MINDELL: Good afternoon. It's my pleasure to
introduce and to welcome you to the MIT 150 symposium on
brains, minds, and machines. My name is David
Mindell, and I'm chair of the MIT 150
Steering Committee, and I just wanted to say a
few words about MIT's 150th that provide a little bit of
context for today's symposium. In 1853, William Barton Rogers
came north from Virginia to pursue his dream of a new
kind of technical education, an education that would
mix the world of science and the useful arts,
theory and practice, what we have come to know as mens
et manus, or mind and hand. Nine years later-- eight
years later, in 1861, the governor of Massachusetts
fulfilled Roger's dream by signing MIT'S charter
on April 10 of that year, creating this unique
and innovative educational institution. Now, 150 years and
two weeks later, we're celebrating
MIT's accomplishments in a whole variety of fields
of ideas and inventions that changed our world and
helped define it today. And of the courageous
professors, students, graduates, and alumni
who have gone forth from this place to make their
contributions to the world. The MIT 150 celebrations
include 150 days of events, concerts,
other festivals. Today, we're on the 117th day,
and we've already begun to see, over the past 117 days, the
emergence of tomorrow's MIT, one that's united by
ambitious intellectual agendas and focused on both its
core research domains and on the Institute's
relationship to the wider world. Within the 150th anniversary,
the intellectual [? core ?] is a series that
comprises the core of MIT. Thinkers students,
researchers, and professors talking about great ideas,
contemplating the world, and perhaps even making
a little progress on some of the interesting problems. This is the essence of 150
symposia series, the sixth and last of which
we're opening today. The other symposia have
focused on economics and finance back in
January, conquering cancer through the convergence
of science and engineering in March, women's leadership
in science and engineering, also at the end of
March, computation and the transformation of
practically everything, two weeks ago, the
future of exploration in earth, air, ocean,
and space last week, and today's brains,
minds, and machines. Each of these symposia were
chosen for the leading faculty, the exciting ideas, the new
ideas, and the symposium's focus on more than one
department and more than one school. Of course they in no
way cover the full range of the interesting research
that goes on at the Institute, but they all represent
cutting-edge work that epitomizes what
is best about MIT. My thanks particularly
today to professors Irene Heim, Tommy Poggio,
and Josh Tenenbaum for their willingness to
step forward and organize this historic gathering. And to all of our
participants and all of you for taking the time to be here
to share your insights as well. On a personal note, I find
this symposium particularly exciting, not least
because I've spent a good deal of my
career studying the prehistory of
cybernetics and currently work on topics of
human and machine relationships in
complex systems, but also because from the
very first pre-proposal that we reviewed
on this symposium, we saw how it coincides
with the new intelligence initiative at MIT and represents
a genuinely new synthesis. I could not think
of a better way to culminate the
MIT 150 symposium series than with this event. Nothing could better illustrate
MIT's seminal history in these areas, its synthetic
intellectual culture, and the great promise
for the future than what you're about to see
in the following two days. I'll now introduce the Dean of
Science at MIT, Marc Kastner. Thank you. KASTNER: Thank you, David. David said it well. This is an extremely
exciting topic. I love it in particular
because in addition to the deep intellectual
challenges, it already-- the new intelligence
initiative has already involved faculty members from
all of the five schools at MIT. The School of Science, with
its brain and cognitive science department. The School of
Engineering, obviously. But also the School of
Humanities, Arts and Social Sciences, where linguistics, and
philosophy, and economics sit. The Sloan School of
Business, and the School of Architecture Urban Planning,
which houses the Media Lab. So this is an
intellectual challenge which draws together faculty
members from all across MIT, and that makes it very exciting. So this problem has a
great history at MIT, but also has a
great future at MIT. I want to add my thanks to
professors Heim, Poggio, and Tenenbaum. And a special thanks to Lore and
Pat McGovern and the McGovern Institute for hosting
a reception tomorrow. So on with the show. It should be fun. POGGIO: So on behalf of my
co-organizers, Irene Heim and Josh Tenenbaum, and all the
MIT 150 organization and people of my group that will
be helping you around in the next couple
of days, I would like to welcome you
to this symposium and to this first
panel of today. We are in a typical
gritty MIT classroom. We kept you outside in the heat
just to emphasize the point. We are very close to where the
old barracks were, Building 20. Back in the '50s
and '60s there was a remarkable
intellectual activity in the old country that
started around the new physics and engineering of electronics
and information processing. And if MIT was one of the
main centers of this whirlwind of activity, Building 20
was really the center of it, serving as a magical incubator
for a number of fields as diverse as information
theory, cybernetics, neural networks, linguistics,
artificial intelligence, and neuroscience. Researchers in the
building included Claude Shannon, Norbert Wiener,
Walter Pitts, McCulloch, Noam Chomsky, Maurice
[? Harley, ?] Jerry Lettvin, Marvin Minsky. And the intellectual
[? ferment ?] eventually converged in the
ambitious attempt to understanding intelligence
and replicating it in computers and machines. Marvin Minsky and
Seymour Papert to were key in starting
the new field of artificial intelligence. Noam Chomsky and
David Marr pioneered linguistics, and
cognitive science, and [? computation ?]
neuroscience. And I would like to remember in
particular Jerry Lettvin, who died about a week ago. He was part of that group. He was one of the giants in
this MIT history of research between the brain and the mind. He had a unique culture. He was unique. He was loved by everybody. We all miss him a lot. Artificial intelligence
started 50 years ago, about. Machine learning started
growing under the name of neural networks
about 25 years ago. And academic research in these
two areas is one of the main reasons for the emergence in the
last five years, I would say, of remarkably
successful applications, commercial and not commercial,
like Deep Blue, Google search, [? Kinect, ?] Watson
[? Mobileye. ?] Each of these systems-- computers that play
chess, search the web, recognize human gestures,
answer to [? Jeopardy, ?] allow cars to see, is at human-level
performance in a narrow domain. But none of these systems can
be said to be intelligent, these intelligence. And the problem of intelligence,
and of how the brain produces intelligence, and how to make
really intelligent machines, is still wide open. So the main thesis
to be discussed in this symposium, the way
Josh and I thought about it, is that a new effort in
curiosity-driven research, basic research,
is needed in order to understand intelligence
and understand the brain. That this new basic research
should, this time, not only rely on computer
science, but also integrated with neuroscience
and cognitive science. And it should also integrate
tightly different aspects, facets, of intelligence, such
as vision, planning, language, social intelligence. So I believe that
50 years later, it is the time to try again. This symposium is
a way to find out whether we all agree that it
is indeed time to try again, and if so how? So now, I will start
the real symposium. And I have the
pleasure to introduce the moderator of this
extraordinary panel, Steve Pinker. You all know Steve, so he
does not need an introduction. You all know him from his books,
starting with The Language Instinct, a great book. Steve was a colleague in
my department here at MIT, and now we are loaning him back
for a few days from Harvard. Steve. PINKER: Thank you, Tony. Welcome to the golden age. I'll look at the original roots
of artificial intelligence, cognitive science,
and neuroscience. Moderating this panel
is a daunting challenge that puts me in mind of the
famous quotation from John F. Kennedy when he hosted a
dinner at the White House for all of the living
Nobel Laureates of the Western hemisphere. And he said "this is the
most extraordinary collection of talent and human
knowledge that has ever been gathered together
at the White House, with the possible exception
of when Thomas Jefferson dined alone." It's not clear who the Thomas
Jefferson figure equivalent would be in this case,
although I imagine there might be some
people in the audience who would nominate our
late colleague, the inimitable Jerry Lettvin. The second reason this
is a daunting challenge is that our distinguished
panelists represent a remarkable diversity
of interests, from the biology of soil
roundworms to the nature of logic, truth, and meaning. Nonetheless, I think there is a
common thread that all of them are contributors to
what I think of as one of the great revolutions
in human thought, which is the scientific and
understanding of life and mind in terms of information,
computation, and feedback and control. Well into the 20th
century, life and mind remained scientific mysteries. Life was thought of as this
mysterious substance called protoplasm, some kind
of quivering gel, animated by an élan vital. The mind was thought of
as a portion of some realm of the soul or spirit. Or, according to the
dogma of behaviorism, something that didn't exist
at all, just one big category error. But then, in the middle
decades of the 20th century, ideas of thinkers like Turing,
Church, von Neumann, Wiener, Shannon, Weaver,
McCulloch, and Pitts gave us a rigorous
language in which to understand the concepts of
information and computation and apply them to
domesticate these formerly mysterious realms, in the
process revolutionizing biology and psychology. They gave us what became the
insight that the stuff of life is not some magical
protoplasm, but rather matter that's organized
by information. And today, when we
discuss heredity, we use the language
of linguistics. We talk about the genetic code. We talk about DNA
sequences being synonymous, or meaningless,
or palindromic, or stored in libraries. Even the relation between
hereditary information and the actual meat and
juices of the organism we explain with concepts
from information, namely transcription and translation. The metaphor is profound. Similarly, the stuff
of thought is no longer thought to be some
kind of ghostly spirit, nor a mirage or category
error, but also can be understood in
terms of information. That beliefs are a
kind of representation, thinking a kind of
computation or transformation, and action a problem of control,
in the engineer's sense. These ideas we take
for granted now, but I am always
struck going back to earlier great thinkers
in biology and psychology, how much they
floundered without it. When one reads great
philosophers of mind, like Hume, or great
biologists like Darwin, I often wish that I could
reach back over the centuries and tell them a few things
about the modern science of information, because one
could see that they were flailing around with hydraulic
and mechanical analogies that could be so clearly
explicated by what we know now about information
and computation. I think it was the 1950s and
early 1960s that was a turning point in both fields, and the
six people in today's panel were all, in different
ways, instrumental in making it happen. I don't think I'm going
to offend anyone's vanity by introducing them and inviting
them to speak in order of age. Sydney Brenner is a biologist. All of us know that 58 years-- BRENNER: [INAUDIBLE] PINKER: Everyone knows that
58 years ago Watson and Crick explicated the structure of DNA. But the DNA would
be useless if there wasn't some way
for the information that it contained
to actually affect the development and
functioning of the organism. The discovery of
the genetic code is something that we owe to our
first speaker, Sydney Brenner, working in collaboration with
Francis Crick and others. As if that weren't
enough, the discovery of the operation
of the genetic code and mechanisms of
transcription into RNA and translation into
protein, Sydney Brenner was also instrumental in the
modern science of development. How the information
coded in the genes actually builds a
three-dimensional functioning origin, and, for that
matter, neuroscience, both through his choice
of the lowly roundworm, Caenorhabditis elegans. C. elegans, often known
as Sydney's worm, which has exactly 959
cells, 302 neurons, and therefore offers a perfect
opportunity to reverse engineer the process of
development and the wiring of the nervous system. In recognition of
this accomplishment, Sydney was awarded
the 2002 Nobel Prize in Physiology or Medicine. But perhaps an even
greater recognition is that he has been immortalized
in the Linnaean taxonomy, a sister species of C. elegans,
Caenorhabditis brenneri, has been named after
Sydney Brenner. Sydney is senior
distinguished fellow at the Crick-Jacobs center
at the Salk Institute for Biological Studies. Marvin Minsky is a
computer scientist. And he is widely
recognized as one of the founders,
perhaps the founder, of artificial intelligence,
cognitive science, and robotics. He is responsible,
among other things, for the first neural network
simulator, the first computer simulation of semantic
memory, one of the first music synthesizers, first
mechanical arm, and the first
programmable Logo Turtle. He has, since then, been
a major theoretician on how to apply the
computational analysis to problems such as vision,
reasoning, learning, common sense, consciousness,
emotion, and so on. He has been recognized
with the Association of Computing Machinery's
Turing Prize, as well as the Japan Prize. But perhaps most
significantly, he inspired Arthur C. Clarke when
he was writing 2001 a Space Odyssey and served as
consultant for that movie, in particular for
the computer HAL. Marvin Minsky is Professor
of Media Arts and Sciences Emeritus at MIT. Noam Chomsky is a linguist
who revolutionized the study of
language by changing the very nature of the questions
that the field attempts to answer. Noam pointed out that the most
profound mystery of language is first of all that any
competent speaker can produce or comprehend an
infinite number of novel sentences. Therefore, our
knowledge of language has to be captured by a
recursive, generative system. Noam explored the
mathematics of such systems, has developed a
number of theories of what kind of recursive
generative system is implemented in
the human mind, and inspired the modern
science of psycholinguistics that explores how language
is processed in real-time. Noam also set, as a
problem for linguistics, the project of figuring
out how children acquire a first language without formal
instruction in an astonishingly short period of time, suggesting
that children are innately equipped with a
universal grammar of the abstract principles
behind language, an idea that led to the
modern science of language acquisition. Noam is also largely
responsible for the overthrow of behaviorism as the main
dogma in the study of psychology and philosophy,
for rehabilitating the philosophical
approach of rationalism, and for making the
concepts of innateness and modularity respectable
in the study of mind. Chomsky has been recognized
among many prizes, with the Kyoto Prize, the
Helmholtz Medal, and the Ben Franklin Medal. He is also the most
cited living scholar, according to citation
counts, and has become such a fixture
of popular culture that Woody Allen had him feature
prominently in his New Yorker story "The Whore of Mensa". And I believe that
Noam is the only MIT professor whose
lecture has ever been featured on the
B-side of an album by the rock group Chumbawumba. Noam is Institute
Professor Emeritus in the Department of Linguistics
and Philosophy here at MIT. Emilio Bizzi is a
neuroscientist who made early contributions to
our understanding of sleep, but has spent most of
his career studying the process of movement. Emilio is responsible
for setting the agenda for the
neuroscience of movement, pointing out that the
movement of animals is not simply a
set of knee jerks, or pressing a button
and a lever moving, but rather has goal-directedness
that a simple muscle motion would be useless
if it was executed the same way regardless of the
starting position of the body, and rather that movements
have to be organized towards positions in space. Movement is therefore
an intelligent process at every level of
the nervous system. Emilio is also largely
responsible for the maturation of neuroscience as a
field, which did not even exist as a name when
he entered the field. And was the founding director
of the Whitaker College at MIT, and founding head
of the Department of Brain and Cognitive
Sciences, in which I was privileged to
teach for many years. In addition to his
scientific brilliance, Emilio is widely respected as a
man of judgment and erudition, and his wisdom
has been tapped in many scholarly organizations. Together with prizes such
as the [? Empedocles ?] Prize and the President of Italy
Gold Medal for Achievements in Science Prize,
Emilio has served as the president of the American
Academy of Arts and Sciences. Emilio is currently Institute
Professor of Neuroscience here at MIT. Barbara Partee is a linguist. She was a student
in the first class of the fabled MIT graduate
program in linguistics. And as the first recipient of
a National Science Foundation Fellowship in linguistics, her
graduate career symbolically inaugurates the
appearance of linguistics as an official science. Barbara transformed the field
of linguistics, which hitherto had concentrated on
syntax and phonology, by putting semantics,
the study of meaning, on a formal,
rigorous foundation, tying linguistics to
the world of logic and the concepts of
meaning and truth. She remains the world's
foremost semanticist, and moreover, trained
all of the world's other foremost semanticists. When she retired
recently, her students presented her with
a genealogical tree of all of the people
that she has trained. They noted that it has
a depth of four, which means that she has trained
several great, great, grand students, and
it has 176 nodes. Barbara has been recognized
with the Max Planck Research Award for the Max
Planck Society, and is currently distinguished
University Professor Emerita, that's feminine for Emeritus, at
the University of Massachusetts at Amherst. Patrick Winston is a
computer scientist. He was one of the
first researchers to give a rigorous analysis
of the concept of learning in the framework of
modern symbolic artificial intelligence, and his work
on the concept of learning continues to be
influential today. He was also instrumental
in transforming artificial intelligence
from a bunch of hackers in plaid flannel shirts to
a respectable academic field through his textbook, through
directing the Artificial Intelligence Laboratory at
MIT for most of its existence, and his famous course in
artificial intelligence, as well as starting a
number of companies. Patrick has been recognized by
a number of teaching awards, including the MacVicar, the
Baker, and the Graduate Student Council Award here at MIT. And is widely known and beloved,
aside from his contributions to artificial intelligence,
in his training of teachers through his famous
IAP talk, "How To Speak" which has
influenced many of us. Among the many suggestions for
how to keep a class engaged, he suggested that every
lecturer should cultivate some eccentricity, whether
it be wearing a rope belt, or tugging a lock of
your hair, or erasing the Blackboard with both hands. I was influenced by
this as a postdoc, and ever since, I
have always lectured wearing a gaudy necktie. Patrick is Ford Professor
of Artificial Intelligence and Computer Science at MIT. I have asked the six panelists
to spend 10 minutes each, sharing any personal
thoughts that they think the audience would enjoy on the
birth of the modern sciences of life and mind, and their
reflections on the origins, key questions, key discoveries,
and open challenges of your field. So we'll start with Sydney. I'd like to ask each of you
to speak for about 10 minutes, and then I will ask you
to amplify, reflect, ask each other questions, and so on. I'll put a little timer
here to just remind you how much time has elapsed. BRENNER: I've shrunk
in recent years. Well, I thought I'd give-- to explain why I'm
here, I thought I'd give two classes of reasons. The first will be
sentimental, and I'd like to say that my association
with artificial intelligence has always been as an
interested spectator. I don't think I ever
played in the game. My interest in it came
from a very long friendship with Seymour Papert. We grew up together
in South Africa. We shared a room in the
Department of Physiology. I taught Seymour
neurophysiology. He taught me mathematics. And I came to the conclusion
he was both the better student and the better teacher. It was the connection
with Seymour, that when I finally
grew up, that brought me into association with
the Department of Artificial Intelligence. And I sent one of my
best students here. I wouldn't say he was a student. One of my best colleagues
here, David Marr. And so those are the reasons
why I've always been. And in later years, which
I ask you to ponder on, there was a great adventure
here producing something called a thinking machine. In fact, there was a
corporation named after that. And I suppose that's
what we were after. We have to resuscitate
thinking machines. Now I'd like to give you the
mental reasons why I'm here. I am very influenced
in about 1950, '51 it was, reading an
article by John von Neumann called "The Theory of
Self-Reproducing Machines". It had been published in a
book called The Hixon Symposium of Cerebral Mechanisms. And that's a very
interesting book to read through, because you
can see all the false leads. So that paper from
Neumann went unattended to at the actual basic logical
construct for the way the genes work. And in fact, the whole of this
is done without any reference to biology. And indeed, when the
biologists discovered that this was the
machinery, then nobody had mentioned von Neumann. In fact, they all paid very
great respect to Schrodinger. Schrodinger wrote a book
called What Is Life? Everybody claimed to read it. I read it. I didn't understand it. So it had no influence on me. But in thinking back
on it some years ago, I came to the conclusion
that Schrodinger had made a fundamental mistake. He said the genes contained
the program for development, as we would put it this day,
and the means to execute them. What von Neumann
said, they didn't contain the means of execution. They contained the description
of the means of execution. In other words, the program
is not self-reading. You'll have to build
a reader for it. And that's, of course, what
von Neumann's [INAUDIBLE],, and without this, you can't
make a self-reproducing machine because it has to transmit
to the next machine a description of
the means to do it. And I think that this is
the fundamental thing that lies behind this. And so if you like,
if you want to say I've got this text in
DNA, it's a long sequence, can we read it? Can I look in there and
say yes, that's a zebra, and it's going to be
able to do these things? And that is if we believe
in what we can do. So when you take this, and you
try to analyze the difference, analyze the relationship
between what we inherit and how we can perform. And the big argument
at the time, that there is a connection
between genes and behavior, which gave rise to a whole
lot of other problems, such as saying that
intelligence is something that is inherited with the genome. Those are not the
questions, really, to ask, because I think you
must divide the problem into two as, indeed, it is divided
into two in von Neumann's view of it. The first thing is how do
the genes specify and build a machine that
performs the behavior? And how does that machine
perform the behavior? That is a separate question. Of course the two are
connected, as indeed they are, but they must be distinguished,
because what we are asking is, if we're looking
at the behavior, the behavior is represented
[? and ?] the genome [? has ?] a description of how to
build a machine that behaves. And you see this is very
important to get that through, because the deepest problem
is how did all of this evolve? Because you can only
change the description. So there are very
interesting questions that are attached to this. And in following
this line of thought, I thought that the only way
to give a scientific theory of a nervous system is to ask,
how does the wiring diagram, if I can call it that,
compute behavior? Because if we know
how this is done, we can look at the
deeper computation later, which is how is the script
translated into the machinery that builds this? And in fact, I think
a lot of science will now go to what I call
the forward question, which is how do we connect
the output of a system with its wiring diagram? Which is the thing I
think we have to solve. But of course, this
is a grand thing, and one of the
things you learn is that there is a difference
between vision and eyesight. I learned this by
describing a friend of mine as a man of great vision
but poor eyesight. So we have to have good
eyesight to implement anything like this. And that is the
story of the worm. The worm has 302 neurons. Seemed to me it was
a finite problem. We could come to the
end of description, and then after that, we
could deal with the questions that everybody
raises when you do modeling, which is the skeptic
who stands there and says how do you know there's not
another wire that starts in the big toe,
runs up at the back, and connects this with this? You have to be able to
say we know all the wires. Then you can proceed. So this was, then, to get to a
total analysis of the structure of the nervous system,
the structure of the brain of the worm, 302 neurons, which
will he finally accomplished. Took 20 years, because we had
to many, many other romances with computers in between. We tried to mechanize this,
but to do this in the 1960s, was impossible. Nowadays, it can be done,
and there's huge activity now in getting these
wiring diagrams specified. In my opinion, sometimes
overzealously applied, because it's not going to
answer the question that we wanted to answer. If I take a worm of exactly
the same genetic structure, will I find exactly the
same wiring diagram? Today, you could
ask sure, I've just cut a section of
this mouse's brain, and I've found these synapses. And you say well, maybe
if you delayed your knife a half an hour, would you
have found the same synapses? And indeed, if you
go to another mouse and look at the same cell, is
it going to be the same there? However. So I think that in
approaching this question, I think that we are on the
threshold of really asking very serious questions, both
in biology and in relationship to the behavior of
complex systems. And that's why I think this
initiative is very, very important at the time-- important to do
now, because I think we all have to take a completely
different approach to this in the future. As you well know, my
colleague Francis Crick got interested in
consciousness at one stage. And I was asked, some time ago. what did I think of
consciousness as a problem? The great physicist,
Wigner, thought that consciousness would be the
explanation as why you can't predict anything with
quantum mechanics, that there would be
this added ingredient. My view is that I think
consciousness will never be solved, but will disappear. Now, 50 years from
now, people will look back and say
what did they think they were talking about here? Right? I'll tell you one other
thing that has the same thing and has disappeared. It is a thing called
embryological determination. We had many discussions
in the '50s and '60s. Is determination different
from differentiation? Are they different processes? Nobody talks that way
anymore, because we have a completely
different view of this. And this is what I
would like to say is going to be changing
those views, changing many of the other views of which
I'm not equipped to talk about. But I'll tell you the
one view that I would like to see very much changed. And that is the view of
what a gene stands for. And in doing this, I will
just deliver a little parable in my sermon. It was said that when we
sequence the human genome, and we sequence the
chimpanzee genome, we will find one extra
gene in the human. That'll be the
gene for language, and we'll call it
the Chomsky gene. But there's an
alternative explanation, which is during the revolution,
chimpanzees discovered talking gets you into trouble. And so they evolved a
language suppressor gene. Then we'll call it
the Chimski gene. Thank you very much. PINKER: Marvin. MINSKY: I have too many notes. When Brenner started
working on his worm, C. elegans, that
was a big project. 1960, was it? And to map out the nervous
system of this animal as well as the
rest of it, you had to make very small
slices with a microtome and trace the nerves and
other organs in every layer. And that's a tremendous
amount of work to do by hand. And he had employees, graduate
students, I don't know what, and this process was
taking a long time. At the same time, McCarthy and
I had started the Artificial Intelligence Group here
and had done a lot of work on computer vision,
even by 1960. So I visited Brenner
and suggested that he should import a couple
of our graduate students who would automate
the process of vision. And Brenner said no. And I said why? And he said well,
both of our fields are in their very early stages,
and I have these budding genetic biologists or whatever,
and you have these budding computer experts. And if you let any of them
into my lab, all of my students will realize that
your subject is much more exciting and easier. Do you remember that? And it took me a
while to realize he was absolutely right. I don't know where to start. I love MIT, and I've been
here in a sense since 1957, because I started at
Harvard as an undergraduate, and I could talk about
that for two days. But in the course of-- and then I went to graduate
school at Princeton. And I'm not sure where
I'm going with this. But every time I've got
interested in doing something, I was incredibly lucky. I never had to do any real work. If I had a mathematics
problem, well, when I was a
sophomore at Harvard, I sat down at a table with a
young man named Andrew Gleason who had won the Putnam-- first prize in the Putnam
Mathematical Competition three or maybe four years in a row,
which sort of excluded everyone else in the world. And I asked what he was doing. And he said he was
trying to solve Hilbert's fifth problem, which
is that every continuous-- every locally continuous group-- oh, I forget what it is. Anyway, oh, every continuous
group is differentiable. It's a very nice problem. And I said, how
long do you think it will take to solve that? And he said nine years. I had never met anyone with
a plan of that magnitude only a few years older than me. And I asked what that was,
and he said well, first I'll have to do Dedekind cuts
to show that the thing has to have real numbers
and blah, blah, blah. Anyway, it took him eight years. And I think, somehow I was very
early in my research career, and it seemed to me that
eight years was plausible, let's do things. Anyway, I was just very lucky. I ran into von
Neumann at some point when I was doing my
thesis at Princeton, which was on neural networks. And I got the idea of doing that
from earlier work by McCulloch and lots of people in the 1940s. It turned out that in
1895, Sigmund Freud had written a little
paper on neural networks, but no one would publish it. And it wasn't published
till around 1950. Well, I guess the
same question came up, because I was in the math
department at Princeton and they asked von Neumann is
this PhD thesis mathematics? And I'm happy to say that
he replied if it isn't now, it soon will be. And anyway, there was
something great about the era, and it's hard to talk about how
things started because it was so different from it is today. See, 1950 is shortly
after World War II, and the country was
very prosperous, and there were lots of
research institutions, because there were
a lot of monopolies. I mean, a company like General
Electric, or Westinghouse, or even CBS were pretty huge. And they-- and Bell
Laboratories, of course, was a monopoly. The others only acted like it. And so they would start
out different projects. And one, I guess
John McCarthy and I in the summer of 1951 or two-- anyway, we got summer
jobs at Bell Labs and someone told us not to
work on any problem that would take less than 30 years. They didn't have the
tenure deadline either. Now to get tenure you have to
be quick, because legally, I think tenure is seven years, but
they like it to be six years, because you don't want
to keep people hanging. And what's more, if you
fire somebody before that, you have to pay about
a year termination fee and oh well, blah, blah. So it's very hard to
get a research grant for five years, which
was sort of the standard that NIH and many of
these laboratories had. So any questions? Anyway, we started this
artificial Intelligence laboratory. And the first few-- I'll just stop in the
middle of the sentence when time is up, because
I never make plans. I could talk-- PINKER: What? MINSKY: Oh, it's an iPhone. I have one. Mine is [? pink. ?] Anyway,
how did all this happen? When I was a child, there were
a lot of books in the house, and I got interested
in reading them. And it seemed to me that
they fell into two classes. One was fiction,
and that was novels. This is a joke I've
made many times. And I read a few novels,
and it seemed to me that they were
all the same, even by great writers from
Shakespeare on down. And there are about six or
seven sins, or the major ways that people screw
up their lives. And so a typical
piece of fiction describes a situation
where something goes wrong, and you try to fix it, and
then something else goes wrong. And then this goes on until
finally you cure them all or you die. It doesn't make much difference. So that's what general
literature is about. And science fiction is
about everything else. So somehow, Isaac
Asimov lived nearby when he had just finished. And just as I ran out of the
Jules Verne, and HG Wells, and Aldous Huxley, and John
Campbell, and the early science fiction writers,
exactly the right people started to turn up, which
was Robert Heinlein in 1942, I guess, and Isaac
Asimov a little later, and Frederick Poe. I met Isaac because he lived
in Newton, which is nearby, and at this time, or
somewhat later anyway, we were building the first robots. Pretty much the-- although
a guy named Grey Walter, neurologist in the
west of England had made some interesting ones. And I invited Asimov to come and
see our robots, and he refused. And this happened a few times. And finally I said why not? And he said I'm imagining
really intelligent robots of the future. And if I came and-- I'm sure that your
robots are really stupid. Which they were. They could play a very
slow game of ping-pong and a few things like that. You might wonder why
aren't there any robots that you can send in to fix the
Japanese reactor that's broken? Reactors, I should say. The answer is that
for the last-- there was a lot of progress in
robotics in the 1960s and '70s, then something went wrong. If you go to Carnegie Mellon,
I don't want to mention names, or anywhere else,
you'll find students are slaving over robots that
play basketball, or soccer, or dance, or make
funny faces at you, and they're not
making them smarter. They don't use or don't realize
that they need something like artificial intelligence. So I'd like to see a return
back to the old days. Brenner mentioned
consciousness I think, and I agree entirely that
it's a very bad word. Because if you look at my
recent book, which is badly named The Motion
Machine, it says notice that people use the
word consciousness for 35 very different things, like
remembering what you've recently done, or making
certain kinds of choices, or reacting to certain
stimuli, and so forth. And so virtually everything we
don't understand about the mind when people do things is
attributed to this word. So anyway, that's
one of the problems. And yet I see that professional
people in various sciences still use the distinction
between conscious and not. And as far as I can see, you
can't get rid of that word. What's its main use? Its main use is deciding
when someone is a criminal. If they run over you by
accident, well, that's very bad and you might lose your license. If you run over
someone on purpose, that's intentional,
conscious, blah, blah, blah. And so we have this strange
situation in psychology that I don't see in
other sciences which is to use words that have
been around for a long time without-- I'm down on the 10%
of my notes, so. PINKER: Thank you. Noam Chomsky. CHOMSKY: Well, I kind of like
that idea of the language suppressor gene. That actually has a venerable
history, which maybe you know. In the 17th century, when
Westerners were discovering all kinds of exotic creatures
like apes, and [INAUDIBLE],, and others, and they
weren't sure which was which, there was
a lot of discussion about whether that
ape, orangutans, you know, apes can speak. They didn't seem to, and there
was a lot of debate about why they didn't. And there was a proposal
that there was-- they didn't call it
genes in those days, but there was the analog of
a language suppressor gene. Louis Racine, who was the
son of the famous dramatist, suggested that apes are really
more intelligent than humans, and the proof of it is
that they don't speak. Because they knew that
if they did speak, we would enslave them. So they keep quiet. This, incidentally,
led to the conclusion that a brilliant father
can't have a brilliant son. I was in our [? early ?]
Building 20 in the golden age. And it actually
was a golden age. But I got here in 1955. It was quite an exciting place. A lot of enthusiasm, innovation. A very lively
interaction among people of widely different fields. It's a kind of
community which I think will be very hard, if even
possible, to reconstruct. I also have many very
warm memories of it. In fact, some of them
very hot memories. Now those of you who
were around in those days may remember that
over the summer, Building 20 was unbearably hot. You could barely survive. Morris Halle and I then
shared a small office and we decided we
would try to get the Institute to put in and
air conditioner, a window air conditioner. So we sent a message up
through the bureaucracy, and it finally reached
whatever high office it got to, and something came
back down to us finally with a message saying
you can't put in an air conditioner because it
wouldn't be consistent with the decor of Building 20. Those of you who have ever
seen a picture of Building 20 would know what this meant. Fortunately, we could find a
friendly janitor who for $10 was willing to break
the rules and we were able to survive the summer. Back a few years earlier,
in fact, going back to the time of the famous
paper, Alan Turing's paper on machine
intelligence in 1950. Early '50s, there
was a small group of graduate students
down the road who were dissatisfied with the
prevailing conceptions of how to understand thought,
behavior, language, and so on. And we were trying to
see if we can construct another way of looking
at these topics, which might be more promising
and integrated better into the general sciences. And Turing's comments
had a certain resonance. You may recall that
in this paper, which is about machine
intelligence, he begins by saying that the
question whether machines can think is too meaningless
to deserve discussion. He didn't explain
why, but he presumably meant that it's a question
of what kind of metaphor you are willing to accept. So it's like asking do
airplanes really fly? Or do submarines really swim? If you want to extend
the metaphor, yeah. If not, no. But it's not a factual question. He nevertheless went
on to say that it would be a very good idea
to construct hard problems to see if you can
design machines, meaning hardware and
software, to solve them. And the famous
proposal of his was what he called his
Imitation Game, later came to be
called the Turing Test. He thought that
might be an incentive to develop better
machines, better software. And for that reason
it's good to do it, but we're not asking
do machines think? He also suggested that
maybe in the course of time, he said 50 years,
people will come-- because of this
work, people will come to think about
thinking in a different way. You can ask whether
that's happened. The machines-- it
certainly was an incentive to develop better machines. And that had a certain
analogy to the kinds of things we were concerned with. There is for one
thing, because when you look at language
seriously, it wasn't really done at that time,
unfortunately. You can see right
off that it's-- what each of us has
internalized is some kind of computational system. A system which, in fact,
determines an unbounded array of structured
expressions, each of which has a dual interpretation
[? that's ?] interpreted by the sensory motor system as
some kind of externalization, sound, or we know
no other modalities, and it's interpreted at a
internal, roughly speaking, kind of a thought
system, called sometimes the conceptual
intentional system, as having a specific meaning,
which then enters into planning of action and so on. So that's a
computational system. And the constant understanding
of computational systems had advanced quite considerably
by the mid-20th century, in large part because
of Turing's work. And that sort of fit naturally. Also, his provisos
made some sense for us. There is a standard question,
what counts as a language? And I think Turing's
response was accurate. The question's too
meaningless to deserve discussion for the same
reasons as machines thinking. If you want to call
the communication systems of hundreds of species
of bees language, okay, you can call it that. That means you're accepting
a certain metaphor. If you don't want to
call it that, don't. It's not a factual question. The facts of the matter are
these systems differ radically. Every animal
communication system known differs radically in
its basic principles and structural principles
of use [? others ?] from human language. And human language is,
in fact, [? here ?] it's different
from Turing's case. It is, in fact, a particular
biological system, one that each of us has
internalized, that, at its core, a computational
system with the kind of properties that I mentioned. And there's no problem
here about constructing hard problems to deal with,
because it was quickly discovered, as
soon as the effort to construct such computational
systems was undertaken, that almost nothing
was understood. Everything you looked
at was a puzzle. And the more you learn, the
more puzzles you discover. That was kind of
surprising at the time, because it was assumed,
the prevailing conception was that everything
was more or less known. There aren't any real
problems about language. It's just a matter of-- well, a famous version
was [INAUDIBLE],, a highly influential
philosopher, most influential, whose picture was
that language is just a collection of sentences
associated with one another and with stimuli
by the mechanism of conditioned response. So all there is to
do is just trace the individual
cases of histories of conditioned response
and you get the answers. Among linguists,
it was very widely assumed that
languages can differ in virtually any possible way
with virtually no constraints. And the only problem
in linguistics, I remember this
as a student, was to collect more data from a
wider variety of languages and apply to them the techniques
of analysis which were assumed to be more or less understood. And that's the field. But nothing could be puzzling. On the other hand,
as soon as the effort was undertaken to construct
an actual computational system with the properties
that I mentioned, it was discovered that
everything is a puzzle. Nothing is understood. And some of the puzzles are
quite simple, and some of them are still outstanding, in fact. So just to be concrete,
take the simple sentence can eagles that fly swim? Okay. We understand that it means
we're asking a question about whether they can swim. We're not asking a question
about whether they can fly. But that's a puzzle. Why doesn't the "can"
relate to the closest verb? Why does it relate to
the more remote verb? One which, in fact, is
closest in a different sense. It's structurally closest. So similarly, we can say are
eagles that fly swimming? But we can't say are
eagles that flying swim? Now that's a reasonable thought. It's asking is it the case that
eagles that are flying swim? But somehow we can't say it. The design of
language prevents us from articulating a
perfectly fine thought except by paraphrases. And there is a question
why that should be true. From a computational
point of view, counting computing the closest
verb, computing linear order and linear closeness,
is far simpler than computing structural
closeness, which requires complex assumptions
about the structure of the object. So there's a puzzle. And that puzzle had been
around for thousands of years, except nobody thought
it was a puzzle. It's one of [? really ?]
many such cases. I should mention that in
the last couple of years, sort of a substantial
industry has developed to try to deal with
it on computational grounds. And I won't talk about that. I don't think it gets anywhere. But it's a real puzzle. And furthermore, that
principle of using structural distance
but not linear distance is all over the place. You find it in structure after
structure in all languages, and it seems to just be some
universal property of language, and a very puzzling one. Well, discoveries
of that kind are kind of reminiscent
of the earliest moments of modern science. Go back to say 1600. For thousands of
years, scientists had been quite satisfied with
an explanation for such things as why stones fall
down and steam goes up. They're seeking
their natural place. End of that story. When Galileo and a
few others allowed themselves to be
puzzled about that, you start getting
modern science. And I think that continues. The capacity to be puzzled
about what looks sort of obvious is a good property to cultivate. And it's an awakening, and
that began at that time. And it turns out there's
enormous linguistic evidence that this is the
way things work. There's also, in recent
years, some evidence from the neurosciences. There's some interesting
investigations being carried out by a
very good group at Milan. The linguist in the group, Andre
[? Amaro ?] is well-known here. He's been here many times. They've been investigating
the reaction of people to two types of stimuli. One all new to them. [? Oral ?] systems that
meet the conditions, the basic conditions
of what's called universal grammar of the
general principles of language, and others that violate these
conditions and use things like linear order. So for example, an
invented language in which to negate a sentence,
you take the negative word and you put it in the third
position in the sentence, let's say. And it turns out that
brain activity is quite different in these cases. In the case of a normal
language, which people have never been exposed to,
Broca's area, language areas, are activated in the normal way. But in the case of
linear order, it's not. The language areas don't
have the normal activation. Now, people may figure it out,
but they're figuring it out as some kind of
puzzle to solve, not using their
linguistic capacities. Well, all of this ought
to be puzzling, and it is. And in fact, as far as I know,
the only serious proposal as to how to explain it
has pretty far-reaching conclusions. The natural assumption
is that at the point at which the computations are
taking place in the brain, there just is no order. There's no ordering at all. All there is is hierarchy. So the only thing
the brain can compute is minimal structural
distance, because there's no linear order. Well, that looks pretty
plausible, in fact, from many points of view. One reason is that if you look
at the syntax and semantics of language, for a very
broad core class of cases, order doesn't matter. What matters is hierarchy. Furthermore, if
you look at the-- so that suggests that one
of those two interfaces, the semantic interface
with the thought system, just doesn't care about order. On the other hand, the
sensory motor system must have some kind of
arrangement depending on what the form of externalization is. If it's speech, it'll
be linear order. If it's signed, it'll be
various things in parallel. But it has to have
some kind of ordering. So it's reasonable to suppose
that the ordering is simply a reflex of the
externalization, but doesn't enter into the core
properties of language, which simply give you
the thought, what's used in the thought system. Now that effectively
means, if it's correct, and it seems to be, that in
the informal sense of the term design, you know,
[? not ?] designer, but in the informal
sense of the term design, language is designed for thought
and not for externalization. Externalization is
an ancillary process. And that would
mean, incidentally, that communication,
which is just a special case of
externalization, is an even more ancillary
process, contrary to what's widely believed. That has significant
implications for the study of
evolution of language. I won't go into them, but
you can think of through. And for the nature of
design of language, where [? there are ?]
other kinds of evidence that have similar-- that lead to
similar conclusions. It says a lot about the
architecture of mind, if this is correct. For example, one
class of cases is kind of a ubiquitous phenomena
in language, sometimes called displacement. You hear something
in one position, but you interpret
it somewhere else. So if you say what did John see? You have to interpret the
word "what" in two positions. And where it is, where it's
kind of an operator asking-- telling you-- asking some
question about what person or something like that. And also in another position
as the object of see. Just as in John saw
the book, or something, where it's getting its semantic
role as the object of the verb. It's got to be interpreted
in those positions. It's as if the mind is hearing
something like for which thing X, John saw X, where
the "for which" thing is an operator ranging
over the variable. That shows up in other ways. That unpronounced
element turns out to really be there for
the mental processes that are taking place. You can see that in
sentences like say, they expect to see each other,
where the phrase "each other" is referring back to they. Say the men expect
to see each other. On the other hand, if you
had a another noun in between in the right position
it wouldn't work. So if you say, the men expect
Mary to see each other, then that somehow breaks it up. Each other can't
go back to the men. Well what about who do the
men expect to see each other? Well, that's like the men
expect John to see each other. Each other doesn't
go back to the men. But there's nothing there
in the form that you hear. It's just like they expect-- the men expect to
see each other. Well, that suggests strongly
that the mind is actually hearing, interpreting
for which person X, the men expect X
to see each other. And that X is like John
when you pronounce it. And things like that can
get pretty complicated. So if you take a sentence
like say, they expect-- they think that every artist
likes one of his pictures best. Like maybe the first
one he painted. If you take one of
his pictures and you ask a question about it, so
which one of his pictures do they expect every
artist to like best? The answer would be,
well, his first one, even though which
of his pictures is not in the right
position to be bound by the quantifier every. And that becomes clearer if
you make a different question. Suppose you say,
which of his pictures convinced the museum that
every artist paints flowers? Well, there's no relation
in every and his there, though it's
structurally think it through about the same
as the one that works. There's really only one
plausible interpretation for that, and that is that the
phrase which of his pictures is actually appearing twice. It's appearing where it's
getting its semantic role as the object of like,
and it's appearing where it is being interpreted
as an operator arranging over the variable. And these examples proliferate
into pretty complicated cases when you look at them closely. Turns out, interestingly,
that all of this follows if you develop an
optimal computational system, one that meets the condition
of perfect computational efficiency as the least possible
computational operations. Then it turns out that
what goes to the mind ought to have all these copies. They don't get erased. On the other hand, what goes to
the mouth has them all erased. Except for one to tell
you that it's there. That turns out to be
optimal computation. But that has a consequence. It means that
optimal computation, the core of language design, is
oriented towards the meaning. The mapping to the
transformation change to what comes out of
the mouth is causing communicative difficulties. Anyone who's ever worked
on a parsing program trying to figure out how to
mechanically interpret a sentence structure and
meaning of a sentence knows that one of
the hardest problems is what's called
filler gap problems. You hear the word what at the
beginning of the sentence, and you've got to
find where is the gap, the unpronounced element,
that this what is related to. If you pronounced them all,
that wouldn't be a problem. Most of the cases
would be solved. But efficient computation
leads to the consequence that it undermines
communicative efficiency. That's pretty much like the case
of are eagles that flyings-- are eagles that flying swim? Nice thought, but
you can't say it. And again, optimal
operation of the operations is leading to
communicative difficulties. And quite generally,
there's an interesting class of cases known now
where you have conflicts between computational efficiency
and communicative efficiency. And computational
efficiency wins hands down in every case that's
understood, which again suggests that
the design of language is really for meaning. It's for somatic and
intentional interpretation out of organized speech
acts and so on, and that the
externalization of it, the fact that it sort of appears
in the outside world somehow, is just a secondary process. Communication, tertiary process. Well, that reorients
our conception about how the mind works, but
it looks pretty plausible. Well another issue-- kind
of [? go ?] long sometimes. Another minute? Yeah. Going back to the '50s, it was
of course understood right away that none of this can-- it's impossible for
any of these things to be learned by association,
conditioning, induction, any simple mechanism. So it must be the case that
these examples of the kind that I mentioned, which are
known very early by children, we have a little
evidence, they must result from the other
two possible factors in development. One of them is some
genetic endowment, which is a species
property apparently. All humans share essentially
the same endowment. No group difference is known. So that's one. And the other is
just laws of nature. The kinds of laws
that determine, say, that a cell divides
into spheres, not tubes. So genetics is involved
some, but not much. The interaction of
these two factors ought to be able to yield
results of this kind. By now there's a fair amount
of evidence supporting that the kinds of things I
mentioned illustrate [? it. ?] Of course, languages
differ all over the place. That was known too. So you have to show that
these principles that you're postulating are going
to apply universally. That does not mean, contrary to
a widely held misunderstanding that you can read in
journals like Nature and Science and
others, it does not mean that there are going to
be descriptive generalizations about language that
hold universally. In fact, it might
turn out there's no descriptive
generalizations at all, and you have a very
rich genetic structure. That's not the case,
but it could turn out, because the things that
you see are reflections of internal processes
that are going on that meet other conditions,
like efficient computation. But it must be
somehow that you've got to apply it to everything. Well, in the '50s,
in the golden age, a few languages were studied. The ones that people knew
in [INAUDIBLE] at that time. Actually, one of the
first extensive studies was on an American
Indian language. That's the first dissertation
in the department. Actually, it was in the [? EE ?]
department, because we didn't have a formal department then. It was on Turkish. There was of course
work on English. [? Marcel ?] was
working on Russian. By the 1960s, as the
department developed and people came in from all
over, the range of languages investigated
expanded enormously. In the '80s and since,
it's just exploded. There is now intensive
investigation of languages, with a very wide type
typological variety. All kinds of new problems
being discovered, sometimes new answers. Sometimes they lead to
further problems and so on. At the same time, what
Steve mentioned before, the study of language
acquisition really took off. Actually, Steve's work
was part of the early work on this in the '80s. Based on trying to discover
how the option's [? a ?] variation of language,
which is all that has to be picked up by a child. The rest is fixed. How those are set on
the basis of data, and by now quite a
lot of work on that. All of this, I'll just finish by
saying that plenty of problems remain. I don't want to
overstate, but there's some fairly striking
and reasonably plausible conclusions which have
far-reaching implications. All of this, however,
is restricted to what's going on inside the
internal computing system. What's going on inside the mind. Now somehow, language has to be
related to the outside world. And because there
are two interfaces, it's going to have to be
related in two different ways. At the sensory motor
interface, the question is hard but sort of understood. That's the topic of the acoustic
and articulatory phonetics, which has been studied
pretty intensively. [? Orally, ?] too, for 60 years. And a lot of results. A lot of problems. But at least the
problem's kind of in hand. What about the other end? How does the meaning side
relate to the outside world? Well, there is a doctrine that's
widely held that's supposed to give an answer to this. It's sometimes called the
referentialist doctrine. It holds that the elementary
semantic units, let's say words for simplicity, they
relate to the outside world by a relation of reference,
picking something out, or denotation for predicates,
picking out a class of things. So like cow, the
word cow picks out cows where cows are something
that a physicist could identify in principle. The only problem-- and
that's very widely held. It was known in classical
Greece that that's not correct. They didn't have much
of a deep analysis, but examples showing
it doesn't work. In the 17th century,
in what we ought to call the first
cognitive revolution, many of the
significant questions were raised, but are
still puzzling today. It was widely understood
that that doesn't work. As Hume, David Hume
put it, summarizing a century of inquiry, that
the way we categorize things-- the things we
categorize, he said, are fictitious, created by the
mind using Gestalt properties, notions of cause and effect,
notions of psychic continuity for sentient objects. John Locke studied this. And other properties that a
physicist cannot identify. They are imposed by the mind
on reflexive experience. And those are the
entities, if you like, sort of mental entities
that we construct. And they're somehow
related to the world. Now, this poses a huge problem. There's no animal
communication system known that has anything
remotely like this. There, the referentialist
doctrine apparently holds. So if you take say, cries
of a vervet monkey, each cry is key to some identifiable
physical event. Like leaves are
moving, we interpret that as meaning an eagle's
coming, all the vervets run and hide. I don't know. How they interpret
is another question. But or else it's an internal
sort of hormonal phenomena, like I'm hungry or
something like that. Every communication system
that's known works like that, but human language doesn't
work like that at all. You take a look at the words
of the language, none of them work like that. And that's a real problem. For one thing, it's a
tremendous evolutionary problem. Like where did all
this come from? Totally unknown, and
maybe unknowable, but it's also a problem
for the nature of language and for the acquisition
of language. Like how do children find
these properties which they get to understand very quickly? In fact, things like
children's fairy tales exploit them effectively. Well, these are
major mysteries that remain on the horizon
right at the core of what language and thought are about. And there are plenty
of other ones. So I'd just suggest again that
the capacity to be puzzled is a good one to cultivate. PINKER: Thank you
very much, Noam. Emilio Bizzi. BIZZI: Well, one of the-- can you hear me? Yeah? One of the goals of
artificial intelligence, as I understand it, was to
build intelligent machines. And there's a class, of course,
of intelligence machines that move like robots. And so what I'm going
to discuss is movement, not in machines, but movement
from a biological perspective. And then toward the end, I
will discuss the interaction between artificial systems like
robots and biological systems that move. But for the initial
part of my discussion here, I like to
point out something that I can illustrate
with an example. Let's say that I want to
reach this pair of glasses. Well, this is something
extremely simple. We do it hundreds
of times per day. We have done it for thousands
and thousands of times. So what's the big deal about it? Well, this simple gesture here
points out an important fact. And that is that what seems
to be a very simple action, in reality, the neural
processes that subserve, that make it possible for
this action to be expressed, are of great complexity. And this complexity is, to a
certain extent, mysterious. We know a little bit about it. We know that some
of the problem, some of the computations, are
beginning to be seen. But there are an
enormous amount of things that have to do with
actions that at this point remain fairly mysterious. So let me start with what
we have glimpsed recently in the last few years. And that has to do with-- and
I'll go back to this example. If I want to touch
this pair of glasses, then I have to do a number
of things simultaneously. I have to move my eyes,
my eye muscles, my neck muscles, my trunk muscles,
and of course, then there arm muscles. Now, from an anatomical
point of view, all these muscles
are made of elements, which are the muscle fibers. And each group of muscle
fibers receives a fiber from the central nervous
system, and particularly from, in this case, from
the spinal cord. So what we have here is that to
implement this simple gesture, the central nervous
system has to control an enormous [? piece, ?] an
enormous number of elements. That is the muscle fibers
that make up the muscles. So this is in a sense
a gigantic problem, because we have an extremely
tough computational problem. How to arrange this
distribution of signals to a very vast space. Well, in the last few years,
a number of investigators have identified elements
of the architecture of the central nervous system
that deals with movement that has indicated a modularity. And this modularity has been
identified predominantly in the spinal cord. It's maybe also the upper
part of the brain stem. But in any case, most of
it is in the spinal cord. And what it does,
it means that there are groups of interneurons
that manage to put together in a unit a group of muscles. What does that mean? It means that the
number of degrees of freedom, this vast space,
has been reduced dramatically in such a way that
the descending fibers from the cortex
that convey from the brain down to the spinal cord the
information for movement, all they have to do is to combine. They activate these various
modules in the spinal cord, combine them, and
provide coefficient of activations for each one. And this view of how
the brain manages to be so effective
in producing movement is derived from experimental
work that in this laboratory, in my laboratory, and
in other laboratories, has been performed
in the last 10 years. Now there are other things
that are more mysterious. When I do this simple movement
here, this movement is learned, has been learned somehow
during the course of life. The site of learning,
motor learning, is certainly the motor
cortex of the brain. So the frontal areas
of the frontal lobe. Now, these areas of the
frontal lobe of course connect it with the subcortical
nuclei, with the cerebellum, and so on. And certainly all these
areas, in conjunction with the connectivity
of the spinal cord, represent the circuitry that
encode the signals, the memory signals that are necessary
to do this simple action. But here there is a catch. When I do these
simple actions, I can do them like in
front of you now, but I can do it by moving my
body in a different posture. I can be reclining,
and nevertheless I can accomplish the same goal. So this is-- it's very tough to
understand how the signals that have been memorized, which
specify particular muscles that have to be activated in
that particular context can, in another context,
still be just as effective. So this is a question
of generalization. And we-- at least
I don't understand how the central
nervous system manages to do this process of
generalization so effectively. There are also other
computational problems that are quite difficult to
see how they are implemented in the central nervous system. And that is I can reach
this pair of glasses. If there is an obstacle,
I can go up this way, I can go down this way. So this is a
question of planning. How [? batteries ?]
are extremely good at planning their action
depending on the environment. And how, again, this memory
that guides the movements, how can that be translated into
[? when ?] a signal is modified in order to accomplish
pathways that each time are potentially different. It depends on the environment. Now there are other things that
are somewhat less mysterious. We know that among
the vertebrates, a certain amount of learning
goes through by imitation. And recently, some
light has been shed on this process of
learning by imitation. Neurons have been found
in the frontal lobe that discharge when the
subject sees an action. And when the subject
repeats that action, the similar action. So these are called
mirror neurons, and they are an important
feature of the model system that seem to provide the basis
for learning by imitation. Now although there are all these
tough computational problems that need to be
understood, I am optimistic that in the next few years we
will make a lot of progress. And the reason is that there
are many laboratories scattered in various parts
of the world that are pursuing the issues of
robotics, humanoid robotics. That is, what they
are trying to do is to put these properties
that I've described to you, generalization, planning,
learning by imitation, and so on in machines. And I don't know how
far this effort-- how much these people
have accomplished. But nevertheless,
the fact that there is this intellectual attitude
toward implementing in machines [? lay ?] solutions
to try to find the computational
solutions that provide these properties to machines
is a way to really understand, is a way to understand. When you start to
make things, beginning to face the computational
problems, and understand. There is also-- and I'm
finishing [INAUDIBLE].. There is also a new field,
which is somewhat promising. And that is to place
sets of microelectrodes into the brain of-- it has been tried with
humans, and is usually tried in animals, and
connect the output of the signals that come
out of these microelectrodes and connect it with machines. So this is a brain-machine
interaction, which obviously has tremendous importance,
both in understanding the workings of the
central nervous system, and also the the practical
significance in rehabilitation of amputees and so on. Now for the time being,
there are technical problems that prevent the
quick implementation of brain-machine interactions. And those that have
to do with the brain reacts to the contact with
these permanent [? contact ?] with these electrodes
in the brain. They are essentially
rejected after a while. But it's conceivable
that more different types of different technologies could
be used in order to get around this problem. So this is the two reasons
why I have a certain amount of optimism that for the
future, this [? methodic ?] intelligence will be understood. Thank you. PINKER: Thank, you Emilio. Barbara Partee. PARTEE: For me, the adventure
began just 50 years ago, here at MIT in 1961. The Chomskyan revolution
had just begun, and Noam Chomsky and Morris
Halle had just opened up a PhD program in linguistics. And I came in the first class. I want to start by
thanking Chomsky and Halle for building that program. And I thank MIT and the Research
Laboratory of Electronics for supporting it. I'm indebted to Chomsky
for revolutionizing the field of linguistics and
making it into a field whose excitement has never waned. Chomsky redefined
linguistics as the study of human linguistic
competence, making linguistics one of the early
pillars of cognitive science. At the center of the
Chomskyan revolution was syntax, generative
grammar, a finite specification of the infinite set of
sentences of a language. That launched an extremely
productive research program, but it didn't include semantics. Chomsky considered meaning
scientifically intractable, and he wasn't alone. When linguists did soon tried
to add semantics generative grammar, there were
two big obstacles. A lack of good formal
tools, and the absence of any non-subjective
notion of meaning. It was the UCLA logician
Richard Montague who revolutionized the study
of semantics in the late 1960s. He built on successes
in formalizing the syntax and semantics
of logical languages, the work of Frege,
Tarski, and others. Montague himself
developed a higher order typed intentional logic
with a model theory, and with its help, he
was able to formalize a significant part of the
syntax and semantics of English within a clearly articulated
theoretical framework. The result was what we
now call formal semantics. Emmon Bach has summed up
Chomsky's and Montague's cumulative innovations thus. Chomsky showed
that English can be described as a formal system. Montague showed
that English can be described as an
interpreted formal system. For me, as a young syntactician
at UCLA in the late '60s, Montague's work was eye-opening. Linguists had been
building tree-like semantic representations. Okay for capturing certain
things like scope ambiguity, but otherwise leading
to endless arguments about competing representations. We linguists never dreamed
of truth conditions being relevant for linguistics. But Montague's work, and
David Lewis's, showed that truth conditions are
crucial for giving semantics a non-subjective foundation. A linguistically
exciting part was that with such a
rich logic, we could get a close match between
syntactic constituents and semantic constituents. And meanings of sentences
could be built up recursively and compositionally from
the meanings of their parts, of their syntactic parts. One brief illustration. Bertrand Russell had argued
that natural language syntax is logically very
misleading, since it puts John and every man into the
same syntactic category of noun phrases. Whereas in first order
logic, John walks, and every man walks, must
have radically different syntactic structures. Montague showed that natural
language syntax does not have to be logically
misleading at all. All noun phrases can be treated
uniformly in higher order logic as so-called
generalized quantifiers. And English
subject-predicate sentences, like John walks,
and every man walks, can then be interpreted
in a similar manner straightforwardly. Generalized quantifier
theory is now one of many rich topics
in formal semantics, and it can explain many
things that we couldn't explain with just syntax. I started working on Montague
grammar in about 1970 to try to integrate it into
linguistics by finding ways to put Chomsky's and
Montague's work together. From the early '70s,
collaborative work among philosophers,
logicians, and linguists in the US and Europe
built in many directions. One important result is that
with a serious semantics to share the load,
syntax doesn't have to do all of the work
that we once imagined. Syntactic and semantic advances
now often develop in tandem, each informing the other. By the late 1980s,
formal semantics was well established within
linguistics departments in the US. In Europe, it may be in
linguistics or in philosophy. Among middle generation
leaders of formal semantics, I'll just mention two who are
involved in this symposium and who were my PhD students
at UMass in the 1980s. Irene Heim, head of the
Department of Linguistics and Philosophy
here, became MIT's first formal semantics
faculty member in 1989. And Gennaro Chierchia is now
Head of Linguistics at Harvard. Both have made seminal
contributions that have helped shape the field. I have to mention one
seeming paradox which relates to open challenges
as well as to past progress. I stressed how the Chomskyan
revolution made linguistics an early pillar of
cognitive science. Yet Frege's anti-psychologistic
stance, shared by Montague, was crucial in the foundations
of formal semantics. Frege argued that truth
conditions, and not mental ideas, have
to be at the core of the meaning of a sentence. And the work of generations
of linguists and philosophers has shown the fruitfulness
of that approach. First, we have to formalize what
the semantics of our language is, what our sentences say about
how the world is, then figure out how our knowledge
of it, such as it is, is acquired and exploited. This stance might seem to
exclude formal semantics from cognitive science, but
I believe on the contrary, it makes the
contributions of semantics to cognitive science all
the more interesting. Human language is a
remarkable achievement. Part of what's remarkable
is how we implicitly recognize and navigate the
social construction of meaning. When we converse,
we simultaneously exchange information
and negotiate meaning. David Lewis's classic
work on convention was an early
milestone in exploring the relation between
individual competence, what's in the head of
one language user, and social intelligence, an
important dimension that is still probably under-explored. As for newer
directions, the best understood parts of
formal semantics concern the semantic compositions
of parts from wholes. No, of wholes-- sorry. Of wholes from parts, of course. Yeah. The semantics of-- I
proofread this several times. What we might call the
semantics of syntax. There has also
been great progress on formalizing parts
of pragmatics involving the interaction of meaning,
language use, and context, including the study of how
context both affects and is affected by interpretation. And studies in
language processing now include formal
semantics and pragmatics. And game theoretic approaches
are having a growing influence. Computational formal
semantics is now a subfield of
computational linguistics, contributing to both
theoretical and applied goals. And there's promising early
work on universals and typology in semantics with
innovative fieldwork uncovering semantic and
pragmatic subtleties of various indigenous languages. And just as in
syntax, such languages prove to be as semantically
rich and complex as more familiar European languages. There are many challenges
facing formal semantics and its relatives. These are still young fields. I'll mention just
two that I think are important,
both for the field and for the goals
of this symposium. First, the semantics of
[? sentencial ?] structures is increasingly well-understood,
but lexical semantics is really hard, and formal
methods are weakest here. It's in lexical semantics that
we most clearly cannot equate the language with
our knowledge of it. As Hilary Putnam
observed years ago, the fact that he doesn't
himself know anything that would distinguish a
beech tree from an elm tree does not mean that beech and elm
are synonyms in his language. The lexicon has lots of
linguistically important substructure, but it's
also the part of language that interfaces most strongly
with non-linguistic knowledge, with encyclopedic knowledge,
with our commonsense ontology, with visual imagery, with
cultural associations, and on and on. And connected with
that, another challenge is how to build formal semantics
into real-time processing models, whether we think of
psychological models of how we do it or
computational models that might do it in an applied way. Models that involve
the integration of linguistic, and not
specifically linguistic knowledge. This concerns not only lexical
semantics, but also all kinds of context dependence. Implicit content,
meaning shifts, and nonliteral uses of language. I know there is progress. I'm not an expert
in those areas. But I believe this is a domain
where major interdisciplinary innovation and cooperation
may be crucial. Just an illustration
of the challenges. I'm pretty sure that the most
successful search engines do not do any formal semantic
analysis of questions that we ask or of texts
that they are searching. I've heard of some
beginnings of some nice work in that direction, but large
scale, all-purpose, fast search engines, or quote "intelligent"
machines that really know and use semantics as we
study it are probably still far in the future. And in conclusion, I
would say I would suppose that really knowing
semantics is a prerequisite for any use of language
to be called intelligent. So if there is to be
a new basic research effort to understand
intelligence and the brain, semantics is ready to contribute
to it and to gain from it. Thank you. PINKER: Thank you Barbara. Patrick Winston, I
think you're going to be a motile organism, as I recall. Would you like to
use the podium? WINSTON: I think
I'll not use that. I've never been very good
at introductory statements, so I haven't prepared one. I'll ask myself a few
questions instead. My first question is, well,
what do you think so far? And that's a difficult
question for me to answer, because
several of the speakers, you may have noticed,
ran out of time. I was particularly
disappointed that Marvin was unable to give us a detailed
account of his assessment of the progress that's been
made in artificial intelligence in the past 20
years, so I decided to dedicate a little of my
time to doing a simulation of Marvin, and here it is. And that concludes my
simulation of Marvin's account of the past 20 years. Now it's certainly the
case that many people would contest the view that
there's been no progress, but I don't think anyone would
contest the view that there could have been more
progress in the past 20 years than there has been. And I think it's informative to
think about why that might be, because we have no lamp to
guide us into the future except by the lamp of what
has [? passed ?] before. And I think that in my
view, what went wrong went wrong in the '80s. And the first thing
that went wrong is that we all discovered
that artificial intelligence technology was actually
useful, and that led to a kind of
commercial distraction, from which the field
has never recovered. So that's one problem. Another problem, of
course, is that happened to be the decade in
which the Cold War ended. And as a consequence
of that, there were shifts in
funding patterns that contributed to the problem,
because sponsors were no longer as interested in being
princes of science, but rather became
much more interested in short-term goals,
demonstrations, and the like. But I think the most
insidious problem was what I'd like to label
the mechanistic Balkanization of the field. What happened in that period was
that artificial intelligence, as a field, matured
and began to break up into communities that were
coagulating around mechanisms rather than problems. So it was in the
'80s that we began to see conferences dedicated
to neural nets, conferences dedicated to genetic algorithms,
conferences dedicated to probabilistic inference. And when you dedicate
your conferences to subjects of that
kind of mechanisms, then there's a
tendency to not work on fundamental problems
but rather those problems that the mechanisms
can actually deal with. This tended to affect not
only the conferences that were being held, but also
the kind of jobs that were offered by universities. Those job descriptions tended
to have phrases in them like neural nets, probabilistic
inference, genetic algorithms, and the like. I think it was in about 1995
that I had a horrible nightmare as a consequence of this. I was on the search committee
for the Electrical Engineering and Computer Science
Department at the time. And in my nightmare,
we had put out a call for people interested
in making an application to work in MIT as a professor
in the Electrical Engineering and Computer Science Department. And Jesus applied. And after due consideration
of the application, we decided to send
the standard letter. Thank you for your
interest in our department. We regret that with
over 400 applicants, we cannot interview anyone. And besides, this year we're
not looking for a theologian. It was because of that
kind of Balkanization, which focused everyone
on particular rather narrow elements of the field. So what to do? Some people say well,
Winston, the trouble with you is you're just a hopeless
romantic about the past. But that's not entirely
true, because I'm also romantic about the future. It's just right now
that may not be so hot. And I think the future
does offer some hope. And you might say why? And I think the reason is
because we now are gradually becoming less stupid
about what to do. So you might say, well, what is
it that you think we should do? And then my response
would be well, I think we should take
the right approach. And then you would say
what is the right approach? What do you call your approach? And I would reply, well, I
call my approach the right way. And then you might
say, well, what does your approach
have to offer? And then I would say that my
approach, what my approach has to offer is a focus on the
fundamental differences between our human species
and other primates. And in particular to
others of the Homo genus. And you might say,
well, what are those properties that
separate us from, for instance, the Neanderthals? And who knows? But if we want a clue,
the best thing to do is to ask people who
study the question. So I've been much influenced
by, for example, the cultural-- rather the paleoanthropologist
Ian Tattersall who tells us that
we, as a species, coexisted quite happily with
the Neanderthals for about 100,000 years. And during that time, we made
approximately the same kind of stone tools. And during that time there
was approximately no progress in our making of stone tools. But then about 60,000 years
ago or so something magical happened to the human species. It probably happened
in southern Africa. And it probably happened
in a neck-down population of maybe many
hundred, but certainly not more than a few
thousand individuals. And once we got
that new property, it spread throughout
the human population. And within 10,000 years
we had either wiped out or outcompeted the
Neanderthals, wiped out a lot of species along our
path throughout the world and never looked back. And so what is it then that
was that magic property? Once again, who knows? But we can speculate. And I've heard Noam say things
like it was the capacity, perhaps, to take two concepts
and put them together to make a third concept, and to
do that without limit and without disturbing the
concepts that got merged together. I think that's probably right. And I would hazard to layer
a piece on top of that, because I think that
that capability, in turn, makes it possible for
us to describe events. And once we can
describe events, it's natural to develop a capacity
to stringing them together into stories. And once we have stories,
then we can take two stories and blend them together
to make another story. And once we have that, we have
at least a kind of creativity. And once we have that,
we have a species that's much different from any other. So I've come to believe that
that story understanding is one of the great
things that's enabled by the inner language that's
underneath our communication language. And I think that the
past 50,000 years has been a period of time
in which we have become increasingly awash in stories. We are soaked in stories
from our birth to our death. We start with fairy tales. We continue on through
religious parables. We go on to literature
and history. We end up perhaps with law
school or medical school. And throughout that whole
period of education, we are essentially
accumulating and learning to manipulate the stories
that come to us in our culture and in our environment. So that's what I think
ought to be done. And now you might ask an actual
question, what kind of property do you need in order to study
these particular questions that derive from this approach? And the answer is that
there's only one property that you really
need, and that is, I think, naive
optimism in the face of overwhelming
contraindication. But that's okay. I don't mind that,
because at the very least, I think that through
the study of stories that we will develop at least
sharper questions to ask in the next generation. I also think that it's possible
to do the kinds of things that I do better today
than I was able to do them some years ago, because I think
I've gradually become less stupid about the steps that need
to be taken in order to make progress. So here are the
steps that I think you need to take in
order to make progress. First of all, you need to
characterize the behavior. Next, after you've
characterized the behavior, you need to formulate a
computational problem. And once you've formulated
a computational problem, then you could take
step three, which is to propose a
computational solution. And all of these things
are the natural objectives of classical psychology. But then there's a fourth step. Once you have the
proposed solution to the computational problem,
then the kind of thing that I do I could refer to
as exploratory development, building systems
that you believe will have the behavior
that you have characterized in virtue of the
computational solution that you have proposed. And when you do that, you
discover, to your horror and amazement, that
you left things out, that your solution
is incomplete, and you have to go back
through that cycle again, and again, and again. So I think that's four steps. The computational problem--
rather the behavior, the computational problem with
all of the constraint necessary to solve it, a
proposed solution, exploratory development,
and as one of my students, [? Leonard ?] [? Federov, ?]
mentioned to me this morning, that isn't enough either,
because at step four, you might be in the same
situation that the Romans were when they had catapults. You can improve them, but
you didn't understand them until you had a notion
of Newtonian mechanics. So the fifth step,
then, is you have to look back over
the first four steps and crystallize out
the principles that made everything possible. So that's what I think
ought to be done. And that's the sort
of thing that I do. But as I say that,
I think it would be easy to suppose
that I think everything happens in symbol
land, merely because we are a symbolic species. But I don't think everything
happens in symbol land, because I think we make
use of the stuff that was there before. In particular, we have to
solve a lot of problems with our perceptual apparatus. So I could say, for example,
to you, Sebastian kissed Erin. And then I could ask did
Sebastian touch Erin? And you would immediately
reply yes, of course, because you can imagine it. And you don't feel like you're
doing syllogistic reasoning. You feel like you're
imagining the situation and reading the answer off of
the imagined situation with you with your perceptual apparatus. And I think that's right. I think we know a lot
of things latently and we don't have
to be told them. We don't have to
be told that if you kiss somebody you touch them,
because we can figure that out on the fly. And then, of course,
we can cache it in our symbolic
systems if we want, but the point is that we
can compute that stuff when we need it. So we know vastly more
than we actually know. Here's another example
that I am very fond of. I borrowed it from [? Shimon ?]
[? Oman. ?] It's the example of, well, what am I doing? It's not a trick question. I'm drinking. Now what am I doing? I'm toasting. Now what am I doing? I'm admiring this fine
[INAUDIBLE] glass. But then for the
next example I have to ask you to exercise your
own power of imagination, because I don't have
a slide that does it. What I want you to
imagine is that I'm a cat, and my mouth is
pointed to the sky. And up there above my
mouth is a leaking faucet. What am I doing? I'm drinking. Even though that kind of
drinking doesn't look, visually, anything like
the kind of drinking I did with my first
example, yet somehow we label those both as drinking. So my view is that
it must be the case that this kind of drinking
and that kind of drinking is telling the same story. That there's a
story about thirst. That there's a
story about motion. There's a story about
liquid moving through space into your mouth. And that's the
story of drinking, and you can recognize it from
a single frame as a drinking story even though it's very
different from other frames, from other videos you've seen
that tell the drinking story. So that's what I think. And the last question
I'll ask myself is what I've tried to
contribute here today. And the answer is I tried to
talk a little bit about why I think we haven't made progress
in the field to the extent that we should have, what
I think we ought to do now in order to make progress,
and the steps that I think need to be taken
by people who are trying to make that progress. And an emphasis, too, on
the idea that we're not just a symbolic species
[? or ?] symbolic nature, and that our [? interlanguage ?]
capability make possible an interaction with our
perceptual apparatus that I think would not be possible
in a dog or a chimpanzee. And finally, I think as part
of the contribution package, I'd like to say that I think
that if we do all this, then we'll have a much better
understanding of how we work. And I think that
will lead to what I would like to call, with
perhaps a touch of irony, unthinkable value
to the species. PINKER: Thank you, Patrick. Tommy Poggio tells me that
we have another 15 minutes, and I would hate to
let this opportunity pass with this extraordinary
collection of speakers without some exploration
of perhaps some themes that cross-cut some of
the presentations. I'm going to take the liberty
of asking a couple of questions myself to try to draw
out some connections among the fascinating
presentations we just heard. And I'll start with
Sydney Brenner. You have inaugurated the
study of the nervous system of a creature with 302 neurons. Many of us are interested in the
neuroscience of a creature with [? 100 ?] billion
[? 302 ?] neurons, give or take a few 100,000. And we have heard,
this very evening, suggestions that the genetic
code contains information that is exploited in the
development of the capacity, say, to use human
language together with the thoughts that
are expressed in language. You referred jocularly
to the Chomsky gene, the single hypothetical gene
that turns us into a human, but more seriously, do
you have any thoughts on how we might eventually
narrow the gap between systems like C. elegans,
where we clearly have an enormous amount
of understanding, and the organism that
many of the rest of us are interested in and
the possible information in the genome that results
in the innate unfolding of cognitive abilities? BRENNER: Well, I
think it has to go beyond these hardwired systems. And I think there is a
biological invention that occurred, which allowed us
to expand the possibilities of the system. If I can give one
example of this, I think that it would be
extremely interesting to ask is it just going to be more
of the same stuff that somehow culminates in intelligence
and all the other properties associated? Or is there some revolutionary
change in [? principal? ?] So I think the
important thing is in that once we get away and
have a way of building bigger nervous systems, we have
the potential, then, to have not fix it. So there are many such
systems in biology. The immune system is. It says you can learn
things from the past, which is [? true ?] by animals being
selected because of things, but you must have
a way of meeting unforeseen circumstances. And I think we have to
have something like that. It needn't be coded
like the immune system. We have to have a flexible
system in which we can achieve the result but we need
additional input of experience, if you like. So perhaps our genomes only
decide how to make a baby and all the rest of
it has to follow. But [? they ?] give you the
capacity to do these things. And that is the-- that, I think, is the
very interesting thing. On the other hand,
I am still very interested in the
accumulation that comes from the
processes of evolution. And evolution, of course,
naturally selects for organisms and, so to speak,
guarantees their survival. In fact, it could be argued
that the trouble with us, as human beings, is that we
stopped biological evolution, [? where ?] sitting
inside of ourselves is a quarter of a million year
old genome, totally unsuited, maladapted to the environment
we need to create-- that we've created
as part of history. And I think we should be clear
about that, because there is still, if you like, deep
down inside there is an animal. And the animal is still very
much like a rat or a mouse. Of course it doesn't
do the same things, but it is designed, a system
designed for function, for survival. I think what we've
been able to do by the expansion of other
parts of the brain is, in fact, in many cases,
to be able to suppress it. I think there's a
very good example. You can see this very clearly. And that's why I think the
basic part of the brain that we can still
explain from the genome and all these other things
resides in the hypothalamus. The hypothalamus is
Freud's id, all right? Now what happens is
the frontal cortex sends inhibitory stimulus
down to the hypothalamus and says stop
behaving like a beast. So all those things which we
regret in our constitution, like greed, lust,
desire, avarice, those are encoded,
really, in the genes that affect the hypothalamus. What we have is this
inhibitory thing. So that means that
we can suppress this and sometimes we don't
want to or can't and so on. So I think that the layering
of the system, which it distinguishes that. And therefore, you will have
to work in this field here that looks at this. Everybody knows
that you can take-- if you take a rat and let it
smell a cat, has to smell it, that's encoded in the genome and
it undergoes a complete panic. Of course you can condition
that to the vision of a cat. Then you show the animal
a cat, complete panic. You take it out and
you just destroy 3,000 cells in the hypothalamus
and you put it back in the cage. You can show it the cat. It'll saunter out of this,
walk in front of the cat. The amusing thing, now
the cat is perplexed. However, I'll just
give you that. And I think a lot
of this is in this, and I think this is where
biology can tell you what the ground process is,
what's encoded in our genes. Many things are like that. But I think the rest of
it is all the other stuff that we've seen. So we have, if you like, a
machine within a machine. We have [? then ?] added on
top of this a machine which is capable of flexibility,
[? and ?] is capable of changing it. So that's why I don't
believe that tracing out all the connections-- I think that's a
form of insanity. And then we'll be able
to discover exactly this. I think that is a
form of insanity. I mean, today, nobody can
describe to you in detail all the physical events that
occur in a computer chip when you take a
picture in your camera which has the telephone
attachment to it. And nobody wants to
describe it anymore, because the basic
principles are there. We've got a [? buffet. ?] So I think we have to
look at this [? still ?] with the whole thing of
layers of organization. And we must learn to
know what we can do and what others can
do together with us. That's why it's got to
be multiple discipline. PINKER: Thank you. I have a question for
both Noam and Marvin that I think is on the minds
of many people in this room, and I know it's been expressed
in some of the questions that I've received by
email prior to this event. There is a narrative in
which the new direction of both artificial intelligence
and cognitive science is one that makes
a great deal more use of probabilistic
information that is gleaned from enormous amounts
of experience during learning. This is manifested in
branches of cognitive science such as neural networks
and connectionism, Bayesian inference models,
application of machine learning to intelligence. Many of the models, both
of Tommy Poggio and Josh Tenenbaum, in the classic
work from the golden age, and indeed in many of
the models since then, including the models
of generative grammar and models of semantic
memory, probabilities don't play a big role. Is the narrative that says
that the direction of the field is in making use
of massive amounts of statistical
information via learning-- well, maybe I'll ask you
to complete that sentence. What is the-- well, I'll let
you complete the sentence. Noam? CHOMSKY: It's true. There's been a lot
of work on trying to apply statistical models to
various linguistic problems. I think there have
been some successes, but a lot of failures. These successes, to
my knowledge at least, there's no time to
go into details, but the successes
that I know of are those that integrate
statistical analysis with some universal
grammar property, some fundamental
properties of language. When they're integrated, you
sometimes do get results. The simplest case, maybe, is
the problem of identifying words in a running discourse,
something apparently children do, you know? They hear a running discourse,
they pick out units. And the obvious units
are words, which are really phonological units. And there's a natural
property that-- I wrote about it in the 1950s. I mean, it was just
taken for granted that if you just
take a look at the-- if you have a series
of sounds and you look at the transitional
probabilities at each point, what's likely to come next,
when you get to a word boundary, the probabilities go way down. You don't know
what's coming next. If it's internal to a word,
you can predict pretty well. So if you kind of trace
transitional probabilities, you ought to get something
like word boundaries. Actually, I wrote
about it in 1955. And I assumed that
that's correct. Turns out to be false. It was shown, actually, by
Charles Yang, a former student here, [? got the ?] PhD in the
Computer Science Department, that if you apply that method,
it basically gives syllables in a language like English. On the other hand, if
you apply that message under a constraint,
namely the constraint that a word has what's
called a prosodic peak, you know, like a pitch
stress peak, which is true, then you get much
better results. Now there's more recent work,
which is still in [? press, ?] by [? Shukla, ?]
[? Aslund, ?] and others, which shows that you get still
better results if you apply that together with what are
called prosodic phrases. And it turns out that
a sentence, let's say, has units of pitch, and stress,
and so on, which are connected, related to the
syntactic structure. Actually in ways which were
studied, maybe first seriously, by another former student, Lisa
Selkirk a colleague of Barbara. But when you connect, when
you interact prosodic peaks with transitional
probabilities, then you get a pretty good identification
of word boundaries. Well, you know that's
the kind of work that I think makes sense. And there are more
complex examples, but it's a simple example of
the kind of thing that can work. On the other hand,
there's a lot of work which tries to do sophisticated
statistical analysis, you know, Bayesian,
and so on and so forth, without any concern for the
actual structure of language. As far as I'm aware, that
only achieves success in a very odd sense of success. There is this notion
of success, which has developed in
computational cognitive science in recent
years, which I think is novel in the
history of science. It interprets success as
approximating unanalyzed data. So for example, if
you were to study bee communication
this way, instead of doing the complex experiments
that bee scientists do, you know, like having
bees fly to an island to see if they leave an odor
trail and this sort of thing. If you simply did extensive
videotaping of bees swarming, okay, and you did
a lot of statistical analysis of it, you would get a
pretty good prediction for what bees are likely
to do next time they swarm. Actually get a better prediction
than bee scientists do, and they wouldn't care, because
they're not trying to do that. And you can make it a better
and better approximation by more videotapes and
more statistics and so on. I mean, actually, you
could do physics this way. Instead of studying things
like balls rolling down frictionless planes, which
can't happen in nature, if you took a ton of
videotapes of what's happening outside
my office window, let's say, bees flying
and various things, and you did an extensive
analysis of them, you would get some
kind of prediction of what's likely to
happen next, certainly way better than anybody in the
physics department could give. Well, that's a notion of
success, which is, I think, novel. I don't know of anything like
it in the history of science. And in those terms, you
get some kind of successes. And if you look at the
literature in the field, a lot of these papers
are listed as successes. And when you look
at them carefully, they're successes in
this particular sense, not the sense that science
has ever been interested in. But it does give you
ways of approximating unanalyzed data, analysis
corpus, and so on and so forth. I don't know of any
other cases, frankly. So there are successes where
things are integrated with some of the properties of
language, but I know of none in which they're not. PINKER: Thank you. To my tremendous
regret, and I imagine of many people in
the audience, I'm going to have to bring the
proceedings to a close, and we'll have to be left
with Patrick's interpretation of what Marvin's response would
have been to the question. I'd like to thank Sydney
Brenner, Marvin Minsky, Noam Chomsky, Emilio Bizzi, Barbara
Partee, Patrick Winston for an enormously
stimulating session. And thanks to all of you.
