Unintentional ASMR Gold with a VERY Soft Spoken Mathematician | Marvin Minsky Interview

I think the first time I started to think about that was when I was an undergraduate at Harvard and I was looking through Widener Library and I ran across a big thick book called mathematical biophysics and now we're talking the late 1940s and there weren't such words around I opened it it was full of strange little articles edited by a prodigious guy Nicholas Reshevsky at the University of Illinois I think maybe Chicago and it had chapters about theories of how cells might divide and how population's grow and maybe 40 or 50 little chapters and one of the chapters was about simulated neural networks by McCulloch and Pitt's in 1941 and I had been curious about psychology because that's a long story but I was trying to decide what to do and one thing that seemed interesting to do was mathematics but there were other people who were good at mathematics very good and in mathematics there's no point in being second-best because it's it's different from other fields and I was interested in biology and there seem to be people pretty good at that and chemistry I had a professor Louis Caesar and it looked like that was under control and then there was psychology and as far as I could see there wasn't anyone good at that except maybe Sigmund Freud 50 years before and but what to do about it because people didn't seem to have any theories of how thinking word and here was this strange paper with ideas that in fact were completely new about finite state machines and things like that and I got very excited I don't think I saw where this field could go but I had been reading psychology and I did see that nobody had a theory for example of how learning works they had philosophical theories that well you have ideas and somehow they get connected and when something happens you make a new idea and you put it somewhere in your mind and later you fish it out and there didn't seem to be any theories of how this knowledge could be represented or retrieved or how you could rub two of them together and get a third one and I could see in the McCulloch Pitts ideas which were just little switches connected to each other that there was a way that perhaps information her knowledge could be represented and the paper was in three parts and I couldn't understand the third one and after a long time I decided that whatever it was it must be wrong and that's very important because that gives you something to do and I worked on various ways to fix it and I couldn't it finally got fixed much later in 1956 by a mathematician named Stephen Kleene II who also read the paper and said this doesn't seem right and he knew exactly what to do about it and incidentally his theory of how to represent finite state machines in switches and so forth was exactly the same as another theory that two professors trying to member their names at MIT had made for calculating the impedance of a complicated electrical circuit and I don't know if anyone had noticed that but these two theories one is called regular expressions by Stephen Kleene II and his theory was so nice and elegant that it's used in all search engines today in almost exactly the form that he invented and the Mason and Zimmerman I think were the professors and the signal flow graphs are used everywhere for calculating how electrical circuits would work but those are ten years apart in my experience but interesting to see two theories in completely different fields that are exactly the same I think when I was a child I didn't have the feeling that I could solve problems that other people could solve on the contrary I found things were quite difficult and when I tried to read mathematics it would take an hour a page and I'd get some of the ideas but not others and usually it would be six months later that suddenly it would click and so I think I thought of myself as sort of slow on the other hand I thought of everyone else as incredibly slow but I didn't think of myself as particularly creative and it's just but I never grew up in some sense and as far as I can tell I've been getting better at things slowly and steadily and it was only when I was older that I noticed that most people work on something they do something wonderful and then they get stuck and I started to make theories of why do people get stuck and how to avoid it and the best thing is to if you've done something you should be ashamed of it instead of proud of it and I notice a lot of people keep saying well I thought of that a long time ago and and that sort of thing and they keep trying to get recognition and why bother I think I was incredibly lucky all my life because when I went in grade school I had interesting teachers and there was a there was a school for unusual students in New York that opened up called the spire school and it had lots of unusual children and then we and so I in that environment I was with people who were more or less my own age but who knew a lot and I've had the good fortune always to be in that situation by one accident or maybe my father's clever planning or something and then I went to Fieldston school in New York ethical culture school which was a wonderfully smart place with extraordinary teachers and fourth grade I had somebody who saw that I had read a chemistry book and he gave me the laboratory and I was allowed to synthesize things and and so forth and I had some friends there who I'm still in contact with because they were good thinkers then their high school of science was a miraculous place because most of the high school teachers were PhD refugees because we're talking about the early 1940s and all the smart people in the world who didn't get killed came to America as far as I could see in fact when I got to Harvard the first thing was gee these kids are not nearly as smart as the ones at the high school of science and they're finding these courses hard and it seemed like a step back and over I was there just for a year and I had a calculus teacher who was also the wrestling teacher and that was pretty good but most of the year was exceedingly and a great English teacher Dudley Fitz translated plays from Greek and back but the kids were mostly jocks of various sorts and but then Harvard again was a world of good fortune because I met a great mathematician shortly after I got there named Andrew Gleason and I didn't know it at the time or maybe he didn't either but he was one of the greatest mathematicians in the world and the math department had ten or twenty people who each of whom had created some field and so forth I met a great psychologist young assistant professor named George Miller who is now recognized as one of the pioneers of cognitive psychology and when I met him I he knew I had read this McCulloch Pitts paper and he said he couldn't understand chapter 3 also and I said well don't worry about it looks like it's wrong and we became great friends because no one else could and in fact when I did invent learning machine George Miller first he gave me a laboratory in the psychology department when I was an undergraduate and then he got money from the Air Force or somewhere to actually build this machine so forth so when I was an undergraduate I actually had a couple of laboratories another professor Welch John Welsh Gate when I told him I was interested in neurons he gave me a big laboratory because Harvard had just built this gigantic new biology building with hundreds of rooms and it had been designed with some foresight so that it was more than twice as large as anyone needed and I happened to go there and say I would like to do some experiments and so we said well here's this suite of rooms which included a black photographic laboratory with where you could go in and experience sensory deprivation and all sorts of equipments and so I got to I was interested in the inhibitory nerve of the crayfish claw it turns out this is a wonderful animal Welsh recommended it because the nerves in this thing are so big that you can see them and if you have a magnifying glass you can really see them and you can move them around with the tweezers and connect them to your alligator clip and here you're doing neurophysiology with a screwdriver the crayfish doesn't seem to mind having its claws snipped off because it snaps off it has a detachable joint and it sits in its tank for the rest of the year and grows another one so I didn't feel any ethical problems about this wonderful animal did some of that work but did some of that work motivate your development of optical devices well maybe the most important experience I had was meeting Warren McCulloch and Warren McCulloch was a philosopher and physiologist and great poet one I think maybe a hundred years from now he will be seen as one of the great philosophers of the 20th century right now he was sort of well known in the early days of cybernetics Norbert Wiener and gadgets but he's been forgotten and he would look at a problem and think of some new way to to do something with it and one thing he was I believe the first person to invent circuits that would work if you break any part of them how do you make a reliable circuit that's redundant enough that that it will correct some errors at least and he wasn't good at mathematics but he just worked out all the possible simple examples of this and found one which was self-repairing did many things like that and I followed him around for a couple of years and I think the reason I developed so well in this field is that I didn't listen so much to what Andrew Gleason said when he showed me how to prove some theorems are or what McCulloch said about this particular theory but I was always asking how did he think of that and sometimes I'd ask him and he'd tell me some wrong because nobody knows how they think of things but the idea that what students should learn from their teachers is how they work not the subject that they're teaching and I think it was just a great accident that I encountered this Warren McCulloch who was interested in that as much as in the he was interested in how he would think and tried to explain it and even today which is 40 years later sometimes when I'm stuck writing something for example I could hear his voice he's saying oh that's too pretentious or that's not pretentious enough I think I've accumulated a cloud of these people there four or five people that I worked with for several years and whenever I'm stuck I can hear all of her Selfridge or dick fine men Andy Gleason say oh you're wasting your time why didn't he look at it this way it's almost as though I've made little copies of these guys there's a little richard fineman and there's there's a Theodore sturgeon this science fiction writer who I tracked around with and because his science wasn't very good but his intuitions about it were good and he could write these wonderful things and create these images and I just wanted to know how to do that never got very good at it but but I I can sudden I say what would Richard search and say or what would Isaac Asimov say I have about ten of these characters that I can exploit what did you want to learn from Richard Fineman how he got such good examples of things and then made theories but what period did you work with him I think I had met him in the 70s early 70s it was interesting I was travelling around Los Angeles with a friend of mine edward fredkin who was a incredibly innovative thinker and has discovered all sorts of little theories of things he started the first company that did image processing and word processing and that sort of thing and he was one of three or four people I've known who were never afraid to do something normally when you say let's let's do this or that somebody will say well that would be very hard and so forth but there are three or four people that I've I've known John McCarthy Oliver Selfridge Fineman if you think of doing something he'll say let's do it and then we'll do it the next day or right away and usually get anything done to get anything done you have to convince a lot of people and make a plan and so forth but there's I've worked with a few of these people who say if that's a good idea maybe we can do it tonight and instead of next year and so until the 1980s I never wrote a proposal right just was always in the environment where there would be somebody like Jerry Wiesner of MIT John McCarthy and I had started working on artificial intelligence in about nineteen six 1958-59 when we both came to MIT and we had a couple of students working on it and Jerry Wiesner came by once and said how are you doing and he said we're doing fine but it would be nice if we could support three or four more graduate students and he said well go over and see Henry Zimmerman who I think it was immerman and say I said that he should give you a lab and two days later we had this little lab of three or four rooms and a large pile of money which IBM had given to MIT to for the advancement of computer science and nobody knew what to do with it so they gave it to us and not not a bad move right and for many years that kept happening we'd think of something to do and I had a great teacher in college Joe Licklider Lickliter and Miller were assistant professors when I was an undergraduate and we did a lot of little experiments together and then about 1962 I think Licklider went to Washington to start a new advanced research project that was called ARPA just started up and he said to them well there's some people at MIT who have all sorts of nice ideas one of our students had built a good robot another had built a machine that showed promise of doing some good vision research Larry Roberts and then Larry Roberts also had this idea of an Internet whew I mean that idea came from several people but Licklider got them him and Ivan Sutherland to come to Washington to help run this department so now we I was in this situation that Licklider had sent a big budget to MIT to do chyme sharing which had been invented by John McCarthy and at fredkin and a few other people also it had been vented in England about the same time using a computer with multiple terminals like we went to visit IBM about that and we went to Bell Labs also to suggest that they be working on that and the research director at IBM thought that was a really bad idea we we explained the idea which is that each time somebody presses a key on a terminal it would interrupt the program that the computer was running and jump over to switch over to the program that was not running for this particular person and if you had ten people typing on these terminals at five or ten characters a second that would mean that poor computer is being interrupted a hundred times per second to switch programs and this was this research director said well why would you want to do that and we would say well it takes six months to develop a program because you run a batch and then it doesn't work and you get the results back and you see it stopped at instruction 94 and you figure out why and then you punch a new deck of cards and put it in and the next day you try again whereas with time sharing you could correct it you could change this instruction right now and try it again and so in one day you could do 50 of these instead of a hundred days and he said well that's terrible why don't people just think more carefully and write the program so they're not full of bugs and so IBM didn't get into time to sharing until after MIT succeed success of successfully made such systems and years later I had a sudden flash of really what what was bothering this research director and I think he said well if somebody interrupted me a hundred times per second I would never get anything but identifying an important difference between humans and computers right um apparently didn't fully grasp and right and making computers easier to use I suppose wasn't in their business interests anyway all right making them solve problems faster that is I'm sure making them easy to use was but but that came from none of the large companies ever did very much for computers it was all hackers here and there and their ideas gradually filtered up I was in wrestling class and I was it was surprisingly interesting but one day in fact I got to be what I thought was pretty good but I was in the class of people from some weight up to 137 and then one day they weighed us and he said well you weigh a hundred thirty-eight so now you have to be in this class the next class which is from 138 to 147 or something and then I was the worst one and I decided there was nothing to this skill and generally I developed an attitude towards sports which is that there's absolutely no point to it the people who are good and it are maybe 2% faster in some reflexes and they're a lot slower and others like worrying about whether they're going to get hurt and there's just no point and when I see 20,000 people in a stadium watching them my first thought or last thought is why don't they hire one very good critic to watch them what's old it's a waste of time to have 20,000 people have a mediocre thoughts about it then why don't they just hire someone who will evaluate it and basketball is the best example because it isn't even statistically significant if you see a score like 103 to 97 that's less than one Sigma and one shouldn't regard that as a victory at all so it's very unscientific well eventually they'll catch on but it might be a couple of hundred years before our culture has adapted well when as I said I went to Harvard as an undergraduate and it was wonderful and I had a neurology lab in a psychology lab and that was great most students never got that I happened to be at the right place at the right time and I was majoring in physics for a while and my grades weren't particularly good so I thought I should make up for that by writing a good thesis and throw it out you couldn't write a thesis in physics they just didn't have it bachelor's thesis so Gleason said my mathematician Francis why don't you just switch to the math department you can write a thesis there so I wrote a nice thesis about fixed points on spheres was pretty exotic I sorted there was an unsolved problem and I solved half of it I got some really striking results wasn't what was the problem the problem was it was known that if you have three points on a sphere suppose you have a sphere it's like the earth it has an altitude if it turns out that if you take three points in around the equator equally spaced there's some place you can put them where the altitude of all three points will be equal in other words if you had a 3/3 well that's that's theorem it was proved by professor Kakutani Yale but it was only true for three points on a great circle and it seemed to me this ought to be for any triangle that is you oughta be able to put it somewhere and rotate it so that like a three-legged stool so it would stand straight up I couldn't quite prove that but I proved that it was true for several different shapes of triangles and then I got stuck and a couple of years later this strange person I never heard of named Freeman Dyson proved the thing in general and he sent me this paper fans I didn't believe anyone could possibly be that smart this was just so strange but anyway Gleason said I should just switch to major in mathematics and I could write a thesis and I did and then I said this is and I was a senior so I said well I'd like to stay here and Gleason said no you should not stay here you've been here for years and you've observed a lot of what we have to teach you here and now you must go to Princeton so I felt very rejected turned out that Princeton was the other place that had the other half of the great map well they were all over the place but Princeton had another full set of great mathematicians or von Neumann von Neumann who became my thesis advisors not quite my thesis advisor but anyway I felt rejected but I said well ok and I went to Princeton girdle there was Princeton and girdle I had lunch with girdle once he was wearing gloves because he was afraid of germs Einstein who I couldn't understand because I wasn't used to German accents but and and that was a great place and then I met I had met all of her Selfridge who was this pioneering researcher who had known McCulloch and Pitt's in fact he had been Walter Pitts roommate and he was at Lincoln lab and he invited me to join his research group there who were inventing all sorts of things and then I got a message from the MIT math department inviting me to come and be a professor so all this happened I was just being pushed around I never actually made any long-range plans or applied for anything well I was always trying to find the simplest way to solve a problem and I think that that could lead anywhere because nobody knows what the simplest solution is what was the opportunity at MIT when you arrived in the math department what what did you see needed to be done and where were the rooms and labs that you described do it what weasoner got you there right off the bat but what sort of things were yours for the taking do you think well of course they did when I arrived at MIT McCarthy I think had been there for a year and he was already had laid the groundwork for catching students and potentially good mathematicians and perverting them into being computer science computer science was just growing in the you're talking about 1960 there weren't very many theories at all and today I'd say that computer science is is a whole new area of science that was never even imagined before except by a few pioneers like girdle and Turing and posted a handful of people had had visions that there would be something like mathematics for complicated processes mathematics itself is it's really only good for very simple things because if you have 10 or 20 equations of different kinds there's nothing you can do what you can do is take one or two equations and study them very thoroughly and build great hours of theories about those things but if there are 10 different things interacting mathematics is helpless computers are helpless in at understanding them but in some sense you can they let you experiment with things you could never do by hand or in your head and so then you could discover phenomena and then simplify things down to see well where does this new phenomena come phenomenon come from and what part of the system caused it and progress comes from from taking a complicated thing with behavior you can't understand and gradually breaking it down of course some things no one's ever broken down and we don't understand them applied mathematics was not very was not filled with so many great ideas as as pure mathematics which took very simple sets of axioms or assumptions and built huge towers and in fact when I was in graduate school the most exciting thing in the world was this to me was this field called algebraic topology topology which is the principles of geometry where you don't actually care about the shapes of things but just the properties of the shapes like are all the parts connected in a simple way or are there holes in it or is it twisted or things like that and strangely the hardest problem it was 100 year old problem practically was was this suppose you have a plain two-dimensional surface and you draw a curve that never crosses itself but it closes so it's in topology that's considered a circle because you just care about how it's connected and you don't care about its shape well everybody knows that if you do that then there's exactly one inside and one outside it divides the plane into these two sets of things and not three and no one had ever proved that the first proof was around 1935 so this was called the jordan curve theorem not sure whether jordan had the first solution or was the first one to state the problem clearly and it's sort of obvious that it's true and the reason why it's hard is that what if the curve isn't really smooth but it it Wiggles a lot and if it Wiggles an infinite number of times before it gets here maybe there could be some little part of the plane that you sort of almost outlined it and that's very strange they said there is a in three dimensions there is a strange phenomenon that math I think they're called Antoine sets there was a mathematician named Antoine who discovered this very simple example imagine a regular chained bicycle chain not a bicycle yeah anyway you have a link like this and so here's two legs so now let's have another link in another link and that's called the chain now close it by putting the last link so now you have a ring of Link's now if you put a string through the middle of that you can't get it out there's nothing stopping it but you bump it to this chain and well you could try to push it through but that wouldn't help and so here's an Antoine set the set of Link's just just think of that each of these links does not divide space up very much but it does have the property that if something's going through it you can't get it out right because it hits the walls now here's this chain where there are a lot of links and there none of them are touching each other so that shouldn't make much difference sure that if they're not they're not touching each other and yet if you put a string through if you had one link here and a string you could just go around it with this Antoine chain or regular chain this closed if you have a strange still can't get it out but it's not being stopped by any particular link I mean it hits this link so you go here so what what's that somehow this said in three dimensions is dividing space up in some queer way and there's nothing like it in two dimensions two dimensions if you have a look you can't have a lot of little circles because you could go around them in three dimensions you can in four dimensions it's much more complicated and nobody knows what happens really so I got fascinated with that and this is a long story but after a while I finally understood a proof by check named check CEC h of the jordan curve theorem and of all the experiences i can remember in mathematics the my feeling of accomplishment was greater of understanding checks proof than anything I ever proved myself is a strange thing but it was that this proof occurs in a sort of infinite space of things all messing around and step by step he shows that it that something happens and so here's a case of it's like appreciating a Shakespeare play when you can't write your own but you still might say oh I understood something about this play that nobody else did or something like that it's not that I created it so that it's always bothered me that if I do something myself and other people admire it I regard it a will that I couldn't have done it unless it was obvious so that's another way not to get stuck you don't if you have a theory and it turns out to be wrong great now as a chance to do something better and you run into people who don't like their theories being proved wrong not a problem for you well no because then now you've got another to problem problem to solve can I make a theory that includes this and and the exceptions to it and so forth how did you mention that weasoner was the sort of impetus behind the formation of your lab with McCarthy what happened in the beginning and how did that become a mission to really specify what's going on in the human brain well when I built this learning machine and that had really started before this was a machine that made connections between things if you would give it a little problem is usually the problem was a something like a little rat simulated rat in a maze and if it managed to make the right turns to get the cheese then you reward the Machine and it changed the probability that it would take this it would increase the probability of taking the same paths again and so it did in fact learn to solve some simple problems but after a while I could see that it wasn't going to understand how it solved them so it couldn't what was missing is it could accumulate conditioned reflexes all right but it couldn't say oh I've learned a lot about this and I still haven't done this and what's the reason and I might have gotten stuck with this machine for a long time except another friend of mine named ray solomonoff had found a different theory of how take a bunch of data and make good generalizations from it because you can think of any theory of learning as saying you've had a lot of experiences is there a sort of higher level simple thing that they're all examples of andreas Altmann I've invented this other way of making generalizations and I instantly said this is much better than everything the psychologists have done since Pavlov and Watson and so forth in 1900 and so suddenly seeing this new idea of ray solomonoff which also occurred to a Russian named Cole magar up about the same time and later independently a guy named gregory chaitin in i think he was in argentina but when I first saw this new idea by ray solomonoff I got the idea that everything in psychology was too low-level and couldn't handle the right kinds of abstractions ray solomonoff theory said if something happens you must make a lot of descriptions of it and see which description is shortest because it must have the most significant abstractions in it like if you could have a short description that gives the same result as a long one it must be better or whatever it said and over the last 40 years that because I think that discovery was around 1957 so from the beginning the search for artificial intelligence was also a search to define processes that were fundamentally in psychology yes right what why do descriptions work and how do you make description if something happens in the old psychology you would somehow make some very crude representation an image or a record of exactly what happened and connected to another one and that might work to explain how maybe some fish or simple mammals think but it doesn't explain how you could think about what you recently been thinking and the so although I had very deeply involved in trying to improve reinforcement theory and the traditional statistical theories of psychology almost the moment I saw Solomon s idea I realized all this stuff could never get anywhere and that was the reason why my learning machine couldn't transfer what it had learned from this maze to another maze that was similar and so forth what kinds of machines did you work with in those early days to your experiments and to demonstrate your results well in the early days there were relays and vacuum tubes and you could build almost anything out of relays although it was slow because Claude Shannon had published of master's thesis in I think in 1947 giving Shannon was a remarkable discovery he made two major discoveries each of which started a whole new field and solved almost all the problems in it so in 1947 I think it was he published this master's thesis about switching circuits and nothing much has happened since then that was he did the whole thing and then 1950 published this theory of the amount of number of symbols it takes to represent some information and for about ten years people worked on various aspects of theories and found better proofs for the ones in Shannon's 1950 paper but but essentially he had solved all the important problems that vacuum tubes well he lays right and the snark machine this neural analog reinforcement machine was it had about 400 vacuum tubes and about a couple of hundred relays and a bicycle chain so that when something happened and you want to increase the probability a little motor would turn on and a bicycle chain would turn a volume control - it was all electromechanical and you could now write a description of such a machine with a hundred computer instructions I suppose and make it run a billion times faster what was the biggest technological breakthrough in those early years that allowed you much deeper access to the kinds of questions that maybe you are limited in exploring with the early computer make a very simple one namely the invention of the language Lisp Li SP by John McCarthy Lisp is a language computer language where there are only about eight or nine basic instructions but but these instructions are arranged in a structure called a list and most of the instructions are on how to change a list so in this in this language you can write a computer program and then you can write another computer program that will edit and modify the first one now we have languages the popular languages today are still Lisp is 1960 I would say the Great languages today are 2 a fairly large extent pre-1960 because they can't understand their own instruction it's hard to write a program in C that can understand a C program and say oh if this happens I should modify the program to do this or that now you can in fact do that and people become so expert at this that they can write Lisp like programs in in C or Java or these other programs but it's hard and I shouldn't knock them because they're easier to learn to be good to write complicated programs with but the big change was going from thinking of a program as a sequence of commands to thinking of a program has a structure sitting in the computer that another program can manipulate so this made it possible in principle to make a program that could even think about itself now no one actually did that much and that's what I'm still trying to start a project which has programs that mostly spend their time thinking about why those programs themselves succeeded or failed to do something else and have access to a lot of advice about how can I change myself to be better at doing that this is just not catching on right now and I'm having trouble convincing other people too to go in that direction you know you're stuck for the government right but I think I'll write a book about something else until they get ready for it your book gum Society of mind presents a metaphor for the operation of the brain that is fundamentally different from traditional psychology what is that well most of traditional psychology tried to imitate physics and physics had a wonderful modus operandi uh if you see a phenomenon what should you do and the one thing you could do is find the simplest sets of laws that would predict that and for example Ptolemy tried to explain the behavior of planets and he said well they were going almost in circles it seems but they're not quite circles so what could we do we could maybe we could combine a little circle that's turning faster with a big circle and so if the planet looks like it's the circle only it's it's going a little too far here and there let's add another circle which is added to the first one but it goes half as fast so then it'll bulge out here so that was called epicycles and the ancients used that to this idea by saying well everything's made of circles but their circles are rotating at different speeds and different sizes and it takes quite a few circles to match the description of a planet planets motion and Kepler discovered that well but you could do it much better with just one ellipse ellipse is slightly more complicated than a circle cos it it's like it has two radii rather than one so it's a little worse and that made a tremendous difference because that explained the behavior of planet to great precision eventually you discovered that that the orbit of Mars is a little bit affected by the orbit of Jupiter so it's not quite an ellipse and Newton discovered the right law which was even simpler which is that planets attract each other with a force that's one over the square of the distance and times the mass and Newton discovered three laws which did almost everything for mechanics it wasn't it explained everything except electricity and Maxwell added a couple more laws for more laws than that so now he had seven laws and Einstein discovered that Maxwell laws could be reduced to much less and so physics ends up in Einstein's time with about five laws and then court things got worse because quantum mechanics was just being discovered partly Einsteins fault psychology was attempting to mirror psychology was I called it physics Envy in in honor of Freud psychology said well we've got to find four or five simple laws that explain learning and if you look at old psychology textbooks there everybody has a little set of laws like the most the most obvious phenomena that everybody observed is if you ask a person to remember a list of 10 items you say I went to the store and I bought some soda and cabbage and spinach and chicken wings and so forth if you say ten of those and you say what did I buy the other person was so well I know that you bought cabbage and soda and chicken wings and they won't remember the ones in between so people made up a law of recency which says that you remember the most recent thing most and then they made up a law of primacy which is you remember the first thing most then maybe there's another law which is you remember the loudest thing and for many years psychologists tried to imitate Newton to get a small handful of laws to explain how memory works or perception works or this event there was one honest psychologist named Hall who did the same thing only he got a hundred and twenty laws how do you develop an artificial intelligence / psychology theory that doesn't have physics Envy oh well yes like the last paragraph would have been so this Occam's razor or finding the simplest theory were tremendously well in physics and it worked pretty well in some other sciences but in psychology it does nothing but harm and a simple reason is that we already know that the brain has three or four hundred different kinds of machinery and they're each somewhat different we know how three or four of them work like a little bit about the visual system and the cerebellum but we don't know how any of the dozens of brain centers work in the frontal lobe and the parietal cortex and the language areas and so forth but one thing we can be sure of is the brain wouldn't have 400 different kinds of machines unless they were specialized to behave in different ways and do different things so the Society of mind starts out pretty much in the opposite way and says let's take all the things or a lot of the things that people do and try to find simple explanations for how each of those could be done and then let's not try to find a simple machine that does all of those but let's say let's try to find some way that a few hundred of these different things could be organized so that and that the whole thing would work and in fact the book didn't get my first book the Society of mind didn't come up with a good theory of that so it's basically what we call a bottom-up theory which takes a lot of things and different phenomena and explains them in different ways you know it's a bottom-up right theory how do you figure out how these things right yeah this society of mine had four or five ideas about how these would be organized but most of the book is it has I think great many good ideas about how different aspects of thinking works but it doesn't have a picture of how they're organized and the new book which took another 20 years is top-down and it says that let's start by imagining that the mind has a lot of different things it can do resources like a roomful of different computers then which should you use and when and the answer is that or the answer I propose is that you have to have some goals so there's a chapter on what goals are and how they work and that chapter comes from some research in the nineteenth early 1960s by my colleagues Alan Newell and Herbert Simon effective if I can interrupt myself for many years there were just two major centers of research on artificial intelligence and one was the group with McCarthy and me and later peppered at MIT and the other was Simon and Newell at Carnegie Mellon and they also had a crowd of students who produced great little theories and their students went other places in the field spread anyway one thing that Newell and Simon did was to develop a theory of how would a machine have a goal and their answer was it's sort of like the kind of feedback things that Norbert Wiener described in cybernetics but it's different in order for a machine to have a goal it has to have some kind of picture or a representation or description of a future situation now you'd say informally and it would like to have that situation but like is no good because that's burying that's what you're trying to explain however what it could have is a machine that also has a description of what you have now and this other description of what you would quote want and it finds differences between these and eliminates them so having an active goal is to have a description of a future system situation and the present situation and then a process which does things to make them the same by changing the present situation now another thing you might do is change your goal of the future one and that's if you're really uncomfortable in that you want something and can't have it then you can always just change edit the goal and settle for something less but that's another story so this is the new book is a little spit centered around this 1960 idea of Newell and Simon which I call a difference engine and it says in order to accomplish anything the brain has to be full of difference engines of various sorts in other words there's no point in just reacting to things you have to react to things in order to reduce a difference that you don't like like if you're hungry you have to get food or reduce the absence of food so you you can always express things this way okay so that's a simple picture of an animal it has a set of goals namely these difference reducing machines it has set some machinery to turn some goals on and off so whatever those are let's not worry about them because they would have even whatever they are they evolved because the creatures who didn't have them died out animals that lost the urge to eat doesn't matter what else they think though they'll go away so what's the next step well the next step is what if you then you need another mechanism which says well I've been pursuing this goal for a long time and nothing happened so I call that a critic the critic says there's something wrong something has gone wrong with what you're doing and know this best example is I kept doing things and I didn't achieve a goal so what should I do I should change my strategy of course I could change the goal too and sometimes we do that but the main idea is to think of the next level of the mind as a bunch of critics which are watching what's going on and looking for failure if there's a success it's not important then you do something in low-level psychology to make that more likely to happen that's trivial obviously you want to learn what was successful but it's not profound however if it fails you have to do something really good namely do something new instead of so traditional learning is how to do something old again that's why I discarded it in the 1960s because some enough suggested another way so the top level of the new book called the emotion machine is that the important thing that neurologists should look for are these things called critics which fire off when some effort to achieve a goal fails and what should you do well you should think in a different way you might change some of the goals or at least sub goals and then you have a chance to to succeed again so that's the other idea and that becomes a very rich idea because that leads you to ask well what kind of goals do people have and there's two answers to that one is that evolution provides the vital ones like if you don't have if you don't drink enough you'll die and so forth and also ideas like consciousness become manifestations of these very complex critics interacting and reaching some sort of right cold right because suppose some critics don't work either then you want a higher one which says what's wrong with the critics I've been using and that means you have to start thinking about your recent thoughts you stopped thinking about what you did and you think about how you were thinking and to me the word consciousness is the name for there is no such thing as consciousness but there's dozens of processes that involve memories of what you've been recently doing and new ways to represent things and describing things in language rather than images and and so forth and in fact the existence of the word consciousness is the main reason why psychology you could ask why is Aristotle writing and William James writing just as good as popular writing today in psychology if you read William James you say oh he's better than this guy who's telling you how to think and if you look at Aristotle you'll see his discussion of ethics is just as good as this president's ethics adviser and so forth it's because they got stuck thinking that these words like consciousness and ethics are things rather than big complicated structures that might have a better explanation so anyway these the we have five or six levels of thinking him the higher level is one where you make models of your whole self but they're simplified and you say what would happen if I did this without actually doing it stuff like that those are the things we call consciousness and I think there's about 50 of them let's um talk a little bit about the span of time the you are at you've been at MIT it spans the late 1950s to the present yeah they think for a moment about how students have changed in that time but before you answer tell me a little bit about the beginnings of the Media Lab and how you got involved that's in the middle of all this right well when I came to MIT first the first thing is that it's hard for people today to imagine what it was like to be in a golden age world war 2 was over if you were a kid who wanted to learn about electronics you could go to something called a surplus store which I did incessantly and you could buy a gadget that today would have cost half a million dollars full of parts and gears and take it apart and rearrange it there are no surplus stores today if there were they would have these integrated circuit things that you can't understand and take apart and you'd have parts with 80 pins or 200 pins a computer processor is is just intractable so first of all we had free free laboratories also we had companies like Heathkit which for a very small price would sell you the parts to make an oscilloscope so tens of thousands of young people were making their own laboratories what else about the Golden Age well there were these great professors who came from Europe and in fact filled high schools okay when I got to MIT the wars were over the new one was about to start I suppose but that's another story and the universities were expanding mit was growing it never it decided not to grow much mit has 4,000 undergraduates and it's going to stay that way and Caltech has 1,000 Boston University has 50,000 so some universities grew but what happened at MIT was that the faculty grew so there were more graduate students than undergraduates and they're more laboratories than you can imagine so every student at MIT if they want can be in a laboratory it's heaven if you want to do something the world is open to you and now as an assistant professor here I am and I have the smartest people in the world as far as I could tell I got some some names of reycarts while wrote me from high school and he became one of the great inventors of the century Gerry Sussman I knew from Danny Hillis from high school these were kids who wrote in there said I hear you're working on making Thinking Machines so I didn't do anything I just I was just there in the right place and the world was beginning to hear about cybernetics and AI and so forth and the right people just came and as I said before Jerry Wiesner kept getting money and Larry Roberts and Joe Licklider every time we needed something or room for more students somebody would hand it over this stopped around 1980 by a strange political accident Senator Mansfield who was a great liberal decided that the Defense Department might be a dangerous influence and he got Congress to pass some rule that the Defense Department can't do basic research should only support research with military application it's a great example of whatever you want to call it unintended consequence of shooting yourself in the head and but anyway but right after that the Media Lab okay then now there was a thing we're doing this interview in this very building where Nicholas Negroponte who had some training as an architect got this idea that computers were going to be important and media was going to change and by the year 2000 there wouldn't be any paper anymore and all sorts of ideas that were correct except that people didn't do the right thing and so he had started this Media Lab and in fact some of the most exciting things in computers had happened not in the place you'd expect it to but in the civil engineering where Professor Charlie Miller had developed graphics so that people could envision buildings and move them around and Negroponte 'yes it was called the architecture machine laboratory and that was doing similar things finding new ways to improve communication he invented something called zero bandwidth what do you call television by phone there's a name anyway kilometer II is there a bandwidth no well this is a joke yeah for a long time Bell Labs and other people had been trying to make television available over telephone so you could see who you were talking to and they failed it was the technology wasn't ready it was too expensive in fact I had a videophone it was called but no one else no one else had one except Nicolas that Bell Labs gave us I came to call each other yes anyway zero bandwidth television was a demonstration made some by some students in the in the pre Media Lab which was very clever it was a complicated sound processing thing which sometimes could guess what emotion you had from the sound of your voice wasn't it was better than chance it could tell when you were laughing horse and it could guess when you were smiling and if your voice lowered and slowed down it would guess that you had a less happy expression so they managed to get a few graphics on the screen you're talking to someone and you're not seeing that there's no pic there's no camera looking at the other person but it's guess it has a cartoon face and it was uncanny because it worked just well enough that it looked like you were looking at the person who was talking that disappeared they never even published it and I just noticed last week some laboratory and MIT which said oh we're going to make something that listens to the voice and shows the expression of the speaker so 30 little 30 year hiatus there but it's it's the old timers fault for not even publishing it it was just such fun and they just showed it to each other and was that the spirit of the media live in the beginning fun exciting it was you ideas exactly it was disciplinary it was exactly Nicholas had the idea that he wanted to expand the laboratory because there are so many things that he couldn't do in the architecture department and he got a wonderful idea how would you fund how would you fund this kind of research which nobody was doing and he went to companies that had didn't even know what research was and they all piled in they said oh we're worried does the various newspaper chains for example heard the prediction there wouldn't be any paper pretty soon because everybody would have things like iPhones and they wouldn't need paper well that didn't happen for 30 years more than Nicolas expected but who cares they were scared so they started giving his new Media Lab money to say what's at Steelcase wonderful company they began to realize that since you could work from home maybe people wouldn't need office furniture what will happen to them when the office disappears so some visionary people and Steelcase gave us first they gave us a lot of the office equipment all the chairs not this one but all the chairs in the Media Lab were really deluxe Steelcase modern things but anyway the nice thing about the Media Lab was Nicolas is inspiration to see yes you can fund research if you explain to people why they needed and for almost 20 years it was just like the Golden Age that I got into it when the AI lab started the Media Lab again started lots enough money it poured in that we could do anything we want it had enough sponsors that whatever you did one of the sponsors would be pleased and Nikolas invented a kind of sharing of property and rights and so forth that there was great happiness from I think it started in nineteen eighty four or five eighty four probably so during an here honey for 20 years now it's getting harder to support because this these ideas have spread and the Media Lab is working hard right now to what's the next revolution can the new director reproduce this I certainly hope he can but it's it might have been a historic moment Nikolas has said that he could never have started the lab 10 years later because they were doing things that the other departments were doing by then so it sounds like what you're saying is that in your period at MIT you scored not one but two golden ages yeah a big one and a little one right not bad and the second golden age starting in about 1963 I started to work with Seymour Peppard and I had never been interested in education and that side of engineering so we worked together for 20 years and then when Nicholas started the Media Lab he had some great engineers and great hackers of all sorts and he also invited me and peppered and we started to move our activities so my artificial intelligence and Seymour's new ways in education started to develop here in the in this new environment and again it was a golden age in the sense that if we got an idea there would be someone to support it today things are different the United States has very few basic research institutions the government is broke if you look at the National Science Foundation they're now in the situation where they can barely fund one out of a hundred proposals now suppose some scientist proposes something that is going to take two years that's fine if he gets it suppose 200 scientists do that and one gets the support they have spent probably a hundred man years of wasted time writing these applications and so now we might be better off of if we close the research facility completely and the United States is headed down a a drastically destructive tract what kind of students came to MIT in the late 50s and contrast that with the kinds of students you see today and how broadly have the concerns and interests of students at MIT changed at the time you've been here I don't think I could say very much about are we recording your question yeah yeah I don't think I have a very good picture of that I have a qualitative sense that we're still getting wonderfully we're still getting some of the best possible students but we have a lot of machinery for losing the best ones very rapidly because they take courses in business and they take course a lot of them will go into management science as you know when a field has the word science in it it isn't but it tries and also if the student gets a pretty good idea then in spite of the great internet bubble of the of the year 2000 or whenever it was they can get support to start a company what this means is that the students who do the most exciting research as an undergraduate or even a graduate student are very likely not to become a professor in the Golden Age as I mentioned almost all of my graduate students became professors virtually every one somewhere or other usually a very good place now very few students become professors because they get jobs in startups or in the industrial research laboratories like Google and Yahoo and computer related places like that we even Microsoft which employ thousands of people who eventually produce nothing in most cases it disappears they just sometimes they're just hired so that they won't go somewhere else I don't know but the future is fairly bleak for students now because they can't they can't look forward to a career in research it's just closed some are going to China China is starting research laboratories where we're closing them you invented a microscope what was that was a great story well I've one of the reasons that I hung around McCulloch and the neurological community was to find out what they knew about how neurons worked and know about how the brain works and it turns out that there isn't to this day there's a great gap in neuroscience because we know a great deal about how individual neurons work and how they connect to each other through these complicated little things called synapses and when one neuron gets excited it sends some chemicals over to the next one and these chemicals start new activities and a lot is known about how this works and the conditions under which these synapses grow and become stronger conductors are more quick quick to act and so forth then we know a little bit about what happens in the relation between two cells and almost nothing about what happens when there's a hundred cells and most of the brain of the human brain mammalian brains in general most of the brains are actually they're not really made of cells so much as columns of cells these columns were discovered around 1950 and most of the brain here's this bunch of 500 or a thousand cells which acts as a functional unit and we're just beginning to find out what these do in the case of the visual of vision we know a lot about what the columns do in the case of the cerebellum and the hippocampus we know a little bit and in the case of the frontal lobes we're reflective thinking goes on we don't know anything at all and well what was the question you wanted a microscope yes and one reason so one problem was that you could try to guess what these columns did but you couldn't find a wiring diagram of one so I started to think about all we had were very thin sections with a people use diamond knives or broken glass microscope slides microscope slides two-dimensional right slices now the interesting property of the brain is that if you of course it's pretty transparent that there's no pigment in the brain cells to speak of so you have to stain them and there's no empty space that tissue is full of cells many of them are nerve cells and others are other kinds of connective tissue cells and so forth and the connective tissue cells in the brain look pretty much like what you'd think brain cells use each of them have thousands of fibers coming out and so forth well if you stained all the nerve cells the thing which is black there's a wonderful stain which uses osmium of all things rare metal and when it stains in neuron it stains the whole thing and a neuron maybe a whole millimeter in or more in size and some of its wires go go 20 millimeters or more and if you stain them all then if you take a section that's more than a thousandth of an inch thick it'd be completely black so nobody had three-dimensional pictures of what happens because the mic receivin a thin microscope slide is so dense that no light very little light gets through today we're looking for a 3d brain viewer yes and so the question is okay if the if you can't get the light through then what can you do and I thought of it and one of the reasons is if you of course you can get light through if you shine a bright enough light through but then this light that comes through it's pretty useless because it's bounced off something it's called scattering and it's going this way in this way finally it comes out this way and you don't know where it came from and I figured out a very simple way by combining two microscopes back-to-back looking at the same point that this microscope if light got scattered before it reached the point you're looking at by something else into this it then that would be collected by the second one and there'll be no good but if you put a pinhole at each end then any light that went the wrong way significantly would just get rejected so now I could use an extremely bright light and just collect the race that went straight through and now how many there were this is it so now almost every laboratory in the world uses in this thing unfortunately it took more than 20 years between the first one I built and the second one anyone else built so that the patent disappeared but I get lots of letters and emails from people who say thanks for making this gadget the funny part is that by the time I finished it that was exactly the time when I had read ray solomonoff spay purr and decided it wouldn't help to know how the nervous system is wired until you have high level theories to interpret it so although I after building it I used it to look at worms and blood cells and things like that I never actually used it to look at a neuron it's possible that not everyone at MIT would smile so broadly as they described it in one of their patents expiring before they could take advantage of it well it's too bad I could have used if I had a billion dollars I could do my project now there you go I could do my project here yep last question a theme of your story in terms of how you describe your success it seems to be that you are the right place at the right time that you entered into a golden age and well one other thing is if somebody does something better than you don't waste your time never compete right always go away and do something that that nobody else does better so I kept moving around if you'll indulge me for just a moment with the sentimental possibility that there is something about Marvin Minsky that has been passed on to the many students that have encountered you at MIT what would it be that you imparted to them other than be sure to be at the right place at the right time that's not very useful one I think the useful one is if you get stuck don't try too hard to fix it but find another way and because if you get stuck it's because you're not good at good enough at that and you probably can't fix that so find someone else who can do it but if you've got stuck it's probably because you found a really good problem and so find another really good problem is MIT a great place to find another way when you get stuck it certainly was you know that the nature of things changed gradually in the legal structure when I was a I came as an assistant professor without applying because William Martin who was chairman of the mathematics department thought hey I heard this this good guy at Lincoln and maybe computers and things like that have a future I don't I never actually found out why and a couple of things happen one thing happened is that I was teaching for courses as things were than those days and I said I could teach no I wasn't I just need two courses each term but still if you're teaching every other day I found this hard because after I'd give a lecture it would take me a day to figure out what I did wrong with it and I also needed a day to say well what should I talk about tomorrow and so I couldn't get any research done so one day I was walking down the hall and there was a great mathematician scientist Peter Elias who was in charge of the EE department he said how's it going I said well I wish I could teach all my four courses in one term and and do research the other term and he said well why don't you come over to our department and we'll let you do that oh he said well what did to happen when you asked them and the math the math department said well what if everyone did this and I thought well why not but they thought it was bad and then when I got there I turned out the e Department had so much money that professors only had to teach two courses but anyway one day Peter came by and he said oh we decided you should get tenure and I had never thought about that and I hadn't first of all I've never had the idea of staying at MIT forever in fact I went to another couple of schools and hung around and after a while I didn't like it there because at MIT practically every student is really good and it's just wonderful other schools you'd have to search for anyway that's that's what's not at the point but what's the difference is that today when a student when somebody becomes an assistant professor they have six years to make a reputation and get tenure and they think about it all the time and they arrange their career so they do one big thing instead of several they don't waste their time they have they publish a lot of papers a candidate for tenure here might have written 30 papers which are all almost the same it's a scandal they write slightly different papers they somehow get them in journals and they count them and what happens is these people are so narrow in a field because they're so desperate to make these points that I don't know how to conclude this paragraph so the situation is very different and this is all because of well-meaning civil rights laws and you know the promotion process has to be very open and it's bad if people promote their favorite friends and you shouldn't promote people from the same institution or it'll get inbred and they have all sorts of rules so they made this seven year rule it's very rigid and it turns out it's six years it's really five because there's also another law which is if you fire somebody you have to pay their salary for a year this has nothing to do with anything so really you have to make the tenure decision pretty almost firm when they've in their fifth year so the pressure is enormous and so it's harder to get stuck in this era you're almost forced to get stuck yeah and and finally the chances of getting tenure is small because because they're not making many new professors and you know there's another factor in all of this which is the lounge effort you're not allowed to fire people because of age the United States pretty much in England professors have to retire at 60 but the age of the life expectancy has been growing three months per year for the last 50 years so people are living 12 years longer now than when I started college so the number vacancies for new professors is slowly being eaten away by mere longevity besides everything else and so the pressure begs to be said another example of you being at the right place at the right time mm-hmm well of us or not I'm lucky not to have gotten old bike while I did it but but that's just luck too oh thank you Marvin you

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Published: Thu Jun 04 2020