Presidential Address

The Importance ofImportance18

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Presidential Address

AAAI-98 Presidential Address

The Importanceof ImportanceDavid Waltz■ Human intelligence is shaped by what is mostimportant to us—the things that cause ecstasy,
despair, pleasure, pain, and other intense emotions. The ability to separate the important fromthe unimportant underlies such faculties as attention, focusing, situation and outcome assessment,
priority setting, judgment, taste, goal selection,
credit assignment, the selection of relevant memories and precedents, and learning from experience.
AI has for the most part focused on logic and reasoning in artificial situations where only relevant
variables and operators are specified and has paid
insufficient attention to processes of reducing therichness and disorganization of the real world to aform where logical reasoning can be applied. This
article discusses the role of importance judgment
in intelligence; provides some examples ofresearch that make use of importance judgments;and offers suggestions for new mechanisms, architectures, applications, and research directions forAI.

I

t’s a great pleasure to address you all. I haveviewed this as a unique opportunity to
examine what I think about AI as a field andto say what I think to you without having to
run it by reviewers. I’d like to talk about what
I think is really important in the field and inparticular what we’re doing that’s important
and what’s important that we aren’t doing,with more emphasis on the latter. Roughly
what I’ll cover today is the following: first, thegood news, briefly. AI really is doing well, andI’ll try to give a brief overview as I see it. Then
I’ll digress to ask some basic questions about
intelligence: What are brains really for? How
did they evolve? Why do we have them? I’llargue that brains and intelligence are rooted inmatters of life and death, important issues,
things that people regard as juicy, things thatpeople really care about. And then I’ll move tothe bad news: Assuming that its goal is to really

understand and model intelligence, AI isneglecting many essential topics. I’ll talk about
specific issues in perception, deep understanding of language, creativity, and logic. I’ll makesome suggestions about what we as a fieldshould do based on what I’ve said. If I don’t
offend everybody at least a little, I’ve failed.
First the good news: AI is thriving. Unlike
earlier times, AI encompasses a broad range ofareas and topics. If you look through the current AAAI conference proceedings, I thinkyou’ll see this. The narrow focus on logic alone
is gone. When AI people make predictions,which of course we do and should do, they’remuch more muted; I don’t think we’re going to
get in trouble for the kinds of predictions we’remaking today. There’s a significant industrial
effort and many applications. Membership in
the organization is stable now. It had dropped
for a long time—from 15,000 during the expert
systems craze—but it’s now been around 5,500
for several years and maybe has even gone up
a little bit. If you count institutional memberships, it’s more like 7000. We have very highstandards for paper acceptance. In this conference, the acceptance rate was 32 percent, andjournals in AI are strong. Advances in computing hardware have been a huge help to AI. For
instance, in the practical speech recognition
systems of today, there aren’t many new ideas
compared to what we knew in the seventies.
(There are a few: For example, hidden Markov
models (HMMs) are important and new since
the seventies.) But overall, much of the credit
for making AI become real in this and othercases should go to computing power increases.
The earlier ideas were adequate, but the computing power was inadequate.
There’s a very impressive range of very interesting AI applications. Rather than running
through a long list, I’ll point you to the IAAI

Copyright © 1999, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1999 / $2.00

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Presidential Address…
understanding
cognition—
understanding
the way thebrain works,
what themind does—
is a keyscientific
grandchallenge.

proceedings this year and other years. Many
grand challenges, first stated many years ago,
have been achieved. We have truly
autonomous vehicles such as Dean Pomerleau’s NO HANDS ACROSS AMERICA project, theautonomous vehicle that drove from Washington to San Diego, with all but 30 miles hands
off, and SOJOURNER, which wasn’t very smart
but certainly made a big splash and will be followed by more interesting systems. In automatic gene finding, AI techniques were usedby Steve Salzburg to find the Lyme disease andsyphilis genes recently. There have also been
the automatic classification of galaxies andstars and winning game programs.
In addition, AI has a very impressive recordof spin-off ideas: Lisp, SCHEME, PROLOG, maybe
SMALLTALK, garbage collection, object-oriented
programming. Lots of programming ideas
came out of AI labs; so did the mouse, thegraphic user interface, software agents, data
mining, telerobots, metasearching, PCs, laser
printing, time sharing, symbolic math aids,
computer games, graphics, and others.
Many important future applications will
require progress in AI: high-quality translation; high-precision web search, really finding
just what you want as opposed to finding a listthat hopefully contains what you want; content-based image matching, finding the image
you want; intelligent robots, intelligent environments, smart offices, and houses; andautonomous space systems.
Moreover, I think it’s very clear that understanding cognition—understanding the way
the brain works, what the mind does—is a key
scientific grand challenge. I think that within
the next 20 years, we will have available
affordable computing power sufficient tomatch human abilities, at least in principle.
We’ll have a lot more knowledge of the brain
and mind. So the future looks very rosy in thatregard.
Here is a chart, with thanks to Hans
Moravec (figure 1). You can see that computers
are very quickly approaching the chimphuman-whale cluster on the upper right. And
it won’t be too long after that before thesecreatures will be overtaken if trends continue.
And there’s very good reason to believe thattrends will continue.

What Are Brains For?
That was the good news. Let’s step back andlook at the Big Picture: What is the brain really
doing, what is intelligence about? This is a personal view, but I feel pretty confident and20

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strong about it. The central phenomena ofintelligence are tightly related to life and deathissues. We have brains because they reallymade a difference to human survivability as
individuals and as a species. What exactly is AI
trying to study, understand, and explain? The
study of intelligence is an ill-posed problem:
What is intelligence? A better question is Whatis intelligence for? That one I think we havesome chance of answering. And the answer to
it is that any kind of intelligent phenomenon
that we see, whether physical or behavioral, isthere because it really serves the organism’s
survivability purposes. Brains are for matters oflife and death. Brains are primarily organized
to perform fast action selection in order to promote survival. Brains make decisions on issues
such as fight or flight, on the finding of foodand water, on the selecting of mates and reproducing, and in only a slight exaggeration, I
think the basic operations the brain is doing isasking over and over again, quickly, Can I eat
this, can it eat me? Now, it doesn’t seem to us
that this is happening. Partially this is because
given the degree of intelligence and development that we have as a culture, we’ve createda world where food is plentiful, and predators
or dangers are kept at bay. But that doesn’t tell
us much about where the brain came from. We
surely haven’t changed very much physicallyin 5000 years. Five thousand years ago theworld was a much more dangerous and uncertain place. We’ve been able to relax about
these life and death issues because we’ve created a supportive, protective culture. But I thinkthat the brain arose during much less secure
times.
How did the brain actually come to accomplish fast action selection? It accomplished it
through the evolution of a lot of critical structures: innate drive, reward, and evaluation systems. We know what we like. We know whensomething we experience is good or bad. We
don’t have to be taught that; in fact, that’s themechanism of teaching. The brain consists
largely of many parallel, short, interacting
chains of neurons that connect attention andperception systems to action systems. We
know the chains are short because we can act
on things within roughly 100 milliseconds,
and given the latencies of neurons, which areon the order of a millisecond, there can’t bemany more than 100 layers of neurons
between inputs and outputs. That’s very short!
There’s not much chance for backtracking or
serial reasoning. Of course, the paths could bevery broad and bushy: There could be many
parallel paths and many neurons involved, but
the depth can’t be very great.

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Human

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powersof ten)

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1K CM-5 (1992)
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Cray-1(1977)
IBM 3090 Mainframe (1990)
Pentium PC (1993)
IBM PC (1983)

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Creatures

IBM Mainframe (1953)

Computers

0
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Figure 1. Relative Compute Power and Memory.
The other very important things we have aremany metasystems that watch other systems,
that in particular watch the actions of theseshort chains (and other parts of our brains)and look for regularities that they can predict.
They can generate shortcuts—essentially predictive mechanisms—and also try to generatemetaobservations and learning controls. Thisis very important for intelligence and probably
one area in which people are clearly differentiated from other creatures. There’s been a lot of
discussion over what drives learning. One popular theory is that learning is failure driven. I
think that’s roughly true, but I’d like to broaden it a bit: I think learning is surprise driven.
Whenever things are predictable or emotionally mild, not much changes in the brain. Why
should it change? The world is more or less inour control, we know what’s going on, andthere’s no particular reason to change anything. But when we’re surprised, when expectations are violated, and when we encounter
novel items—things we’ve never seen before,
situations we know we’ve never been in
before—then I believe we drop into a differentmode in which we note and remember items
much more vividly. The more surprising, themore alarming, the more novel, the more isremembered.
Why are surprises remembered? Well, we
need to assign credit. When we encountersomething novel, we don’t know what tothink of it. We do have to note such items inorder to later judge their importance. (Curiosity is also tied with this. We may seek out surprises if they aren’t coming fast enough for us,
and our learning rate is stagnating.) We learn
disproportionately from intense experiences.
Think of your first romantic experience. You
probably thought about it for a long time afterward. You probably still remember it pretty
vividly, if you’re anything like me. What’s
going on?
Consider great accomplishments, things
that you worked very hard on and finallyachieved, things that were extremely lucky,
praise that you’ve gotten from people that you
admire, experiences of terror or pain, deaths of
loved ones, abuse, humiliation, failures; allthese things cause us to experience intensely.
In such cases, we note and remember many
relatively raw features, both ones that were
present at the time that the shocking or surprising event happened and ones from aroundthat time (songs, weather, clothing we were
wearing, particular locations, and so on). We
tend to need a long time to get over intense
events, and we may obsess over them oversome period of time. I believe that such obsessing corresponds to substantial reorganizationsof our minds/brains.
What’s the purpose of obsessing? Why

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Presidential Address…
the brain andmind “workthrough”
intenseexperiencesby repeatedrehearsal,
daydreams,
talking tooneself. In theprocess, weconstructdemons thatcan give us
timely alerts…

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would it be valuable to remember many
details, most irrelevant? Imagine that we experience two very similar situations, one casethat turns out very positive and another similar situation that turns out extremely negative.
How are we to ever learn to predict the difference in the future? If we’ve generalized the firstexperience to a high degree, selecting minimal
patterns of features, there’s no way in which
we can later reliably learn the differences
between the two, since certain features, now
lost, may be essential clues to telling the difference. So I think one important reason weremember such things vividly and go over thefeatures of the experiences is so we can later
identify the differences between these situations and differentiate them. We don’t necessarily create accurate causal models. We can be
haunted by experiences even though we know
rationally they may never happen again. Even
those of us who are highly scientific may feelat least hints of superstition—“well, things
turned out well when I did x, and even though
I can’t see a causal relation from x to the outcome, I might as well do x again.”
What’s the value of obsessing? I think thebrain and mind “work through” intense experiences by repeated rehearsal, daydreams, talking to oneself. In the process, we constructdemons that can give us timely alerts: We can
avoid bad things in the future, avert disasters,
or we can seize opportunities that present
themselves. By rehearsing situations, we canconstruct demons that give us very early warnings, noting the faintest signs of dangers andopportunities ahead. I think we assign values
to people, objects, places, situations, events,
and relations by constructing a number of very
quick static evaluators. These provide us withan instant impression of whether something isgood or bad. There are recent psychologicalstudies by Bargh that strongly suggest thatpeople have quick (often unconscious) positive and negative reactions to just about everything.
Language and mental imagery play a criticalrole in this. Our basic perceptual systems arefast and feed forward. To modify these systems
and build new ones, I think we use internallanguage and mental imagery to “reexperience” events and “what-if” scenarios, so that
we can build new chains for when we
encounter similar situations again, as well as
hypothetical situations that we have imaginedin this process. The results are new, very fast
feed-forward perception-action chains as well
as self-descriptive prediction systems.
If events are sufficiently negative, they canbe debilitating. Battle fatigue and delayed

stress syndrome are examples of experiences so
intense that every subsequent experience islikely to contain some reminders of it. That is,certain events can generate so many features—all associated with strong negativity—that any day to day experience will share somefeatures that cause the victim to recollect andrelive the negative experience. And I think we
all know sad stories of adult maladaptations
that can be traced to abuse in childhood.

Animal Intelligence
Much of this is shared with animals. I had
hoped to bring a videotape that had beenshown by Pat Churchland, but I’ll have to settle for describing it to you. We are not alone, I
believe, in being smart and self-reflective inexactly the sense I’ve been talking about. Pat
showed a video of a baboon being put in frontof a mirror. This was a large mirror, a wall-sized
mirror, very clear and sharp. The baboon
looked at the mirror, saw another baboon,
lunged at “the other baboon,” which in turn ofcourse lunged back at her. She jumped back insurprise, and then made a threatening movement, mirrored by the other baboon, and shethen just ran off in a panic. The video thenshowed a chimp being put in front of the samemirror. The chimp had initially the same reactions: She jumped toward the mirror, and theother chimp jumped toward the mirror. The
chimp then just stood back, making somefaces and hand motions and watching the“other chimp” doing the same things. And
then there was a cut to a scene where thechimpanzee was turned back to the mirror,
examining her own rump and then up close,
looking in her own mouth, with an expressionthat conveyed “ooh, what’s this?” It was really
startling. There was no question that the
chimp was self-reflective, that she realized that
not only was she seeing herself, but also realizing that she had found a new method for discovering things about herself, things she’dnever been able to see or know before.

Animal Superstition
Konrad Lorenz was very fond of geese andimprinted himself on many of them. He’d bepresent when they hatched, and they would
imprint on him and follow him everywhere.
He was essentially their mother. He had one
particular favorite goose who followed him
everywhere, except that the goose would neverfollow him into his house. Geese are said to beafraid of going into dark places, so the goose
would always stop at the door. But one day, thePresidential Address

goose absent-mindedly followed Lorenz into
the house and suddenly found herself in thiscompletely dark place and panicked. Whengeese panic because of darkness, they’ll fly
toward light. So she flew to a window andstood by the window and cooled down, andafter her eyes adjusted to the darkness, she
resumed following him around the house.
Thereafter the goose followed him into thehouse every day: The goose would follow him
in, go and stand by the window for a while,and then return to following him. But gradually, over time, the goose started only making
a sort of detour toward the window, no longer
standing by the window, but just making adeviation in her path. One day the gooseabsent-mindedly forgot to make the deviationin path after coming in the door, suddenly
realized it, flew in a panic to the window, andcooled out for a while. So what was going on
there? Lorenz argued the goose was truly
superstitious, that somehow, even though thegoose now knew the house well, and felt safe
there, she needed to make that little deviationin her path, or else who knew what could happen? We humans aren’t so different.

The Bad NewsAI has for the most part neglected these sorts
of issues. And I think this is a serious problem,
because it raises the question about whether
AI, if it continues on its current course, is reallyup to the challenge of fulfilling its stated longterm goals. I would argue that most AI research
to date has been dictated largely by applications that people have chosen to work on or byavailable methods. And so it’s rather like looking under the light for your keys even though
you know you lost them somewhere else
because it’s easiest to see there. AI has ignored
most of neuroscience—some neuroscienceknowledge has come into AI via the neuralnets community, which has made a serious
attempt to follow neuroscience—but AI reallyhasn’t taken neuroscience seriously, eventhough it is a very fast growing and relevant
body of knowledge. It’s very clear that FMRI
(functional magnetic resonance imaging) studies support society of minds and/or PDP-likemodels; mental activity is highly distributed.
This hasn’t had much of an impact on AI. And
it ought to because, even as a practical matter,
we are in the age of distributed computers.
I’m going to talk about these three topics: (1)perception, situation assessment, and action; (2)meaning and deep language understanding (asopposed to statistical processing or web searching); and (3) generativity and creativity.

… experiments show that young
children, too young to have been able toexplore the world with their hands,
understand in some fairly deep way thephysics of the world ….

Perception, Situation
Assessment, and Action
Most of the AI work in perception, situationassessment, and action concerns symbolic reasoning about situations. AI models alreadyassume that somehow we’ve discovered theobjects and relations that are important in asituation. Very little work has been done on
the problem of actually turning real, complex
scenes into symbolic situation descriptions.
And I would argue that this is where most ofintelligence really lies.
Perceptual pattern recognition is in a very
primitive state. Human Go champions face nothreat from AI programs anytime soon. Why isthat? Well, people are very good at and, I
believe, very dependent on being able to makeperceptual pattern judgments. In chess, this isalso true. But chess is a sufficiently easier gamethat has been attacked successfully by computers in a very different fashion.
Why hasn’t AI looked at this sort of thing
more carefully? I think there are two main reasons: One is that perception really is complex
and difficult. It depends on extensive innate
wiring. Broad principles just aren’t going towork. There are some principles, but there area lot of them, and few apply generally. Some
examples: Animals clearly have extensive
innate perceptual abilities. Animals have mating rituals and dances that are extremely elaborate (another topic of Lorenz if you’re interested in details). But clearly these are notlearned; they’re innate in the creature. Ungulates—cattle, sheep, horses—can walk and they
can find their mother’s udders, they don’tbump into things, and they do all this immediately from the time they are born. They
don’t learn this. It’s wired in. Human experiments show that young children, too young tohave been able to explore the world with theirhands, understand in some fairly deep way thephysics of the world, as discussed later. In contrast, most of AI work in vision—which, by the

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Presidential Address

Identical

NonidenticalFigure 2. When Two Object Parts Are Seen behind an Occluder, 4-1/2-month-olds Assume There Is One Object If the Parts Are
Identical (left) and Two Objects If the Parts Are Dissimilar (right) (Bremnan, Slater, and Butterworth 1997).
From A. Slater, “Visual Perception and Its Organization in Early Infancy,” © 1997. Reprinted by permission of Psychology Press Ltd.

way, I think is a good and thriving field—hasconcentrated on low level vision—edge finding, segmentation, object recognition, 3-D
structure and so on—and hasn’t really treatedperception, that is, the problem of how one
selects what’s really important from among all
the possible segmented objects.

Evidence for Innate Perceptual
Knowledge in Humans
To make clear the importance of innate wiringin human perception, here are some experiments from Renee Baillergeon and Elizabeth
Spelke. The basic form of their experiments is
this: An infant sits in a seat and looks at some
sort of display and if the child attends to thedisplay, then the presumption is the child isinterested in it, there is something novel about
it, something surprising or interesting, but if
the child starts getting bored, looking aroundand fidgeting, then the presumption is thechild is not interested anymore because thedisplayed situation is boringly familiar. Thissounds shaky, but researchers have been able
to replicate very strong, clear effects. In thefirst experiment (figure 2), a 4-1/2 month oldinfant is shown a scene consisting of three
regions, one occluding the other two. In thiscase, if you show the child the figure where theoccluded regions are similar and then pull on
one of the regions, but only one piece comes
out, the child is very surprised. The child
expects that both regions are part of the same24

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object and that they will move together. If you
take a scene with dissimilar occluded regions,pull on one, and both pieces move together,
the child is surprised. The child expects
because the regions are different that they are
not part of the same object. And this is judged
by the child long before the child could havefigured out such things by playing with
objects. Figure 3 shows a similar display: If you
show a block with occluded regions moving ina coordinated manner behind it until the childgets bored, you then remove the occluding
object to show either a complete rod or two
short rods joined invisibly. Now, to a 4-1/2
month old, the left bottom display (the complete rod) is extremely boring: The child has
already figured out that the rod is continued
behind the occluding object. But the situationon the right is very interesting: The child isvery surprised that these aren’t really joined. Itturns out that before 4-1/2 months, the effectis exactly opposite, and nobody really quite
understands why that is. But it can’t have been
judged from experience because an infant of
this age still lacks hand-eye coordination andcan’t have had any physical experience withsuch situations.
Figure 4 requires some explanation. The bottom line of the figure depicts a block—theshaded object on the left—and a thin board,which is the object on the right. The child sees
the board rolling up to and then past vertical,
coming to lean on the block, then coming forward and lying flat on the table. And that’sPresidential Address

dull and uninteresting to the child. The middle display, an impossible event, represents thefollowing: The board rolls up, goes back towhere the block should be but is not supportedby the block (which has been removed by the
researcher), allowing the board to go flat on
the table. The board then rolls forward andthen the block appears; so it’s as though theblock somehow disappeared and reappeared
during the process. This is very surprising to
infants. So in some sense they really understand the object ought to be there, that it
ought to support this board, and if it doesn’t,
something is really wrong. So there has to be a
lot of innate wiring—really a lot of knowledge—that’s built into us. To build a perception system, that information somehow has tobe there. It’s not likely to be learned easily—it
was learned evolutionarily, but for AI programs, it would have to be programmed in.

Habituation Display

Test Displays

Links between
Perception and Action
The second reason that perception is difficult isthat perception is goal-directed and, therefore,
varies as goals change: Perception is tightly
linked with a rich set of goals and actions. Of
course, not all aspects of perception are goaldirected. Some parts of scenes are inherently
salient and “stand out”: things that are very
big, or moving very fast, or very bright, orfamiliar (for example, people you know). But
in general, perception is responsible for assessing scenes relative to the assessor’s goals andexperiences. You see in the scene opportunities
and dangers, things you can eat, things thatcan eat you, metaphorically of course. There’s
a large body of work by J. J. Gibson from the
1950s and 1960s that talks about perception in
exactly this form. Gibson described the perceived world as consisting of affordances, forexample, the things that the scenes offer to us,
the possibilities and risks. I don’t think general
purpose representation is even possible, except
in the very, very simplest domains, where one
has, for instance, plane surfaces with polyhedra. How do you make sense of complex scenes
and situations?
To illustrate this in part, consider figure 5.
This is a picture from the National Geographic,
and at the first level of analysis, it’s an orangutan in a tree. This is a hard scene to parsewith many very small regions. I’d like you to
imagine yourself in the following situation:
Suppose you’re really in the scene, and this iswhat you’re seeing, you’re there. If you’re aphotographer looking at that scene, what
would you see? I would argue that first of all

Figure 3. Habituation and Test Displays in Experiments’
Perception of Partly Occluded Objects.
During habituation, the rod moved back and forth behind the occluder (Bremnan, Slater, and Butterworth 1997). From A. Slater, “Visual Perception and Its Organization in Early Infancy, © 1997. Reprinted by permission of Psychology Press Ltd.

as a photographer for National Geographic,
when you look at this you’d want to be able
to show enough of the surroundings to givean idea of the habitat and terrain in which
this orangutan lives, how much visibility
there is, how much she wants to be hidden
versus being able to see what’s coming interms of predators, what kind of foliage orvegetation this creature might eat, whether
she’s in a nest or not. You’d want to showhow high off the ground she is, have a pleasing composition to the picture, and so on.
Now suppose instead that you are a collectorof animals for a zoo, then what would you
see? Well, I think you’d see something very
different. You first of all might look and say,
“Hmm, can I sneak up on this creature? Is
there any way for this creature to get away?

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Habituation Event

Test Events
Possible Event

Impossible Event

Figure 4. Schematic Representation of the Possible and Impossible Test Events Used in the Principal Experiment (Baillargeon,
Spelke, and Wasserman 1995) (courtesy of Renee Baillargeon, University of Illinois at Urbana-Champaign).

Could the creature jump to this nearest tree?
Could I get there in time? Is there a path? How
would I put something, a net or something,out to catch it? Could I hit her with a dart
from here?” You’d be looking at very different
parts of the scene and in different ways. Third,
imagine that you are a member of the World
Wildlife Federation, knowing that there might
be a collector, a poacher, nearby. Then what

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would you look at? Well, I think you’d lookaround the bases of the trees and ask yourself,“Is someone sneaking up on this creature. Is
there some way I could intercept such a person in time, etc.?” You’d look at different partsof the scene, you’d look at them in different
ways, and you’d look at them in terms ofpaths, trajectories, distances, escape routes,
accessibility routes, and so on.

Presidential Address

Figure 5. A Complex Scene (Knott, C., and Laman, T. 1998. Orangutans in the Wild. National Geographic 194(2): 30–56).
Courtesy Timothy Laman, National Geographic Society Image Collection.

AI Successes in Perception andLanguage
Now, I don’t want you to think that all thenews is bad. There are a number of AI successes
and promising projects that address the issues
I’ve been discussing. I’ll list just a few. Successful systems that have perception and action forthe most part don’t do anything terribly complex, but they do take real world scenes and dosomething useful with them. I mentioned
RALPH, the autonomous driving system used in
the NO HANDS ACROSS AMERICA project. RALPH
looks at the real world, and it finds travel lanes,
other vehicles, road obstacles, hazards, exits(to avoid), intersections, and so on, and it does
something with them, namely, it changes the
direction of the car and may hit the brakes.
Autonomous planetary explorers like SOJOURNER have been pretty dumb to date, but SOJOURNER’s descendants will do things such as locatinginteresting targets, for example, rocks or theirhome base, devising paths to get to these targets while avoiding obstacles such as big rocks

or cliffs that they might not be able to climb
over or might fall down into. And there is alarge body of work by Rod Brooks et al. on various reactive robots that interact with the realworld.

Evolution of Language
Language and meaning are quintessential features of human intelligence. Language is notjust for I/O. Early on in AI’s history there were
influential articles that argued that the central
core topics of AI were logic, reasoning, andknowledge representation and that language
and vision were peripheral operations for thecentral reasoning core. I disagree; language iscentral, not peripheral.
The origins of language are unclear, but it’s
fun to speculate. My guess is that language is
the intersection of (1) the ability to reify
objects perceptually, that is, the ability to create coherent mental chunks that can be
named; (2) the richer association of possibilities that a larger brain offers—more possible

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Presidential Addressconnections; (3) metaoperator developmentthat lets us learn via self-observation andrehearsal as I mentioned earlier; and (4) preexisting signaling systems, shared with animals.
I believe there may have been a synergy
between these abilities that led to a criticaladvantage for early ancestors and eventuallydeveloped quite significantly, giving hugeadvantages to hunters and gatherers and ultimately leading to civilization. (There are manyother proposals. One seriously advanced theory is that gossip is the root of language; thatgossip is extremely important for social bonding, stratification of society; and consequently,that we are wired to love gossip, much as we’re
wired to love food and sex. It seems to be truethat most of us love to hear and tell juicy stories about the people around us. Language
thus developed initially to let us gossip more
successfully! Others have suggested that language developed because women preferred
poetic men. I think that these factors may haveplayed some role, but not the central one.)
I think language evolved through interlocking synergies. The relatively undeveloped stateof human newborns made it possible for us tolearn a great deal more, including language,
because we were born incompletely developed.
There’s a natural course of innate developmentthat would occur anyway, but its direction andcontent are influenced by outside experience—by the language heard around us, byperception, by the particular circumstances wefind ourselves in. In turn, of course, the helplessness of lengthened childhoods requires stable families and societies. And so language
itself may also have played a role of increasing
the fitness of families and societies that madepossible the longer childhoods and support ofchildren until they were able to go off on theirown. And so these forces may have formed asynergy that was important in language development. Language makes culture possible: we
don’t even have culture without language.
Memes—the things that we name and share as
the standard units into which we divide theworld—are language items. They might bethings that we discover ourselves perceptually,
but they might not be. Language makes theminescapable. And of course language itself is amajor element of culture. Language itself cansubstitute for direct experience so much ofwhat I said about perception is also true of language. Language allows us to experience things
that we can’t see directly, things that are faraway in space or time, as well as accumulated
culture, and more abstract things: morals,
hints, metaconcepts, stories, hypotheticals,
rules, and so on. Language is self-extending, so

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that once a certain level of proficiency is developed, people can reify objects and events even
if they haven’t seen them perceptually. (Perhaps we can’t even avoid reifying experienced
items.) And I think we need importance judgment for abstracting correctly—there are a lotof ways we can abstract. How do we do it properly? And we clearly need judgment for understanding things such as anaphora, humor,
fables, or parables. A fable isn’t just a story
about some creatures. It’s really about somebigger issue, and we somehow learn early tounderstand that.
Language is related to and depends on perception. The possible meaning structures forutterances are more constrained than in thecase of perception, which is much more openended. But many of the arguments made forperception also hold for language. Language
can have special meanings and affordances forthe understander: You can hurt or inspire
somebody very effectively with language.
Probably for many of you the most intense
experiences you’ve had—positive or negative—have been language-mediated rather
than perceptually mediated. Some of what
your parents said to you, your boss said to you,your child said to you have likely had a huge
impact.
Much language requires perceptual reasoning. I wrote an article a long time ago when I
was already thinking about these sorts ofthings. The central example I used was, “Mydachshund bit our postman on the ear.” Now
why is that an odd sentence? It’s odd because
postmen’s ears aren’t ordinarily anywhere neardachshunds’ mouths. So one needs to invent
some sort of story for how this could happen,
how this could possibly make sense: maybe
the postman fell down, or the postman pickedup the dachshund to pet him. But this requires
perceptual reasoning, possibly involving theperceptual system, even though the similarsentence, “My doberman bit our postman on
the ear,” is not problematic—a doberman is big
enough to reach the ear. But the fact that theperceptual system can be pulled into action if
necessary, is, I think, significant.
How can we build systems that understandlanguage? I think text based learning methods,
statistical methods, simply won’t do it all,unless we somehow solve perception and canuse perception to support language learningfrom experience, which is a big if. Humans
learn language with the aid of perception.
Symbol grounding is very important. Language experience has to be, to be really understood, linked into the structure of the corresponding situation, which, like language, also

Presidential Address

Figure 6. Video Sequence to Be Classified as an Event by HOWARD(Siskind and Morris 1996) (courtesy of NEC Research Institute).

has meaning and context. As with perception,
I believe that much of language is innate.
Chomsky’s main point is that syntax is largely
innate, and I think this is moderately wellestablished, although the scope of innate functionality is certainly still arguable. Lessarguable are the facts that deaf children spontaneously sign and that twins are frequently
observed to invent private languages of theirown. And there are many other examples.
I’m going to show you a video of HOWARD,
one of the best systems for actually understanding real, complicated, moving scenes.
This is work by Jeff Siskind who’s at NEC
Research Institute now. The work was originally done while he was at the University ofToronto. Figure 6 shows a sample from Jeff’s
current follow-up of this earlier research. The
following is the audio track of the videotape:
People can describe what they see.
They describe not only objects like blocks,

but also events like picking things up.
This video presents Howard, a computer
program with the ability to describe
events. Our approach is motivated by asimple observation: We pass movies of
events through an edge detector. Whilepeople couldn’t recognize the objects
from stationary edges alone, they could
recognize events depicted by edges inmotion.
An event pass can be described by the
motion profile of its participant objects. A
picking up event has two sub-events:
First, the agent moves toward the patient
while the patient is at rest above thesource. Then the agent moves with thepatient away from the source. Our event
models attempts to capture these motion
profiles.
We don’t use detailed image understanding to perform event recognition.

FALL 1999 29

Presidential Address

Figure 7. Parse Trees for Sentences in English, Korean, and German (courtesy of NEC Research Institute).

Only the rough position, orientation,
shape, and size of the participant objects
are needed. We represent this information
by ellipses centered on the participants.
Our training and classification procedures
operate solely on the streams of ellipse
parameters produced by our tracking procedure.
We process the movie one frame at atime to find regions of pixels that areeither moving or are brightly colored.
First, we ignore pixels with either low saturation or value. Then we group nearby
similarly colored pixels into regions discarding those that are too small. Opticalflow is then used to find moving objects
in the non-brightly colored pixels. Next,
we group nearby moving pixels into
regions, again discarding those that are
too small. We use both the colored andthe moving regions to track the object.
Finally, we fit an ellipse to each region.

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Subsequent processing uses only ellipse
data.
Here you see the colored and moving
regions found by applying our tracker toa sample movie. Once the ellipses areplaced in each frame, we find the ellipse
correspondences between frames. Our
technique attempts to fill in missing
ellipses and filter out spurious ellipses.
We processed 72 movies with our tracker. Of these, 36 were randomly selected astraining movies, 6 movies for each of the
6 event classes. We constructed a single
hidden Markov model for each eventclass. We then classified all 72 movies,
both the original training movies andthose not used for training, against all 6
event models. Our models correctly classified all 36 training movies and 35 out of
the 36 test movies.
You are watching the results of our classifier now. Different events can have dif-Presidential Address

ferent numbers of participant objects. Our
pick up and put down models have threeparticipants, while our push, pull, drop,
and throw models have two. Movies areclassified against only those models withthe same or fewer numbers of participants
as the ellipses found by our tracker. Ourclassifier can correctly match a subset ofthe ellipses in this three ellipse movie tothe drop model, which has only two participants. It appears that poor tracking
caused one drop movie to be misclassified
as a throwing event.
I should comment that Jeff can now show
you a real time demo on his desk in normal
light thanks to computer power increases. He’s
trying to significantly increase the set of possible events. On one hand, that doesn’t seemvery impressive: It’s a long way from being able
to do what you’d really like a system to do,namely, describe what’s going on, given any
possible scene. On the other hand, the realworld, with its shadows and complexity, ishard to work with, as any of you who have
tried it can attest.
Overall, to really build intelligent systems, I
suspect that large amounts of handcoding aregoing to be essential. Fortunately, a lot of work
has been done in recent years. We have examples such as WORDNET and CYC. In the subsections to follow I’ll tell you about the PAPPI system and work by Inquizit, Inc., that I recently
encountered. Inquizit has built a huge natural
language processing system with many levels
and functionalities.

Figure 8. INQUIZIT, as Applied to an Employee Manual, Answers theQuestion “Who Can Train Employees?” (courtesy of Kathy
Dahlgren, InQuizit Technologies, Santa Monica, California).

Query: Can they can an employee over drug addiction?

PAPPI
PAPPI is the work of Sandiway Fong of the NEC
Research Institute. PAPPI parses a broad range ofdifficult sentences from 12 languages. It’s a“principles and parameters” parser, inspired byNoam Chomsky’s ideas. Figure 7 shows parsetrees for sentences in several languages: English, Korean, and German. PAPPI comprises
about a quarter million lines of PROLOG code,and it’s able to handle new languages by simply setting parameters correctly, an impressive
feat. Sandiway’s group has been able to add
new languages, including complicated languages such as Hungarian or Turkish, based on
one summer’s work with a linguistics grad student for each language.

INQUIZITINQUIZIT, a product of Inquizit Technologies, isa very interesting system, written by Kathy
Dahlgren and Ed Stabler over a 14 year period.
INQUIZIT has an extensive word sense lexicon
with 350,000 word senses as well as a morpho-

Figure 9. INQUIZIT with the Question “Can They Can an Employee
over Drug Addiction?”(courtesy of Kathy Dahlgren, InQuizit Technologies, Santa Monica, California).

logical analyzer that lets it actually represent
over a million word senses. INQUIZIT has an
ontology with inheritance, and a “naive
semantics” with first order predicate calculus
tests to determine when the various definitions can apply. Figure 8 shows INQUIZIT applied
to an employee manual, answering the question, “Who trains employees?” The point here
is to show that INQUIZIT knows a large number

FALL 1999 31

Presidential Address

Generativity and Creativity
Query: What crimes should be reported?

Figure 10. INQUIZIT with the Question “What Crimes Should be Reported?”(courtesy of Kathy Dahlgren, InQuizit Technologies,
Santa Monica, California).

of meanings for “trains”—railroad cars, a parallel line of people, and so on—and it’s able topick the correct meaning—“instruct”—basedon the values of predicates it applies to the surrounding sentence. Figure 9 shows a sentence
being used to do text retrieval; the highlighted
text is what’s retrieved. The question here is,“Can they can an employee over drug addiction?” The example illustrates first that INQUIZIT
can pick the right meanings for each occurrence of can, but more importantly that INQUIZIT can match text based on deep meanings—
note that the word can is never used in thematched passage. INQUIZIT understands that inthis case can means terminate. Figure 10 showsa third example, “What crimes should bereported?’’ In this case, the word crimes neverappears either but the text matched says, “Areyou aware of any fraud, embezzlement, inventory shortage?” and so on, and INQUIZIT is able
to use its ontology to judge that those words
are specific examples of crimes and matchthem. Building PAPPI and INQUIZIT (and CYC) has
required a huge amount of hand effort. The
bad news is that I think we will need to tackle
a number of similarly large tasks in order tobuild systems that are really intelligent. The
good news is that it’s possible that this can bedone successfully for very large—butfinite—domains.

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Art, science, and technology depend criticallyon our ability to create ideas and artifacts thathave never been seen before and to create novel relationships between people, objects, andideas. Doing this depends clearly on developing meaning in perceptual pattern systems andon creativity, but creativity is not just theprovince of arts and sciences. Infants andadults of all ages illustrate creativity: infants by
their play activity, by their language, by understanding, and use of metaphor and analogy.
We aren’t going to have systems we ever regard
as smart if all they do is react to what they see.
And in fact they can’t really understand language unless they are able to make some senseof analogy and so on. And they won’t be able
to express things very concisely unless they
can also use language and use it appropriately.
I know this is important, but I’m not certain
how to attack this as an AI problem. Some reasonable starting places can be found in thework of Ken Forbus and Dedre Gentner, Maggie Boden, Harold Cohen, John Koza, Doug
Lenat, and Doug Hofstadter.

The Role of Logic
A possible objection to what I have said so far
is, “Isn’t everything built on logic?” A substantial community within AI has long held theview that the human brain somehow implements a perfect logic machine and that all
intelligence is built on top of this. A corollary
to this view is that in order to create a fully
intelligent system all we need to do is create aperfect logic machine and then build everything on top of it. I’d ask you the following
question: If that’s true, why are adults so miserable at reasoning and hopeless at formal reasoning for the most part? (None of you ofcourse. You’re all great at it! But if you take theoverall population, they’re not so great.) We
have cases of people with Williams syndrome—kids who speak extremely articulately,
who have great understanding about themselves, social situations, and other people, but
who are absolutely hopeless in reasoning
about the physical world, and have very low
IQs. If intelligence was based on a common
logic reasoning system, how could such a caseexist of people who are so good in language
and social reasoning but so poor about mostother things?
My view is that logic is really the fruit oflearning, one of the last things we learn, notthe system on which everything is based. Logic
is the culmination of repeatedly detecting pat-Presidential Address

terns and abstracting them from specific
events and schemas and then from these evermore abstract schemas. However, I think thatwhen we do this, we also retain polymorphic
features so that we can apply the patterns andschemas if and only if they are appropriate. As
evidence, we have work by Tversky who
showed that people are much better at logic
problems when they’re stated as social situations, but when exactly the same schematic
problem is stated as As, Bs, xs, and ys, people
are miserable at solving them. We understandenvy, jealousy, competition, and such stuff
well, but we have trouble reasoning about sets
and relations. We also have such gems as thecompeting proverbs: “Absence makes the heart
grow fonder’’ and “Out of sight, out of mind.”
They’re both sort of right, sometimes. We
know when one or the other applies.
Logic lets us talk about unimportant things.
It lets us take diverse objects—important andunimportant—and turn them into objects ofequal importance. In logic, all objects, operators, and so on tend to be uniform, and thiscan be extremely important and useful, but it’svery different I think from what intelligence isusually doing, namely, judging what’s important under the circumstances.

Summary
Perception, action, language, and creativity are
central to intelligence. They aren’t just I/Ofunctions, they aren’t just peripheral operations that AI’s going to need to connect thereal systems to the outside world. There’s very
little—perhaps no—evidence for a central
CPU. There’s lots of evidence for the “societyof mind.” What humans think is important
really ought to have a role in AI—how could it
not? Understanding systems need goals andvalues to guide their actions. If they’re to act
appropriately, they need to make importancejudgments and choices. And of course if intelligent systems are going to understand us,
they’ll need to be able to model us. They’regoing to need to be able to understand why
we’ve done what we’ve done, why we say what
we say. If they can’t do that, they’re going to bepretty useless as colleagues or assistants. Soeven if they didn’t need to have their own
emotions and goals, they’d have to be able tosimulate them in order to deal with us.
So what should AI be doing? I certainly
don’t want you to go away thinking that weshould stop doing applications. I am also verymuch in favor of continuing research on reasoning, logic, and so on—it’s great work.
Applications are absolutely critical for contin-

ued credibility and funding. The work on logic, reasoning, and knowledge representation
has been the heart and soul of what lets us do
the AI applications we have done. Don’t stop!
But as a field we need to do more. We need tohave a greater emphasis on science. We need
to be more problem driven, not solution driven. Even if we have great hammers, we haveto avoid the tendency to see the world as madeup completely of nails. I deeply believe weneed broader scholarship. To do AI properly,
we have to know a lot of areas. We have toknow about neuroscience, we have to know
about psychology, we have to know about linguistics. This is hard. There’s a lot to know. I
strongly recommend that you cultivate relationships with people who can be good informants so you don’t necessarily have to study
the whole literature. When you discover partsof the literature that you really need to know,
go and learn them. We aren’t going to accomplish our largest goals unless we are prepared
to do that. We need this both for inspirationand to avoid reinventing the wheel—or reinventing it hexagonally. Future AI applications
will really require us to model language, perception, and reasoning well. Other people canuse statistics, other people can use simple stuff
that we’ve already shown how to do. Whatmakes us special? We need to have something
new. We need to have something that’s better.

ServiceThat’s the main thrust of what I wanted to saytechnically. I do want to add a postscript. Thisis very important. In addition to research, I
really believe that AI has to give much more
attention to service. This was underlined in a
recent trip that several of us made to NSF, thefirst of a series of trips I hope we will make toother agencies as well, part of an education
process. The story we heard over and overagain was that AI has not given sufficientattention to volunteering (nor has CS in general). AI is not well understood by other people
in CS, not understood well by funders, by legislators, or by the public, and it’s not going tobe well understood unless people in AI are willing to go and be part of the decision making
and contracting functions of government inour country—and I’m sure this is true of othercountries as well. This is a critical step toward
influencing policies and funding.
Service is also critical for conferences, for
editing of journals and conference proceedings, and prompt reviewing. Review andreturn your papers more quickly. Let’s notmake excuses for why it takes two or three
years for papers to appear. We can do better

FALL 1999 33

Presidential Address

Source List
Creativity
Gentner, D.; Brem, S.; Ferguson, R..; Markman, A.; Levidow,
B.; Wolff, P.; and Forbus, K. 1997. Analogical Reasoning andConceptual Change: A Case Study of Johannes Kepler. The
Journal of the Learning Sciences 6(1): 3–40.
RALPH
Jochem, T., and Pomerleau. D. 1996. Life in the Fast Lane. AI
Magazine 17(2): 11–50.
Chess
IBM. 1996. DEEP BLUE Chess Program. IBM Research Products
and Projects. Available at www.research.ibm.com/research/systems.html#chess.
GeneralWaltz, D. L. 1997. Artificial Intelligence: Realizing the Ultimate Promises of Computing. AI Magazine 18(3): 49–52. Also
available at www.cs.washington.edu/homes/lazowska/cra/
ai.html.
Dachshund-Postman Example
Waltz, D. L. 1981. Toward a Detailed Model of Processing for
Language Describing the Physical World. In Proceedings of
the Seventh International Joint Conference on Artificial Intelligence, 1–6. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.
SOJOURNER
MarsMap—Demos. SOUJOURNER. ca. 1999. Available at
img.arc.nasa.gov/Pathfinder/marsmap2/www_demo.html.
Lyme Disease
Fraser, C. M.; Casjens, S.; Huang, W. M.; Sutton, G. G.; Clayton, R.; Lathigra, R.; White, O.; Ketchum, K. A.; Dodson, R.;
Hickey, E. K.; Gwinn, M.; Dougherty, B.; Tomb, J. F.; Fleischmann, R. D.; Richardson, D.; Peterson, J.; Kerlavage, A. R.;
Quackenbush, J.; Salzberg, S.; Hanson, M.; van Vugt, R.;
Palmer, N.; Adams, M. D.; Gocayne, J.; Venter, J. C.; et al.
1997. Genomic Sequence of a Lyme Disease Spirochaete, Borrelia burgdorfori. Nature 390(6660): 553, 555. Available at
http://bric.postech.ac.kr/review/genetics_molbiol/97/12_11.html.
Syphilis
UPI. 1998. Key to Syphilis Vaccine May Be Found. UPI Science
News, 16 July. Available at www.medserv.dk/health/98/07/17/
story03.htm.
Star and Galaxy Classification
DIMACS. 1998. Astrophysics and Algorithms: A Discrete
Math and Computer Science Workshop on Massive Astronomical Data Sets, 6–8 May, Princeton, N.J. Available atdimacs.rutgers.edu/Workshops/Astro/abstracts.html.
Animal Behavior
Glass, J. 1998. The Animal within Us: Lessons about Life from
Our Animal Ancestors. Corona del Mar, Calif.: Donington
Press.
Good general reference that generally agrees with myviews, but adds significant new ideas on reasoning, ourinner monologues, territoriality, and other important
topics.
Lorenz, K. 1966. On Aggression. New York: Harcourt, Brace.
Perception
Goleman, D. 1995. Brain May Tag All Perceptions with a Value. New York Times, 8 August, C1 (Science Section).
Reports on values attached to perceptions, a report on
the work of Jonathan Bargh.
Affordances
Gibson, J. J. 1979. The Ecological Approach to Visual Perception.
Mahwah, N.J.: Lawrence Erlbaum. Available atwww.sfc.keio.ac.jp/~masanao/affordance/home.html
Theories of the Mind
Moyers, B. 1988. Interview with Patricia Churchland on the
PBS Program “A World of Ideas.”
A brief synopsis of this videotape can be found atwww.dolphininstitute.org/isc/text/rs_brain.htm.
Animal Morality and Superstition
Lorenz, K. 1991. Objective Morality. Journal of Social and Biological Structures 14(4): 455–471. Also available at www.percep.demon.co.uk/morality.htm.
Robots
AMRL. 1994. Project 3: Motion Control for Space Robotics:
Behavior-Based 3D Motion. Autonomous Mobile Robotics
Lab, University of Maryland, College Park, Maryland.
This site contains reactive robot design information.
Cañamero, D. 1996. Research Interests, AI Lab, Massachusetts
InstituteofTechnology.
Availableatalphabits.ai.mit.edu/people/lola/research.html.
General information, including the Zoo Project, headedby Rodney Brooks, is presented.
Moravec, H. 1988. Mind Children: The Future of Robot andHuman Intelligence. Cambridge, Mass.: Harvard University
Press.

than that. Treat reviewing as an important
activity, not a nuisance that you only get toafter you’ve done all the more important stuff.

Executive Council Actions
The last thing I’d like to do is to tell you about
what the executive council is doing. I tried to34

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decide when I became AAAI President what
was the most important thing to do. I decided
that the most important thing to do is to makesome real use of the large amount of moneythat we’re fortunate enough to have as anorganization, to help AI become a better
field—not to merely save the money for aPresidential Address

rainy day. I think we should be careful that it’s
not wasted, but I think we really can use thefunds we have to do something great.
Here are some of the actions that the council
has agreed to recently:
We’re going to create a number of new
prizes for AI members. I think this isimportant because nobody gets a Nobel
Prize (or Turing Award) without having
gotten other prizes first. In fact you’reunlikely to get a prize from any othercomputer society unless you’ve alreadygotten one from AI. We need to recognize
the great work that’s done in the field andneed to create heroes and heroines andhelp people’s careers.
We’re going to create high school andundergraduate prizes to encourage people
so they will want to go into AI as a fieldand so that they will know what AI’s
about.
We’d like to sponsor two international
science fair teams to come to AAAI, findones that are doing great work that happens to bear on our topic and perhaps
encourage them.
We want to put some AI technology on
our web site. Our web site ought to somehow reflect the great things that are goingon in the field. It ought to be distinctive.
It shouldn’t be lagging behind the rest of
the field.
We’re going to try to commission a science writer or perhaps several science writers to write about AI. If the word isn’t getting out the way it should be, let’s makesure that we hire someone who’s persuasive and articulate and tell them what
we’re doing and let them make our story.
To encourage people to go into government service from within the field, we’regoing to create service awards for AI people who serve in government positions.
Bruce Buchanan has already done a lot ofwork on creating high school information
web pages and brochures that should
appear fairly shortly. We get a lot ofrequests from high schools, and we’regoing to try to answer them better than
we have in the past.
Finally, we’re going to increase the budget for the national conference scholarships to let more students, who could otherwise not afford to come, come to theconference. Next year we’re going to try
something interesting called “CHI-Care.”This was Jim Hendler’s suggestion. The
CHI conference has offered this for awhile now. Basically, they have children

take part in the conference program: The
kids write a newsletter about the conference, take pictures of the speakers, interview them, mingle with the conference
participants, help out at booths, whatever. Apparently this is hugely successful—a
lot of fun for the kids and also anotherway of getting them interested in thefield. Next year we’re going to leave theconference fee the same, but we’re going
to include tutorials, so everyone can go tothem. We’re going to try to continue toincrease the cooperation with collocatedconferences and continue the series of
educational visits to NSF, DOD, NLM, andNIH funders.
And finally we’re going to take a member’s survey and act on the good ideas.
Thank you very much for your attention. I
actually managed to finish before the end of
my hour. Have a great conference!

References
Baillargeon, R.; Spelke, E.; and Wasserman, S. 1995.
Object Permanence in Five-Month-Old Infants. Cognition 20:191–208.
Bremner, G.; Slater, A.; and Butterworth, G. 1997.
Infant Development: Recent Advances. Philadelphia: Psychology Press.
Knott, C., and Laman, T. 1998. Orangutans in the
Wild. National Geographic 194(2): 30–56.
Siskind, J. M., and Morris, Q. 1996. A MaximumLikelihood Approach to Visual Event Classification.
In ECCV96, Proceedings of the Fourth European Conference on Computer Vision, 347–360. New York:
Springer-Verlag.
David Waltz has been
vice-president of theComputer
ScienceResearch Division of
NEC Research Institutein Princeton, New Jersey, and adjunct professor of computer scienceat Brandeis University
since 1993. He is currently president of the AAAI and
is a fellow of the AAAI, a fellow of the ACM, andsenior member of IEEE, and a former chair of ACM
SIGART. Before moving to NEC Research, Waltz
directed the data mining and text retrieval effort at
Thinking Machines Corp. for 9 years, following 11
years on the faculty at the University of Illinois at
Urbana-Champaign. Waltz received all his degrees
from MIT. His research interests have included constraint propagation, computer vision, massively parallel systems for both relational and text databases,
memory-based and case-based reasoning systems andtheir applications, protein structure prediction using
hybrid neural net and memory-based methods, andconnectionist models for natural language processing. His e-mail address is waltz@research.nj. nec.com.

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