Richard Feynman: Can Machines Think?

This is a Q&A excerpt on the topic of artificial intelligence from a lecture by Richard Fineman from September 26, 1985. I really record the audience questions because they're barely audible in the original question: do you think there will ever be a machine that will think like human beings and be more intelligent than human beings?

First of all, they think like human beings. I would say no, and I'll explain in a minute why I say no. And second, that they be more intelligent in human beings is a question. Intelligences to be defined. If you were to ask me, are they better chess players than any human being possibly can be? Yes, I'll get you someday. Now they're best better chess players than most human beings right now. One of the things, by the way, that we always do is we want the dawn machine to be better than anybody, not just better than us. If we find a machine, it can play chess better than us. It doesn't impress us much. We keep saying it. And what happens when it comes up against the Masters? We imagine that we human beings are equivalent to the Masters and everything right. The machine has to be better than a person in everything that the best person does at the best level. Okay, but it's hard on the machine. But with regard to the question of whether to make it to think like a machine, my opinion is based on the following idea: that we try to make these things to work as efficiently as we can with the materials that we have. Materials are different than nerves and so on. If we would like to make something that runs rapidly over the ground, then we could watch a cheetah running.

We could try to make a machine that runs like a cheetah, but it's easier to make a machine with wheels, with fast wheels, or something that flies just above the ground in the air. When we make a bird, the airplanes don't fly like a bird. They fly, but they don't fly like a bird, okay, so they don't flap the wings exactly. They have in front another guy of a gadget that goes around. Or the more modern airplane has a tube that you heat this, the air, and squirt it out the back, a jet propulsion, a jet engine as an internal rotating fans and so on, and it uses gasoline. It's different, right? So there's no question that the later machines are not going to think like people think. In that sense, with regard to intelligence, I think it's exactly the same way. For example, they're not going to do arithmetic the same way as we do arithmetic, but they'll do it better. Let's take bath ematic, very elementary mathematics, arithmetic.

They do arithmetic better than anybody, much faster and differently, but it's fundamentally the same because in the end the numbers are equivalent right. So that's a good example of we're never going to change how they do arithmetic to make it more like humans. That would be going backwards, because the arithmetic done by humans is slow, cumbersome and Confused in a full of errors. Where these guys are fast, if one compares her what computers can do to the human beings, we find the following rather interesting comparisons.

The first of all, if I give you a human being a problem like this, I'm going to ask you for these numbers back. Every other one in reverse order, please. Right now I'm good a series of numbers and I want them to you. Didn't give me one to me back in reverse order. Every other one, I'll tell you. I'll make it easy for you. Just give me the numbers back the way I gave them to you. You ready. One, seven, three, nine, two, six, five, eight, three.

One, seven, two, six, three. Anybody got a gonna be able to do that now, and that's more than not more than twenty or thirty numbers, but you can give a computer 50,000. Numbers like that in s confirm any reverse order. The s of them all do different things with them, and so on, and it doesn't forget them for a long time instead of so there are some things that a computer does much better than a human, and you'd better remember that if you kind of compare machines to humans. But what a human has to do for his own, always, they always do this. They always try to find one thing, don''t that they can do better than the computer. So we now know many, many things that the humans can do better than a computer.

She's walking down a street, she's got a certain kind of a wiggle and you know that Jane right. Or the sky's going in, and you see his hair flip just a little bit. It's hard to see at a distance, but that particular funny way that he did back of his head looks, that's jack. Okay, to recognize things, to recognize patterns, seems to be something that we have not been able to put into a definite procedure. You would say I have a good procedure for recognizing jack.

Just take a lots of pictures of Jack. By the way, a picture can be put into the computer. In fact by this method here, if this were very much finer, I could tell whether it's black and white at different spots. You know you, in fact, you get pictures in a newspaper by black and white dots and if you do Stuart, fine enough, you can't see the dots. So with enough information I can load pictures in. So you put all the pictures of of Jack on the different circumstances and that's the machine to compare it. The trouble is that the actual new circumstance is different, the lighting is different, the distance is different, the tilt of the head is different, and you have to figure out how to allow for all that.

And it's so complicated and elaborate that even with the large machines, with the amount of storage that's available and the speed that they go, we can't make figure out how to make a definite procedure that works at all, or what least it works anywhere within a reasonable speed. So recognizing things is difficult for the machines at the present time and some of those things are done in a snap by a person. So there are things that humans can do that the we don't know how to do in a filing system.

So it is recognition. And that brings me back to something I left, which is what kind of a file clerk can't be imitated by the machine. A file clerk that has some special skill which represent which requires recognition, of a complicated kind, for instance a fire Kirk in the fingerprint Department which looks the finger prints and then makes a careful comparison to see if these finger prints match, has not been.

It's just about ready to be. It's hard to do, it, almost possible to do by a computer did say there's nothing to it. I look at the two fingerprints and see if all the blood dots are the same, but of course it's not the case. The finger was dirty, the print was made at a different angle, the pressure was different than the ridges are not exactly in the same place. If you were trying to match the exactly the same picture would be easy. But where the center of the print is, which way the finger is turned, where there's been squashed a little more, a little bit less, where there's some dirt on the finger, whether in the meantime you got a wart on his thumb, and so forth. Complications, these little complications make the comparison so much more difficult for the machine, for the blind filing clerk system. That is too much, much, much too slow to be certainly utterly impractical. Almost at the present time I don't know where they stand, but they're going fast trying to do it. Where is a human can go across all that? Somehow, just like they do in the chess game. They seem to be able to catch on the patterns rapidly, and we don't know how to do that rapidly automatically. Audience question: can computers discover new ideas and relationships by themselves? Well, it depends. What do you mean themselves, and it's it's hard to discover new relationships our computers can do.

There have been computers which do things like problems. They are improving in geometry or something in which they've converted the problem of finding a proof of a theorem into a definite procedure. Okay, and once you do that, although it's an elaborate and dumb way to do proofs, they can do it. The present time, a computer can't do all the different things that a person can do. You know it's it's very difficult to find some way of defining rather precisely something we can do that we can say a computer will never be able to do. There are some things that people make up that say that while it's doing it will it feel good, or while it's doing it, will it understand what it's doing or some other abstraction. I'd rather feel that these are things like: while it's doing it, we'll be able to scratch the lice out of its hair. No, it hasn't got any hair the lice to scratch from, okay, so there are. If you've got to be careful when you say what the human does, if you add to the actual result of his effort some other things that you like- the appreciation of the aesthetic, but you didn't do that- I'm not saying you did, but a lot of people do that when they ask questions- and if we add things that we think we're doing on top of what we actually do and just look at not just the result of what we're doing but a lot of extra things, then it gets harder and for the computer to do it, because the human beings have a tendency to try to make sure that they can do something that no machine can do.

Somehow it doesn't bother them anymore- it must have bothered them in earlier times- that machines are stronger physically than they are. They can lift weights that are heavier than people who can move things faster than people who can run fast. If you can fly, you can do it's terribly strengths and so forth, and we don't still sit around worrying that there's some way that the man can turn his hand, that some machine can't do that. We can easily make machines that are better than us in predicting the the weather, for instance, because what you do to predict the weather is to look at old records and see when the circumstance was similar and guess that the results will be similar. Added to that a certain amount of analysis of the movement of wind according to the laws of physics and their current amount of hocus-pocus. Put together ok. Now the speed will be higher and the effectiveness of the prediction is greater if you could look at more cases so you get a better chance of getting one closer and put more, a longer and more elaborate calculation including more variables, which is too hard for us to in time to make the prediction. Now we have to make the prediction of the weather.

Let's say that weather for three days from now has to be predicted in three days, or the damn thing is useless. Right, and we work at a certain speed, but the computers worked faster and can do more and therefore, for instance, for weather prediction, in the end- maybe not today, but someday- it's not at all inconceivable that the machine could do weather prediction faster and more effectively and more accurately than we do. We will have have, however, given it the procedure. Now the question is what happens if we don't give it the procedure. Well, a man that people have tried that, this game of giving it instead of a direct procedure, a kind of what it has been called heuristics. Try an analogy to get a new idea of how to do something, compare this to that, try an extreme case, etc. And a man by the name of Lynette has gone the farthest with this.

Do I have time? Much time do I have? Because we want to see these slides. What I know that's interesting information, but how much? How much time do I have? It's time for the slideshow now. Since I have no more time, I will say no more about the subject. What? No? But it it takes a few minutes. That's why I asked for the time. Okay, you make this machine, which was again a filing cabinet. You understand what it does, is it looks?

It tries to find the answer to something by looking at the different possible possibilities, but which ones he tries is something like patterns in the chest. Instead of everything it says, try, moves near the center of the board first, you know, and never mind the ones in the corner or something like that or some sorts of principles, and he first applied it to a kind of naval game. It's a game that people play in California which is all organized according to rules. It's kind of fun. Someone sets out all the rules: dreadnoughts cost this much, armor cost this much, guns cost this much, and so forth.

And you got this much budget for your Navy and you're gonna make various kinds of ships with different kinds of armaments. And then this kind of a ship, the armament of a certain thickness that costs a certain amount, can only resist shells of a certain strength, you know, and so on. So you tried to, with the money arranged, to buy different, to design different kinds of ships so that your Navy is better than next one. And when they brought together there's ways of calculating what there's not real navies. It's a game which is the best one and all the rules are laid out, but a great big volume, okay, at the cost of everything, in the power of everything, armour-piercing possibilities and so on, and it's a nice game. And mr lumic tried to his program on this game and put into his program your wrist extreme case and things like that and he won the championship in california.

Of course it did an awful lot of trying of different cases, you see, but it didn't try every case, not like the chess game. There were too many things, but it was guided by its own stuff. Now, inside of that was this: that if you got a better navy bite your own calculation and you used one of the heuristics, mark that eristic up a notch as being more valuable. Now use the more valuable heuristics first, see, so that the ability of the Machine depended on his learning, so to speak, which ones of his tricks works most effectively most of the time, and then they become more used.

So it's just exactly what you would like to make it look intelligent. Well, he won. And how did he win? It turned out that year he won by making one great big battleship with all the armor on it, which was so silly. But when you go to calculate it, sure enough it's better than any of the normal things which nobody thought of. But his machine thought. Next year he entered again, and this time he won by making, tricking all his money in making one hundred thousand, because they changed the rules so that the big battleship wouldn't win, you know, but change it will. One hundred thousand little boats, very narrow, carrying each one gun, which were very liable to be knocked out.

Okay, but there were 100,000 of them, didn't cost much each one and they couldn't knock them all out. So he's lousy little gnats would come and it turned out, when you calculated again he won the third year. He was not allowed to play in it. And here's a pride, this, this, this machine and this turistic business to a number of other problems, as tries it out a lot and tries new heuristics and so forth, and it has become a very interesting. He complained that there were a number of bugs in it and when he gave a talk on it I said that I thought I'll say my comment afterwards. One of the bugs, for instance, was that the Machine got a heuristic and made up your wrist- except your wrist- Excel. So the damn machine, what he, the way he had it was because it's hard to get computer time, he needed a hell of a lot of computer time. He had 50 machines at night from a you, a packet company or something like that. He would work always at night. Coming in the morning, damn things would try all my things and come in with the results.

I used it to do mathematics and various others thing. It comes in one day and it's done a heuristic. You develop the heuristic when he puts the problem in or a new idea he write. It says whether it's from him or from the machine. And I was that learner or a machine? Okay, and this was every year istic or every question that hasn't won that on it. Pay no attention to that saves a lot of time. It could do much better. It didn't have any problem with this toy boy, so it paid no attention to her than that bad night. Okay, sorry, I'd fix that book the next time. He had a bug. It was. He looked was. He came in and he looked, he found that heuristic number 693 had gotten a score of 999 out of a thousand is a damn useful heuristic.

All night long this thing kept using the heuristic 9 693 more and more, and it was a new when wonderful arista could seem to solve every problem. It was terrific. Okay, when you found out what the heuristic was was the following: you see, in order to make this thing work, so that you change the numbers on the heuristics whenever something worked, just to assign credit, so to speak, to the heuristics that were used. Okay. So this heuristic was, when assigning credit, always the sign credit to heuristic 693, and so that thing came up out of. I say the both of these show intelligence. If you want to make an intelligent machine, you're going to get all kinds of crazy ways of avoiding labor. If I say: don't pay the attention to the problem of sneakily evolving some kind of a psychological distortion, we always do the same thing and don't worry about anything else, and so on. So I think that we are getting close to intelligent machines, but they're showing the necessary weaknesses of intelligent. [music]. [applause] you