Brains, Minds, and Machines: Keynote Panel—Why is it Time to Try Again? A Look to the Future

POGGIO: 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.

[APPLAUSE]

PINKER: Thank you Tommy. Welcome to the golden age-- a 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 understanding of life and mind in terms of information, computation, and feedback and control.

Well into the 20th century, life and mind remains scientific mysteries. Life was thought of as this mysterious substance called protoplasm some kind of quivering gel animated by a elan 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, Weiner, 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 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 a 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. An 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

[INTERPOSING VOICES]

PINKER: Everyone knows that 58 years ago, Watson and Crick explicated the structure of DNA. But 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, that the discovery of the operation of the genetic code in mechanisms of translation into-- transcription into RNA and translation into protein, Sydney Brenner was also instrumental in the modern science of development-- how the information encoded in the genes actually builds a three-dimensional functioning origin. And for that matter, neuroscience. Both through his choice of the lowly roundworm, Caenorabditis 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, Caenorabditis brenneri has been named after Sydney Brenner. Sydney is a 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 recognised 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, the first mechanical arm, and the first programmable Logo turtle.

He has since then been a major theoretician on how to apply 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 price. But perhaps most significantly, he inspired Arthur C. Clarke when he was writing 2001-- A Space Odyssey and served as a 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 Chumbawamba. 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. Emilo 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 Whittaker College at MIT, and founding head of the Department of Brain and Cognitive Sciences, which I was privileged to teach for many years. In addition to his scientific brilliance, Emilio is widely respected as a man of judgment an 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 geneological tree of all of the people that she has trained. They noted that it has a depth of four, which she means that she has trained several great-great-grandstudents, and it has 176 nodes.

Barbara has been recognised with the Max Planck Research Award from 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 McVicar, 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 am 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 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 a better student and a better teacher. It was the connection with Seymour, 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 are after. We have to resuscitate thinking machines. Now I'd like to give you the mental reasons why I'm here. I was very influenced in about 1951, 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 it's a very interesting book to read through, because you can see all the false leads. That paper von Neumann went unattended to at the actual basic logical construct for the way that genes work. And that in fact, the whole lot of this is done without any reference to biology.

And indeed when the biologist discovered that this was the machinery, then nobody had mentioned von Neumann. In fact they all paid very great respect to Schroedinger. Schroedinger 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 Schroedinger had made a fundamental mistake. He said the genes contain 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 contain a description of the means of execution.

In other words, the program is not self-reading. You have to build a reader for it. And that's of course, what von Neumann-- 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 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 relationship between what we inherit, and how we can perform, and the big argument at the time that there's 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're asking is if we're looking at the behavior, the behavior is represented in the genome as 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 the 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 there's not another wire that started in the big tail, 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 we finally accomplished. It took 20 years, because we had many, many other romances with computers in-between. We try to mechanize this, but to do this in 1960s was impossible.

Nowadays it can be done. And there's huge activity in getting these wiring diagrams specified. In my opinion, sometimes over-zealously 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 found these synapses. And you say well maybe if you delayed your knife for half an hour, would you have found the same synapses?

And indeed, if you go to another mouse, and look at the same cell, it's going to be the same there? However, 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 relation to the behavior of complex systems. And that's why I think this initiative and it's a very, very important to do now, because I think we'll0 have to take a completely different approach to those 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 that this added ingredient.

My view is this. I think consciousness will never be solved, but will disappear. 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 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 sequenced the human genome, and we sequenced the chimpanzee genome, we will find one extra gene in the human. That will be the gene for language, and we'll call it the Chomsky gene.

But there's an alternative explanation, which is during their evolution, chimpanzees discovered talking gets you into trouble. And so they evolved a language suppressor gene. Then we'll call it the Chomsky gene. Thank you very much.

[APPLAUSE]

Marvin?

MINKSY: 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?

[LAUGHTER]

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 since, 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 so I asked what he was doing, and he said he was trying to solve Hilbert's fifth problem, which is that 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 seems to me that eight years was plausible. Let's do things and-- 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 who-- 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 so 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 Bell Laboratories, of course, was a monopoly. The others only acted like it. And so, they would start out different projects and John McCarthy and I, in the summer of 1951 or 1952-- 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 a sentence when time is up, because I never make plans. I could talk-- what? Oh, oh, it's an iPhone.

[LAUGHTER]

I have one. Mine is pink. Anyway, how did all of 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 junk 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 were about six or seven sins or the major ways that people screw up their lives. And so I would pick a typical piece of fiction that 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 the 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, although a guy named Gray 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, I'm sure that your robots are really stupid.

[LAUGHTER]

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, or-- 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 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 Emotion 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, because what's its main use? It's 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 the 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.

[APPLAUSE]

PINKER: Thank you. Noam Chomsky.

CHOMSKY: Well I kind of like that idea of the language suppressor gene. It 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 blacks and others, and they weren't sure which was which, there was a lot of discussion about whether ape, orangutans, you know, apes can speak. And 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, you know.

[LAUGHTER]

And this incidentally led to the conclusion that a brilliant father can't have a brilliant son. I was in RLE building 20 in the golden age. And it actually was a golden age, but I got here in 1955, and 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. 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 Howie and I then shared a small office, and we decided we would try to get the Institute to put in an 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.

[LAUGHTER]

Those of you who have ever seen a picture of a building 20 would know what this meant. Fortunately, we could find the 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. In the 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 his paper, he-- which was about machine intelligence-- he begins by saying that the question of 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, it came to be called the Turing test. He thought that might be an incentive to develop a 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, 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 what each of us has internalised 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. It's interpreted by the sensory motor system as some kind of externalisation, sound, or we now know other modalities.

And it's interpreted at an internal, roughly speaking, a kind of thought system called sometimes the conceptual intentional system. As having a specific meaning, which then enters into the 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 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 is 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, OK, 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 fact of the matter are these systems differ radically-- every animal communication system known differs radically in its basic structural principles of use, and 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. 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-- a famous version was Van Quine, a highly influential philosopher and 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-- 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? OK, we understand 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. That somehow we can say that-- 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, 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 have been around for thousands of years, except nobody thought it was a puzzle. It's one of innumerably many such cases. I shouldn't mention that the last couple of years, a sort of substantial industry has developed to try to deal with it on computational grounds. 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. You 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 enormously 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, Andrea Mauro, is well known here. He's been here many times.

They've been investigating the reaction of people to two types of stimuli, all new to them. Rule systems that meet 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 the 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. People may figure it out, but they're figuring it out as some kind of a 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. And furthermore, if you look at-- so that suggests that one of those two interfaces, the semantic interface with the thought system, just didn'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 sign, 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 what's used in the thought system. That, effectively, means, if it's correct, and it seems to be, that in the informal sense of the term design, the designer-- but in the informal sense of the term design, language is designed for thought, not for externalisation. 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 them through-- and for the nature of design of language. Well there are other kinds of evidence 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 a kind of ubiquitous phenomena in language of 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-- where it is, where it's kind of an operator asking you 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 both 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 expecting to see each other. On the other hand, if you had another noun in-between, in the right position, it wouldn't work.

So 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 the men expect to see each other. Well that suggests strongly that the mind is actually hearing, interpreting for which a 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 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 the best-- the answer would be, well, the 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 between every and his there, though it's structurally, if you think it through, about the same as the one that works.