Tingle & Sleep in uASMR Class | Marvin Minsky | Society of Mind Lecture at MIT | Part 1: Intro

[00:00:00]So what I'm going to do in this course is discuss mostly ideas that are already in the book called the emotion machine. I'm sorry I used that title.

[00:00:20]Uh and the older book called the society of mind which are the books are not quite the same. They overlap a bit in material but they're sort of complimentary. Uh, I like the old one better because the chapters are all one page long and they're moderately independent. So, if you don't like one, you can skip it. uh the new book is much denser and it has a smaller number of long chapters and I think it's um over the years I got lots of reactions from um young people in high school for example uh almost all of whom uh liked the society of mind and found it easy to read and seem to understand it. Uh there are lots of criticisms by older people who uh maybe some of them found it harder to put so many fragments together. Who knows? But uh most of this class, most of the things I'd like to say are in those books. So, it's really like a big seminar and I'll my hope is that everyone who comes to this class would have a couple of questions that they'd like uh to discuss and if I can't answer them maybe some others if you can. So I'd like to think of this as a super seminar and normally I don't prepare lectures and uh I just start off asking if there are any questions and if they're not I get really pissed off because but anyway I'm going to start with a series of slides. Unlike most species uh or kinds of animals, humans have only been around a few million years and uh they're very clever compared to other animals, but it's not clear how long they will last. And when we go, we might take all the others with us. So there are a whole set of serious problems that are arising because there are so many humans.

[00:03:00]And here's just a little list of things.

[00:03:08]There's a better list in a book by the astronomer royal Martin Lee.

[00:03:20]uh of England.

[00:03:23]Anybody know the title?

[00:03:25]>> Our final hour.

[00:03:27]>> Yes, our final hour. It's a slightly scary title.

[00:03:32]And when I was a teenager, World War II came to an end.

[00:03:54]I didn't believe the first one was real because it was in Hiroshima.

[00:03:59]So I assumed that the US had somehow made a big underground underwater tanker with 20,000 tons of TNT and some few grams of radium or something and blown it up in the harbor and first it it flew an airplane over dropping some little thing and this was to fool the Japanese into thinking that we have an atomic bomb. Um but uh when they did it again over Nagasaki that wasn't feasible. So and when I was in grade school sometimes if I said something very bright I would hear a teacher saying maybe he's another J. Robert Oppenheimer because uh that was the name of a scientist who had been head of the Manhattan project and he was I think three or four years earlier in in uh grade school than I was.

[00:05:17]And I thought it was very strange for a person to have a first name as just being a letter rather than a name. And uh many years later when I was at Princeton in graduate school, I met the Robert Oppenheimer and that was a great pleasure. And in fact, he took me to lunch with a couple of other people I admired, namely Girdle and Einstein, which was very exciting, except I couldn't understand Einstein because I wasn't used to people with a strong German accent.

[00:06:05]But I understood Girdle just fine.

[00:06:08]And after that lunch was over, I went and spent about a year learning about touring machines and trying to prove theorems about them and so forth. So anyway, um in the course of these talks, we'll run across a few of these people.

[00:06:34]And here's a big list of the people that I'm mostly indebted to for the ideas in the society of mind and the emotion machine.

[00:06:50]It would be nice to have met Aristotle because uh no one really knows much about him. that uh you really should read. Just skim through some of that and you'll find that uh this is a really smart guy. We don't know if he wrote this stuff or if it were compiled by his students. Like a lot of Fineman's writing is and Vonoman, Von Noman's writing is edited from notes by their students. And uh anyway, the astonishing thing about Aristotle is that he seems to be slightly more imaginative than most uh cognitive scientists you'll run into in the present day.

[00:07:49]It would have been nice to know Spinosa and Kant and the others also Freud wrote 30 or 40 books. So, did he fall off this list?

[00:08:07]>> There he is. There's something about Greek culture because it had science. It had experiments. Somebody has a theory and they say and like Epimemenities Lucriccious uh somewhere in the society mind I think I quoted Lucriccius about uh translucent objects and he says they're they have the particular appearance because the rays of light bounce many times before they get to the surface so you can't tell where they started and I don't find in Eastern philosophy theories that say here's what I think and here's a reason why I've looked at Buddhist stuff and it's it's strange lists of psychological principles every one of which is looks pretty wrong and they make nice uh two-dimensional diagrams but no evidence for any of them so I don't know whether to take it seriously Obviously in the Arabic world they got up to the middle of high school algebra.

[00:09:22]Most cultures they never got to the critical point of getting theories doing experiments discussing them and then throwing them out. And so if you look at Buddhist philosophy it's 2500 years old.

[00:09:43]If you look at Greek physics, yes, Archimedes almost got calculus and he got lots of nice principles and Buddha mentions at some point if you want to weigh an elephant uh put him in a boat and then take the elephant out and put rocks in till the boat sinks to the same level. So there you see a good idea. But if you look at the history of the culture, if people still say this thousand-y old stuff is good, then you should say, "No, it's not."

[00:10:21]>> So, it's good to know history, but if the history doesn't get anywhere, then you don't want to admire it too much because you have to ask why did it stop?

[00:10:30]What went wrong? And usually it went wrong because barbarians came in and well, you know, what happened to Archimedes?

[00:10:38]uh some some Roman killed him. But uh because humans are five million years old. So what took it so long? And >> in most cultures it might be religion which is a sort of science that doesn't use evidence and in fact kills people who try to get it. And so there there systematic reasons why most cultures failed. And uh maybe somebody has written is there a book on why science disappeared ex except once.

[00:11:17]It's a rather remarkable isn't it? After all the idea if somebody says something and somebody else says okay let's do an experiment to see if that's right.

[00:11:28]You don't have to very be very bright.

[00:11:31]So how come it didn't happen all the time everywhere?

[00:11:37]Oh, come on. And Martin Ree is the Royal Astronomer and has that book about the the last hour or whatever. And uh I'm making another longer list, but uh he has lots of obvious disasters like some high school student looks looks up the the genetic sequence for smallpox virus has been published.

[00:12:07]And now you can of nucleotides and send it somewhere. They'll make it for about 50 cents or a dollar per nucleotide for so for a couple of hundred dollars you can make a virus or a few hundred and so one possibility is that some high school student makes some smallox only gets it wrong and it kills everyone. So lots of disasters like that and no one knows what to do about that because uh the the DNA synthesis machinery are is becoming less and less expensive and u so there are lots of other things that could happen. Um but one particular one is this graph which I just made up. Um, an interesting fact is that since 1950 when the first antibiotics started to appear, as I mentioned, I was a a kid in the 1940s and uh, penicellin had just uh, hit the stands and there wasn't much of it and there was a researcher uh, lived a few blocks from us whose dog had cancer and so uh its father I don't know what you call the owner of a dog uh sneaked some penicellin out of the lab and uh gave it to the dog who died anyway but he said well nobody's tried penicellin on cancer yet maybe it will work and A lot of people were mad at him because he probably cost some human its life, but he said he might have saved a billion humans their lives. So, ethics.

[00:14:14]Ethicists are people who give reasons not to do things. And I'm not saying they're wrong, but it's a funny job.

[00:14:25]So anyway, uh since that sort of thing happened and medicine began to advance, people have been living one year longer every 12.

[00:14:36]So it's uh 60 years since 1950.

[00:14:43]Another So um that's five of those six. So they're living six or seven years longer now than they were when I was born.

[00:14:55]And uh somebody mentioned that that curve stopped the last few years uh for other reasons. But uh anyway, if you extrapolated that uh you find that the lifespan is going to keep increasing.

[00:15:16]how much we don't know. Um, another problem is that you might discover enough about genetics to get rid of most of the serious diseases.

[00:15:28]Maybe just 20 or 30 genes are responsible for most deaths right now.

[00:15:35]And if you could fix those, which we can't do yet. There's no way to change a gene in a person because invading all the cells is pretty massive intervention.

[00:15:47]But we'll get around that. And then it might be that people will suddenly start living 200 or 300 years.

[00:15:56]At some point the population has to slow down and so you have you can only reach equilibrium with one child per family and probably less than that and so all the work has to be done by two or 300 year olds and let's hope they're good and healthy.

[00:16:17]Um, so anyway, I think it's very important that we get smart robots because we're going to have to stem the population and I hope people will live longer and blah blah blah. And so these robots have to be smart enough to replace most people.

[00:16:36]And um, how do you make something smart?

[00:16:45]Well, artificial intelligence is the field whose goal with has been to make machines that do things that we regard as smart or intelligent or whatever you want to call it.

[00:17:02]And the idea of seriously making machines smart has roots that go back to a few pioneers like Linets who wrote about Automa and that sort of thing.

[00:17:20]But the idea of a general purpose computer didn't appear till the 1930s and 40s in some sense. The first form of the general purpose computer appears in really in the 1920s and 30s with the work of a mathematician who Amal Post at NYU who I happened to never meet but we had some friends in common. He had the idea of production rules and basically rule-based systems and proved various theorems about them. Uh then Kurt Girdle uh showed that if you had something like a computer or a procedure that uh had the right kinds of rules. It could compute all sorts of things. But there were some things it couldn't compute, unsolvable problems. And that became an exciting branch of mathematics.

[00:18:23]And the uh the star thinker in that field was Alan Turing who invented a very simple kind of universal generalpurpose computer.

[00:18:35]Instead of a random access memory, it just had a tape which it could write on and read and change symbols and would go back and forth. And if it's in state X at C symbol Y, it will print symbol Z over the X and move to the left or right. And just a bunch of rules like that was enough to make a universal computer.

[00:19:05]And so from about 1936, it was sort of clear to a large mathematical community that these were great things. And a couple of general purpose like computers, very simple ones, were built in the 1930s and more in the 1940s.

[00:19:30]And in the 1950s, big companies started to make big computers which were rooms full of equipment and but as you know most programs could only do some particular thing and none of them were very smart.

[00:19:53]Um whereas a human can handle lots of kinds of situations and if you have one that that you've never seen before, there's a good chance you'll think of a new way to deal with that and so forth. And so how do you make a machine that doesn't get stuck almost all the time?

[00:20:19]And I like to use the word resourcefulness, although I left an R out of that one.

[00:20:37]Is there a shorter word?

[00:20:43]My favorite example of a situation where a person is born more or less with a dozen different ways of dealing with something. And the problem that I imagine that you're dealing with is this. My favorite example is I'm thirsty.

[00:21:10]So I see that glass of water and I do that and get it. Actually I am on the other hand if I were here I would never in a whole lifetime do this.

[00:21:33]You never walk out a window by mistake.

[00:21:36]It's we're incredibly reliable. So, uh, how do I know how far it is? And that slide shows you 12 different ways that your vision system, that's only your vision system, has to measure distances.

[00:21:56]So, gradients, if things are sort of blurry, then they must be pretty far away.

[00:22:04]That's on a foggy day outside. And uh here's a situation. If you assume those are both chairs of the same size and you know that this chair is about twice as far away as that, although you don't well and you know how far away they are pretty much by the absolute size.

[00:22:32]If you have two eyes that work well, then if something is less than 30 feet away, you can make a pretty good estimate of its distance by focusing both eyes on some feature. And your brain can tell how far apart your eyes are looking. So that's there's 12 different things.

[00:22:55]Uh lots of people are missing half of those. Uh lots of people have very poor vision in one eye.

[00:23:05]Uh some people cannot fuse stereo images even though both eyes are seem have 2020 vision. Uh and in some cases nobody knows why they can't do that.

[00:23:25]I think I once took a test for being a pilot and they wanted to be sure you could do stereo vision which seemed very strange because if you're an airplane and you're less less than 30 or 40 feet away from something it's too then yes you can't use stereo you could use stereo but it's too Right.

[00:24:00]See if you can think of an example where a person has even more 12 of these. But it's pretty amazing, isn't it?

[00:24:12]This is too hard to read.

[00:24:18]Somehow I found in an Aristotle essay the idea that you should represent things in multiple ways.

[00:24:30]You might describe a house. One person might describe a house as a shelter against destruction by wind, rain, and heat. Another might describe it as a construction of stones, bricks, and timbers. But a third possible description would say it was in that form in that material with that purpose.

[00:24:53]So you see there's two different descriptions. One is the functional description. It's a shelter. The second one is a structural description how it's made.

[00:25:05]Aristotle says which is the better description. And he dismisses the material one or the functional one. is not rather the person who combines both in a single statement.

[00:25:22]And then I found a paragraph by Fineman who says every theoretical physicist who is any good knows six or seven seven different ways to represent exactly the same physics and you know that they're all equivalent and uh but you keep them all in your head hoping that they will give you different ideas for guessing guessing. I should put more dots.

[00:26:00]That whole argument is to say that um the interesting thing about people is that they have so many ways to do things and perceive things and think of things.

[00:26:13]And in some cases, we even know that there are different parts of the brain that are involved in one aspect or another of constructing those different representations or descriptions.

[00:26:29]If you look at the one of my favorite books weighs about 20 pounds. It's the book on the nervous system by Candle and Schwarz and the index to that book is quite a lot of pages long and it mentions 400 different structures in the brain.

[00:26:58]So the brain is not like the well I shouldn't make fun of the liver because for all I know the liver has 400 different uh many processes for doing things but the brain has distinguishable areas that seem to perform several hundred different functions and uh with a microscope that at first they all look pretty much the same but if you Look closely, you see different slightly different patterns of how the most layers of the cortex of the brain. Most parts of it have six layers and each has a population of different kinds of cells. There a lot of cross connections up and down and sideways to other.

[00:27:46]They're arranged in columns of between 400 and a thousand cells. And you have a couple of million of those. And there are lots of differences between the columns in different areas. And we know some of the functions. Most cases we don't know much about how any of them actually work with the main exception of vision where the functions of the cells in the visual cortex are fairly well understood at low levels. So we know how that part of the brain finds the edges and boundaries of of different areas and textures and regions of the visual field. But we do not know uh even a little bit about how the brain recognizes something as a chair and an overhead projector and a CRT screen, that sort of thing.

[00:28:53]So the kind of question that I got interested in was how can you have a system which has a very large number of different kinds of computers each of which by itself might be relatively simple or might not I suppose and how could you put them together into a larger system which could do things like learn language and uh prove theorems and convince people to do things that they would never have dreamed of doing five minutes earlier and stuff like that.

[00:29:43]Now the first sort of things I was interested in was in fact how to make how to simulate simple kinds of nerve cells because in the 1950s there was about almost a hundred years really more like 50 years of science discovering things about neurons and nerve cells, the axons and dendrites that they use to communicate with other neurons. So if you go back to 1890, you find a few anatomists discovering some of the functions of or connections of neurons in the brain and you find a few experimental physicists.

[00:30:36]There was no oscilloscope yet, but there were very uh high gain amp galvanometers which could which could detect pulse pulses going along a nerve fiber. And uh by 1900 it was pretty clear that part of the activity in a nerve cell was chemical and part was electrical. And by 1920 or 30 with the cathode ray tube appearing mostly because of television but um it became possible to do a lot of neurohysiology by sticking needles in brains. The vacuum tube appears around 1900 or and you can make amplifiers that can see millolts and then microvolts. So in the beginning of the 20th century there was lots of progress. By 1950 we knew a lot about the nervous system but we still don't know much about how you learn something uh in the brain.

[00:31:50]It's quite clear that the things called synapses are involved. the connections between two neurons become better at conducting nerve impulses under some conditions. Uh but no one knows how higher level knowledge is represented in the brain yet. And the society of mind book had a lot of theories about that.

[00:32:19]And in particular there was a theory called K lines, knowledge lines or something that um came partly from me and partly from a couple of other researchers named David Waltz and Jordan Pollock.

[00:32:38]And uh that's a sort of nice theory of how neural networks might remember higher level concepts. And for some reason, although that's that kind of work is from around 1980, which is 30 years ago, it has not hit the neuroscience community.

[00:33:01]So if you look at the so at the emotion machine book or the society of mind in in Amazon, you might run across a review by a a neurologist named Richard Restack who says that uh Minsky makes up a lot of concepts like kines and micronmes and stuff like that that nobody's ever heard of and there's no evidence for them and he ignores the possibility that it isn't the nerve cells in the brain that are important, but the supporting tissues called GA which hold the neurons up and feed them. And he goes on for a couple of insane paragraphs.

[00:33:46]It's very interesting because it doesn't occur to him that you can't look for something until you have the idea of it.

[00:33:56]And so here's this 30-year-old idea of kines and go and ask your favorite neurologist neuroscientist what it is. And he said, "Oh, I think that's some AI thing, but where's the evidence for it?"

[00:34:16]What do you suppose is my reaction to that?

[00:34:22]Who's supposed to get the evidence?

[00:34:27]So, it seems to me that there's a strange field in neuroscience, which is that it doesn't want new ideas unless you've proved them.

[00:34:38]So, I try to have conversations with them, but get somewhat tired of it.

[00:34:50]But in this course I'm taking the opposite approach which is that uh we don't want a theory of thinking.

[00:34:58]We want a lot of them because probably psychology is not like physics.

[00:35:07]What's the most wonderful thing about physics?

[00:35:10]most wonderful thing is that they have unified theories.

[00:35:19]There wasn't much of a unified theory until Newton and he got these three laws, wonderful laws.

[00:35:30]uh one was the gravitational idea that that things bodies attract each other with a force that's the inverse square of the distance between them. Another is that uh kinetic energy is conserved.

[00:35:50]I forget what the third one oh equal reaction is equal and opposite. If you hit some, if two things collide, they transfer equal amount of momentum to both. There was a little problem up to Newton's time. Galileo got some of those ideas. And my impression from reading him is that he has a dim idea that there are two things around. There's kinetic energy, which is MV, and there's Whoops. momentum is mv and there's kinetic energy which is mv squared and he doesn't have the clear idea that there are two different things here and you can't blame him I would think what you wouldn't think that two two quantities would combine in two different ways to make two important different concepts well that got clear to Newton somehow And Galileo is a bit muddled. Although he gets almost all the consequences of those things right, but he doesn't get the orbits and things to come out.

[00:37:08]Well, anyway, what's happened in artificial intelligence like most fields is that people said, well, let's try to understand thinking and psychology and let's use physics as our model. And so, what we want is to get a very small number of universal laws.

[00:37:33]And a lot of psychologists struggled around to do that. And then they gradually separated so that there were some psychologists like Bill Estes who worked out some very nice mathematical rules for reinforcementbased learning. Got a simple rule. If you designed an experiment, right, it predicted pretty well how many trials it would take a rat or a pigeon or a dog or whatever to uh learn a certain thing from trial and error. And SD's got a set of four or five rules which looked like Newton's laws. And if you designed your experiment very carefully and shielded the animal from noise and everything else, which is what a physicist would do for a physics experiment, uh the reinforcement theories got some pretty good uh models of how to make a machine learn, but they weren't good enough.

[00:38:51]So here's a whole list of things that happened in the early years of cognitive psychology when people were trying to make theories of thinking and they were imitating the physicists by physics envy to borrow a term of Freud there.

[00:39:14]The idea is can you find a few simple rules that will apply to very broad classes of psychological phenomena.

[00:39:24]And this led to various kinds of projects.

[00:39:33]uh lots of neural network and reinforcement and statistical based methods led to learning machines that were pretty good at learning in some kinds of situations but and they're becoming very popular but I don't like them because if you have a lot of variables like 50 or 100 to use a probabilistic analysis you have to think of all combinations of those variables because if two of them are combined in something like a exclusive or manner u you know I I just put the light pen in a pocket it's either in a left pocket or a right pocket can't be both uh that's an exor uh that will cause a lot of trouble to a learning machine and if there are 100 variable ables, there's no way you could decide which of the two to the 100th boolean combinations of those variables you should think about. And so lots of statistical learning systems are good for lots of applications, but they just won't cut it to solve hard problems where the hypothesis is a little bit complicated and has seven or eight variables with complicated interactions.

[00:41:00]So, um, most statistical learning people assume that if you get a lot of partial ones, then you can look for combinations of ones that have high correlations with with uh the result, then you can start combining them and things get better and better. However, if the uh mathematically if an effect you're looking for depends on the exclusive or of several variables, there's no way to approach that by successive approximations. If any one of the variables is missing, there won't be any correlation of the phenomenon with the others. Anyway, that's a long story. Um but I think it's worth complaining about because almost all young people who start working on artificial intelligence look around and say what's popular statistical learning so I'll do that. That's exactly the way to kill yourself scientifically.

[00:42:07]You don't want to get the most popular thing. You want to see what am I really good at that's different and what are the chances that that would provide another thing. So you well end of end of long speech.

[00:42:25]uh another problem in the last 30 years and I'm I'm sort of as you'll see during my lectures I think a lot of wonderful things happened between 1950 when the idea of AI first got articulated 1950s and then u the 20 years after that from 1960 to 1980 A lot of early experiments and I'll show you some of them um looked very promising.

[00:43:04]In fact, they may be Here we go.

[00:43:22]1961, Jim Slaggel was a young graduate student here at MIT.

[00:43:41]He was blind.

[00:43:44]uh he had gotten some retinal degeneration thing in his first or second year of high school.

[00:43:54]He was told that he would lose all his vision and there was no treatment or hope. So he learned Braille while he could still see.

[00:44:06]And when he got to MIT, he was completely blind.

[00:44:11]But there was a nice big parking lot in Technology Square and he would ride a bicycle and people like Susman and Winston and whoever was around would yell at him telling him where the next obstacle would be. And uh Jim got better and better at that.

[00:44:35]And uh nothing would stop him.

[00:44:40]and he decided he would write a program that oh I wrote a program that would take any formula and find its derivative was really easy because they're just about five rules like if there's a product uv then you compute u * the derivative of v and plus v * you know u dv plus v du. So I wrote a 20line list program that did all the algebraic expressions and what it would do is put D's in and then at the right place and then it would go back through the expression again wherever it saw a D it would do the derivative of the thing after that and nothing to it. So Slaggel said, "Well, I'll do integrals."

[00:45:31]And we all said, "Well, that's very hard. Nobody knows how to do it." And in fact, in Providence at the uh home of the American Mathematical Society, there is a big library called the Baitman Manuscript Project, which has been collecting all known integrals for a hundred years.

[00:45:56]And when everybody when anybody finds a new integral that they can integrate in closed form, they send the formulas to the Bman manuscript project and uh some hackers there were developed ways to index it. So if you had an integral and you didn't know how to integrate it, you could look it up. That was pretty big.

[00:46:25]I should say that Slaggel succeeded in uh writing a program that managed to do all of the kinds of integrals that one usually found on uh the first year calculus course at MIT and got an A in those. Couldn't do word problems.

[00:46:50]And the uncanny thing is that if it was a problem that usually took a MIT student five or 10 minutes, Slaggo's program would take five or 10 minutes.

[00:47:01]It's running on a IBM 701 with 20 millisecond cycle time. It's incredibly slow.

[00:47:11]You can type almost that fast.

[00:47:15]uh and 16k of words of memory.

[00:47:24]So there's no significance whatever to the this accident of time. It would now take a microscond or so be a hundred million time thousand million times faster than a student uh quite remarkable. I don't have a slide.

[00:47:46]Joel Moses then uh Slaggel went and graduated. Joel Moses was another student who was is he provost now or what?

[00:47:58]>> What?

[00:48:01]>> He got tired of it. a terrific student and he set up a project called Maxima for project max symbolic something or other algebra and um got people uh several people all over the country working on integration and at some point a couple of them Bobby Cavanus and forget the other one found a procedure that could in fact integrate everything every algebraic expression that has can be integrated in closed form. I forget the couple of constraints on it and uh that became a widely used system.

[00:48:43]It ultimately got replaced by uh Steven Wolram's Mathematica but Maxima was sort of the worldass symbolic mathematician for quite a few years and uh Moses mentioned to me he had read Slaggel's program thesis and it took him a couple of weeks to understand the two pages of or three pages of lisp that uh Slaggel had written because um being blind, Slaggel had tried to get the thing into as compact a form as possible.

[00:49:35]But that's symbolic.

[00:49:38]It's too easy. Here's an more ambitious one which was three years later Dan Babro who is now a vice president doing something at Xerox and it solved problems like this. The gas consumption of my car is 15 miles per gallon. The distance between Boston and New York is 250 miles.

[00:50:02]What is the number of gallons used on a trip between Boston and New York?

[00:50:09]and it chomps away and solves that.

[00:50:14]It has about a hundred rules.

[00:50:17]It doesn't really know what any of those words mean, but uh it thinks that the word is is equals the distance between doesn't care what Boston and New York is. It has a format thing which says the distance between two things and uh it never bothers to you see because the phrase do Boston and New York occurs twice in the example. It just replaces that by some symbol.

[00:50:50]It was fairly remarkable. And generally, if you had an algebra problem and you told it to Bobro, Bobro could type something in and it would solve it. If you typed it in, uh, it probably wouldn't, but it was, you know, it had more than half a chance or less about half a chance. So, it was pretty good.

[00:51:14]And uh if you look at an outof print book I wrote called I compiled called semantic information processing most of Bob's program is in that. So that's 1964.

[00:51:31]I'll skip which is perhaps the most interesting program.

[00:51:36]This was a program where you could talk to a robot that I don't have a good picture on the slide, but they're they're a bunch of blocks of different colors. They're all cubes in the or rectangular blocks. And you can say, uh, which is the largest block on top of the big blue block and it would answer you. And you could say, "Put the large red block on top of the small green block." And it would do that. And um Winterrad's program was of course a symbolic one. We actually built a robot and um I guess we built it second. Our friends at Stanford in built a robot and they imported Winterrad's program and they had the robot actually performing these uh operations that you told it to do by typing and it was pretty exciting.

[00:52:50]My favorite program in that period was this one because it's so psychological.

[00:53:00]This is called a geometrical analogy test and it's on some IQ tests. A is to be as C is to which of the following five and Evans wrote a set of rules which uh were pretty good at this did as well as 16 year olds.

[00:53:25]and it picks this one. And if you ask it why, it says something like I don't have a reason that it moves the largest object down or something like that. Makes up different reasons.

[00:53:51]So you see in some sense we're going backwards in age because we're going from calculus to algebra to simple analogies.

[00:54:04]Oh, there it is.

[00:54:08]That's one where the largest object moves down. I don't know why I have two of them.

[00:54:19]These are for another lecture.

[00:54:32]So that was a period in which we picked problems that people considered hard because they were mathematical.

[00:54:42]But when you think about it more, you see well those math things are just procedures and it's once you know what lelas and uh Gaus and those mathematicians Newton and people did you can write down systematic procedures for integrating or for um solving simultaneous algebraic constraint equations or things like that and so there's very little to it. So in some sense if you look at the what you're doing in math in high school in in education you're going from hard to easy.

[00:55:25]It's just that people aren't most people aren't very good at obeying really simple rules because it's so hideously boring or something.

[00:55:37]So we gradually started to ask well why can't we make machines understand everyday things and um the things that everyone regards as common sense and uh people can do so you don't need machines to do them.

[00:55:55]One of my favorite examples is why can you pull something with a string but not push?

[00:56:04]And there's been a lot of publicity recently about that interesting uh program written at I group at IBM called Watson, which is good at uh finding facts about sports people and celebrities and politics and and so forth.

[00:56:32]But there's no way it could understand why you could push pull something with a string but not push.

[00:56:39]And I don't know of any program that that has that concept or way of dealing with it. So that's what I got interested in.

[00:56:50]And starting around the maybe the middle 1970s or late 1970s, several of us started to uh stop doing the easy stuff and trying to make theories of how you would do the kinds of things that people are uniquely good at. I don't know if animals Well, I don't know. I'm sure a monkey wouldn't try to push anything with a string.

[00:57:26]Maybe it does it very quickly and you don't notice.

[00:57:34]And one aspect of common sense thinking is going right back to that idea vision having a dozen dozen different systems is that whatever a person normally is doing they are probably representing it in several different ways.

[00:57:52]And here's an actual scene of two kids named Julie and Henry who are playing with blocks.

[00:58:03]It's pretty hard to see those blocks.

[00:58:07]And you can think that Julie is thinking seven thoughts.

[00:58:16]I'd like to see a longer list. Maybe a good essay would be to take a few examples and say, "What are the most common micro worlds?"

[00:58:27]see physical, social, emotional, mental, instrumental, whatever that is, visual, tactile, spatial.

[00:58:37]She's thinking all these things. What if I pulled out that bottom block? You can't see the tower very well.

[00:58:44]Should I help him or knock his tower down?

[00:58:48]How would he react?

[00:58:50]I forgot where I left the arch shaped block. That was real. Uh, it's somewhere over here, but I don't think it maybe it's that. I don't know.

[00:59:06]I remember when it happened, she mentioned that she reached around and it wasn't where she thought it was.

[00:59:14]So, common sense thinking involves this.

[00:59:19]Um, in most cases, I think several representations. I don't know if it's as many as seven or maybe 20 or what, but uh that's the kind of thing we want to know how to do.

[00:59:39]In the next lecture, I'll talk about a model of how I think thinking works.

[00:59:49]Um, what's the difference between us and our ancestors?

[00:59:56]We know we have a larger brain.

[00:59:59]But if you think about it, if you took the brain that you already had in say uh try to remember the name of the little monkey that looks like a squirrel, jumps around in trees. Lemur.

[01:00:17]I don't I forget. I'll have to.

[01:00:22]Anyway, if you just made the brain bigger, then the poor animal would be slower and heavier and would need more food and take longer to reproduce.

[01:00:32]The joke about more difficulty to give birth. I don't know if any animal has the problem that humans have.

[01:00:40]A lot of people die and so on. So, how did we evolve new ways to think and so forth?

[01:00:55]And the my first book, the uh society of mind, had this theory that uh maybe we evolved in series of higher and higher levels or management structures built on on the earlier ones.

[01:01:20]And this particular picture suggests that I got this idea from uh segment Freud's early theories.

[01:01:32]Uh there's been a lot of Freud bashing recently. So you can look on the web. I forget the authors, but there are a couple of books saying that he made up all his data and there's no evidence that he ever cured anyone and that he lied about uh all the data in mentioned in his 30 or 40 books and so forth.

[01:02:00]But the funny part is that if you look at his first major book 1895 called the interpretation of dreams, it sort of outlines his theory that most of thinking is unconscious and its processes you can't get access to. And it has a little bit about sex, but that's not a major feature.

[01:02:24]And it's just full of great ideas that the cognitive psychologist finally began to get in the 1960s again and never give credit to Freud. So, uh, he may well have made up his data, but if you have a very good theory and nobody will listen to you, what can you do?

[01:02:51]his friend Rudolph Fleece listened to him and there was a uh another paper on how the neurons might be involved in thinking which was also written around 1895 but never got published till 1950 by uh forget who called project for a scientific psychology And it's full of ideas that if they had been published might have changed everything because what's on your mind? Who has what would you like to hear about or who has another theory?

[01:03:41]When he talks about neurotransmitters, it's as though he thinks that chemical has some real significance.

[01:03:49]Any chemical would have the same function as any other one, provided there's another receptor that causes something to happen in a cell membrane.

[01:03:59]So, you don't want to regard acetylcholine or epinephrine as having a mental significance. It's just a it's just another pulse but very low resolution.

[01:04:13]And uh yes, a neurochemical might affect all the neurons a little bit and raise the average amount of activity uh of some big population of cells and reduce the average activity of some others. But that's nothing like thinking. That's like saying in order to understand how a car works, what's the most insulting thing I could say?

[01:04:43]Or to understand how a computer works, you have to understand the arsenic and phosphorus andor what's the other one?

[01:04:54]You have to understand these atoms that are what?

[01:04:57]>> Yeah. Well, that's the that's the matrix.

[01:05:01]So, there are these one part in a million impurities.

[01:05:05]And that's what's important about a computer, isn't it? The fact that the transistor has gain and so forth. Well, no.

[01:05:14]The trouble with the computer is the transistors. That's why practically every transistor in a computer is mated to another one in opposite phase to form a flip-flop whose properties are exactly the same except one in a quadrillion times.

[01:05:38]In other words, everything chemical about a computer is irrelevant. And I suspect that almost everything chemical about the brain is unimportant except that it causes it helps to make the columns in the cortex which are complicated arrangements of several hundred cells work reliably whereas the neuroscientist is looking for the secret in the sodium.

[01:06:12]When a neuron fires, the important thing is that that lets the sodium in and the potassium out or vice versa. I forget which at 500 mill volts. It's really quite a colossal event, but it has no significant. It's only when it's attached to a flip-flop or to something like a kline which has an encoder and decoder uh of a digital sort uh every few microns of its length that you get something functional. So the trouble is the poor neuroscientist started out with too much knowledge about the wrong thing.

[01:06:54]The chemistry of the neuron firing is very interesting and complicated and cute.

[01:07:02]And in the case of the electric eel, you know what happened there? The neuron synapse, it got rid of the next neuron and it just in the electric eel, you have a bunch of synapses or motor end plates, they're called uh in series. So instead of half a volt, if you have 300 of those, you get 150 volts. I think the electric shock that a electric eel can give you is about 300 volts. And uh this can cause you to drown promptly if if you are in the wrong wave when it happens to bump into you. I don't know why I'm rambling this way.

[01:07:46]Uh, you're welcome to study neuroscience, but please try to help them instead of learn from them.

[01:07:54]Now, Mike Travers has a thesis, Tony Hearn, there are three master's thesis on kines. They sort of got them to work to solve some simple problems. But, uh, I'd go further. I've never met a neuroscientist who knows the pioneering work of Newell and Simon in the late 1950s. So there's something wrong with that community.

[01:08:24]They're just ignorant.

[01:08:27]It's uh they're proud of it. Oh, well, I spent some time learning neuroscience when I was I once had a great stroke of luck when I was a I guess I was a junior at Harvard and there was a great new biology building that was just constructed. You probably know it's a great big thing with two rhinoceroses. What are those?

[01:08:55]So this building was just finished and half occupied because it was made with the future. So I wandered over there and I met a professor named John Welsh.

[01:09:12]And I said, "I'd like to learn neurology."

[01:09:16]And he said, "Great. Well, I have an extra lab.

[01:09:21]Why don't you why don't you study the crayfish claw?

[01:09:26]I said great. So he gave me this lab which had four rooms and a dark room and a lot of equipment and nobody there and he had worked on crayfish. There was somebody who went every week up to Walden Pond or somewhere and caught crayfish and bring them back.

[01:09:51]And I was a radio amateur hacker at the time. So good at electronics. So I got my crayfish and Welsh showed me how to The great thing about this preparation is you can take the crayfish and if you claw and if you hold it just right go snap it comes off. grows another one. Takes a couple of years.

[01:10:16]Uh, and then there's this white thing hanging out, which is the nerve. And it turns out it's six nerves, one big one and a few little ones.

[01:10:28]And if you keep it in ringer solution, whatever that is, it can live for several days.

[01:10:35]So, I got a lot of switches and little inductors and things and made a gadget and mounted this thing with six wires going to these nerves. And then I programmed it to reach down and pick up a pencil like that and wave it around.

[01:10:59]Well, that's obviously completely trivial. And all the neuroscientists came around and gasped and said, "That's incredible. How did you do that?"

[01:11:14]They had never thought of putting the thing back together and making it work.

[01:11:20]Anyway, I always reminding myself that I'm the luckiest person in the world because uh every time I wanted to do something, I just happened to find the right person and they'd give me a lab.

[01:11:47]I got an idea for a microscope and there was this great professor PCEL who got the Nobel Prize after a while and he said that sounds like it would work.

[01:12:01]Uh why don't you take this lab?

[01:12:05]Uh it was in the Jefferson.

[01:12:16]Anyway, yeah, >> this idea, let's map the whole brain, a hundred billion things, and then people like Rest Tech said, "Oh, and there's a thousand supporting cells for each neuron." He's just glorying in the obscurity of it rather than trying to contribute something. Anyway, if you run into him, give him my regards.

[01:12:44]I really wonder how somebody can write something like that.

[01:12:52]Suppose one part of the brain is doing something and it's in some particular state that's very important like I don't know that I've just seen a glass of water.

[01:13:08]Then another part of the brain would like to know there's a glass of water in this in the uh environment and I've been looking for one so I should try to take over and uh do something about that. Now at the moment there's no theory of what happens in different parts of the brain for a simple thing like that to happen.

[01:13:36]no theory at all except they use the word association or or they talk about what are the purposeful neurons goal forget. Okay. So my theory is that there there are a bunch of things which are massive collections of of nerve fibers, maybe a few hundred or a few thousand.

[01:14:06]And when the visual system sees an apple, it turns on 50 of those wires.

[01:14:14]And when it sees a pair, it turns on a different 100 or 50 of those wires, but about 20 of them are the same. so forth.

[01:14:22]In other words, it's like the edge of a punched card.

[01:14:26]Have you ever seen a card-based retrieval system? If you have a book that has uh suppose it's about physics and biology and uh sumatra and a typical 5 by eight card has 80 holes in the top edge. So what you do is if it's if it's Somatra, you punch eight of these holes at random particular set. They're assigned to the Somatra. And then if it's I forget what my first two examples were, but you punch eight or 10 holes for each of the other two words. So now there are 24 punches. Only probably four or five of them are duplicates. So you're punching about 20 holes. And now if something is looking for the cards that have were punched for those three things, even if there are 30 or 40 other holes punched in the cart, you stick your 20 wires through the whole deck and lift it up and only cards fall out that had those three categories punched for.

[01:15:37]So you see, even though you had 80 holes, you could punch combinations of up to a million different categories into that and put a bunch of wires through. You'll get all of the ones that were punched for those cate the categories you're looking for and you might get three or four other cards that will come down also because all of the eight holes were punched for for some category by accident. Do you get the picture?

[01:16:07]I'll send you a reference.

[01:16:09]It was invented by a uh in the 19 early 1940s by a uh Cambridge scientist here named Calvin Moors and was widely used in libraries for information retrieval until computers came along.

[01:16:29]But anyway, that's the sort of thing you could look for in a brain if you had the concept in your head of Zato coding. But I've never met a neuroscientist who ever heard of such a thing. So you have this whole community which doesn't have a set of very clear ideas about different ways that knowledge or symbols could be represented in neural activity. So good luck to them when they get their big map. Uh they'll still have to say what do I do with a 100red billion of these interconnections uh which was every year we'd tell them what we had done. uh they didn't they didn't want to hear what we wanted to do and things have turned the opposite.

[01:17:26]So what would happen is every year we'd say we did these great things and uh we might do some more went on for about 20 years and it was and then it fell apart.

[01:17:44]One thing, it's a nice story. There was a great liberal senator, Mike Mansfield, and unfortunately he got the idea that the defense department was getting too big and influential.

[01:18:03]So he got Congress to pass a law that Arba shouldn't be allowed to support anything that didn't have direct military application and Congress went for this and all of a sudden uh a lot of research disappeared basic research. It didn't bother us much because we made up applications and said, "Well, this will make a military robot that will go out and uh do something bad."

[01:18:42]But anyway, around 1980, uh the um the the funding for that sort of thing just dried up because of this political accident.

[01:18:58]It was just an accident that ARPA mainly through the office of naval research was funding basic research. And that's that was a bit of history.

[01:19:10]uh if you look back at uh the year 1900 or so, you see people like Einstein making these nice theories. But Einstein wasn't a very abstract mathematician.

[01:19:27]So he had a mathematician named Hermon Vile um polishing his tensors and things for him.

[01:19:36]and Herman Vile's son Joe was at the office of naval research in my early time and uh that office had spent a lot of secret money getting scientists out of eur Europe while Hitler was marching around and sending them to places like Princeton and other forms of heaven in the in Cambridge.

[01:20:06]And again, one of the reasons I was lucky is that uh I was here and all these, you know, if you had a mathematical question, you could find the best mathematician in the world down the block somewhere. And uh Joe Vile was partly responsible for that. and the ONR uh was piping all that money to us for work on early AI.

[01:20:38]So it's a very sad thing of the maybe the most influential liberal in the US government actually ruined everything by accident.

[01:20:52]ARPA changed its name to DARPA.

[01:20:55]It was advanced research projects agency and it had to call itself defense advanced research project agency.

[01:21:08]The number of people working on advanced ideas in AI is has gotten smaller and smaller as the right now the around 1980 rule-based systems became popular. There are lots of things to do.

[01:21:27]uh right now statisticalbased inference systems are becoming popular and as I said these things are tremendously useful but the problem is if you have a statistical system the important part is guessing what are the plausible hypotheses and then making up the then finding out how many instances of that are correlated with such and such. So it's a nice idea but but the hard problem is this is the abstract symbolic problem of what sets of variables are worth considering at all when there are a lot of them. So to me the uh the most exciting projects are the kind that Winston is developing for reasoning about real life situations and the one that Henry Lieberman would you stand up Henry Lieberman runs a worldclass group that's working on common sense knowledge and uh informal reasoning. And it seems to me that that's the critical thing that all the other systems will need.

[01:22:39]Uh in the meantime, there are people working on logical inference which has the same problem that statistical inference has. Namely, how do you guess which combinations of variables are worth thinking about? Then it seems to me that the statistics isn't so important. In fact, there's a great researcher named Douglas Lenat in Austin, Texas, who once made an interesting AI system that u was good at uh making predictions and guessing explanations for things. And it was sort of like a probabilistic system. It had a lot of hypotheses and every time one of them was useful in solving a problem, it moved it up one on the list.

[01:23:28]So, Lennet's thing never used any numbers.

[01:23:32]It didn't say this is successful 73 of the time and now it's successful 736 4825 of the time.

[01:23:44]What it would do is if something was useful, it would move it up past another hypothesis. Every now and then would put a new one in. Well, if you're doing if you're trying to solve a problem, what do you need to know? You want to know what's the most use what's the most likely to be useful one and try that.

[01:24:06]You don't care how likely it is to be useful as long as it's the most, right?

[01:24:12]I mean, if it's one in a million, maybe you should say, "I'm getting out of here.

[01:24:19]I don't I shouldn't be working in this field at all or uh uh get a better problem."

[01:24:26]But Lennis thing did rather wonderfully at making theories by just changing the ranking of the hypothesis that it was considered. No numbers.

[01:24:43]it it did something very cute. uh he gave it examples of arithmetic and it actually it was rather long effort and it actually learned to do some arithmetic and it invented the idea of division and the idea of prime number which was some number that wasn't divisible by anything.

[01:25:12]It decided that nine was a prime.

[01:25:17]Didn't do much harm and uh it crept along and it got better and better and uh it invented modular arithmetic by accident at some point and uh it's a PhD thesis. A lot of people didn't believe this PhD thesis because Lennet lost the program tape.

[01:25:44]So, uh, he was under some cloud of suspicion for people thinking he might have faked it, but who cares? Anyway, uh I think there's a lesson there which is that uh let's start with something that works and then uh if it's really good then hire a mathematician who might be able to optimize it a little. But the important thing was the order and the a good statistical one might waste a lot of time because here's this one that's 78 and here's this one that's 56 and it's the next one down and you get a lot of experience and it goes up to 57 and 58 and it never you know might be a long time before it gets past the other one because you're doing arithmetic whereas in Lennets it would just pop up past the other one then it would get tried right away and if it were no good it would get knocked down again. So it's a real question of I don't know is mathematics is great and I love it and a lot of you do but there should be a name for when it's actually slowing you down and wasting your time because there's a better way that's not formal.

[01:27:13]It's a strange question because there is music everywhere. Uh on the other hand, I have several friends who are who are a musical and so when I have this theory that uh music is a way of teaching you to represent things in an orderly fashion and stuff like that. Um well I have three of my colleagues who aren't musical but they dance so they may it may be that I don't know the answer.

[01:27:58]It's interesting the the theory the first theory in my paper is that when you have a lot of complicated things happening then the only way to learn is to represent things that happen and then look at the differences between things that are similar and then try to explain the differences.

[01:28:24]Right? I mean what else is there? Maybe there's something else. In order to become intelligent and understand things, you have to be able to compare things. And to me, the most important feature of what's called music is that it's divided into measures.

[01:28:49]Pa and measures are the same number of beats or whatever they are. And so now you can say da da da da da da da da da da. What's the difference?

[01:29:06]You change the eighth notes in the second one. The last four eighth notes.

[01:29:10]No, the two before last to a quarter note.

[01:29:15]So, uh, you're taking things that were in different times and you're superimposing those times and now you can see the difference. And the reason you can see the difference is that uh, you have things called measures and the measures have things called beats and so things get knocked into very good frames. Now there's some Indian music which has 14 measures for a phrase and some of the measures go seven and five and I can make no sense of that stuff whatever and I've tried fairly hard but not very so I don't understand how Indians can think any of you can handle Indian music.

[01:30:07]Maybe there's something different about their cookia or maybe they have absolute pitch in some sense, which is a bad thing to have because if you're if you're listening to a piece composed by a composer who doesn't have absolute pitch, then you're reading all sorts of things into the music that shouldn't be there.

[01:30:33]And if you're and the opposite would be true. Uh I read music criticism sometimes and maybe the reviewer says and after the second and third movement he finally returns to the initial key of E flat major. What a relief.

[01:30:53]Well, uh, I once had absolute pitch for a couple of weeks, uh, cuz I ran a tuning fork in my room for a month and, uh, and I didn't like it because you can't listen to Bach anymore.

[01:31:19]Oh, well, it's a good question. Uh, why do people like music? And um I don't know any other paper like mine. If you ever find one, I'd like to see it because if you go to a big library, there are thousands of books about music.

[01:31:36]And if you open one, it's mostly Burlio's complaining that somebody wouldn't give him enough money to hire a big enough chorus.

[01:31:52]Can't you what happens if you sit back and just think for a while? Uh you wouldn't know if your body had disappeared for for would you?

[01:32:10]There are all sorts of strange ideas about existence and why do you think there's a world?

[01:32:26]One of the things that bugs me is people say, "Well, who created it?" And that can't make any sense because this is just a possible world. Suppose there are a whole lot of possible worlds and there's one real one. How could you ever how could you possibly know which one you're in?

[01:32:45]And then you could say, well, didn't someone have to make it?

[01:32:51]And what's the next thing you'd ask?

[01:32:54]Well, who made the maker?

[01:32:57]So the body mind thing seems to me that uh once you have a computer uh it's it can be its own world. It just can sit the program can spend half the time simulating a world and half the time uh thinking about what it's like to be in it.

[01:33:21]>> Yeah.

[01:33:23]>> What would it mean to say the universe exists?

[01:33:27]The universe is in the universe.

[01:33:30]So, so there's something wrong with thinking about.

[01:33:36]So, there are only possible worlds.

[01:33:39]There's no, it doesn't make any sense to pick one of them out and say that's the real one. Well, you can't tell because five minutes from now everything might change. So, phys explains anything.

[01:33:55]You just have to take what you've got and make the best of it.

[01:34:00]Artificial intelligence means to me uh making a system that is resourceful and doesn't get stuck. And so if you have a system and also it's a how do you put it? Uh some definitions are not stationary like uh what's popular.

[01:34:32]Popular is what's popular now.

[01:34:37]There there isn't any such thing as popular music in terms of the music. So I know there were There was once a little department called systems analysis at Tufts which had a couple of rather good philosophers trying to make general theory of everything and they were writing nice little papers and it got it moved along.

[01:35:10]But then there was a Senator McCarthy you've probably heard of, and he announced that he had evidence that the one of the principal investigators had slept with his wife before they were married.

[01:35:29]Well, Tufts was very frightened at this and abolished that department.

[01:35:37]and Bill Schutz went to California and started Eselin and had a good time for the next 50 years.

[01:35:50]More stories.

[01:35:52]I look over at Carnegie Melon and there are some nice projects and the most popular one is robot soccer. And here are these little robots kicking a ball around.

[01:36:04]They're Sony. What are they called?

[01:36:10]>> Yes, the Sony IBOS. Sony stopped making the IBOS, but it respected Carnegie and it made a little stash, secret stash of IBOS to send to Carnegie if when the present ones break.

[01:36:26]But my impression of AI projects that have robots is that they do less less less than projects that don't. The reason is if you have a robot like Asimo made by Sony, no >> Honda, Asimo can get in the backseat of a car with some effort. usually falls over.

[01:37:00]However, if you simulate a stick figure in a computer getting into a stick figure of a car, then you can make it learn to do that and get better and better. And so all AI projects without robots are way ahead of all AI projects with robots.

[01:37:18]And the profound reason is that robots are usually expensive and they're always being fixed. So if you have five students and the robot is being fixed, I don't know what they're doing, but they have to wait. Whereas if you have a stick figure robot, then uh you can just run it on this, although it might be a little slower than your main frame.

[01:37:47]Probably not.

[01:37:51]Well, but it just seems to me that a large amount of our brain is involved with highly evolved locomotion mechanis mechanisms.

[01:38:06]And uh as I said, when you're sitting back with your eyes closed in a chair thinking about something, then it's not clear how much of that machinery is important. But it might be that I have a I have a strange paper on I don't know if it's try to remember its name. It's called u do you think I can actually get a I can't remember the name of the title.

[01:39:03]Oh, I give up. Um, in the older theories of psychology, everything is learned by experience in the real world. So, conditioning and reinforcement and so forth.

[01:39:22]uh in this theory I call internal grounding.

[01:39:27]I make a conjecture. Suppose the brain has a little piece of nerve tissue which consists of a few neurons arranged to make not a flip-flop but a what would you call a three or a four flop? A flip-flop with three or four states. Let's say three.

[01:39:51]So when you put a certain input, it goes from I couldn't find the chalk.

[01:40:04]So here are three states and here's a certain input. That means if you're in that state, you go to this and if you pop that input again, it does this. And if you say uh go clockw go counterclockwise it goes. So if three of them get you back where you were. But if I go this this and that, that would mean to go like this, this, and back. So this would be that means that's equivalent to just going one.

[01:40:44]Get the idea? In other words, imagine that there's a little world inside your brain which is very small and only has three states and you have actions that you can perform on it and you have an inner eye which can see which of the three points of that triangle you're on.

[01:41:04]Then you could learn by experience that if you go left, left, left, you're back where you were. But if you go left, right, left, right, you're back where you are. And if you go left, left, right, that's like going one left.

[01:41:20]In other words, you could imagine a brain that starts out before it connects itself to the real world, it starts by having the top level of the brain connected to a little internal world which just has three or four states and you get very good at manipulating that.

[01:41:43]Then you add more sensory systems to the outer world and you get to get learn ways to get around in the real world. So I call that the internal grounding hypothesis.

[01:41:56]And my suggestion is maybe somewhere in the human brain, there's a little structure that's somewhat like that which is used by the frontal part of the cortex to make very abstract ideas.

[01:42:15]You understand? The more abstract an idea is, the simpler and more stupid and elementary it is. Abstract doesn't mean hard. Abstract means stupid.

[01:42:27]Real real things like this are infinitely complicated.

[01:42:34]So we might have and I wouldn't dare suggest this to a neuroscientist.

[01:42:41]There might be some little brain center somewhere near the frontal cortex that allows the frontal cortex to do some uh predicting and planning and induction about uh very simple few simple finite state arrangements.

[01:43:01]Who knows would you look for it?

[01:43:06]Well, if you were a neuroscientist, you could say, "Oh, that's completely different from anything I ever heard.

[01:43:12]Let's look for it." And if you're wrong, you've wasted a year. And if you're right, then you become the new uh Ramoni Kahal or someone. Who's the best?

[01:43:32]Who's the currently best neuroscientist?

[01:43:38]Maybe it's late.

[01:43:43]One more question.

[01:43:46]One last question.

[01:43:48]it. However, his his projects, what was it called?

[01:43:53]>> Cog, >> disappeared without a trace.

[01:43:59]That theory was so wrong that it got a national award and it corrupted AI research in Japan for several years. I can't understand.

[01:44:10]Brooks became popular because he said maybe the important things about thinking is that there's no internal representation. You're just reacting to situations and you have a big library of how to react to each situation.

[01:44:26]Well, David Hume had that idea and he was a popular philosopher for hundreds of years, but it went nowhere and it's gone. And so is Rod.

[01:44:41]However, he is one of the great robot designers and he may be the instrumental in fixing the great Japanese nuclear meltdown because they're shipping some of his robots out there. The problem is, can it open the door?

[01:45:02]So far, no robot can open the door even though it's not locked.