3. Cognitive Architectures

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This is great. I get ASMR from people's voices most and this worked well. Thanks for the post. Also if this got you asmr, try watching this https://www.youtube.com/watch?v=qw_Iwcos8aQ

👍︎︎ 1 👤︎︎ u/Mister_Red_Bird 📅︎︎ Aug 13 2015 🗫︎ replies
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The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or to view additional materials from hundreds of MIT courses, visit mitopencourseware@ocw.mit.edu. PROFESSOR: So really, what my main concern has been for quite a few years is to make some theory of what makes people able to solve so many kinds of problems. I guess, if you ran through the spectrum of all the animals, you'd find lots of problems that some animals can solve and people can't, like how many of you could build a beaver dam and/or termite nest. So there are all sorts of things that evolution manages to produce. But maybe the most impressive one is what the human infant can do just by hanging around for 10, or 20, or 30 years and watching what other humans can do. So we can solve all sorts of problems, and my quarrel with most of the artificial intelligence community has been that the great success of science in the last 500 years really has been in physics. And it's been rewarded by finding little sets of rules, like Newton's three laws, and Maxwell's four laws, and Einstein's one law or two, that explained a huge range of everyday phenomena. Of course, in the 1920s and '30s, that apple cart got upset. Actually, Einstein himself, who had discovered the first quantum phenomena, namely the quantization of photons, had produced various scientific laboratory observations that were inexplicable in terms of either Maxwell, or Newton, or Einstein's earlier formulations. So my picture of the history is that in the 19th century and a little bit earlier going back to Lock, and Spinoza, and Hume, and a few of those philosophers, even Immanuel Kant, they had some pretty good psychological ideas. And as I mentioned the other day, I suspect that Aristotle was more like a modern cognitive psychologist and had even better ideas. But we've probably lost a lot of them, because there are no tape recorders. Who knows what Aristotle and Plato said that their students didn't write down? Because it sounded silly. The idea that we developed around here, mostly, Seymour Papert, and a lot of students-- Pat Winston was one of the great stars of that period. --was the idea that to get anything like human intellectual abilities, you're going to have to have all sorts of high level representations. So one has to say, the old conditioned reflex of stimulus producing a response isn't good enough. The stimulus has to be represented by some kind of semantic structure somewhere in the brain or mind. So far as I know, it's only in the theories of not even modern artificial intelligence, but the AI of the '60s, and '70s, and '80s, that people thought about what could be the internal representation of the kinds of things that we think about. And even more important, if one of those representations, you see something, or you remember some incident. And your brain represents it in some way. And if that way doesn't work, you take a breath. And you sort of stumble around and find another way to represent it. Maybe when the original event first happened, you represented it in three or four ways. So we're beginning to see-- did anybody hear Ferucci's talk? The Watson guy was up here a couple of days ago. I missed it, but they haven't made a technical publication as far as I know of how this Watson program works. But it sounds like it's something of a interesting society of mind like structure, and it'd be nice if they would-- has anybody read any long paper on it? There have been a lot of press reports. Have you seen anything, Pat? Anyway, they seem to have done some sorts of commonsense reasoning. As I said the other day, I doubt that Watson could understand why you can pull something with a string, but you can't push. Actually, I don't know if any existing program can understand that yet. I saw some amazing demonstrations Monday by Steve Wolfram of his Wolfram Alpha, which doesn't do much common sense reasoning. But what it does do is, if you put in a sentence, it finds five or 10 different representations, anything you can find that's sort of mathematical. So when you ask a question, it gives you 10 answers, and it's much better than previous systems. Because it doesn't-- well, Google gives you a quarter million answers. But that's too many. Anyway, I'm just going to talk a little bit more, and everybody should be trying to think of a question that the rest of the class might answer. So there are lots of different kinds of problems that people can solve going back to the first one, like which moving object out there is my mother and which might be a potential threat. So there are a lot of kinds of problems that we solve, and I've never seen any discussion in psychology books of what are the principal activities of common sense thinking. Somehow, they don't have-- or people don't-- before computers, there really wasn't any way to think about high level thinking. Because there weren't any technically usable ways to describe complicated processes. The idea of a conditional expression was barely on the threshold of psychology, so what kinds of problems do we have? And if you take some particular problem, like I find these days, I can't get the top off bottles. So how do I solve that? And there are lots of answers. One is you look for somebody who looks really strong. Or you reach into your pocket, and you probably have one of these and so on. There must be some way to put it on the floor, and step on it, and kick it with the other foot. So there are lots of problems that we're facing every day. And if you look in traditional cognitive psychology-- well, what's the worst theory? The worst and the best theory got popular in the 1980s, and it was called rule based systems. And you just have a big library, which says, if you have a soda bottle and you can't get the cap off, then do this, or that, or the other. So some people decided, well, that's really all you need. Rod Brooks in the 1980s sort of said, we don't need those fancy theories that people, like Minsky, and Papert, and Winston are working on. Why not just say for each situation in the outer world have a rules that says how to deal with that situation? Let's make a hierarchy of them, and he described a system that sort of looked like the priority interrupt system in a computer. And he won all sorts of prizes for this really bad idea that spread around the world, but it solved a lot of problems. There are things about priority interrupt that aren't obvious, like suppose you have-- in the first computers, there was some problem. Because what should you do, if there's several signals coming into the computer, and you want to respond to them? And some of the signals are very fast and very short. Then you might think, well, I should give the highest priority to the signal that's going to be there the shortest time or something like that. The funny part is that when you made such a system, the result was that, if you had a computer that was responding to some signal that's coming in at a-- I'm talking about the days when computers were only working at a few kilohertz, few thousand operations a second. God, that's slow, a million times shorter than what you have in your pocket. And if you give priority to the signals that have to be reacted to very fast, then what happens if you type to those computers? It would never see them, because it's always-- I saw this happening once. And finally, somebody realized that you should give the highest priority to the inputs that come in least frequently, because there's always-- otherwise, if there's something coming in very frequently, you'll just always be responding to it. Any of you run into this? It took me a while to figure out why. Anyway, there are lots of kinds of problems. And the other day, I was complaining that we didn't have enough ways to do this. We had hundreds of words for emotions, and here's a couple of dozen. They're in chapter seven and eight actually most of these. So here's a bunch of words for describing ways to think, but they're not very technical. So you can talk about remorse, and sorrow, and blah, blah, blah. Hundreds and hundreds of words for feelings, and it's a lot of effort to find a dozen words for intellectual, for-- what should I call them? --problem solving processes. So it's curious to me that the great field called cognitive psychology has not focused in that direction. Anyway, here's about 20 or 30 of them. And you'll find them scattered through chapters seven and eight. Here's my favorite one, and I don't know of any proper name for it. But if you're trying to solve a problem, and you're stuck, and the example that comes to my mind is, if I'm trying to remember someone's name, I can tell when it's hopeless. And the reason is that for somehow or other, I know that there's a huge tree of choices. That's one way to represent what's going on, and I might know that-- I'm sure that name has a Z in it. So you search around and try everything you can. But of course, it doesn't have a Z, so the way to solve that problem is to give up. And then a couple of minutes later, the name occurs to you. And you have no idea how it happened and so forth. Anyway, the long story is that Papert, and I, and lots of really great students in the '60s and '70s spent a lot of time making little bottles of problem solvers that didn't work. And we discovered that you needed something else, and we had put that in. Other people would come and say, that's hopeless. You're putting in more things than you need. And my conclusion is that, wow, it's the opposite of physics. In physics, you're always trying to find-- what is it called? --Occam's razor. Never have more structure than you need, because what? Well, it'll waste your time, but my feeling was, never have less than you'll need. But you don't know how many you'll need. So what I did, I had four of these, and then I forced myself to put in two more. And people ask, what's the difference between self models and self-conscious processes? And I don't care. Well, what's the difference between self-conscious and reflective? I don't care. And the reason is that, wow, it's nice to have a box that isn't full yet. So if you find something that your previous theory-- going back to Brooks, he was so successful getting simple robots to work that he concluded that the things didn't need any internal representations at all. And for some mysterious reason, the Artificial Intelligence Society gave him their annual big prize for this very wrong idea, and it caused AI research to sort of half collapse in places, like Japan. He said, oh, rule based systems is all we need. Anybody want to defend him? The odd thing is, if you talk to Brooks, he's one of the best philosophers you'll ever meet. And he says, oh yes, of course, that's wrong, but it helps people do research and get things done. And as, I think, I mentioned the other day when the 3 Mile Island thing happened, there was no way to get into the reactor. That was 1980. And 30 years later when the-- how do you pronounce it? --Fukushima accident happened, there was no robot that could go in and open a door. I don't know who to blame for that. Maybe us. But my picture of the history is that the places that did research on robotics, there were quite a few places. And for example, Carnegie Mellon was very impressive in getting the Sony dogs to play soccer, and they're still at it. And I think I mentioned that Sony still has a stock of-- what's it called? AUDIENCE: AIBOs. PROFESSOR: Say it again. AUDIENCE: AIBOs. PROFESSOR: FIBO? AUDIENCE: AIBO, A-I-B-O. PROFESSOR: All right, AIBOs, but the trouble is they're always broken. There was a robot here called Cog that Brooks made, and it sometimes worked. But usually, it wasn't working, so only one student at that time could experiment with the robot. What was that wonderful project of trying to make a walking machine for four years in-- there was a project to make a robot walk. And there was only one of it, so first, only one student at a time can do research on it. And most of the time, something's broken, and you're fixing it. So you end up that you sort of get five or 10 hours a week on your laboratory physical robot. At the same time, Ed Friedkin had a student who tried to make a walking robot, and it was a stick figure on the screen. I forgot the student's name. But anyway, he simulated gravity and a few other things. And in a couple of weeks, he had a pretty good robot that could walk, and go around turns, and bank. And if you simulated an oily floor, it could slip and fall, which we considered the high point of the demo actually. So there we find-- anyway, I've sort of asked you to read my two books for this course. But those are not the only good texts about artificial intelligence. And if you want to dig deeper, it might be a good idea to go to the web and type in Aaron Sloman, S-L-O-M-A-N. And you'll get to his website, which is something like that. And Sloman is a sort of philosopher who can program. There are a handful of them in the world, and he has lots of interesting ideas that nobody's gotten to carry out. So I recommend. Who else is-- Pat, do you ever recommend anyone else? PAT: No. PROFESSOR: What? I'm trying to think. I mean, if you're looking for philosophers, Dan Dennett has a lot of ideas. But Sloman is the only person, I'd say, is a sort of real professional philosopher, who tries to program, at least, some of his ideas. And he has successful students, who have made larger systems work. So if you get tired of me, and you ought to, then go look at this guy, and see who he recommends. OK, who has a good question to ask? AUDIENCE: So Marty, I'm talking about how we have a lot of words for emotions. Why can we only have one word for cause? PROFESSOR: It's a mystery, but I spent most of the couple of days making this list bigger. But these aren't-- you know, these are things that you do when you're thinking. You make analogies. If you have multiple goals, you try to pick the most important one. Or in some cases, if you have several goals, maybe you should try to achieve the easiest one, and there's a chance that it will lead you into what to do about the harder ones. But a lot of people think mostly in England that logic is a good way to do reasoning, and that's completely wrong. Because in logic, first of all, you can't do analogies at all, except at a very high level. It takes four or five nested quantifiers to say, A is to B as C is to which of the following five. So I've never seen anyone do analogical thinking using formalogic, first order or higher order predicate calculus. What's logic good for? Its great after you've solved a problem. Because then you can formalize what you did and see if some of the things you did weren't unnecessary. In other words, after you've got the solution to a problem, what you've got by going through a big search, you finally found a path from A to Z. And now, you can see if the assumptions that you had to make to bridge all these various little gaps were all essential or not. Yes? AUDIENCE: What kind of examples would you say that logic came to analogies? Like, well, water is [INAUDIBLE] containment, like why [INAUDIBLE]? PROFESSOR: Well, because you have to make a list of hypotheses, and then let me see if I can find Evans. The trouble is-- darn, Evans name is in a picture. And Word can't look inside its pictures. Can PowerPoint find words in its illustrations? Why don't I use PowerPoint? Because I've discovered that PowerPoint can't read pictures made by other programs in the Microsoft Word suite. The drawing program in Word is pretty good, and then there's an operation in Word, which will make a PowerPoint out of what you drew. And it's 25 years since Microsoft hasn't fixed the fatal errors that it makes when you do that. In other words, I don't think that the PowerPoint and Word people communicate. And they both make a lot of money, so that might be that might be the reason. Where was I? AUDIENCE: Why logic can't do [INAUDIBLE].. PROFESSOR: Well, you can do anything in logic, if you try hard enough, but A is to B as C is to X is a four part relation. And you'd need a whole pile of quantifiers, and how would you know what to do next? Yes? AUDIENCE: Talk a bit about the situation in which we are able to perform some sort of action, like really fluently and really well, but we cannot describe what we're doing. And the example I give is, say, I'm an expert African drummer from Africa, and I can make these really complicated rhythms. But if you asked me, what did you just do? I had no idea how to describe it. And in that case, do you think the person is capable of-- I guess, do you think the person-- we can say that the person understands this, even though they cannot explain it. PROFESSOR: Well, if you take an extreme form of that, you can't explain why you used any particular word for anything. There's no reason. It's remarkable how well people can do in everyday life to tell people how they got an idea. But when you look at it, it doesn't say how you would program a machine to do it. So there's something very peculiar about the idea that-- it goes back to this idea that people have free will and so forth. Suppose, I say, look at this and say, this has a constriction at this point. Why did I say constriction? How do you get any-- how do you decide what word to use for something? You have no idea, so it's a very general question. It's not clear that the different parts of the frontal lobes, which might have something to do with making plans and analyzing certain kinds of situations, have any access to what happens in the Broca or-- what's the speech production area? Broca, and I'm trying to find the name of the other one. It's connected by a cable that's about a quarter inch thick. AUDIENCE: Is that the Wernicke? PROFESSOR: Wernicke, yeah. We have no idea how those work as far as I've never seen any publication in neuroscience that says, here's a theory of what happens in Wernicke's area. Have any of you ever seen one? What do those people think about it, what they'll tell you about? I was reading something, which said, it's going to be very hard to understand these areas. Because each neuron is connected to 100,000 little fibers. Well, some of them are. And I bet they don't do much, except sort of set the bias for some large collection of other neurons. But if you ask somebody, how did you think of such a word? They will tell you some story or anecdote. But they won't be able to describe some sort of procedure, which is, say, in terms of a language, like lisp. And say, I can't this and that, and I took the clutter of this in the car of that. And I put them in this register, and then I swapped that with-- You don't see theories of how the mind works in psychology today. The only parts are they know a little bit about some aspects of vision, because you can track the paths of images from the retina to what's called the primary visual cortex. And people have been able to figure out what some of those cortical columns do. And if you go back to an animal, like the frog, then researchers, like [? Bitsey ?] and others, have figured out how the equivalent of the cerebellum in the frog. They've got almost the whole circuit of how when the frog sees a fly, it manages to turn its head that way, and stick its tongue out, and catch it. But in the case of a human, I've never seen any theory of how any person thinks of anything. There's artificial intelligence, which has high level theories of semantic representations. And there's neuroscience, which has good theories of some parts of locomotion and some parts of sensory systems. And to this day, there's nothing much in between. David, here, has decided to go from one to the other, and a former student of mine Bob Hearn has done a little bit on both. And I bet there are 20 or 30 people around the country, who are trying to bridge the gap between symbolic artificial intelligence and mappings of the nervous system. But it's very rare, and I don't know who you could ask to get support to work on a problem like that for five years. Yeah? AUDIENCE: So presumably to build a human life for artificial intelligence, we need to perfectly model our own intelligence, which means that we are the system. We ourself are the system that we're trying the understand. PROFESSOR: Well, it doesn't have to be exactly. I mean, people are different, and the typical person looks like they have 400 different brain centers doing slightly different things or very different things. And we have these examples. In many cases, if you lose a lot of your brain, you're very badly damaged. And in other cases, you recover and become just about as smart as you were. There's probably a few cases, where you got rid of something that was holding you back, but it's hard to prove that. We don't need a theory of how people work yet, and the nice thing about AI is that we could eventually get models, which are pretty good at solving what people call everyday common sense problems. And probably in many respects, they're not the way the human mind works, but it doesn't matter. But once you've got-- if I had a program, which was pretty good at understanding why you can pull with a string but not push, then there's a fair chance you could say, well, that seems to resemble what people do. I'll do this few psychological experiments and see what's wrong with that theory and how to change it. So at some point, there'll be people making AI systems, comparing them to particular people, and trying to make them fit. The trouble is nowadays, it takes a few months, if you get a really good new idea, to program it. I think there's something wrong with programming languages, and what we need is a-- we need a programming language, where the instructions describe goals and then subgoals. And then finally, you might say, well, let's represent this concept by a number or a semantic network of some sort. Yes? AUDIENCE: That idea of having a programming language where you define goals. PROFESSOR: Is there a goal oriented language? AUDIENCE: So there is kind of one. If you think about it, if you squint hard enough at something, like SQL, where you tell it here, I want to find the top 10 people in my database with this high value. And then you don't worry about how the system goes about doing that. In a sense, that's redefining your goal [INAUDIBLE].. But you got to switch a little bit. PROFESSOR: What's it called? AUDIENCE: SQL. PROFESSOR: SQL. AUDIENCE: [INAUDIBLE] database and curates it [INAUDIBLE].. PROFESSOR: Oh, right. Yes, I guess database query languages are on the track, but Wolfram Alpha seems to be better than I thought. Well, he was running it, and Steve Wolfram was giving this demo at a meeting we were at on Monday. And he'd say, well, maybe I'll just say this, and it always worked. So maybe either the language is better than I thought, or Wolfram is better than I thought or something. Remarkable guy. Yes? AUDIENCE: So I liked this example of you only remember a name after you've given up consciously trying to think about it. Do you think this is a matter of us being able to set up back our processes, and then there's either some delay. Like we give off- there's some delay in the process, where we don't have the ability to correctly terminate processes. Do you think this only works for memory, or could it work for other things? Like could I start an arithmetic operation, and then give up, and then it'll come to me later? PROFESSOR: Well, there's a lot of nice questions about things like that. How many processes can you run at once in your brain? And I was having a sort of argument the other day about music, and I was wondering if-- I see a big difference between Bach and the composers who do counterpoint. Counterpoint, you usually have several versions of a very similar idea. Maybe there's one theme, and you have it playing. And then another voice comes in. And it has that theme upside down, or a variation of it, or in some cases, exactly the same. And then it's called a canon. So the tour de force in classical music is when you have two, or three, or four versions of the same thought going on at once at different times. And my feeling was that in popular music, or if you take a typical band, then there might be four people. And they're doing different things at the same time. Usually, not the same musical tunes. But there's a rhythm, and there's a tympani. And there's various instruments doing different things, but you don't have several doing the same thing. I might be wrong, and somebody said, well, some popular music has a lot of counterpoint. I'm just not familiar with it. But I think that's-- if you're trying to solve a hard problem, it's fairly easy to look at the problem in several different ways. But what's hard is to look at it in several almost the same ways that are slightly different. Because probably, if you believe that the brain is made of agents, or resources, or whatever, you probably don't have duplicate copies of ones that do important things. Because that would take up too much real estate. Anyway, I might be completely wrong about jazz. Somebody, maybe they have just as complicated overlapping things as Bach and the contrapuntal composers did. Yeah? AUDIENCE: What is the ultimate goal of artificial intelligence? Is it some sort of application, or is it more philosophical? PROFESSOR: Oh, everyone has different goals or ones. AUDIENCE: In your opinion. PROFESSOR: I think we're going to need it, because the disaster that we're working our way toward is that people are going to live longer. And they'll become slightly less able, so we'll have billions of 200-year-old people who can barely get around. And there won't be enough people to import from underdeveloped countries to, or they won't be able to afford them. So we're going to have to have machines that take care of us. Of course, that's just a transient. Because at some point, then you'll download your brain into a machine and fix everything that's wrong. So we'll need robots for a few years or a few decades. And then we'll be them, and we won't need them anymore. But it's an important problem. What's going to happen in the next 100 years? You're going to have 20 billion 200-year-olds and nobody to take care of them, unless we get AI. Nobody seems particularly sad about that. How long-- oh, another anecdote. I was once giving a lecture and talking about people living a long time. And nobody in the audience seemed interested, and I'd say, well, suppose you could live 400 years. And most of the people-- then I asked, what was the trouble? They said, wouldn't it be boring? So then I tried it, again, in a couple of other lectures. And if you ask a bunch of scientists, how would you like to live 400 hundreds years? Everyone says, yay, and you ask them why. And they say, well, I'm working on a problem that I might not have time to solve. But if I had 400 years, I bet I could get somewhere on it, and the other people don't have any goal. That's my cold blooded view of the typical non-scientist. There's nothing for them to do in the long run. Who can think of what should people do? What's your goal? How many of you want to live 400 years? Wow, there must be scientists here. Try it on some crowd and let me know what happens. Are people really afraid. Yeah? AUDIENCE: I think the differentiating factor is whether or not your 400 years is just going to be the repetition of 100 years experience, or if it'll start to like take off, then you'll start to learn better. You'll progress. PROFESSOR: Right. I've seen 30 issues of the Big Bang, and I don't look forward to the next one anymore. Because they're getting to be all the same. Although, it's the only thing on TV that has scientists. Seriously, I hardly read anything, except journals and science fiction. Yeah? AUDIENCE: What's the motivation to have robots take care of as we age as opposed to enhancing our own cognitive abilities, or our prosthetic body, or something more societiable? What's the joy of living, if you can't do anything, and somebody takes care of you? PROFESSOR: I can't think of any advantage, except that medicine isn't getting-- you know, the age of unhandicapped people went up at one year every four since the late 1940s. So the lifespan is-- so that's 60 years. So people are living 15 years longer on the average than they did when I was born or even more than that. But it's leveled off lately. Now I suspected you only have to fix a dozen genes, or who knows? Nobody really has a good estimate, but you can probably double the lifespan, if you could fix. Nobody knows, but maybe there's just a dozen processes that would fix a lot of things. And then you could live longer without deteriorating, and lots of people might get bored. But they'll self select. I don't know. What's your answer? AUDIENCE: I feel that AI is more-- the goal is not to help take care of people, but to complement what we already have to entertain us. PROFESSOR: You could also look at them as our descendants. And we will have them replace us and just as a lot of people consider their children to be the next generation of them. And I know a lot of people who don't, so it's not a universal. What's the point of anything? I don't want to get in-- we might be the only intelligent life in the universe. And in that case, it's very important that we solve all our problems and make sure that something intelligent persists. I think Carl Sagan had some argument of that sort. If you were sure that there were lots of others, then it wouldn't seem so important. Who is the new Carl Sagan? Is there any? Is there a public scientist? AUDIENCE: [INAUDIBLE]. PROFESSOR: Who? AUDIENCE: He's the guy who is on Nova all the time. PROFESSOR: Oh, Tyson? AUDIENCE: Bryan Green. PROFESSOR: Bryan Green, he's very good. Tyson is the astrophysicist. Bryan Green is a great actor. He's quite impressive. Yeah? AUDIENCE: When would you say a routine has sense of self? Like when you think there's something that like a self inside us, partly, because there's some processes [INAUDIBLE]. But when would you say [INAUDIBLE]?? PROFESSOR: Well, I think that's a funny question. Because if we're programming it, we can make sure that the machine has a very good abstract, but correct model of how it works, which people don't. So people have a sense of self, but it's only a sense of self. And it's just plain wrong in almost every respect. So it's a really funny question. Because when you make a machine that really has a good useful representation of what it is and how it works, it might be quite different, have different attitudes than a person does. Like you might not consider itself very valuable and say, oh, I could make something that's even better than me and jump into that. So it wouldn't have the-- it might not have any self protective reaction. Because if you could improve yourself, then you don't want not to. Whereas we're in a state, where there's nothing much we could do, except try to keep living, and we don't have any alternative. It's a stupid thing to say. I can't imagine getting tired of living, but lots of people do. Yeah? AUDIENCE: What do you think about creative thinking as a way of thinking? And where does this thinking completely come from or anything that comes after? PROFESSOR: I had a little section about that somewhere that I wrote, which was the difference between artists and scientists or engineers. And engineers have a very nice situation, because they know what they want. Because somebody's ordered them to make a-- in the last month, three times, I've walked away from my computer. How many of you have a Mac with the magnetic thing? And three times, I pulled it by tripping on this, and it fell to the floor and didn't break. And I've had Macs for 20 odd years or since 1980-- when did they start? 30 years, and they have the regular jack power supply in the old days. And I don't remember. And usually, when you pull the cord, it comes out. Here is this cord that Steve Jobs and everybody designed very carefully, so that when you pull it, nothing bad would happen. But it does. How do you account for that? AUDIENCE: It used to be better with the old plugs were perpendicular to the plus, and now it's kind of-- PROFESSOR: Well, it's quite a wide angle. AUDIENCE: Right, so it works at a certain angle. The cable now instead of naturally lining that area actually naturally lies in the area where it doesn't work. PROFESSOR: Well, what it needs is a little ramp, so that it would slide out. I mean, it would only take a minute to file it down, so that it would slide out. AUDIENCE: Right. PROFESSOR: But they didn't. I forget why I mentioned that, but-- AUDIENCE: [INAUDIBLE]. PROFESSOR: Right, so what's the term doing an artist and an engineer? Well, when you do a painting, it seems to me, if you're already good at painting, then 9/10ths of the problem is, what should I paint? So you can think of an artist as 10% skill and 90% trying to figure out what the problem is to solve. Whereas for the engineer, somebody's told him what to do, make a better cabled connector. So he's going to spend 90% of his time actually solving the problem and only 10% of the time trying to decide what problem to solve. So I don't see any difference between artists and engineers, except that the artist has more problems to solve than he could possibly solve and usually ends up by picking a really dumb one, like let's have a Saint and three angels. Where will I put the third angel? That's the engineering part. It's just improvising, so to me, the media lab makes sense. The artists or semi artists and the scientists are doing almost the same thing. And if you look at the more arty people, they're a little more concerned with human social relations and this and that. And others are more concerned with very technical, specific aspects of signal processing or semantic representations and so on. So I don't see much difference between the arts and the sciences. And then, of course, the great moments are when you run into people, like Leonardo and Michelangelo, who get some idea that requires a great new technical innovation that nobody has ever done. And it's hard to separate them. I think there's some place, where Leonardo realizes that the lens in the eye would mean that the image is upside down on the retina, and he couldn't stand that. So there's a diagram he has, where the cornea is curved enough to invert the image, and then the lens inverts it back again, which is contrary to fact. But he has a sketch showing that he was worried about, if the image were upside down on the retina, wouldn't things look upside down? AUDIENCE: [INAUDIBLE] question. Did you ever heard about [INAUDIBLE] temporal memory, like-- PROFESSOR: Temporal? AUDIENCE: Temporal memory, like there is a system that [INAUDIBLE] at the end of this each year on it. And there's some research. They have a paper on it. PROFESSOR: Well, I'm not sure what-- AUDIENCE: This is Jeff Hawkins project? I don't know. Yeah, it's Jeff Hawkins. PROFESSOR: I haven't heard. About 10 years ago, he said-- Hawkins? AUDIENCE: Yeah, Hawkins. PROFESSOR: Yeah, well, he was talking about 10 years ago, how great it was, and I haven't heard a word of any progress. Is there some? Has anybody heard-- there's a couple of books about it. But I've never seen any claim of that it works. They wrote a ferocious review of the Society of Mind, which came out in 1986. And the Hawkins group existed then and had this talk about a hierarchical memory system. AUDIENCE: [INAUDIBLE]. PROFESSOR: As far as I can tell, it's all a bluff. Nothing happened. I've never seen a report that they have a machine, which solved a problem. Let me know if you find one, because-- oh well. Hawkins got really mad at me for pointing this out, but I was really mad at him for having four of his assistants write a bad book review of my book. So I hope we were even. If anybody can find out whether-- I forget what it's called. Do remember its name? AUDIENCE: [INAUDIBLE]. PROFESSOR: Well, let's find out if it can do anything yet. Hawkins is wealthy enough to support it for a long time, so it should be good by now. Yes? AUDIENCE: Do you think that's going to solve the problem? People first start out with some sort of classification in their of the kind of problem it is, or is that not necessary? PROFESSOR: Yes, well, there's this huge book called Human Problem Solving, which I don't know how many of you know the names of Newell and Simon. Originally, it was Newell, Shaw, and Simon. Believe it or not, in the late 1950s, they did some of the first really productive AI research. And then, I think, in 1970, so that's sort of after 12 years of discovering interesting things. Their main discovery was the gadget that they called GPS, which is not global positioning satellite, but general problem solver. And you can look it up in the index of my book, and there's a sort of one or two page description. But if you ever get some spare time, search the web for their early paper by Newell and Simon on how GPS worked. Because it's really fascinating. What it did is it looked at a problem, and found some features of it, and then looked up in a table saying that, if there's this difference between what you have and what you want, use such and such a method. So it was sort of what I called it. I renamed it a difference engine as a sort of joke, because the first computer in history was the one called the difference engine. But it was for predicting tides and things. Anyway, they did some beautiful work. And there's this big book, which I think is about 1970, called Human Problem Solving. And what they did is got some people to solve problems, and they trained the people to talk while they're solving the problem. So some of them were a little cryptograms, like if each letter stands for a digit, I've forgotten it. Pat, do you remember the name, one of those problems? John plus Joe-- John plus Jane equals Robert or something. I'm sure that has no solution, but those are called cryptarithmetic. So they had dozens or hundreds of people who would be trained to talk aloud while they're solving little puzzles like that. And then what they did was look at exactly what the people said and how long they took. And in some cases, where they move their eyes, they had an eye tracking machine. And then they wrote programs that showed how this guy solved a couple of these cryptarithmetic problems. Then they ran the program on a new one. And in some rare cases, it actually solved the other problem. So this is a book, which looks at human behavior and makes a theory of what it's doing. And the output is a rule based system, so it's not a very exciting theory. But there's never been anything like it in-- you know, it was like Pavlov discovering conditioned reflexes for rats or dogs. And Newell and Simon are discovering some rather higher level almost a Rodney Brooks like system for how humans solve some problems that most people find pretty hard. Anyway, what there hasn't been is much-- I don't know of any follow-up. They spent years perfecting those experiments, and writing about-- [AUDIO OUT] --results. And anybody know anything like that? What psychologists are trying to make real models of real people solving [INAUDIBLE] problems. [INAUDIBLE] AUDIENCE: Your mic [? is off. ?] PROFESSOR: It has a green light. AUDIENCE: It has a green light, but the switch was up. PROFESSOR: Boo. Oh, [INAUDIBLE]. AUDIENCE: We're all set now. PROFESSOR: [CHUCKLES] Yes. AUDIENCE: Did that [INAUDIBLE] study try to see when a person gave up on a particular problem-solving method [INAUDIBLE] how they switched-- in other words, when they switched to [INAUDIBLE]?? PROFESSOR: It has inexplicable points at which the person suddenly gives up on that representation. And he says, oh, well, I guess R must be 3. Did I erase? Well. Yes, it's got episodes, and they can't account for the-- you have these little jerks in the script where the model changes. And-- [COUGHS] sorry. And they announced those to be mysteries, and say, here's a place where the person has decided the strategy isn't working and starts over, or is changing something. The amazing part is that their model sometimes fits what the person says. For 50 or even 100 steps, the guy's saying, oh, I think z must be 2 and p must be 7. And that means p plus z is 9, and I wonder what's 9. And so their model fits for very long strings, maybe two minutes of the person mumbling to themselves. And then it breaks, and then there's another sequence. So Newell actually spent more than a year after doing it verbally, at tracking the person's eye motions, and trying to correlate the person's eye motions with what the person was talking about. And guess what? None. AUDIENCE: [CHUCKLING] PROFESSOR: It was almost as though you look at something, and then to think about it, you look away. Newell was quite distressed, because he spent about a year crawling over this data trying to figure out what kinds of mental events caused the eyes to change what they were looking at. But when the problem got hard, you would look at a blank part of the thing more often than the place where the problem turned up. So conclusion, that didn't work. When I was a very young student in college, I had a friend named Marcus Singer, who was trying to figure out how the nerve in the forelimb of a frog worked. And so he was operating on tadpoles. And he spent about six weeks moving this sciatic nerve from the leg up to the arm of this tadpole. And then they all got some fungus and died. So I said, what are you going to do? And he said, well, I guess I'll have to do it again. And I switched from biology to mathematics. AUDIENCE: [CHUCKLING] PROFESSOR: But in fact, he discovered the growth hormone that he thought came from the nerve and made the-- if you cut off the limb bud of a tadpole, it'll grow another one and grow a whole-- it was a newt, I'm sorry. It's salamander. It'll grow a new hand. If you wait till it's got a substantial hand, it won't grow a new one. But he discovered the hormone that makes it do that. Yeah. AUDIENCE: One of the questions from the homework that relates to problem-solving. A common theme is having multiple ways to react to the same problem. But how do we choose which options to add as possible reactions to the same problem? PROFESSOR: Oh. So we have a whole lot of if-thens, and we have to choose which if. I don't think I have a good theory of that. Yes, if you have a huge rule-based system and they're-- what does Randy Davis do? What if you have a rule-based system, and a whole lot of ifs fit the condition? Do you just take the one that's most often worked? Or if nothing seems to be working, do you-- you certainly don't want to keep trying the same one. I think I mentioned Doug [? Lenat's ?] rule. Some people will assign probabilities to things, to behaviors, and then pick the way to react in proportional to the probability that that thing has worked in the past. And Doug [? Lenat ?] thought of doing that, but instead, he just put the things in a list. And whenever a hypothesis worked better than another one, he would raise it, push it toward the front of the list. And then whenever there was a choice, he would pick-- of all the rules that fit, he would pick the one at the top of the list. And if that didn't work, it would get demoted. So that's when I became an anti-probability person. That is, if just sorting the things on a list worked pretty well, our probability's going to do much better. No, because if you do probability matching, you're worse off than-- than what? AUDIENCE: [INAUDIBLE] PROFESSOR: Ray Solomonoff discovered that if you have a set of probabilities that something will work, and you have no memory, so that each time you come and try the-- I think I mentioned that the other day, but it's worth emphasizing, because nobody in the world seems to know it. Suppose you have a list of things, p equals this, or that, or that. In other words, suppose there's 100 boxes here, and one of them has a gold brick in it, and the others don't. And so for each box, suppose the probability is 0.9 that this one has the gold brick, and this one as 0.01. And this has 0.01. Let's see, how many of them-- so there's 10 of these. That makes-- Now, what should you do? Suppose you're allowed to keep choosing a box, and you want to get your gold brick as soon as possible. What's the smart thing to do? Should you-- but you have no memory. Maybe the gold brick is decreasing in value, I don't care. But so should you keep trying 0.9 if you have no memory? Of course not. Because if you don't get it the first time, you'll never get it. Whereas if you tried them at random each time, then you'd have 0.9 chance of getting it, so in two trials, you'd have-- what am I saying? In 100 trials, you're pretty sure to get it, but in [? e-hundred ?] trials, almost certain. So if you don't have any memory, then probability matching is not a good idea. Certainly, picking the highest probability is not a good idea, because if you don't get it the first trial, you'll never get it. If you keep using the probabilities at-- what am I saying? Anyway, what do you think is the best thing to do? It's to take the square roots of those probabilities, and then divide them by the sum of the square roots so it adds up to 1. So a lot of psychologists design experiments until they get the [? rat ?] to match the probability. And then they publish it. Sort of like the-- but if the animal is optimal and doesn't have much memory, then it shouldn't match the probability of the unknown. It should-- end of story. Every now and then, I search every few years to see if anybody has noticed this thing, which-- and I've never found it on the web. Yeah. AUDIENCE: So earlier in the class, you mentioned that the rule-based methods didn't work, and that several other methods were tried between the [INAUDIBLE] [? immunities. ?] Could you go into a bit about what these other methods were that have been tried? PROFESSOR: I don't mean to say they don't work. Rule-based methods are great for some kinds of problems. So most systems make money, and if you're trying to make hotel reservations and things, this business of rule-based systems, it has a nice history. A couple of AI researchers, really, notably Ed Feigenbaum, who was a student of Newell and Simon, started a company for making rule-based systems. And company did pretty well for a while, and they maintained that only an expert in artificial intelligence could be really good at making rule-based systems. And so they had a lot of customers, and quite a bit of success for a year or two. And then some people at Arthur D. Little said, oh, we can do that. And they made some systems that worked fine. And the market disappeared, because it turned out that you didn't have to be good at anything in particular to make rule-based systems work. But for doing harder problems, like translating from one language to another, you really needed to have more structure, and you couldn't just take the probabilities of words being in a sentence, but you had to look for diagrams and trigrams, and have some grammar theory, and so forth. But generally, if you have a ordinary data-processing problem, try a rule-based system first, because if you understand what's going on, it's a good chance you'll get things to work. I'm sure that's what the Hawkins thing started out as. I don't have any questions. AUDIENCE: Could I ask another one for the homeworks? PROFESSOR: Sure. AUDIENCE: OK. Computers and machines can use relatively few electronic components to run a batch of different type of thought operations. All that changes is data over which the operation runs. In the [? critics ?] [? lecter ?] model, are resources different bundles of data or different physical parts of the brain? PROFESSOR: Which model? AUDIENCE: The [? critics ?] [? lecter ?] model. PROFESSOR: Oh. Actually, I've never seen a large-scale theory of how the brain connects its-- there doesn't seem to be a global model anywhere. Anybody read any neuroscience books lately? AUDIENCE: [CHUCKLING] PROFESSOR: I mean, I just don't know of any big diagrams. Here's this wonderful behavioral diagram. So how many of you have run across the word "ethology?" Just a few. There's a branch of the psychology of animals, which is-- AUDIENCE: [CHUCKLING] PROFESSOR: Thanks. Which is called ethology. And it's the study of instinctive behavior. And the most famous people in that field-- who? Well, Niko Tinbergen and Konrad Lorenz are the most famous. I've just lost the name of the guy around the 1900 who wrote a lot about the behavior of ants. Anybody ring a bell? So he was the first ethologist. And these people don't study learning because it's hard to-- I don't know why. So they're studying instinctive behavior, which is, what are the things that all fish do of a certain species? And you get these big diagrams. This is from a little book which you really should read called The Study of Instinct. And it's a beautiful book. And if that's not enough, then there's a two-volume similar book by Konrad Lorenz, who was a Austrian researcher. They did a lot of stuff together, these two people. And it's full of diagrams showing the main behaviors that they were able to observe of various low-cost animals. I think I mentioned that I had some fish, and I watched the fish tanks, what they were doing for a very long time, and came to no conclusions at all. And when I finally read Tinbergen and Lorenz, I realized [? that ?] just had never occurred to me to guess what to look for. My favorite one was that whenever a fire engine went by, Lorenz's sticklebacks, the male sticklebags would go crazy and look for a female. Because when the female's in heat, or whatever it's called-- estrus-- the lower abdomen turns red. I think fire engines have turned yellow recently, so I don't know what the sticklebacks do about that. So if you're interested in AI, you really should look at at least one of these people, because that's the first appearance of rule-based systems in great detail in psychology. There weren't any computers yet. There must be 20 questions left. Yeah. AUDIENCE: While we're in the topic of ethology, so I know that early on, people were kind of-- they were careful not to apply ethology to humans until about '60s EO Wilson with sociobiology. So I was wondering about your opinion on that, and maybe you have anecdotes on [INAUDIBLE] pretty controversial around this area especially. PROFESSOR: Oh, I don't know. I sort of grew up with Ed Wilson because we had the same fellowship at Harvard for three years. But he was almost never there, because he was out in the jungle in some little telephone booth watching the birds, or bees, or-- he also had a 26-year-old ant. Aunt, not ant. Ant. A-N-T. I'm not sure what the controversy would have been, but of course, there would be humanists who would say people aren't animals, but. But then what the devil are they? Why aren't they better than the-- [CHUCKLES] You've got to read this. It's a fairly short book. And you'll never see an animal as the same again, because I swear, you start to notice all these little things. You're probably wrong, but you start picking up little pieces of behavior, and trying to figure out what part of the instinct system is it. Lorenz was particularly-- I think in chapter 2 of the emotion machine, I have some quotes from these guys. And Lorenz was particularly interested in how animals got attached to their parents-- that is, for those animals that do get attached to their parents. Like alligator babies live in the alligator's mouth for quite a while. It's a good, safe place. And Lorenz would catch birds just when they're hatching. And within the first day or so, some baby birds get attached to whatever large moving object is nearby. And that was often Konrad Lorenz, rather than the bird's mother, who is supposed to be sitting on the egg when it hatches, and the bird gets attached to the mother. Most birds do, because they have to stay around and get fed. So it is said that wherever Lorenz went in Vienna, there were some ducks or whatever-- birds that had gotten imprinted on him would come out of the sky and land on his shoulder, and no one else. And he has various theories of how they recognize him. But you could do that too. Anyway, that was quite a field, this thing called ethology. And between 1920 and 1950-- 1930, I guess, 1950-- there were lots of people studying the behavior of animals. And Ed Wilson is probably the most well-known successor to Lorenz and Tinbergen. And I think he just wrote a book. Has anybody seen it? He has a huge book called Sociobiology, which is too heavy to read. I've run out of things. Yes. AUDIENCE: Still thinking about the question [INAUDIBLE].. [INAUDIBLE],, The Society of Mind, ideas in that book, [INAUDIBLE] the machinery from it. What would the initial state of the machinery be [INAUDIBLE] start something? Is that dictated by the goals given to it? And by state, I mean the different agents, the resources they have access to. What would that initial state look like? PROFESSOR: He's asking if you made a model of the program to Society of Mind architecture, what would you put in it to start with? I never thought about that. Great question. I guess it depends whether you wanted to be a person, or a marmoset, or chicken, or something. Are there some animals that don't learn anything? Must be. What do the ones that Sydney Brenner studied? AUDIENCE: C. elegans? They [? learned ?] very simple associations. PROFESSOR: The little worms? AUDIENCE: Mm-hmm. PROFESSOR: There was a rumor that if you fed them RNA-- was it them or was it some slightly higher animal? AUDIENCE: It was worms. PROFESSOR: What? AUDIENCE: RNA interference. Is that what you're talking about? Yeah. PROFESSOR: There was one that if you taught a worm to turn left when there was a bright light, or right, and put some of its RNA into another worm, that worm would copy that reaction even though it hadn't been trained. And this was-- AUDIENCE: That wasn't worms. That was slugs. PROFESSOR: Slugs. AUDIENCE: I think it was [INAUDIBLE] replace the [INAUDIBLE] or something. AUDIENCE: Some little snail-like thing. And nobody was ever able to replicate it. So that rumor spread around the world quite happily, and there was a great science fiction story-- I'm trying to remember-- in which somebody got to eat some alien's RNA and got magical powers. AUDIENCE: [CHUCKLING] PROFESSOR: I think it's Larry Niven, who is wonderful at taking little scientific ideas and making a novel out of them. And his wife Marilyn was a undergraduate here. So she introduced me to Larry Niven, and-- I once gave a lecture and he wrote it up. It was one of the big thrills, because Niven is one of my heroes. Imagine writing a book with a good idea in every paragraph. AUDIENCE: [CHUCKLING] Vernor Vinge, and Larry Niven, and Frederik Pohl seem to be able to do that. Or at least on every page. I don't know about every paragraph. Yeah. AUDIENCE: To follow up on that question, it seems to me that you almost were saying that if this machinery exists, the difference between these sort of animals would be in [INAUDIBLE]. And I think on [INAUDIBLE],, we can create like a chicken or a human [INAUDIBLE].. PROFESSOR: Well, no. I don't think that most animals have scripts. Some might, but I'd say that-- I don't know where most animals are, but I sort of make these six levels, and I'd say that none of the animals have this top self-reflective layer except, for all we know, dolphins, and chimpanzees, and whatever. It would be nice to know more about octopuses, because they do so much wonderful things with their eight legs. How does it manage? Have you seen pictures of an octopus picking up a shell, and walking to some quiet place, and it's got-- there's some movies of this on the web. And then it drops the shell and climbs under it and disappears. It's hard to imagine programming a robot to do that. Yeah. AUDIENCE: So I've noticed, both in your books and in lecture, a lot of your models and diagrams seem to have very hierarchical structure to them. But as you [INAUDIBLE] in your book and other places, passing between [INAUDIBLE] feedback and self-reference are all very important [INAUDIBLE].. So I'm curious if you can discuss some of the uses of these very hierarchical models, why you represented so many things that way instead of [INAUDIBLE] theorem. PROFESSOR: Well, it's probably very hard to debug things that aren't. So we need a meta theory. One thing is that, for example, it looks like that all neurons are almost the same. Now, there's lots of difference in geometric features of them, but they all use the same one or two transmitters, and every now and then, you run across people saying, oh, neurons are incredibly complicated. They have 100,000 connections. You can find it if you just look up "neuron" on the web and get these essays explaining that nobody will ever understand them, because typically, a neuron is connected to 100,000 others, and blah, blah, blah. So it must be something inside the neuron that figures out all this stuff. As far as I can see, it looks out almost the opposite. Namely, probably the neuron hasn't changed for half a billion years very much, except in superficial ways in which it grows. Because if you changed any of the genes controlling its metabolism or the way it propagates impulses, then the animal would die before it was born. And so you can't make-- that's why the embryology of all mammals is almost identical. You can't make a change at that level after the first-- before the-- you can't make changes before the first generations of cell divisions, or everything would be clobbered. The architecture would be all screwed up. So I suspect that the people who say, well, maybe the important memories of a neuron are inside it, because there's so many fibers and things. I bet it's sort of like saying the important memory in a computer is in the arsenic and phosphorus atoms of the semiconductor. So I think things have to be hierarchical in evolution, because if you're building later stuff on earlier stuff, then it's very hard to make any changes in the earlier stuff. So as far as I know, the neurons in sea anemones are almost identical to the neurons in mammals, except for the later stages of growth, and the way the fibers ramify, and-- who knows, but there are many people who want to find the secret of the brain in what's inside the neurons rather than outside. It'd be nice to get a textbook on neurology from 50 years in the future, see how much of that stuff mattered. Where's our time machines? Did you have-- AUDIENCE: Yeah. Most systems have a state that they prefer to be in, like a state that they're most comfortable. Do you think the mind has such a state, or would it tend to certain places or something? PROFESSOR: That's an interesting. I don't-- how does that apply to living things? I mean, this battle would rather be here than here, but I'm not sure what you mean. AUDIENCE: Well, so apparently, in Professor Tenenbaum's class, he shows this example of a number game. They'll give you a sequence of numbers, and he'll ask you to find a pattern in it. So for example, if you had a pattern like 10, 40, 50, and 55, he asked the class to come up with different things that could be described in the sequence. And between the choice of, oh, this sequence is a sequence of the multiples of 5 versus a sequence of the matter of 10 or multiples of 11, he says something like-- he phrases it like, the multiples of 5 would have a higher [INAUDIBLE] probability. So that got me thinking, why would that be-- would our minds have a preference for having as few categories as possible in trying to view the world around us, trying to categorize things in as few things as possible is what got me thinking about it. PROFESSOR: Sounds very strange to me, but certainly, if you're going to generate hypotheses, you have to have-- the way you do it depends on what this-- what does this problem remind you of? So I don't see how you could make a general-- if you look at the history of psychology, there are so many efforts to find three laws of motion like Newton's. Is he trying to do that? I mean, here you're talking about people with language, and high-level semantics, and-- let's ask him what he meant. AUDIENCE: Professor [INAUDIBLE]. PROFESSOR: Yeah. AUDIENCE: This is more of a social question, but there's always this debate about how if AI gets to the point where it can take care of humans, will it ever destroy humanity? And do you think that's something that we should fear? And if so, is there some way we can prevent it? PROFESSOR: If you judge by the recent-- by what's happened in AI since 1980, it's hard to imagine anything to fear. But-- AUDIENCE: [CHUCKLING] PROFESSOR: But-- funny you should mention that. I'm just trying to organize a conference sometime next year about disasters. And there's a nice book about disasters by-- what's his name? The Astronomer Royal. What? AUDIENCE: Martin Rees? PROFESSOR: Martin Rees. So he has a nice book, which I just ordered from Amazon, and it came the next day. And it has about 10 disasters, like a big meteor coming and hitting the Earth. I forget the other 10, but I have it in here somewhere. So I generated another list of 10 to go with it. And so there are lots of bad things that could happen. But I think right now, that's not on the top of the list of disasters. Eventually, some hacker ought to be able to stop the net from working because it's not very secure. And while you're at it, you could probably knock out all of the navigation satellites and maybe set off a few nuclear reactors. But I don't think AI is the principal thing to worry about, but it should very suddenly get to be a problem. And there are lots of good science fiction stories. My favorite is the Colossus series by DF Jones. Anybody know-- there was a movie called The Forbin Project, and it's about somebody who builds an AI, and it's trained to do some learning. And it's also the early days of the web, and it starts talking to another computer in Russia. And suddenly, it gets faster and faster, and takes over all the computers in the world, and gets control of all the missiles, because they're linked to the network. And it says, I will destroy all the cities in the world unless you clear off some island and start building the following machine. I think it's Sardinia or someplace. So they get bulldozers. And it starts building another machine, which it calls Colossus 2. And they ask, what's it going to do? And Colossus says, well, you see, I have detected that there's a really bad AI out in space, and it's coming this way, and I have to make myself smarter than it really quick. Anyway, see if you can order the sequel to Colossus. That's the second volume where the invader actually arrives and I forget what happens. And then there's a third one, which was an anticlimax, because I guess DF Jones couldn't think of anything worse that could happen. AUDIENCE: [CHUCKLING] PROFESSOR: But Martin Rees can. Yeah. AUDIENCE: Going back to her question about example, and if a mind has a state that it prefers to be in, would that example be more of a pattern recognition example? So instead of 10, 40, 50, 55, what if it was [? logistical, ?] like, good, fine, great, and you have to come up with a word that could potentially fit in that pattern. And then that pattern could be ways to answer "how are you?" PROFESSOR: Let's do an experiment. How many of you have a resting state? AUDIENCE: [INAUDIBLE] PROFESSOR: Sometimes when I have nothing else to do, I try to think of "Twinkle Twinkle, Little Star" happening with the second one starting in the second measure, and then the third one starts up in the third measure. And when that happens, I start losing the first one. And ever since I was a baby, when I have nothing else to do-- which is almost never-- I try to think of three versions of the same tune at once and usually fail. What do you do when you have nothing else to do? Any volunteers? What's yours? AUDIENCE: I try not to think anything at all. See how long [INAUDIBLE]. PROFESSOR: You try not to, or to? AUDIENCE: Not to. PROFESSOR: Isn't that a sort of a Buddhist thing? AUDIENCE: Guess so. PROFESSOR: Do you ever succeed? How do you get out of it? You have to think, well, enough of this nothingness. If you succeeded, wouldn't you be dead? AUDIENCE: [CHUCKLING] PROFESSOR: Or stuck? AUDIENCE: Eventually, some stimulus will appear that is too interesting to ignore. AUDIENCE: [CHUCKLING] PROFESSOR: Right, and the threshold goes down till even the most boring thing is fascinating. AUDIENCE: Yeah. AUDIENCE: [CHUCKLING] PROFESSOR: Make a good short story. Yeah. AUDIENCE: There was actually a movie that really got to me when I was little. These aliens were trying to infiltrate people's brains, and like their thoughts. And to keep the aliens from infiltrating your thoughts, you had to think of a wall, which didn't make any sense at all, but-- AUDIENCE: [CHUCKLING] AUDIENCE: But now, whenever I try to think of nothing, I just end up thinking of a wall. AUDIENCE: [LAUGHING] PROFESSOR: There are these awful psychoses, and about every bout every five years, I get an email from someone who says that, please help me, there's some people who are putting these terrible ideas in my head. Have you ever gotten one, Pat? And they're sort of scary, because you realize that maybe the person will suddenly figure out that it's you who's doing it, if they-- AUDIENCE: [CHUCKLING] AUDIENCE: [INAUDIBLE] husband [INAUDIBLE] all them together once, and I think they married. AUDIENCE: [LAUGHING] PROFESSOR: I remember there was once-- one of them came to visit-- actually showed up, and he came to visit Norbert Wiener, who is famous for-- I mean, he's the cybernetics person of the world. And this person came in, and he got between Wiener and the door, and started explaining that somebody was putting dirty words in his head and making the grass on their lawn die. And he was sure it was someone in the government. And this was getting pretty scary. And I was near the door, so I went and got [INAUDIBLE]---- it's a true story-- who was nearby, and I got [INAUDIBLE] to come in. And [INAUDIBLE] actually talked this guy down, and took him by the arm, and went somewhere, and I don't know what happened, but Wiener was really scared, because the guy kept keeping him from going out. [INAUDIBLE] was big. Wiener's not very big. AUDIENCE: [CHUCKLING] PROFESSOR: Anyway, that keeps happening. Every few years, I get one. And I don't answer them. He's probably sending it to several people. And I'm sure one of them is much better at it than we are. How many of you have ever had to deal with a obsessed person? How did they find you? AUDIENCE: I don't know. They found a number of people in the media lab, actually. PROFESSOR: Don't answer anything. But if they actually come, then it's not clear what to do. Last question? Thanks for coming.
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Channel: MIT OpenCourseWare
Views: 230,537
Rating: undefined out of 5
Keywords: carl sagan, m.a. bozarth, human problem solving, human behavior
Id: 2KbvJ3iapbc
Channel Id: undefined
Length: 110min 36sec (6636 seconds)
Published: Tue Mar 04 2014
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