[Paleopsych] Meme 32: AI Magazine: What are intelligence? And why?
checker at panix.com
Tue Oct 5 19:43:51 UTC 2004
Meme 32: AI Magazine: What are intelligence? And why?
[This article doesn't say much about artificial intelligence as such, but
it's the best piece I've seen on theories of the evolution of human
intelligence (though the article was written before Geoffrey Miller's idea
that Darwin's other mechanism, sexual selection, came along. Also
discussed is intelligence in other animals.]
What are intelligence? And why? - Innovative Applications of AI
AI Magazine 19(1) (1998 Spring): 91 ff.
by Randall Davis
Relax, there's no mistake in the title. I've indulged a bit of
British-English that I've always found intriguing: the use of the
plural verb with collective nouns (as in "Oxford have won the themes
The selection of verb sense is purposeful and captures one of the main
themes of the article: I want to consider intelligence as a collective
noun. I want to see what we in Al have thought of it and review the
multiple ways in which we've conceived of it. My intention is to make
explicit the assumptions, metaphors, and models that underlie our
I intend to go back to basics here, as a way of reminding us of the
things that we, individually, and collectively, have taken as given,
in part because we have taken multiple different, and sometimes
consistent, things for granted. I believe it will prove useful to
expose the tacit assumptions, models, and metaphors that we carry
around, as a way of understanding both what we're about and why we
sometimes seem to be at odds with one another. That's the first part
of the article.
In the second part of the article, I'll ask you to come along on a
natural history tour -- I'm going to take you away, back to a time
around 4 million years ago when the first hominids arose and consider
how intelligence came to be. We'll take an evolutionary view, consider
intelligence as a natural phenomenon, and ask why it arose. The vague
answer -- that it provided enhanced survival -- turns out not to be
very informative; so, we'll ask, why is intelligence, and more
important, what does that tell us about how we might proceed in AI?
The third part of the article is concerned with what we might call
inhuman problem solving; it explores to what degree intelligence is a
human monopoly. In this part of the article, AI learns about the birds
and the bees: What kinds of animal intelligence are there, and does
that, too, inform our search for human intelligence?
I'll end by considering how we might expand our view, expand our
exploration of intelligence by exploring aspects of it that have
received too little attention. AI has been doing some amount of
consolidation over the past few years, so it may well be time to
speculate where the next interesting and provocative leaps might be
If AI is centrally concerned with intelligence, we ought to start by
considering what behaviors lie at its core. Four behaviors are
commonly used to distinguish intelligent behavior from instinct and
stimulus-response associations: (1) prediction, (2) response to
change, (3) intentional action, and (4) reasoning.
One core capability is our ability to predict the future, that is, to
imagine how things might turn out rather than have to try them. The
essential issue here is imagining, that is, the disconnection of
thought and action. That disconnection gives us the ability to imagine
the consequences of an action before, or instead of, experiencing it,
the ability, as Popper and Raimund (1985) put it, to have our
hypotheses die in our stead. The second element -- response to change
-- is an essential characteristic that distinguishes intelligent
action from inalterable instinct or conditioned reflexes. Intentional
action refers to having a goal and selecting actions appropriate to
achieving the goal. Finally, by reasoning, I mean starting with some
collection of facts and adding to it by any inference method.
Five Views of Reasoning
AI has of course explored all these in a variety of ways. Yet even if
we focus in on just one of them -- intelligent reasoning -- it soon
becomes clear that there have been a multitude of answers explored
within AI as to what we mean by that, that is, what we mean when we
say intelligent reasoning. Given the relative youth of our field, the
answers have often come from work in other fields. Five fields in
particular -- (1) mathematical logic, (2) psychology, (3) biology, (4)
statistics, and (5) economics -- have provided the inspiration for
five distinguishable notions of what constitutes intelligent reasoning
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One view, historically derived from mathematical logic, makes the
assumption that intelligent reasoning is some variety of formal
calculation, typically, deduction; the modern exemplars of this view
in AI are the logicists. A second view, rooted in work in psychology,
sees reasoning as a characteristic human behavior and has given rise
to both the extensive work on human problem solving and the large
collection of knowledge-based systems. A third approach, loosely
rooted in biology, takes the view that the key to reasoning is the
architecture of the machinery that accomplishes it; hence, reasoning
is a characteristic stimulus-response behavior that emerges from
parallel interconnection of a large collection of very, simple
processors. Researchers working on several varieties of connectionism
are descendants of this line of work; work on artificial life also has
roots in the biologically inspired view. A fourth approach, derived
from probability theory, adds to logic the notion of uncertainty,
yielding a view in which reasoning intelligently means obeying the
axioms of probability theory. A fifth view, from economics, adds the
further ingredients of values and preferences, leading to a view of
intelligent reasoning defined by adherence to the tenets of utility
Briefly exploring the historical development of the first two of these
views will illustrate the different conceptions they have of the
fundamental nature of intelligent reasoning and will demonstrate the
deep-seated differences in mind set that arise -- even within our own
field -- as a consequence.
The Logical View: Reasoning as Formal Calculation Consider first the
tradition that uses mathematical logic as a view of intelligent
reasoning. This view has its historical origins in Aristotle's efforts
to accumulate and catalog the syllogisms, in an attempt to determine
what should be taken as a convincing argument. (Note that even at the
outset, there is a hint of the idea that the desired form of reasoning
might be describable in a set of formal rules.) The line continues
with Descartes, whose analytic geometry showed that Euclid's work,
apparently concerned with the stuff of pure thought (lines of zero
width, perfect circles of the sorts only the gods could make), could
in fact be married to algebra, a form of calculation, something mere
mortals could do.
By the time of Leibnitz, the agenda is quite specific and telling: He
sought nothing less than a calculus of thought, one that would permit
the resolution of all human disagreement with the simple invocation
"let us compute." By this time, there is a clear and concrete belief
that as Euclid's once godlike and unreachable geometry could be
captured with algebra, so some (or perhaps any) variety of that
ephemeral stuff called thought might be captured in calculation,
specifically logical deduction.
In the nineteenth century, Boole provided the basis for propositional
calculus in his Laws or Thought; later work by Frege and Peano
provided additional foundation for the modern form of predicate
calculus. Work by Davis, Putnam, and Robinson in the twentieth century
provided the final steps in mechanizing deduction sufficiently to
enable the first automated theorem provers. The modern offspring of
this line of intellectual development include the many efforts that
use first-order logic as a representation and some variety of
deduction as the reasoning engine, as well as the large body of work
with the explicit agenda of making logical reasoning computational,
exemplified by Prolog.
Note we have here the underlying premise that reasoning intelligently
means reasoning logically; anything else is a mistake or an
aberration. Allied with this is the belief that logically, in turn,
means first-order logic, typically sound deduction (although other
models have of course been explored). By simple transitivity, these
two collapse into one key part of the view of intelligent reasoning
underlying logic: Reasoning intelligently means reasoning in the
fashion defined by first-order logic. A second important part of the
view is the allied belief that intelligent reasoning is a process that
can be captured in a formal description, particularly a formal
description that is both precise and concise.
The Psychological View: Reasoning as Human Behavior
But very different
views of the nature of intelligent reasoning are also possible. One
distinctly different view is embedded in the part of AI influenced by
the psychological tradition. That tradition, rooted in the work of
Hebb, Bruner, Miller, and Newell and Simon, broke through the
stimulus-response view demanded by behaviorism and suggested instead
that human problem-solving behavior could usefully be viewed in terms
of goals, plans, and other complex mental structures. Modern
manifestations include work on SOAR (Rosenbloom, Laird, and Newell
1993) as a general mechanism for producing intelligent reasoning and
knowledge-based systems as a means of capturing human expert
Where the logicist tradition takes intelligent reasoning to be a form
of calculation, typically deduction in first-order logic, the
tradition based in psychology takes as the defining characteristic of
intelligent reasoning that it is a particular variety of human
behavior. In the logicist view, the object of interest is thus a
construct definable in formal terms via mathematics, while for those
influenced by the psychological tradition, it is an empirical
phenomenon from the natural world.
There are thus two very different assumptions here about the essential
nature of the fundamental phenomenon to be captured. One of them makes
Al a part of mathematics; the other makes it a part of natural
A second contrast arises in considering the character of the answers
each seeks. The logicist view has traditionally sought compact and
precise characterizations of intelligence, looking for the kind of
characterizations encountered in mathematics (and at times in
physics). The psychological tradition by contrast suggests that
intelligence is not only a natural phenomenon, it is an inherently
complex natural phenomenon: as human anatomy and physiology are
inherently complex systems resulting from a long process of evolution,
so perhaps is intelligence. As such, intelligence may be a large and
fundamentally ad hoc collection of mechanisms and phenomena, one for
which complete and concise descriptions may not be possible.
The point here is that there are a number of different views of what
intelligent reasoning is, even within AI, and it matters which view
you take because it shapes almost everything, from research
methodology to your notion of success.
The Societal View: Reasoning as Emergent Behavior
AI's view of
intelligent reasoning has varied in another dimension as well. We
started out with the straightforward, introspection-driven view that
intelligence resided in, and resulted from, an individual mind. After
all, there seems at first glance to be only one mind inside each of
But this, too, has evolved over time, as AI has considered how
intelligent reasoning can arise from groups of (more or less)
intelligent entities, ranging from the simple units that make up
connectionist networks, to the more complex units in Minsky's (1986)
society of mind, to the intelligent agents involved in collaborative
work. Evolutions like this in our concept of intelligence have as
corollaries a corresponding evolution in our beliefs about where
sources of power are to be found. One of the things I take Minsky to
be arguing in his society of mind theory is that power is going to
arise not from the individual components and their (individual)
capabilities, but from the principles of organization -- how you put
things (even relatively simple things) together in ways that will
cause their interaction to produce intelligence. This leads to the
view of intelligence as an emergent phenomenon -- something that
arises (often in a nonobvious fashion) from the interaction of
individual behaviors. If this is so, we face yet another challenge: If
intelligence arises in unexpected ways from aggregations, then how
will we ever engineer intelligent behavior, that is, purposefully
create any particular variety of it?
Consider then the wide variety of views we in AI have taken of
intelligent reasoning: logical and psychological, statistical and
economic, individual and collaborative. The issue here is not one of
selecting one of these over another (although we all may have our
individual reasons for doing so). The issue is instead the
significance of acknowledging and being aware of the different
conceptions that are being explored and the fundamentally different
assumptions they make. AI has been and will continue to be all these
things; it can embrace all of them simultaneously without fear of
AI: Exploring the Design Space of Intelligences.
remains, of course, to try to unify them. I believe this can in fact
be done, using a view I first heard articulated by Aaron Sloman
(1994), who suggested conceiving of AI as the exploration of the
design space of intelligences.
I believe this is a useful view of what we're about for several
reasons: First, it's more general than the usual conjunction that
defines us as a field interested in both human intelligence and
machine intelligence. Second, the plural -- intelligences --
emphasizes the multiple possibilities of what intelligence is (or are,
as my title suggests). Finally, conceiving of it in terms of a design
space suggests exploring broadly and deeply, thinking about what kinds
of intelligences there are, for there may be many.
This view also helps address the at-times debated issue of the
character of our field: Are we science or engineering, analytic or
synthetic, empirical or theoretical? The answer of course is, "yes."
Different niches of our field have different characters. Where we are
concerned with human intelligence, our work is likely to be more in
the spirit of scientific, analytical, and empirical undertakings.
Where the concern is more one of machine intelligence, the work will
be more engineering, synthetic, and theoretical. But the space is
roughly continuous, it is large, and all these have their place.
Why Is Intelligence?
Next I'd like to turn to the question, "Why is intelligence?" That is,
can we learn from an explicitly evolutionary view? Is there, or could
there be, a paleocognitive science? If so, what would it tell us?
We had best begin by recognizing the difficulty of such an
undertaking. It's challenging for several reasons: First, few of the
relevant things fossilize. I've checked the ancient bits of amber, and
sadly, there are no Jurassic ontologies to be found embedded there;
there are no Paleolithic rule-based systems still available for study;
and although there is speculation that the cave paintings at Lascaux
were the earliest implementation of JAVA, this is, of course,
The examples may be whimsical, but the point is real -- few of the
elements of our intellectual life from prehistoric times are preserved
and available for study. There are even those who suggest the entire
undertaking is doomed from the start. Richard Lewontin (1990), who has
written extensively on evolution, suggests that "if it were our
purpose in this chapter to say what is actually known about the
evolution of human cognition, we would stop at the end of this
sentence" (p. 229).
Luckily, he goes on: "That is not to say that a good deal has not been
written on the subject. Indeed whole books have been devoted to
discussions of the evolution of human cognition and its social
manifestations, but these works are nothing more than a mixture of
pure speculation and inventive stories. Some of these stories might
even be true, but we do not know, nor is it clear ... how we would go
about finding out" (p. 229). Hence, we had better be modest in our
expectations and claims.
A second difficulty lies in the data that are available. Most attempts
to date phenomena are good only to something like a factor of two or
four. The taming of fire, for example, probably occurred around
100,000 years ago, but it might have been 200,000 or even 400,000.
Then there is the profusion of theories about why intelligence arose
(more on those in a moment). Luckily for our purposes, we don't
actually have to know which, if any, of these many theories are
correct. I suggest you attend not to the details of each but to the
overall character of each and what it may tell us about how the mind
might have arisen.
Presumably the mind evolved and should as a consequence have some of
the hallmarks of anything produced by that process. Let's set the
stage then by asking what's known about the nature of evolution, the
process that was presumably in charge of, and at the root of, all
The Nature of Evolution
The first thing to remember about evolution is that it is engaging in
a pastime that's quite familiar to us: blind search. This is sometimes
forgotten when we see the remarkable results -- apparently elegant and
complex systems -- that come from a few million years' worth of
search. The issue is put well in the title of one article -- "The Good
Enough Calculi of Evolving Control Systems: Evolution Is Not
Engineering" (Partridge 1982). The article goes on to contrast
evolution and engineering problem solving: In engineering, we have a
defined problem in the form of design requirements and a library of
design elements available for the solution. But "biology provides no
definition of a problem until it has been revealed by the advantage of
a solution. Without a predefined problem, there is no prerequisite
domain, range, form for a solution, or coordinates for its evaluation,
except that it provides a statistically improved survival function.
This filter selects `good enough' new solutions and thereby identifies
solved problems" (p. R173).
Consider in particular the claim that "biology provides no definition
of a problem until it has been revealed by the advantage of a
solution." The warning here is to be wary of interpreting the results
of evolution as nature's cleverness in solving a problem. It had no
problem to solve; it was just trying out variations.
The consequences of blind search are familiar to us; so, in some ways
what follows seems obvious, but the consequences are nevertheless
worth attending to.(2)
One consequence of random search is that evolution wanders about,
populating niches wherever it finds them in the design space and the
environment. Evolution is not a process of ascent or descent; it's a
branching search space being explored in parallel.
A second consequence is that nature is sometimes a lousy engineer.
There are, for example, futile metabolic cycles in our cells --
apparently circular chemical reactions that go back and forth
producing and unproducing the same molecules and depleting energy
stores for no apparent purpose (Katz 1985).
Third, despite the size of the design space, blind search sometimes
doubles back on itself, and evolution rediscovers the same mechanisms.
One widely cited example is the eye of the mammal and the eye of the
octopus. They are quite similar but for one quite striking fact: The
human eye is backward compared with the octopus (Katz 1985). In the
mammalian eye, the photoreceptors are in the retinal layer nearest the
rear of the eye; as a consequence, light has to go through the retinal
"back plane" before it encounters the photoreceptors.
A second striking example arises in the evolution of lungs in mammals
and birds. Both appear to have arisen from the swim bladders that fish
use to control buoyancy, but birds' lungs are unidirectionally
ventilated, unlike the tidal, bidirectional flow in other vertebrates.
(As a consequence, avian lungs are much more efficient than ours:
Himalayan geese have been observed not only to fly over human climbers
struggling with their oxygen tanks to reach the top of Mt. Everest but
to honk as they do so (Encyclopedia Brittannica 1994-1997); presumably
this is nature's way of reminding us of our place in the scheme of
The differences in end results suggest the different Paths that were
taken to these results, yet the remaining similarities in eyes and
lungs show that evolution can rediscover the same basic mechanisms
despite its random search.
Fourth, there are numerous examples of how nature-is a satisficer, not
an optimizer. For instance, one of the reasons cuckoos can get away
with dropping off their eggs in the nests of other birds is that birds
have only a very crude algorithm for recognizing their eggs and their
chicks (Calvin 1991). The algorithm is good enough, most of the time,
but the cuckoo takes advantage of its only adequate (manifestly
The control of human respiration provides another example. Respiration
is, for the most part, controlled by the level of [CO.sub.2] in the
blood. There appear to be a variety of reasons for this (for example,
controlling [CO.sub.2] is one way to control pH levels in the blood),
but it's still only an adequate system. Its limits are well known to
mountain climbers and divers. Mountain climbers know that they have to
be conscious of the need to breathe at altitude because the thin air
leaves [CO.sub.2] levels in the blood low, eliminating the normal
physiological cues to breathe, even through blood-oxygen levels are
Divers need to understand that hyperventilation is dangerous: It can
drive the [CO.sub.2] level in the blood near zero, but it cannot
increase blood-oxygen saturation past the blood's normal limits. As a
result, the [CO.sub.2] level can stay abnormally low past the time
that oxygen levels have significantly decreased, and the diver will
feel no need to breathe even though blood-oxygen levels are low enough
to lead to blackout.
Fifth, evolution sometimes proceeds by functional conversion, that is,
the adoption of an organ or system serving one purpose to serve
another. The premier example here is bird wings: The structures were
originally developed for thermal regulation (as they are still used in
insects) and, at some point, were coopted for use in flight.
Finally, evolution is conservative: It adds new layers of solutions to
old ones rather than redesigning. This in part accounts for and
produces vestigal organs and systems, and the result is not
necessarily pretty from an engineering viewpoint. As one author put
it, "The human brain is wall-to-wall add-ons, a maze of dinguses and
gizmos patched into the original pattern of a primitive fish brain. No
wonder it isn't easy to understand how it works" (Bickerton 1995, p.
Evolution then is doing random search, and the process is manifest in
the product. As one author put it,
In the natural realm, organisms are not
built by engineers who, with an overall
plan in mind, use only the most
appropriate materials, the most effective design,
and the most reliable construction
techniques. Instead, organisms are patchworks
containing appendixes, uvulas, earlobes,
dewclaws, adenoids, warts, eyebrows,
underarm hair, wisdom teeth, and
toe-nails. They are a meld of ancestral parts
integrated step by step during their
development through a set of tried and true
ontogenetic mechanisms. These
mechanisms ensure matching between disparate
elements such as nerves and muscles, but
they have no overall vision. Natural
ontogenies and natural phylogenies are not
limited by principles of parsimony, and
they have no teleology. Possible organisms
can be overdetermined, unnecessarily
complex, or inefficiently designed (Katz
1985, p. 28).
The important point here for our purposes is that what's manifestly
true of our anatomy may also be true of our cognitive architecture.
Natural intelligence is unlikely to have an overall vision and
unlikely to be limited by principles of parsimony; like our bodies, it
is likely to be overdetermined, unnecessarily complex, and
In the face of that, searching for the minimalism and elegance beloved
by engineers may be a diversion, for it simply may not be there.
Somewhat more crudely put: The human mind is a 400,000-year-old legacy
application. . .and you expected to find structured programming?
All that in turn gives us all the more reason to explore deeply into
the design space of intelligence, for the human solution, and its
sources of power, may be extraordinarily quirky.
The Available Evidence
If we can't rely on the fossil record for preserved bits of cognition,
can it supply other useful information? One observation from the
record of particular relevance is the striking increase in what's
called the encephalization quotient -- the ratio of brain size to body
size. Fossil records give clear evidence that the encephalization
quotient of human ancestors increased by a factor of three to four
over about four million years (Donald 1991). In evolutionary terms,
this is an enormous change over a short period of time. Simply put,
our brains got very big very fast.
This is interesting in part because brains are metabolically very
expensive. In the adult, about 20 percent of our metabolism goes into
maintaining our brains; in children, the brain consumes about 50
percent of metabolic output (Bickerton 1995). This makes the question
all the more pressing: Considering how expensive large brains are, why
do we have them? Why is intelligence? What benefit arose from it?
A second clear piece of evidence, this time from current studies of
the brain, is lateralization: The standard examples are language
(found in the left hemisphere in approximately 93 percent of us) and
the rapid sequencing of voluntary muscles for things such as throwing
(found on the left in 89 percent) (Calvin 1983). This is striking in
part because the human brain has very few anatomical asymmetries; the
observed asymmetries are almost entirely functional (Eccles 1989). It
is also striking because the asymmetry arose with the hominids (Homo
and our ancestors) and appears unique to them; the brains of our
closest living relatives -- apes and monkeys -- are symmetrical both
anatomically and functionally (Eccles 1989).
The interesting question here of course is why, in a world of
symmetry, is the human brain lateralized, even in part?
One useful way to set the stage for the various suggested answers is
to consider the sequence of events that lead to Homo (H.) sapiens.
Figure 1 gives an overview of the last four million years, indicating
the evolutionary span of several of our immediate ancestors and their
average cranial capacity.
[FIGURE 1 GRAPH OMITTED]
If we zoom in on the last 200,000 years, we see a few additional
events of note (figure 2). Speech arrives quite recently, around
200,000 to 400,000 years ago; fire doesn't get tamed until around
100,000 years ago, which is when more advanced tools also begin to
appear. The conversion from hunter-gatherers to a settled society
dependent on the use of agriculture happens roughly 10,000 to 15,000
years ago, about the same time as the cave paintings at Lascaux.
One question to ask about all this is, What changed between four
million years ago and now? Four million years ago, there was
(presumably) nothing we would recognize as human-level intelligence;
now there is. What changed in between?
Theories of the Origin of Intelligence
A variety of theories have been suggested.
Early Man, the Primal Tool Maker
One theory is wrapped up in the
notion that man is a tool maker. The construction of increasingly
elaborate tools both gave early man a survival advantage and produced
evolutionary pressure for yet more elaborate tools and the brains to
build them. Unfortunately, another look at our time scale provides
some disquieting data. The earliest tools show up around 2.5 million
years ago and stay largely unchanged until about 300,000 years ago
(Calvin 1991). Yet during all that time our brains are growing
quickly. The tool theory thus seems unlikely.
Early Man and the Killer Frisbee
A second theory (Calvin 1991, 1983)
is centered on hunting methods and involves passing a device that is
sometimes whimsically referred to as the killer frisbee (figure 3).
It's one of the earliest tools and is more properly called a hand ax
because it was believed to be a handheld ax. The curious thing about
it is that if you look closely, you'll see that all its edges are
sharp -- not a very good idea for something designed to be held in the
[FIGURE 3 ILLUSTRATION OMITTED]
One researcher built replicas of these and discovered that if thrown
like a discus, it flies like a frisbee at first but soon turns on edge
and lands with its sharp edge embedded in the earth. Now add to this
the fact that many of these artifacts have been found in the mud near
ancient waterholes. This led to the theory that the artifacts were
thrown by our ancestors at herds of animals gathered at waterholes,
with the intent of wounding one of them or knocking it down.
But why should throwing things be interesting -- because throwing
accurately requires precise time control of motor neurons. For
example, if you want to throw accurately at a target the size of a
rabbit that's 30 feet away (figure 4), the motor-control problem is
substantial: the time window for release of the projectile is less
than 1 microsecond. But individual neurons are not in general that
accurate temporally. How do we manage?
[FIGURE 4 ILLUSTRATION OMITTED]
One way to get the needed accuracy is to recruit populations of
neurons and synchronize them: Enright (1980) shows how precise timing
can be produced from mutual coupling of heterogeneous, inaccurate
oscillators (that is, those with differing intrinsic average
frequencies and that are individually unreliable on a cycle-to-cycle
basis). With this arrangement, the standard deviation of cycle length
between successive firings is proportional to
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so quadrupling the number of elements cuts the standard deviation in
half. This might account for the ability of our brains to control
muscle action to within fractions of a millisecond, when individual
neurons are an order of magnitude less precise.
The theory then is that our brains grew larger because more neurons
produced an increase in throwing accuracy (or an increase in
projectile speed with no reduction in accuracy), and that in turn
offered a major selective advantage: the ability to take advantage of
a food source -- small mammals -- that was previously untapped by
hominids. A new food source in turn means a new ecological niche ripe
for inhabiting. The advantage resulting from even a limited ability to
make use of a new source of food also provides a stronger and more
immediate selective pressure than is likely to have arisen from other
advantages of a slightly enlarged brain (for example, some limited
The theory has a number of appealing corollaries. It suggests one
source of lateralization because throwing is fundamentally asymmetric:
One-armed throwing is far more accurate and effective than two armed
for any reasonable-sized projectile (imagine baseball pitchers or
outfielders propelling the ball overhead with both arms). As a result,
only the neurons on one side of the brain need be specialized for the
operation (for why this turns out, in nearly 90 percent of us, to be
the left side of the brain, see Calvin ).(3) That
lateralization, which more generally involves precise sequential
muscle control, may in turn have been a key predecessor to language,
which also requires fast and accurate control of musculature.
Thus, the brain may have gotten larger to allow us to hunt better. The
interesting punchline for our purposes is that thinking may be an
extra use of all those neurons that evolved for another purpose.
Early Man and the Killer Climate
A third theory suggests that climate
plays a central role (Calvin 1991). The last few hundred thousand
years of our history have been marked by a series of ice ages. A being
used to surviving in a temperate climate would face a considerable
collection of challenges as the weather worsened and winters arrived.
In order to survive the winter, it would have had to be good enough at
hunting to accumulate extra food beyond the day-to-day needs (hence
the related utility of being able to throw accurately), and then it
would have had to develop both the foresight to put aside some of that
for the winter and the "technology" for doing so. There is, of course,
a stiff Darwinian penalty for failure to be that smart.
Early Man, the Primal Frugivore
A fourth theory suggests that the
crucial element was the evolution of early man into a frugivore, or
fruit eater. Why should this matter -- because you need to be smart to
be a frugivore. Fruit comes in relatively small pieces, so you need to
collect a lot of it, and it must be collected within a relatively
narrow time window. As a consequence, frugivores need good spatial
maps of their environments (so they know where the sources of fruit
are) and good temporal maps (so they know when to show up). Perhaps
this need for good spatial and temporal maps was a force for the
evolution of larger brains.
Early Man, the Primal Psychologist Yet another theory suggests that
our primary use of intelligence is not for making tools, hunting, or
surviving the winter; it's to get along with one another (Humphrey
1976; also see Byrne and Whiten ). This theory is sometimes
called Machiavellian intelligence. In this view, the primary function
of intelligence is the maintenance of social relationships.
The evidence for this comes from several sources, among them the
behavior of monkey troops that have been studied extensively. They are
seen to spend a good proportion of their time servicing and
maintaining their relationships within their groups, tending to issues
of rank and hierarchy and what appear to be allegiances.
A second source of evidence comes from a study (Dunbar 1992) that
plotted group size against neocortex ratio (ratio of neocortex size to
the size of the rest of the brain) for a variety of animals: a nearly
linear relationship emerged. Perhaps this held true for early man as
well: As early group size grew, along with the advantages of larger
groups came increasing demands to be able to understand, predict, and
perhaps even control the behavior of others. We saw earlier that
prediction was a key component of intelligent behavior; what more
complex, fascinating, and useful thing could there be to predict than
the behavior of then other humans?
Early Man, the Primal Linguist
Finally, Bickerton (1995) has suggested
that language was the crucial driving force behind the evolution of
our brains. He starts with the interesting observation that if we look
back at the historical time line, we notice that although brain size
grows roughly steadily for about three million years, progress in the
development of modern culture was not nearly so gradual. In fact,
"instead of a steady ascent ... we find, for 95% of that period, a
monotonous, almost flat line" (Bickerton 1995, p. 47). Almost nothing
happens. It is well after the appearance of H. sapiens, and well after
the leveling off of brain size, that we see the appearance of language
and all the other elements of what we have come to call civilization.
Bickerton calls these the two most shocking facts of human evolution:
(1) our ancestors stagnated so long despite their ever-growing brains
and (2) human culture grew exponentially only after the brain had
ceased to grow. It appears that we showed our most obvious evidence of
intelligence only after our brains stopped growing.
What was it that happened to produce that evidence? He suggests that
the crucial event was some sort of reorganization within the brain, a
reorganization that happened well after size stopped increasing. That
reorganization made possible two essential things: first, a generative
syntax, that is, a true language, and second, thought, that is, the
ability to think about something (like a leopard) without having to
experience the thing perceptually, and equally important, without
having to react to it in the way one would on meeting one.
This leads to what appears to be a crucial distinction between animal
intelligence and human intelligence. Animal intelligence has a here
and now character: With animal calls, for example, there is an
immediate link from the perception to the mind state to the action. If
a monkey sees a leopard, a certain mind state ensues, and a certain
behavior (giving the appropriate call) immediately follows.(4)
Human thought, by contrast, has an unlimited spatiotemporal reference,
by virtue of several important disconnections. Human thought involves
the ability to imagine, the ability to think about something in the
absence of perceptual input, and the ability to imagine without
In human thought we have the ability, the luxury, of
"re-presentation." The pun is intentional and probably educational:
Representations allow us to re-present things to ourselves in the
absence of the thing, so that we can think about it, not just react to
Enormous things change when we have both thought and language. Thought
and its useful disconnection from immediate stimuli and immediate
action is clearly a great boon -- it's the origin of our ability to
have our hypotheses die in our stead. But what about language? For our
purposes, the interesting thing about language is that it makes
knowledge immortal and makes society, not the individual, the
accumulator and repository of knowledge. No longer is an individual's
knowledge limited to what can be experienced and learned in a
lifetime. Language not only allows us to think, it allows us to share
and accumulate the fruits of that thought.
But what then caused our brains to grow over the three million or so
years during which neither language nor thought (as we know them) was
present? What was the evolutionary pressure? The theory suggests that
the life of a successful hunter-gatherer is fact rich and practice
rich. In order to survive as a hunter-gatherer, you need to know a lot
of facts about your world and need to know a fair number of skills.
This then is the hypothesized source of pressure: the increasing
accumulation of survival-relevant information communicated through a
form of protolanguage. Early man needed to store "the vast amount of
lore ... in the collective memories of traditional societies: the uses
of herbs, the habits of animals, aphorisms about human behavior,
detailed knowledge of the spatial environment, anecdotes, old wives'
tales, legends and myths" (Bickerton 1995, p. 63).(5)
Where does this collection of theories (figure 5) leave us? One
obvious caution is that they are unlikely to be either independent or
mutually exclusive. They may be mutually supportive and all true to
some extent, with each of them contributing some amount of the
evolutionary pressure toward larger brains and intelligence.
Figure 5. Theories of the Evolution of Intelligence.
Early man, the primal tool maker
Early man and the killer frisbee
Early man and the killer climate
Early man, the primal frugivore
Early man, the primal psychologist
Early man, the protolinguist
A second point to note is that human intelligence is a natural
phenomenon, born of evolution, and as suggested earlier, the end
product likely shows evidence of the process that created it.
Intelligence is likely to be a layered, multifaceted, and probably
messy collection of phenomena, much like the other products of
It also may be rather indirect. Here's Lewontin (1990) again: "There
may have been no direct natural selection for cognitive ability at
all. Human cognition may have developed as the purely epiphenomenal
consequence of the major increase in brain size, which, in turn, may
have been selected for quite other reasons" (p. 244), for example, any
of the reasons in figure 5.
This, too, suggests a certain amount of caution in our approach to
understanding intelligence, at least of the human variety: The human
mind is not only a 400,000-year-old legacy application, it may have
been written for another purpose and adopted for current usage only
after the fact. In light of that, we should not be too surprised if we
fail to find elegance and simplicity in the workings of intelligence.
Inhuman Problem Solving
As we explore the design space of intelligences, it's interesting to
consider some of the other varieties of intelligence that are out
there, particularly the animal sort. With that, let me turn to the
third part of my article, in which it's time for AI to learn about the
birds and the bees. What do animals know, and (how) do they think?
Clever Hans and Clever Hands
Before we get too far into this, it would we wise to consider a couple
of cautionary tales to ensure the appropriate degree of skepticism
about this difficult subject. The classic cautionary tale concerns a
horse named Clever Hans, raised in Germany around 1900, that gave
every appearance of being able to do arithmetic, tapping out his
answers with his feet (Boakes 1984) (figure 6). He was able to give
the correct answers even without his trainer in the room and became a
focus of a considerable amount of attention and something of a
[FIGURE 6 PHOTO OMITTED]
In the end, it turned out that Hans was not mathematically gifted; his
gift was perceptual. The key clue came when he was asked questions to
which no one in the room knew the answer; in that case, neither did
he. Hans had been attending carefully to his audience and reacting to
the slight changes in posture that occurred when he had given the
correct number of taps.(6)
The clever hands belong to a chimpanzee named Washoe who had been
trained in American Sign Language (Gardner et al. 1989). One day
Washoe, seeing a swan in a pond, gave the sign for water and then
bird. This seemed quite remarkable, as Washoe seemed to be forming
compound nouns -- water bird -- that he had not previously known
(Mithen 1996). But perhaps he had seen the pond and given the sign for
water, then noticed the swan and given the sign for bird. Had he done
so in the opposite order -- bird water -- little excitement would have
The standard caution from both of these tales is always to consider
the simpler explanation -- trainer effects, wishful interpretation of
data, and so on -- before being willing to consider that animals are
indeed capable of thought.
Narrow Intelligence: Birds and Bees
Given that, we can proceed to explore some of the varieties of animal
intelligence that do exist. Several types of rather narrowly defined
intelligence are supported by strong evidence. Among the birds and the
bees, for example, bees are well known to "dance" for their hive mates
to indicate the direction of food sources they have found. Some birds
have a remarkable ability to construct a spatial map. The Clark's
nutcracker, as one example, stores away on the order of 30,000 seeds
in 6,000 sites over the course of the spring and summer and is able to
find about half of those during the winter (Balda and Kamil 1992).
This is a narrowly restricted kind of intelligence but, at 6000
locations, nonetheless impressive.
Broader Intelligence: Primates
Broader forms of intelligence are displayed by some primates. One
particular variety -- the vervet monkey -- has been studied widely in
the wild and has displayed a range of intelligent-seeming behaviors
(Cheney and Seyfarth 1990). One of the important elements in the life
of a monkey group is status -- your place in the dominance hierarchy.
Vervet monkeys give every sign of understanding and being able to
reason using relations such as higher-status-than and
lower-status-than. They can, for example, do simple transitive
inference to establish the place of others in the hierarchy: If A can
beat up B, and B can beat up C, there's no need for A and C to fight;
the result can be inferred (allowing our hypotheses to get battered in
The monkeys also appear capable of classifying relationships as same
or different, understanding, for example, that mother-of is a
different relation from sibling-of. This can matter because if you
fight with Junior, you had better avoid mother-of(Junior) (who might
be tempted to retaliate), but sibling-of(Junior) presents no such
They also seem to have a vocabulary with semantic content -- different
calls that correspond to the notion of leopard, eagle, and python, the
three main monkey predators. That the calls are truly referential is
suggested by the facts that they are given only when appropriate, they
are learned by trial and error by the young monkeys, and the troop
takes appropriate action on hearing one of the calls. Hearing the
eagle call, for instance, all the troop members will look up,
searching for the eagle, then take cover in the bushes. Note that we
have referred to this as a vocabulary, not a language, because it
appears that there is no syntax permitting the construction of
Lies -- Do Monkeys Cry Leopard?
There is also some anecdotal evidence that the monkeys lie to one
another. They have been observed to lie by omission when it concerns
food: When happening on a modest-sized store of food, a monkey may
fail to give the standard call ordinarily given when finding food.
Instead, the lone monkey may simply consume it.
A more intriguing form of misrepresentation has been observed to occur
when two neighboring monkey troops get into battles over territory.
Some of these battles have ended when one of the monkeys gives the
leopard call -- all the combatants scatter, climbing into trees to
escape the predator, but there is in fact no leopard to be found. The
monkeys may be lying to one another as a way of breaking up the fight
(Cheney and Seyfarth 1991).(7)
Psittacine Intelligence: Bird Brains No Longer
One final example of animal intelligence concerns an African Grey
Parrot named Alex who has been trained for quite a few years by Dr.
Irene Pepperberg of the University of Arizona. Alex seems capable of
grasping abstract concepts such as same, different, color, shape, and
numbers (Pepperberg 1991).
A videotape of Alex in action (WNET 1995) is particularly compelling;
even a transcript of the conversation will give you a sense of what's
been accomplished. Pay particular attention to Alex's ability to deal
with, and reason about, abstract concepts and relations.
Narrator. For 17 years, Alex and Dr.
Irene Pepperberg have been working on
the mental powers of parrots. Their efforts
at the University of Arizona have
produced some remarkable results.
Dr. Pepperberg. What shape (holding up
a red square)?
Dr. Pepperberg: Yeah, how many
corners? Say the whole thing.
Alex: Four ... corners.
Dr. Pepperberg: That's right, four
corners. Good birdie.
Alex: Wanna nut.
Dr. Pepperberg: You can't have another
OK, what shape? (holding up a green
Alex: Three ... corners.
Dr. Pepperberg: That's right, three
corners; that's a good boy.
Now tell me, what color (holding the
same green triangle)?
Dr. Pepperberg: Green, ok; here's a nut.
OK, and what toy (holding up a toy
Dr. Pepperberg: Truck; you're a good boy.
OK, let's see if we can do something
(holding two keys, one green plastic,
one red metal; the green is slightly larger).
Tell, me, how many?
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)?
Dr. Pepperberg: Good parrot. You're
right, different color.
Alright, now look, tell me, what color
bigger? What color bigger (same keys)?
Dr. Pepperberg: Green; good boy. Green
bigger. Good parrot.
Oh you're a good boy today. Yes, three
different questions on the same objects.
Dr. Pepperberg: What we've found out is
that a bird with a brain that is so different
from mammals and primates can perform
at the same level as chimpanzees and
dolphins on all the tests that we've used and
performs about at the level of a young,
say, kindergarten-age child.
This is an interesting bit of animal intelligence, in part because of
the careful training and testing that's been done, suggesting that,
unlike Hans, Alex really does understand certain concepts. This is all
the more remarkable given the significant differences between bird and
mammalian brains: Parrot brains are quite primitive by comparison,
with a far smaller cerebral cortex.
These varieties of animal intelligence illustrate two important
points: First, they illuminate for us a number of other
distinguishable points in the design space of intelligences. The
narrow intelligences of birds and bees, clearly more limited than our
own, still offer impressive evidence of understanding and reasoning
about space. Primate intelligence provides evidence of symbolic
reasoning that, although primitive, has some of the character of what
seems central to our own intelligence. Clearly distinguishable from
our own variety of intelligence, yet impressive on their own terms,
these phenomena begin to suggest the depth and breadth of the natural
intelligences that have evolved.
Second, the fact that even some part of that intelligence appears
similar to our own suggests the continuity of the design space. Human
intelligence may be distinct, but it does not sit alone and
unapproachable in the space. There is a large continuum of
possibilities in that space; understanding some of our nearest
neighbors may help us understand our own intelligence. Even admitting
that there can be near neighbors offers a useful perspective.
I can't leave the topic of animal intelligence without paying homage
to one of the true unsung heroes of early AI research. Everyone in AI
knows the monkey and bananas problem of course. But what's shocking,
truly shocking, is that so many of us (myself included) don't know the
real origins of this problem.
Thus, for the generations of AI students (and faculty) who have
struggled with the monkey and bananas problem without knowing its
origins, I give you, the monkey (figure 7):(8)
[FIGURE 7 PHOTO OMITTED]
This one is named Rana; he and several other chimps were the subjects
in an experiment done by gestalt psychologist Wolfgang Kohler (1925)
in 1918. Kohler was studying the intelligence of animals, with
particular attention to the phenomenon of insight, and gave his
subjects a number of problems to solve. Here's Grande, another of the
chimps, hard at work on the most famous of them (figure 8).
[FIGURE 8 PHOTO OMITTED]
Thus, there really was a monkey and a stalk of bananas, and it all
happened back in 1918. just to give you a feeling of how long ago that
was, in 1918, Herb Simon had not yet won the Nobel Prize.
Searching Design Space
In this last segment of the article, I'd like to consider what parts
of the design space of intelligence we might usefully explore more
thoroughly. None of these are unpopulated; people are doing some forms
of the work I'll propose. My suggestion is that there's plenty of room
for others to join them and good reason to want to.
Thinking Is Reliving
One exploration is inspired by looking at alternatives to the usual
view that thinking is a form of internal verbalization. We also seem
to be able to visualize internally and do some of our thinking
visually; we seem to "see" things internally.
As one common example, if I were to ask whether an adult elephant
could fit through your bedroom door, you would most likely attempt to
answer it by reference to some mental image of the doorway and an
There is more than anecdotal evidence to support the proposition that
mental imaging is closely related to perception; a variety of
experimental and clinical data also support the notion. As one
example, patients who had suffered a loss of their left visual field
as a consequence of a stroke showed an interesting form of mental
imagery loss (Bisiach and Luzzatti 1978). These patients were asked to
imagine themselves standing at the northern end of a town square that
they knew well and asked to report the buildings that they could "see"
in their mental image when looking south. Interestingly, they report
what they would in fact be able to see out of the right half of their
visual field; that is, they report buildings to the south and west but
none to the east.
Even more remarkably, if they are then asked to imagine themselves on
the south end of the square looking north and asked to report on what
they "see" in their mental image, they describe the buildings in what
is now the right half of their visual field (that is, buildings to the
north and east) and fail completely to report those on the west side
of the square, even though they had mentioned them only moments
The process going on in using the mind's eye to "see" is thus
remarkably similar in some ways to what happens in using the
anatomical eye to see.
A second source of support for this view comes from the observation of
like-modality interference. If I ask you to hold a visual image in
your mind while you try to detect either a visual or an auditory
stimulus, the ability to detect the visual stimulus is degraded, but
detection of the auditory stimulus remains the same (Segal and Fusella
A third source of evidence comes from experiments done in the 1970s
that explored the nature of visual thinking. One well-known experiment
involved showing subjects images that looked like figure 9 and then
asking whether the two images were two views of the same structure,
albeit rotated (Shepard and Metzler 1971).
[FIGURE 9 ILLUSTRATION OMITTED]
One interesting result of this work was that people seem to do a form
of mental rotation on these images. The primary evidence for this is
that response time is directly proportional to the amount of rotation
necessary to get the figures in alignment.
A second experiment in the same vein involved mental folding (Shepard
and Feng 1972). The task here is to decide whether the two arrows will
meet when each of the pieces of paper shown in figure 10 is folded
into a cube.
[FIGURE 10 ILLUSTRATION OMITTED]
If you introspect as you do this task, I think you'll find that you
are recreating in your mind the sequence of actions you would take
were you to pick up the paper and fold it by hand.
What are we to make of these experiments? I suggest two things: First,
it may be time to take seriously (once again) the notion of visual
reasoning, that is, reasoning with diagrams as things that we look at,
whose visual nature is a central part of the representation.
Second is the suggestion that thinking is a form of reliving. The
usual interpretation of the data from the rotation and folding
experiments is that we think visually. But consider some additional
questions about the experiments: Why does it take time to do the
rotation, and why does the paper get mentally folded one piece at a
time? In the rotation experiment, why don't our eyes simply look at
each block, compute a transform, then do the transformation in one
step? I speculate that the reason is because our thought processes
mimic real life: In solving the problem mentally, we're re-acting out
what we would experience in the physical world.
That's my second suggestion: Take seriously the notion of thinking as
a form of reliving our perceptual and motor experiences. That is,
thinking is not simply the decontextualized manipulation of abstract
symbols (powerful though that may be). Some significant part of our
thinking may be the reuse, or simulation, of our experiences in the
environment. In this sense, vision and language are not simply
input-output channels into a mind where the thinking gets done; they
are instead a significant part of the thought process itself. The same
may be true for our proprioreceptive and motor systems: In mentally
folding the paper, we simulate the experience as it would be were we
to have the paper in hand.
There is, by the way, a plausible evolutionary rationale for this
speculation that thinking is a form of reliving. It's another instance
of functional conversion: Machinery developed for perception turns out
to be useful for thinking. Put differently, visual thinking is the
offline use of our ability to see. We're making use of machinery that
happened to be there for another purpose, as has happened many times
before in evolution.(9)
One further, ambitious speculation concerns the neural machinery that
might support such reliving: Ullman (1996) describes counterstreams, a
pair of complementary, interconnected pathways traveling in opposite
directions between the high-level and low-level visual areas. Roughly
speaking, the pathway from the low-level area does data-driven
processing, but the opposite pathway does model-driven processing. One
possible mechanism for thinking as reliving is the dominant use of the
model-driven pathway to recreate the sorts of excitation patterns that
would result from the actual experience.
One last speculation I'd like to make concerns the power of visual
reasoning and diagrams. The suggestion here is that diagrams are
powerful because they are, among other things, a form of what
Johnson-Laird (1983) called reasoning in the model. Roughly speaking,
that's the idea that some of the reasoning we do is not carried out in
the formal abstract terms of predicate calculus but is instead done by
creating for ourselves a concrete miniworld where we carry out mental
actions and then examine the results.
One familiar example is the use of diagrams when proving theorems in
geometry. The intent is to get a proof of a perfectly general
statement, yet it's much easier to do with a concrete, specific model,
one that we can manipulate and then examine to read off the answers.
Consider, for example, the hypothesis that any triangle can be shown
to be the union of two right triangles.
We might start by drawing a triangle (figure 11a). The proof of course
calls for any triangle, but we find it much easier with a concrete one
in front of us.
[FIGURE 11A ILLUSTRATION OMITTED]
We might then play with it a bit and eventually hit on the idea of
dropping a perpendicular figure 11b).
[FIGURE 11B ILLUSTRATION OMITTED]
Wary of a of, from a single concrete example, we might try a number of
other triangles and eventually come up with a formal abstract proof.
But it's often a lot easier to have a concrete example to work with,
manipulate, and then examine the results of our manipulations.
What works for something as plainly visual as geometric theorems also
seems to work for things that are not nearly so visual, such as
syllogisms. Consider these sentences describing a group of people
(Johnson-Laird 1983, p. 5):
Some of the children have balloons.
Everyone with a balloon has a party hat.
There's evidence that when asked to determine the logical consequences
of these statements, people imagine a concrete instance of a room and
some finite collection of people, then examine it to determine the
The good news about any concrete example is its concreteness; the bad
news is its concreteness, that is, its lack of generality -- as many a
high school geometry student has discovered when he/she drew an
insufficiently general diagram. For diagrams in particular, the
problem is compelling: There's no such thing as an approximate
diagram. Every line drawn has a precise length, every angle a precise
measure. The good news is that diagrams make everything explicit; the
bad news is that they can't possibly avoid it.
Yet there are times when we'd like to marry the virtues of reasoning
in a concrete diagram with the generality that would allow us to draw
a line that was about three inches long or long enough to reach this
That's my last speculation: There may be ways to marry the
concreteness of reasoning in the model with the power and generality
of abstraction. One early step in this direction is discussed in
Stahov, Davis, and Shrobe (1996), who discuss how a specific diagram
can automatically be annotated with constraints that capture the
appropriate general relationships among its parts, but there is
plainly much more to be done.
With that, let me summarize. I want to suggest that intelligence are
many things, and this is true in several senses. Even within AI, and
even with the subfield of inference, intelligence has been conceived
of in a variety of ways, including the logical perspective, which
considers it a part of mathematical logic, and the psychological
perspective, which considers it an empirical phenomenon from the
One way to get a synthesis of these numerous views is to conceive of
AI as the study of the design space of intelligences. I find this an
inspiring way to conceive of our field, in part because of its
inherent plurality of views and in part because it encourages us to
explore broadly and deeply about all the full range of that space.
We have also explored how human intelligence is a natural artifact,
the result of the process of evolution and its parallel, opportunistic
exploration of niches in the design space. As a result, it is likely
to bear all the hallmarks of any product of that process -- it is
likely to be layered, multifaceted, burdened with vestigal components,
and rather messy. This is a second sense in which intelligence are
many things -- it is composed of the many elements that have been
thrown together over evolutionary timescales.
Because of the origins of intelligence and its resulting character, AI
as a discipline is likely to have more in common with biology and
anatomy than it does with mathematics or physics. We may be a long
time collecting a wide variety of mechanisms rather than coming upon a
few minimalist principles.
In exploring inhuman problem solving, we saw that animal intelligence
seems to fit in some narrowly constrained niches, particularly for the
birds and bees, but for primates (and perhaps parrots), there are some
broader varieties of animal intelligence. These other varieties of
intelligence illustrate a number of other distinguishable points in
the design space of intelligences, suggesting the depth and breadth of
the natural intelligences that have evolved and indicating the
continuity of that design space.
Finally, I tried to suggest that there are some niches in the design
space of intelligences that are currently underexplored. There is, for
example, the speculation that thinking is in part visual, and if so,
it might prove very useful to develop representations and reasoning
mechanisms that reason with diagrams (not just about them) and that
take seriously their visual nature.
I speculated that thinking may be a form of reliving, that re-acting
out what we have experienced is one powerful way to think about, and
solve problems in, the world. And finally, I suggested that it may
prove useful to marry the concreteness of reasoning in a model with
the power that arises from reasoning abstractly and generally.
(1.) Table 1 and some of the text following is from Davis, Shrobe, and
(2.) For a detailed exploration of the consequences and their
potentially disquieting implications, see Dennett (1995).
(3.) In brief, he suggests that it arises from the near-universal
habit of women carrying babies in their left arms, probably because
the maternal heartbeat is easier for the baby to hear on that side.
This kept their right arms free for throwing. Hence the first major
league hunter-pitcher may have been what he calls the throwing madonna
(not incidentally, the title of his book).
(4.) That's why the possibility of monkeys "lying" to one another (see
later discussion) is so intriguing -- precisely because it's a break
in the perception-action link.
(5.) Humphrey (1976) also touches on this idea.
(6.) Oskar Phungst, who determined the real nature of Hans's skill,
was able to mimic it so successfully that he could pretend to be a
mentalist, "reading the mind" of someone thinking of a number: Pfungst
simply tapped until he saw the subtle changes in posture that were
unconscious to the subject (Rosenthal 1966).
(7.) For a countervailing view on the question of animal lying, see
the chapter by Nicholas Mackintosh in Khalfa (1994).
(8.) A true-life anecdote concerning life in Cambridge: When I went to
a photographer to have this photo turned into a slide, the man behind
the counter (probably an underpaid psychology graduate student) looked
at the old book with some interest, then laughed at the photo I wanted
reproduced. I pretended to chide him, pointing out that the photo was
of a famous contributor to psychological theory. "A famous contributor
to psychology?" he said. "Then I know who it is." "Who?" I asked. "Why
that's Noam Chimpsky, of course," he replied. Yes, it really happened,
just that way.
(9.) There has been significant controversy concerning the exact
nature and status of mental images; see, for example, Farah (1988),
who reviews some of the alternative theories as well as
neuropsychological evidence for the reality of mental images. One of
the alternative theories suggests that subjects in experiments of the
mental-rotation sort are mentally simulating their experience of
seeing rather than actually using their visual pathways. For our
purposes, that's almost as good: Although literal reuse of the visual
hardware would be a compelling example of functional conversion, there
is also something intriguing in the notion that one part of the brain
can realistically simulate the behavior of other parts.
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Randall Davis is a professor of computer science at the Massachusetts
Institute of Technology, where he works on model-based reasoning
systems for engineering design, problem solving, and troubleshooting.
He has also been active in the area of intellectual property and
software, serving on a number of government studies and as an adviser
to the court in legal cases. He received his undergraduate degree from
Dartmouth College and his Ph.D. from Stanford University. He serves on
several editorial boards, including those for Artificial Intelligence
and AI in Engineering. In 1990, he was named a founding fellow of the
American Association for Artificial Intelligence and served as
president of the association from 1995-1997. His e-mail address is
davis at ai.mit.edu.
[I am sending forth these memes, not because I agree wholeheartedly with
all of them, but to impregnate females of both sexes. Ponder them and
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