"The Thinking Machine" (1961) - MIT Centennial Film

Summary

Highlights

• A child learning letters is used to show how humans recognize patterns and improve through feedback ✏️
• A computer is shown learning to identify letters by being trained with examples and probability-based responses 📚
• The missionaries-and-cannibals puzzle demonstrates how machines can search for logical solutions step by step 🌉
• A checkers program by MIT’s Dr. Arthur Samuel shows computers improving through play and experience ♟️
• Duckling and frog experiments suggest instinct is a kind of built-in programming inherited at birth 🐣
• Visual illusion demos show that what we see is shaped by expectations and prior knowledge 👁️
• A computer-written Western is presented as proof that rule-based systems can produce novel-looking creative work 🤠
• The film closes by framing computers as tools that will reshape work, science, and society in the years ahead ⚙️

Key Takeaways

• Computers may not "think" like humans, but they can learn patterns and solve problems in ways that look intelligent 🤖
• Human perception and instinct are also rule-based, which makes the brain feel a lot more like a programmed system than we usually admit 🧠
• The film treats machine learning, pattern recognition, and game-playing as early signs of artificial intelligence in action 🎯
• Scientists in 1961 were already imagining computers that could support creativity, logic, and decision-making at a much larger scale than before 🚀
• The big message: technology is powerful, but it still needs human wisdom to use it well 🌍

Overview

Chapters

• 00:00 - 05:30: Opening: The Digital Computer and the Future The episode opens by framing the digital computer as a preview of the future, asking whether machines may become as important as the atomic bomb and whether they can truly think. David Wain introduces the topic with MIT professor Jerome B. Wiesner, who admits that the question is still unsettled and that the idea of thinking machines has moved from fantasy toward serious scientific discussion.
• 05:30 - 10:00: Teaching Machines to Recognize Patterns This segment introduces the idea that recognizing letters is a pattern-learning problem, using a child learning the alphabet as the example. The speaker contrasts the child’s mistakes with the broader psychological question of how the brain learns patterns, then suggests that computers may help illuminate the process.
• 10:00 - 15:00: Problem Solving and Machine Learning The segment begins with the classic missionaries-and-cannibals river-crossing problem as an example of a logical puzzle. It explains the rules, the need to avoid outnumbering the missionaries on either side, and shows a person thinking aloud while attempting to solve it. The point is to illustrate how humans approach structured problem solving and how researchers recorded those thought processes to study them.
• 15:00 - 22:00: Programming, Instinct, and Built-In Behavior The speaker explains how computers work at a basic level: simple electrical building blocks can be combined into adders, multipliers, and other operations, and programming means laying out every step a machine must follow. A machine can store instructions once learned, but the comparison between computers and nervous systems should not be taken too far, even though both use electrical signals.
• 22:00 - 30:00: Perception and Human Bias in Seeing The chapter explains that animals and humans are not blank slates: they are born with built-in programming or instinct that helps them survive and interpret the world. Using frog vision experiments, it shows that a frog’s eye does not report everything it sees, but only specific survival-related information such as movement, small edible objects, and danger signals. The chapter then shifts to human perception with Piaget’s milk-and-glass experiment, demonstrating that children rely heavily on visual cues and may incorrectly assume that a taller container holds more milk. It concludes that perception is shaped by expectations and learned assumptions, illustrated by a window illusion in which the brain interprets apparent motion and depth incorrectly. The key idea is that seeing is filtered by the nervous system, so belief and perception influence each other.
• 30:00 - 37:30: Artificial Intelligence on the Stage The speaker challenges the idea that computers cannot be original by pointing out that people themselves are guided by built-in rules and assumptions. He argues that if humans can think while following internal rules, machines should not be dismissed for doing the same. This leads into the claim that computers can even write creative works like plays.
• 37:30 - 47:30: Limits, Risks, and the Future of Thinking Machines The chapter opens by showing that a computer-generated play does not repeat the same script each time; instead, the machine produces different stories, demonstrating both its flexibility and the limits of what it 'knows'—such as the expectation that bad guys should lose. The narrator emphasizes that computers can still fail and often require trial and error to program.
• 47:30 - 55:00: Closing Reflections on Machines and Human Wisdom The speakers reflect on the promise and limits of intelligent machines, arguing that computers will increasingly relieve humans of routine intellectual work and may eventually be capable of thinking, though not necessarily in the same way as people. They express doubt about machines achieving genuinely creative acts like poetry or music in the near future, but expect major repercussions as computers take on more human-like tasks.

"The Thinking Machine" (1961) - MIT Centennial Film Transcription

• Segment 1: 00:00 - 02:30 tomorrow a preview of the future as it begins to take shape and the Laboratories of the world produced by the CBS television network in cooperation with a Massachusetts Institute of Technology [Music] this program is brought to you by AMF American machine and foundry company [Music] good evening I'm David Wain and as all of you are I'm concerned with the world in which we're going to live tomorrow a world in which a new machine the digital computer may be of even greater importance than the atomic bomb Ken machines really think even the scientists argue that one I don't believe that we can say yet that machines do sink I have a basic question which I always ask and that is are these producing anything really new until I see a machine producing genuinely new things I will not agree that machines think I confidently expect that within a matter of 10 or 15 years something will emerge from the laboratories which is not too far from the robot of science fiction Payne [Music] the thinking machine starring David Wain in a moment that story [Music] [Applause] [Music] with me tonight is professor Jerome B who is nur director of the research laboratory of electronics at MIT dr. weasoner but what really worries me today is what's going to happen to us if machines can think and what interests me specifically is can they well that's very hard question to answer if you'd asked me that question just a few years ago I'd have said it was very far-fetched and today I just have to admit I don't really know I suspect if you come back in four or five years I'll say sure they really do think well if
• Segment 2: 00:00 - 02:30 you're confused doctor how do you think I feel professor weasoner I don't think I have
• Segment 3: 02:30 - 05:00 to tell you that the conception of the robot a thinking machine has been man's dream for centuries also his nightmare of course up until recently the exploitation of that dream has been largely in the hands of its fiction writers and my colleagues in the motion picture industry you remember a great robot in the silent film metropolis no I don't think I ever saw that one that I'd like you to see that'd be very nice ok charlie roll them [Music] [Applause] [Music] [Music] [Music] [Applause] [Music] that movie that a professor is still way ahead of us you know is he he is and you know if I'd seen that movie as a youngster I've probably been afraid of science doctorate when that film was made it wasn't too long ago the thinking machine concept was still in the realm of make-believe what do you think about it today well the reason that's such a very hard question to answer is we know so very little about thought processes or about information that makes up thought processes you know there are many things which machines can do today which if they were done by human beings you would certainly call thinking have you ever watched the child try to learn the alphabet I've got a piece of film here that I'd like to have you see do you know that letter and no it looks like an M but it isn't it's a W and then we'll tie down isn't it I'm going to make an X there and you see if you can draw over the same lies that I make down
• Segment 4: 05:00 - 07:30 up down and up can you do that the same morons honey that's right [Music] that's fine what letter is it W that's right good now you see of course she makes mistakes at first the real question is how does she ever learn to get them right I can copy yours that's right you copy mine what do you mean to tell me that you don't even know yet how a child learns the letters of the alphabet psychologists who study the problem have a lot of ideas about what goes on but no real explanation a letter is simply a pattern and we don't yet know how the brain recognizes patterns do you know what letter that is ah no it's a key but do you think you'll ever understand these problems I think so that's one reason we're so interested in the computer and what it can be made to do are you suggesting that the computer can do what this child is doing yes I am the computer is a remarkable machine and later on in the program we're going to see it write an original TV Western but first let's see how the youngster is doing here what letter is it well P now can you tell me what this letter is do you remember this one [Music] it's a w [Music] now let's see if the computer has as much trouble with the alphabet you mean we're going to see the computer do with the child I hope so but that really proved that machines can learn I'll tell you what it does it compares the letter you write with a letter you first showed it and as it gets more and more information about what is called a W for example it is more and more able that are able to make a judgment about whether the letter that's being written on the screen is in fact a dummy or not let's see okay Laurie let's read in the program again well at
• Segment 5: 05:00 - 07:30 least I know what a program is the program's the rules you want the computer to follow now what are they
• Segment 6: 07:30 - 10:00 doing of showing the computer the alphabet for the first time yes in just the same way the teacher did with a child incidentally for convenience you see we're using peas and dummies - okay now we have those characters in the machine we'll try some test with them [Music] call it a day pretty cool but on the other hand it doesn't have much knowledge well here's a p4 [Music] try another Pig should get it this time [Music] Wow well now that's luck that's pure luck no as a matter of fact one thing the computer can't do is fool the teacher on the next try look at the line below the letter the longer the line is sure the computer is of its answer good enough there's a pretty good percentage is indicated by the bottom bar sir very good how do you call that learning well I know that if my wife and I saw our kids going through that process we call it learning so machines really can learn you know we don't really know much about the thought processes and this is why I'm so hesitant but we are studying the problem it's being studied in a very many places and by people in many disciplines you mean psychologists psychiatrists philosophers and so on yes and electrical engineers and mathematicians and others well if the computer is this important live and I heard more about it well the computer is a relatively new thing and we're just really getting an appreciation for the full range of its usefulness many people think it's going to spark a revolution that will change the face of the earth almost as much as the first Industrial Revolution did well now this brings me back to my original question can machines think I mean by that thinking that process we try to avoid when we have a problem to sell you mean like I'm trying to do in avoiding your
• Segment 7: 07:30 - 10:00 question do you remember that puzzle
• Segment 8: 10:00 - 12:30 about the cannibals and the missionaries I do remember that that's when we tried to cross the river with the missionaries and the cannibals without the missionaries getting eaten yes that's the one well out of Carnegie Tech professors Simon and Newell are doing some very interesting work trying to understand that kind of logical problem let me show you this is professor H a Simon three missionaries and three cannibals are trying to cross a broad river so that they can reach a town on the other side they have a rowboat cannibals they have a row boat which will however hold only two at a time all of them know how to row there's one difficulty no group of missionaries and cannibals may be on either side of the river at any time if the cannibals in the group outnumber the missionaries because the missionaries would be eaten now the problem is to plan a series of trips they will get all three missionaries and all three cannibals across the river in the boat but being eaten you have any questions now very well you may start now please remember to say aloud as much as you can about what you're thinking and you may move the missionaries and cannibals to try different combinations of them there's only one boat there just one boat and only two can get in at a time why don't you take them across and see how it would work remembering that the boat always has to come back with someone to get the next load and then I have the missionary bring the boat back and take one of the missionaries careful that man is going to get eaten Collin guess I wouldn't do that is it possible for any of them to swim no I'm afraid there and now I'm swimmers well I'll bring one of the missionaries over by himself poor Barbara you know I sympathize with her I remember how baffled I was the first
• Segment 9: 10:00 - 12:30 time I tried that problem well Barbara's bright and she'll get it
• Segment 10: 12:30 - 15:00 after a bit it's a matter of fact in a moment I'll show you her solution that's why professor Simon asked her to talk aloud as she works they'll have a record what she says and then I'll bring the other one over and that's it very good that's the correct solution and now we'll go and try the same thing out with the computer on the coast oh no now don't tell me you're going to try to get that computer to do the same thing is it found the solution already yeah and then just a second we'll have a chance to look at the answer is that the answer you have there yes as a matter of fact it's both answers we've typed out Barbara's solution on computer paper - can you tell which is which do I have to answer that how does the machine do it doctor well professor Simon thinks that logical process are really quite simple and only appear complex because there are so many of them in cascade what does that mean that the machine tries every possible answer no it does just what a person does it tries those things which seem most likely well how does the machine know it's likely well a person knows what's likely on a basis of experience the machine knows what's most likely on a basis of probabilities or reasonableness that has been programmed into it that is told to it in advance well what else can computers do well many things though as I've already said we're just really beginning to understand the capabilities of the computers I've got some film to illustrate this point which I think will amaze you that man isn't playing checkers against a computer is he sure and it plays pretty well now which colors the Machine playing black the player pushes those switches to tell the Machine his moves incidentally he has to make the machine moves on a checkerboard as well well then how does he determine the machines moves by watching those lights on the
• Segment 11: 12:30 - 15:00 console machine also prints out its booze who's that watching he's dr. Al Samuel MIT graduate now with IBM dr. Samuel programmed the computer to play checkers so he could study machine learning
• Segment 12: 15:00 - 17:30 let's see what it's printing out huh how in the world did it do that back to easier how does a computer work well would take too long to give you a detailed explanation but I think I can give you a simple explanation of the principles if you want to make a little box they can put signals into and every time you put a pulse into it it adds and and with these devices you can make adders you can make multipliers and you make devices which do a number of other mathematical operations now with these various building blocks and it takes just a very few of them you can organize much more complicated macadam a thematical problems and what you do is what we call programming you lay out a series of steps and you have to tell a machine every blessed single step you wanted to do because it can't do anything unless you do this and this we call a program we feed it into the machine and it carries out the operations we've told it to do one at a time of course there is a saving grace here which is very important and once the machine has learned how to do something it can print out into its permanent memory the taper on a piece of paper of the kind we've seen the instructions for doing this and you never have to think about that particular thing again well does the nervous system work like a computer no not really though there are many similarities between computers and living nervous systems but neurophysiologists who work on the problem think there are many more differences than there are similarities fact the matter is though that both systems use electrical signals there's electrical potentials and pulses well then by following the complicated steps that a computer takes the steps and stages in solving a problem can you learn how or more about how the nervous system works well what you can learn is a good deal about thought processes or
• Segment 13: 15:00 - 17:30 at least simulated thought processes by this method but it's very dangerous to carry this analogy too far well if I told you all about my particular problem could you solve that on a computer well it probably takes a psychiatrist right I think I better ask my question over again you have to tell a computer what to do in other words all of the computers that we've seen have been
• Segment 14: 17:30 - 20:00 programmed that is have been told by men what to do yes but you know that's really not a valid argument because there's every evidence that men are programmed to that is they have certain built-in programming now wait a minute dr. Wiesner are you suggesting that men are born with something put in their brains like men put information into a computer yes they're not only born this way but they get programming in other ways that is they're really two ways in which you can get programming there is a hereditary part that is the part you're born with you can also get information or programming in your brain by experience that is by learning well surely in higher animals learning is more important we like to think that anyway can you give me an example of programming in something that's alive yes I can I've got something over here can you tell me what this silhouettes supposed to represent well it doesn't remind me of anything in particular if I hold it up here like this does it look like a goose to you yes it does now if I turn it around what to make you think of well if we're still thinking about birds I'd say it was a hawk did pretty well let's see if a duck can do as well the ducklings used in this experiment had been raised in isolation they had never seen any other birds and there I see is our friend the goose [Music] well there wasn't any reaction at all was there no there wasn't that's what interested professor melzack you're seeing what happened when he first did this experiment in the London Zoo [Music] you are a duck when you worry if you saw a goose you mean he will worry when professor Mao's act changes that goose into a hawk well let's watch that's a really frightened death well not all
• Segment 15: 17:30 - 20:00 Ducks react this way some are simply
• Segment 16: 20:00 - 22:30 weary when they see their first hawk [Music] well mr. Wayne well when I gather from this is that this duck that never had been exposed in its life to any other kind of a bird can now suddenly differentiate between a goose and a hawk now you call that some kind of programming to me that's what I would call instinct well I'd agree with you instinct is the word you use I'm using programming or at least that part of programming which is determined by heredity you see animals seem to start life with a large part of their nervous system knowing what to do that's the reason why we breathe and why our heart beats and why babies know how to cry or when to cry when they need help well do you mean that instinct turns out to be some kind of programming that were born with yes that's using our language let me show you some very interesting research that was done on the Frog last year at MIT by professors leffen and matt serrano which seems to indicate that some animals at least are born with very much more built in information than we used to suspect was the case well does it show that we're born with a program but what it does show is that in the Frog at least the frog's eye reports only very specific information to the brain you mean that the optic nerve of the Frog doesn't report everything it sees no it reports only very specific things things which seem to be very important to the survival of the Frog let's take a look at the next film well now the Frog doesn't pay any attention to those dead flies hidden T hungry sure but his eyes don't see them he's hungry all right watch what if he didn't eat any of the dead flies wanted to eat the fly on the string well you know that's what Professor let's ins trying to find out it seems that the frog only sees things that move here Elevens looking through the microscope into the frog's brain
• Segment 17: 20:00 - 22:30 preparing to put a tiny electrode into one of the fibers in the frog's optic nerve you mean when the fiber sees something it'll send a signal to the electrode that we can see yes and you'll hear it too now professor
• Segment 18: 22:30 - 25:00 Levin is putting a target at semi-circle a hemisphere in front of the frog's eye and then using a magnet on the back of the target he can move a small metal disc around the target until he finds point at which the particular fiber is looking there he's found it see it sends an electrical signal through the electrode but what's the professor doing now well he's trying to find out just what kind of things make this particular fiber react it looks to me as though this fiber reacts every time something moves it does well that could explain why the Frog didn't eat the dead flies they didn't move so he couldn't see them well that's professor Levin's suggestion anyway he said to himself suppose the fibers in a frog's eye look only for specific kinds of things in the world around them now as we've seen one kind of fiber apparently only reports movement here a professor leptin is looking for a different kind you see this fiber reports to the brain when anything small enough to eat moves into view and it keeps reporting as long as the object remained there well why did it stop reporting when the light went out and then failed to report when it came back on well think of the light going off as a shadow passing over it and I'll say and apparently in a dangerous situation the frog's eye only reports the danger not food watch now as a long bar has passed through the field of vision the bar is too straight and too big to be a bug so you see there is no reaction professor let fans work suggest that the fibers from the frog's eye only reports specific things to the brain things related to the Frog survival one group of fibers looks only for sharp edges another group this one seems to be a bug detector well now I was always under the impression that the eye reported all light patterns so are we all I ought to add though that professor let Ben's
• Segment 19: 22:30 - 25:00 theories are not yet accepted by all people in the scientific community or at least in so far as the implications of our human vision are concerned well if professor Levin's theories are correct we would have some explanation for instinct wouldn't we well the existence of building coding or instinct was never really in doubt all this is a very
• Segment 20: 25:00 - 27:30 interesting demonstration of it it's a demonstration of least of one kind of instinct because it's obvious that the Frog didn't learn to recognize flies but was born with this ability already in its brain well now if I may say so people aren't frogs are they well no one really suggests that people are pre coded to the same extent that frogs are but it's very clear that people also are born with certain amount of information built into their nervous system let me show you something that'll prove this point we're going to Geneva to the laboratories of Professor Jean Piaget and his associates this is dr. barbell in Helder working with a youngster of five by the way how's your French mr. Wayne well it's not very good I'm sorry to say well doctor inhaled her here is filling one glass with milk and she's asking the child to fill his with exactly the same amount as she hasn't hers [Music] boy says you've a little too much on the other [Music] is it now the same he's a real stick with me who's that rick wob' my executive and then she wasn't what was in my bra morally no cigar there I would prefer to drink my milk in the tall glass I have poured it all in the tall glass what you and I have we still the same amount more glass but how do you know because it is taller sure let's delightful listener now how
• Segment 21: 27:30 - 30:00 could that child make that mistake well apparently when we're born we rely exclusively on our eyes to form our concepts of the world around us he apparently had certain preconceived notions of the world around him and some of these are wrong and as he grows up he's going to have to learn to correct them well then our eye apparently is programmed to tell us that that which is taller holds more yes and in nature isn't that generally the case well I've always been under the impression that seeing is believing well actually it's just the opposite these experiments indicate but we tend to see only those attributes of objects which our nervous system is designed and programmed to see then seeing isn't believing but believing is seeing well it works both ways but certainly believing is seeing watch this next piece of film [Music] that's a weasoner I've seen this before now that window isn't square you see one side is longer than the other yes but no matter how many times you've seen it I'll bet you still can't see through the illusion [Music] what's the window doing now it's revolving I know who it's revolving of course it is but I'm certain that you don't see it that way you see it as though over oscillating back and forth [Music] well as a matter of fact you're right you see your eyes tell you that anything that is longer is closer to you and your experience tends to confirm this so you have to see the long side of the window as though it were in front even when it's in back it just can't see it revolve now let's cover the window with a cloth the cloth sides are equal in length now maybe your brain will let you see the window rotate look at the bottom corner of the cloth now do you see it revolve
• Segment 22: 27:30 - 30:00 yes I do [Music]
• Segment 23: 30:00 - 32:30 now let's put a tube through the window what you see this time [Music] well that tube is bending back on itself [Music] you know why that's because you've assumed the tube is made of rubber now if you tell yourself that it's made of steel you'll see it cut through the window instead of bending am i right well I have to think real hard that it's steel right but it's still it's still it's - well I'll be yes you are right [Music] now then doesn't that prove that you tend to see what you believe you mean professor that I am somewhat programmed in other words that there are rules built into me that make me react sometimes similarly to a machine yes you and everyone else well I think I'm beginning to get your point I am born with certain rules built into me but I think I can think that as I say I can think therefore I shouldn't be too upset when a machine thinks just because it has rules built into it by a man that's logical isn't what can you show me where a computer can do anything original I think that might help to convince me but depends an awful lot on what you mean by original would you regard writing a television Western as being original how do I have to answer that what do you mean to tell me that computer can really write a play well we can write pretty good plays matter of fact we will see the Playland written by the computer in just a moment but first from AMF an expression of one company's aims in your interest you are saying that a computer can write a play sure I'll show you it won't be as good as Shakespeare but it'll be better than a lot of the westerns you look at I suspect by now you've seen enough of computers in action so let's just watch
• Segment 24: 32:30 - 35:00 the computer print out its play that's Harrison Morse who did most the work on this computer program [Music] [Music] [Music] [Applause] [Music] [Music] [Applause] [Music] well I'll tell you one thing if that computer ever learns to act I'll tear its transistors out by the roots you know that's as close to magic is anything I've ever seen doctor well you know it isn't really magic let's have Doug Ross who's on the staff
• Segment 25: 35:00 - 37:30 at MIT and who supervised the writing of the program for this playlet explain it to us well we had a lot of fun working on this program but we're not just playing games we're trying to illustrate some important things about artificial intelligence just as a human playwright must obey certain rules in order to have a meaningful and understandable play one that seems natural for people to actually act out we must make the computer aware of the same kinds of rules so what we're trying to show are that intelligent behavior is rule obeying behavior we're trying to show what these rules look like and we're trying to show how a computer can be made to do creative work in the type of play that our program is designed to write we have a robber enter wait for the sheriff and when he enters they have a shoot it out and one or the other will die and the winner if any will pick up the money and we'll all walk out the door the human playwright would know already things that we have to teach the computer by programming for instance if the gun is in the hand and the hand is on the robber and the robber is in the corner the human knows immediately that the gun is also in the corner but we must make the computer able to keep track of all these things this switch shows how the computer will choose reasonable alternatives for the sheriff depending upon whether or not the sheriff can see the robber and the robber sees the sheriff the sheriff may wait advance on the robber to get a better shot or try immediately to shoot the robber we've given the computer rules for determining reasonable behavior and we have also given the computer rules for modifying those rules for example we have an inebriation factor which controls the actions of the robber depending upon how much he has had to drink the more the robber has to drink the more inebriated he will become so
• Segment 26: 35:00 - 37:30 that he becomes less and less intelligent in his be a view notice that the computer is still being intelligent and not violating physical rules but the character of the actor is changing as he becomes more and more inebriated and that is one of the main points that we're trying to show here is that intelligent behavior is rule obeying behavior and there is no black magic about doing these things and machines it's marvelous to do the mind machines but far from miraculous well now that makes it seem almost reasonable but even so how can I be sure that the computer wrote the script and
• Segment 27: 37:30 - 40:00 not the scientists well you know if we had written the script and stored it in the machines memory it would print out the same play every time doesn't it no it doesn't as a matter of fact all is the plot is the same the play it writes is different every time it's sort of like a mystery story writer matter of fact we've written about 50 plays and here are several of them the machine has printed up would you like to see another one of them certainly would it okay [Music] [Music] [Applause] [Music] but I can see there's one thing that the
• Segment 28: 40:00 - 42:30 computer doesn't know in television the bad guys supposed to lose you know you know there are many things the computer doesn't know and even worse sometimes the computer just doesn't work but what happens in almost anything for example [Music] well what happened there well you know making a program for a computer involves a fair amount of trial and error what you've just seen with one of the errors well that's wonderful well now look if a machine can do all these things today what's going to happen tomorrow well it depends on many things depends on how much we are able to find out about learning processes as I told you before there are several ways we're going about trying to understand information processing systems including the nervous system and one very important one is working with humans this experiment is taking place in professor walter rosenblith laboratory at MIT through these earphones dr. geyser will put a series of rapid clicks into the subjects ear now he's going to record the signal coming from the electrodes in the headset response to that signal as it appears on the outside of man's skull but hasn't this kind of recording been done for good many years well no recording from the skull has been done for a long time but what you used to get was a picture of all the electrical activity in the brain in these experiments the computer lets us concentrate on the specific electrical activity that's a direct result of the clicks that the man hears in other words dr. weasoner the computer allows you to learn a little more about signals in the human brain than you knew before well the peaks and valleys you see there are a result of the clicks that that man is hearing incidentally as it almost always does pure research has a number of
• Segment 29: 42:30 - 45:00 applied usually unpredictable values as well in this case here we're seeing the John Tracy clinic in Hollywood these techniques are being used to determine deafness and children another way of studying these logical processes we've been talking about that take place in the brain and in giant computers is going on at the Lincoln Laboratory this is a laboratory which we operate for the Department of Defense here is how is one of the world's largest and most versatile computers we call it the tx2 that took a great many men several years to build and we're still working on it improving it all the time this enormous collection of wires tubes transistors and circuits connects the largest computer memory now operating that's the memory over there all that a memory yes it is you know it contains about two and a half million memory units primarily ceramic cores in this case huge as it is it holds only a tiny fraction of the elements that are housed in the human brain people estimate that our brain holds about 10 billion neurons Kenn billion yes and unbelievable as that is this comparison is probably very misleading because neurophysiologists believe that each neuron fulfills many more functions than a single vacuum tube or transistor so you have to use huge installations like this to find out how signals move between the brains neurons well you know this is really pretty small to simulate anything as complicated as the brain this gadget only has about 1,300 neuron like elements if you put signals into the two edges you can make a wave travel through the device and study the behavior of signals in such networks that's what Belmont Farley is going to do here our scientists everywhere I'm using computer machines like this to study learning yes they are you know once an idea like the computer
• Segment 30: 42:30 - 45:00 exists people everywhere begin using it that's one of the reasons why there can't really be any secrets in science for very long a matter of fact I recently saw work a similar to this in Russia well the fabulous machines like this are being developed everywhere what's gonna happen to us all tomorrow who's going to
• Segment 31: 45:00 - 47:30 be in charge machines are men man I hope you know you can always pull the plug a French artist named John tingly tries to prove this point as a matter of fact by building machines that do absolutely nothing [Music] you know modern research can chew up money faster than Bingley's machine doing [Music] [Music] [Music] you know what he's doing here I'm pumping that bicycle enjoy a picture [Music] no that really is wild well now seriously professor do you think that one day machines will really be able to think well I think so but people still disagree about it let's hear what a few scientists have to say about it I don't believe that any of the machines that we know today can think I have a basic question which is do these machines produce anything really new when you consider the great new ideas produced by men like Newton and Darwin and Galileo you'll find that initially they had to throw away the old rules that they'd been brought up with our machines do what they've been told to do they obey the rules that have been fed into them by man and we know of no machines at present which have means of overcoming this limitation I have little doubt that we'll be able to produce machines and computer programs that will behave in a fashion that we speak of as
• Segment 32: 47:30 - 50:00 intelligent that these will be of great aid to man in terms of relieving him of intellectual work that does not fit for human production where my doubt comes in is whether we shall be able to produce machines and machine programs that are capable of creative thinking I doubt very much with the usual type of human vanity that any artificial information processing system will ever be able to do this kind of inventive things that I rather doubt whether it's gonna be possible to do this in our lifetime I'm convinced that machines can and will think I don't mean that machines will behave like men I don't think for a very long time we're gonna have a difficult problem distinguishing a man from a robot and I don't think my daughter will ever marry a computer but I think the computers will be doing the things that men do when we say they're thinking now machines can't write good poetry or produce deathless music yet but I don't see any stumbling block in a line of progress which will enable them to in the long run I'm convinced that machines can and will think in our lifetime well now that's pretty unnerving stuff don't you think it's going to have tremendous repercussions in the days to come I'm sure that it will it's going to have many effects direct and indirect what would you say those effects would be well in the direct effects we're going to put machines to work for us in many ways by indirect I mean that we're going to learn many things while we work with the computers that will help us in other fields that we're interested in for example in the field of mental health social problems and economic problems just to mention a few but how do we put the computer specifically to work well you know my colleague at MIT professor Norbert Wiener says we're living through the Second Industrial Revolution today the first Industrial Revolution being the replacement of manual labor by
• Segment 33: 47:30 - 50:00 machinery that's right and the second then you say will be the assistance of the human mind by the computer and I'm certain that as time goes on we're going to find ways to do many things using the computer which the unaided mind just could not do by itself the future the computer is just hard to imagine let's listen now to Professor Shannon whom we heard briefly at the beginning of the program and also I won't like you to remember that when he talks about robots he doesn't mean well a pretty girl like our good friend dr. rocha yes it was fun created at the beginning of the program but what he really means
• Segment 34: 50:00 - 52:30 is machines that can do things that man want them to do in discussing a problem of simulating the human brain on a computing machine we must carefully distinguished between the accomplishments of the past and what we hope to do in the future certainly the accomplishments of the past have been most impressive we have machines that will translate to some extent from one language to another machines that will prove mathematical theorems machines that will play chess or checkers sometimes even better than the men who designed them these however are in the line of special-purpose computers aimed at particular specific problems what we would like in the future is a more general computing system capable of learning by experience and forming inductive and deductive thoughts this would probably consist of three main parts in the first place that would be sergeant's akin to the human eye or ear whereby the machine can take cognizance of events in its environment in the second place there would be a large general-purpose flexible computer program to learn from experience to form concepts and capable of doing logic in a third place there will be output devices devices in the nature of the human hand capable of allowing a machine to make use of the thoughts that has had of the cognitive processes in order to actually affect the environment work is going on in all of these fronts simultaneously and rapid progress is being made I confidently expect that within 10 or 15 years we will find emerging from the laboratories something not too far from the robotic science fiction pane in any case whatever the result this is certainly one of the most challenging and exciting areas of modern scientific work exciting and challenging but doesn't it worry well sure it worries me but you know the problems posed by the computer are really no different than
• Segment 35: 50:00 - 52:30 the problems we have with other products of technology it's gonna take a great deal of wisdom on our part to manage them but if we do we're going to make a much better world thank you doctor weasoner the story of the next tomorrow program in a moment but first a word from AMF American machine and foundry company [Music] next month over many of these same
• Segment 36: 52:30 - 55:00 stations tomorrow will present big city 1980 a picture of the city you will live in tomorrow check your local listings for the time [Music] you