habits of animals, aphorisms
about human behavior, detailed knowledge ofthe spatial environment, anecdotes, old wives’
tales, legends and myths” (Bickerton 1995, p.
63).5Where does this collection of theories (figure 5) leave us? One obvious caution is thatthey are unlikely to be either independent or… another
theorysuggests thatour primary
use ofintelligence
is not formakingtools,
hunting, or
surviving
the winter;it’s to getalong with
one another.

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Presidential Addressit’s time for AI to learn about the birds and thebees. What do animals know, and (how) do
they think?
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

Figure 5. Theories of the Evolution of Intelligence.
mutually exclusive. They may be mutually
supportive 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 intelligence is a natural phenomenon, born of evolution, and as suggested earlier, the end product 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 Lewontin (1990) again: “There may have been no
direct natural selection for cognitive ability atall. 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 reasonsin 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, weshould not be too surprised if we fail to findelegance and simplicity in the workings ofintelligence.

Inhuman Problem Solving
As we explore the design space of intelligences,it’s interesting to consider some of the othervarieties of intelligence that are out there, particularly the animal sort. With that, let me
turn to the third part of my article, in which102

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Clever Hans and Clever Hands
Before we get too far into this, it would we wise
to consider a couple of cautionary tales toensure 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 givethe correct answers even without his trainer inthe room and became a focus of a considerable
amount 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 questions to which no one in the room knew theanswer; in that case, neither did he. Hans had
been attending carefully to his audience andreacting 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. Thisseemed 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 andgiven 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 followed.
The standard caution from both of thesetales is always to consider the simpler explanation—trainer effects, wishful interpretation ofdata, and so on—before being willing to consider 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 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 awayon the order of 30,000 seeds in 6,000 sites overPresidential Address

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).

the course of the spring and summer and isable 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 bysome primates. One particular variety—thevervet monkey—has been studied widely inthe wild and has displayed a range of intelligent-seeming behaviors (Cheney and Seyfarth1990). One of the important elements in thelife of a monkey group is status—your place inthe dominance hierarchy. Vervet monkeys
give every sign of understanding and being
able to reason using relations such as higherstatus-than and lower-status-than. They can,for example, do simple transitive inference toestablish 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 beinferred (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 matterbecause if you fight with Junior, you had betteravoid mother-of(Junior) (who might be tempted to retaliate), but sibling-of(Junior) presents
no such threat.

They also seem to have a vocabulary withsemantic content—different calls that correspond 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 appropriate, they are learned by trial and error by theyoung 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, 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 permitting the construction of phrases.

Lies—Do Monkeys Cry Leopard?
There is also some anecdotal evidence that themonkeys 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 thestandard 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

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no leopard to be found. The monkeys may belying 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, andnumbers (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 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 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 anothernut.
OK, what shape? (holding up a green
triangle).
Alex: Three…corners.
Dr. Pepperberg: That’s right, three corners; that’s a good boy.
Now tell me, what color (holding thesame 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 different (same keys)?

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Alex: Color.
Dr. Pepperberg: Good parrot. You’reright, 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, threedifferent questions on the same objects.
Good parrot.
Dr. Pepperberg: What we’ve found out isthat 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 andperforms 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 remarkablegiven the significant differences between bird
and mammalian brains: Parrot brains are quiteprimitive by comparison, with a far smaller
cerebral cortex.

Consequences
These varieties of animal intelligence illustrate
two important points: First, they illuminate for
us a number of other distinguishable points inthe design space of intelligences. The narrowintelligences of birds and bees, clearly more
limited than our own, still offer impressive evidence of understanding and reasoning about
space. Primate intelligence provides evidenceof 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 andbreadth 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. Humanintelligence may be distinct, but it does not sit
alone and unapproachable in the space. Thereis a large continuum of possibilities in thatspace; understanding some of our nearest
neighbors may help us understand our own
intelligence. Even admitting that there can benear neighbors offers a useful perspective.

Primate Celebrities
I can’t leave the topic of animal intelligence
without paying homage to one of the truePresidential Address

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’tknow the real origins of this problem.
Thus, for the generations of AI students (andfaculty) 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 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’ll
propose. My suggestion is that there’s plenty ofroom for others to join them and good reason
to want to.

Figure 7. Rana, Star of an Early AI Problem.
Reproduced with permission from Kohler (1969).

Thinking Is Reliving
One exploration is inspired by looking at alternatives 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” things
internally.
As one common example, if I were to ask
whether an adult elephant could fit throughyour bedroom door, you would most likely
attempt to answer it by reference to some mental 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 suffered a loss of their left visual field as a consequence of a stroke showed an interesting formof mental imagery loss (Bisiach and Luzzatti1978). These patients were asked to imagine
themselves standing at the northern end of a
town square that they knew well and asked toreport the buildings that they could “see” inFigure 8. Grande Going for the Gold(en) Bananas.
Reproduced with permission from Kohler (1925).

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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, copyright 1971, American Association for the Advancement of Science.

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 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 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 thesquare, even though they had mentioned
them only moments earlier.
The process going on in using the mind’s
eye to “see” is thus remarkably similar in someways 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 avisual or an auditory stimulus, the ability todetect the visual stimulus is degraded, but
detection 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-known
experiment involved showing subjects images
that looked like figure 9 and then asking
whether the two images were two views of thesame structure, albeit rotated (Shepard andMetzler 1971).
One interesting result of this work was that106

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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 figures in alignment.
A second experiment in the same vein
involved mental folding (Shepard and Feng
1972). The task here is to decide whether thetwo arrows will meet when each of the piecesof paper shown in figure 10 is folded into a
cube.
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 as
things 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 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 mentallyfolded one piece at a time? In the rotation
experiment, why don’t our eyes simply look at
each block, compute a transform, then do thetransformation in one step? I speculate thatthe reason is because our thought processes
mimic real life: In solving the problem mentally, we’re re-acting out what we would experi-Presidential Address

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.

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 decontextualized
manipulation of abstract symbols (powerful
though 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 simply
input-output channels into a mind where thethinking gets done; they are instead a significant part of the thought process itself. The
same may be true for our proprioreceptive andmotor 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 offunctional conversion: Machinery developed
for perception turns out to be useful for thinking. Put differently, visual thinking is theoffline use of our ability to see. We’re making
use of machinery that happened to be there foranother 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 direc-

tions between the high-level and low-levelvisual areas. Roughly speaking, the pathway
from the low-level area does data-driven processing, but the opposite pathway does modeldriven processing. One possible mechanism
for thinking as reliving is the dominant use of
the model-driven pathway to recreate the sortsof excitation patterns that would result fromthe 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 otherthings, 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 doneby creating for ourselves a concrete miniworld
where we carry out mental actions and thenexamine 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 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 of
two right triangles.
We might start by drawing a triangle (figure

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Figure 11. Triangles.
A. A random triangle. B. A random triangle with a perpendicular.

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 proof 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 havea concrete example to work with, manipulate,
and then examine the results of our manipulations.
What works for something as plainly visualas geometric theorems also seems to work forthings that are not nearly so visual, such as syllogisms. 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 determine the logical consequences of these statements, 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 example
is its concreteness; the bad news is its concreteness, that is, its lack of generality—as many ahigh school geometry student has discovered
when he/she drew an insufficiently general diagram. For diagrams in particular, the problem iscompelling: There’s no such thing as an approximate diagram. Every line drawn has a precise
length, 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 marry
the virtues of reasoning in a concrete diagramwith the generality that would allow us to108

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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.

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, intelligence has been conceived of in a variety ofways, including the logical perspective, which
considers it a part of mathematical logic, andthe 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 ofthe design space of intelligences. I find this aninspiring way to conceive of our field, in part
because of its inherent plurality of views andin part because it encourages us to explore
broadly and deeply about all the full range ofthat space.
We have also explored how human intelligence is a natural artifact, the result of theprocess 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

Presidential Address

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 evolutionary timescales.
Because of the origins of intelligence and its
resulting 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 coming upon a few minimalist principles.
In exploring inhuman problem solving, we
saw that animal intelligence seems to fit insome narrowly constrained niches, particularly for the birds and bees, but for primates (andperhaps 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 ofintelligences, suggesting the depth andbreadth of the natural intelligences that have
evolved 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 example, the speculation that thinking is in part visual, and if so, it might prove very useful to develop 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 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 marrythe concreteness of reasoning in a model withthe power that arises from reasoning abstractly
and generally.

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 thathe could pretend to be a mentalist, “reading themind” of someone thinking of a number: Pfungst
simply tapped until he saw the subtle changes inposture 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. “Whythat’s Noam Chimpsky, of course,” he replied. Yes, itreally happened, just that way.
9. There has been significant controversy concerning
the 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 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 thevisual hardware would be a compelling example offunctional conversion, there is also something
intriguing in the notion that one part of the brain
can realistically simulate the behavior of other parts.

References
Balda, R. P., and Kamil, A. C. 1992. Long-Term Spatial Memory in Clark’s Nutcrackers. Animal Behaviour 44:761–769.
Bickerton, D. 1995. Language and Human Behavior.
Seattle, Wash.: University of Washington Press.

Notes

Bisiach, E., and Luzzatti, C. 1995. Unilateral Neglectof Representational Space. Cortex 14:129–133.

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

Boakes, R. 1984. From Darwin to Behaviorism. New
York: Cambridge University Press.

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

Byrne, R. W., and Whiten A., eds. 1988. Machiavellian Intelligence: Social Expertise and the Evolution ofIntellect in Monkeys, Apes, and Humans. Oxford, U.K.:
Clarendon.

3. In brief, he suggests that it arises from the nearuniversal habit of women carrying babies in theirleft arms, probably because the maternal heartbeat iseasier for the baby to hear on that side. This kept
their 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” to
one another (see later discussion) is so
intriguing—precisely because it’s a break in the perception-action link.

Calvin, W. H. 1983. The Throwing Madonna. New
York: McGraw-Hill.
Calvin, W. H. 1991. The Ascent of Mind: Ice Age Climates and the Evolution of Intelligence. New York: Bantam.
Cheney, D. L., and Seyfarth, R. M. 1991. Truth andDeception in Animal Communication. In Cognitive
Ethology, ed. C. Ristau, 127–151. Hillsdale, N.J.:
Lawrence Erlbaum.
Cheney, D. L., and Seyfarth, R. M. 1990. How Monkeys See the World: Inside the Mind of Another Species.

SPRING 1998 109

Presidential Address
Chicago: University of Chicago Press.
Davis, R.; Shrobe, H.; and Szolovits, P. 1993. What Is
a Knowledge Representation? AI Magazine 14(1):
17–33.
Dennett, D. C. 1995. Darwin’s Dangerous Idea. New
York: Simon and Schuster.
Diamond, J. 1992. The Third Chimpanzee. New York:
Harper Collins.
Donald, M. 1991. Origins of the Modern Mind. Cambridge, Mass.: Harvard University Press.
Dunbar, R. I. 1992. Neocortex Size as a Constraint on
Group Size in Primates. Journal of Human Evolution
20:469–493.
Eccles, J. C. 1989. Evolution of the Brain. New York:
Routledge.
Encyclopedia Brittanica. 1994–1997. Respiration andRespiratory Systems: General Feature of the Respiratory Process: Dynamics of Vertebrate Respiratory
Mechanisms: Birds. In Encyclopedia Brittanica. Electronic version. Available at www.eb.com:180/cgibin/g?DocF=macro/5005/50/11.html.
Enright, J. T. 1980. Temporal Precision in Circadian
Systems: A Reliable Neuronal Clock from Unreliable
Components? Science 209:1542–1545.
Farah M. 1980. Is Visual Imagery Really Visual? Overlooked Evidence from Neuropsychology. Psychological Review 95(3): 307–317.
Gardner, R. A.; Gardner, B. T.; and vanCantfort, T.
1989. Teaching Sign Language to Chimpanzees. New
York: State University of New York Press.
Humphrey, N . K. 1976. The Social Function of Intellect. In Growing Points in Ethology, eds. P. Bateson and
R. Hinde, 303–317. New York: Cambridge University
Press.
Hyland, M. E. 1993. Size of Human Groups during
the Paleolithic and the Evolutionary Significance ofIncreased Group Size. Behavioral and Brain Sciences
16(4): 709–710.
Johnson-Laird, P. N. 1983. Mental Models. Cambridge, 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 Exploration of the Limits of the Biological Enterprise. Harvard 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. In
An 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 of
Evolving Control Systems: Evolution Is Not Engineering.
AmericanJournalofPhysiology
242:R173–R177.

110

AI MAGAZINE

Pepperberg, I. R. 1991. A Communicative Approachto Animal Cognition: A Study of Conceptual Abilities of an African Grey Parrot. In Cognitive Ethology,
ed. C. Ristau, 153–186. Hillsdale, N.J.: Lawrence Erlbaum.
Popper, K. R. 1985. Evolutionary Epistemology. In
Popper Selections, ed. D. Miller, 78–86. Princeton, N.J.:
Princeton University Press.
Rosenbloom, P.; Laird, J.; Newell, A., eds. 1993. The
SOAR Papers: Research on Integrated Intelligence. Cambridge, Mass.: MIT Press.
Rosenthal, R. 1966. Experimenter Effects in Behavioral
Research. New York: Meredith.
Segal S., and Fusella, V. 1970. Influence of Imaged
Pictures and Sounds on Detection of Visual and
Auditory Signals. Journal of Experimental Psychology
83:458–464.
Shepard, R. N., and Feng, C. 1972. A ChronometricStudy of Mental Paper Folding. Cognitive Psychology
3:228–243.
Shepard, R. N., and Metzler, J. 1971. Mental Rotationof Three-Dimensional Objects. Science 171:701–703.
Sloman, A. 1994. Explorations in Design Space. In
Proceedings of the Eleventh European Conferenceon Artificial Intelligence, ed. A. G. Cohn, 578–582.
New York: Wiley.
Stahovich T.; Davis, R.; and Shrobe, H. 1996. Generating Multiple New Designs from a Sketch. In Proceedings of the Thirteenth National Conference on
Artificial Intelligence, 1022–1029. Menlo Park,
Calif.: American Association for Artificial Intelligence.
Swisher, C. C., III; Rink, W. J.; Antón, S. C.; Schwarcz,
H. P.; Curtis, G. H.; Suprijo, A.; and Widiasmoro. Latest 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 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 ona number of government studies and as an adviser tothe 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 Intelligenceand AI in Engineering. In 1990, he was named afounding fellow of the American Association for
Artificial Intelligence and served as president of theassociation from 1995–1997. His e-mail address isdavis@ai.mit.edu.