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<v Narrator>There's too much information making your head spin. <v Narrator>Do you feel at the mercy of modern technology? <v Narrator>Does reality sometimes seem like an illusion? <v Narrator>Well, don't worry. It's all in your head. <v Narrator>Your personal computer has the inside information. <v Narrator>Next. <v Announcer>Major funding for this program is provided by the L.K. <v Announcer>Whittier Foundation. <v Narrator>A movie set, a typical scene. <v Narrator>It'll last maybe 20 seconds in the theater. <v Narrator>But right now, it's taking the energies of dozens of people working together. <v Speaker>[Shots fire]
<v Narrator>A movie, Alfred Hitchcock once said, is life with the dull <v Narrator>parts cut out. And there's an element of truth <v Narrator>in that. <v Narrator>We go to a movie to be, well, moved. <v Narrator>It isn't exactly brain surgery, but it can quite literally <v Narrator>change your mind. When you look at a movie you're really watching the end <v Narrator>product of a great deal of behind the scenes activity, not just <v Narrator>on the set. I'm talking about backstage in your brain. <v Narrator>Because in your mind's eye, although you're no more aware <v Narrator>of it than you are of the magic behind the movies, there's a great deal of <v Narrator>activity going on. It's as if you have a kind of personal production company up <v Narrator>here putting together your picture of reality. <v Narrator>What's the picture made of? <v Narrator>Well, nowadays we'd say information. <v Narrator>Our brains are the stars of the information processing business.
<v Narrator>How do they do that? How well do they handle information? <v Narrator>How is the very idea of information changing the way we think about ourselves? <v Narrator>Well, that's what this program is about. <v Narrator>Behind the scenes, inside information. <v Narrator>This was a watershed in the story of information <v Narrator>before its invention. Information could travel only as fast as a horse could <v Narrator>gallop or a ship could sail. <v Narrator>The telegraph, slicing electricity into a code of dots and dashes
<v Narrator>allowed a message to be transmitted at nearly the speed of light. <v Narrator>At its destination, the information could be cast in tangible form, hard <v Narrator>copy. Then circulated to a wider audience. <v Narrator>This linotype machine invented a century ago was a giant step in <v Narrator>the automation of printing. <v Narrator>And with increases in speed and efficiency, printed knowledge began to grow. <v Narrator>In the 1660s, only a handful of journals recorded the discoveries <v Narrator>of science. Today, there are about 40,000.
<v Narrator>The average American newspaper has twice as many pages as it did 20 years <v Narrator>ago. <v Narrator>And it's estimated that total printed knowledge doubles every eight years. <v Narrator>Add to that radios, cellular phones, laptop computers, fax machines, <v Narrator>VCRs wall to wall communication systems and earth <v Narrator>wired for sound and vision. <v Narrator>And it's not surprising that we think of this as the age of information. <v Narrator>But what does that really mean? <v Narrator>Out here there's no technology, but I'm still processing information <v Narrator>at a rate that would make a supercomputer blow its circuits. <v Narrator>Sights, smells, sounds. <v Narrator>That's information. <v Narrator>The extra amount of information we have to process because we live in the age <v Narrator>of information is actually insignificant.
<v Narrator>Here's an example of information transmission. <v Narrator>The original message was information is a difference that makes a difference. <v Narrator>And here's one of the difficulties of communication. <v Narrator>A message can be distorted. <v Narrator>Information reduces uncertainty. <v Narrator>Right now, we don't know if the message will be heads or tails. <v Narrator>And now we know. That's information. <v Narrator>Information <v Narrator>must be expressed in some kind of code. In Morse code letters and numbers <v Narrator>are expressed in dots and dashes. In this Japanese dance form called kabuki, cultural <v Narrator>messages are carried by color, gesture and facial expression. <v Narrator>The idea that information might be a kind of universal vocabulary is recent <v Narrator>and revolutionary. Only since the 1940s have we begun to appreciate
<v Narrator>the power and richness of information as a language for describing reality. <v Narrator>Life itself can be thought of as information. <v Narrator>Genetic messages written in the code of DNA <v Narrator>and our invention of machines that process and store information has offered a new <v Narrator>way of thinking about the most powerful computer known. <v Narrator>It uses the equivalent of 20 watts to do the work of millions of supercomputers. <v Narrator>It's the ultimate personal computer. <v Narrator>Your brain. The brain's computing power is distributed over 10 <v Narrator>billion neurons. Caltech physicist Carver Mead specializes <v Narrator>in microelectronics. <v Carver Mead>The more you distribute information processing, the more effective it is. <v Carver Mead>So anyone that wants to hoard it simply loses <v Carver Mead>effectiveness. <v Carver Mead>It's the nature of information processing, and that is a deep thing. <v Carver Mead>That's not something we knew a long time ago.
<v Carver Mead>We thought maybe it would be like steam engines. <v Carver Mead>You built bigger ones, they'd be more efficient. <v Carver Mead>They're not. The bigger they are, the less efficient they are. <v Carver Mead>That's why the personal computer has taken over most of the computing in the world. <v Carver Mead>It's far more effective than the big computers are. <v Carver Mead>But it goes much further than that. If you divide computing even more finely, <v Carver Mead>it gets even more effective. <v Carver Mead>And the ultimate in that endeavor is the brain. <v Narrator>What does a computer do? <v Narrator>It performs a sequence of steps called a program or algorithm. <v Narrator>Each step must be so clearly described that a machine could carry it out. <v Narrator>An algorithm leaves nothing to the imagination. <v Narrator>A knitting algorithm might go knit one, purl two. <v Narrator>Knit one. Purl two. <v Narrator>An algorithm is a precise series of rules for doing a particular job. <v Narrator>In this case, making a sweater.
<v Narrator>Algorithms are humble things, but according to some researchers, they're what minds <v Narrator>are made of. Oxford University biologist Richard Dawkins. <v Richard Dawkins>I think it may be disturbing for people to realize, <v Richard Dawkins>as I believe they one day will have to realize, that everything that goes <v Richard Dawkins>on inside their minds, their brains and their minds, their soul <v Richard Dawkins>could be replicated on a computer like this. <v Richard Dawkins>That, I think, is a very disturbing thought. <v Richard Dawkins>It disturbs me, but I think it's true. <v Narrator>How could something so apparently spontaneous and intangible as human <v Narrator>creativity be reduced to a set of instructions? <v Narrator>Surely it's a thing of emotions, not equations. <v Narrator>And yet inspiration doesn't come from nowhere. <v Narrator>It's a product of the workings of our brains. <v Narrator>At UC San Diego, Harold Cohen is trying to understand the unconscious
<v Narrator>mental processes of the artist by codifying some of that knowledge <v Narrator>in a computer program he calls Aaron. <v Narrator>Aaron creates all the drawings in this studio. <v Narrator>But Cohen does more than add the finishing touches. <v Narrator>He taught Aaron everything it knows. <v Harold Cohen>I'm responsible for giving the program its own internal model of the outside world. <v Harold Cohen>The program knows enough about not only about how a human being is built. <v Harold Cohen>That is head, body, arms, legs and so forth, <v Harold Cohen>it knows where the articulations are. It knows what the legal range of movement is at <v Harold Cohen>each articulation. <v Harold Cohen>And most important of all, he knows how the body behaves. <v Harold Cohen>A coherently moving body is not a rag doll. <v Harold Cohen>There are laws governing its behavior, how it balances, how it gestures. <v Harold Cohen>All those things are things that the program has knowledge about. <v Narrator>Now, Aaron can tap that knowledge in different ways so that each drawing
<v Narrator>is unique, unpredictable. <v Harold Cohen>We are faced with a completely new set of circumstances. <v Harold Cohen>This has never happened before in human history. <v Harold Cohen>The ability to write a program that in some significant sense can <v Harold Cohen>behave as a sort of surrogate human being. <v Narrator>While Cohen is analyzing the eye of the artist, Carver Mead is studying the eye <v Narrator>of the beholder. The human visual system. <v Narrator>He's trying to create a silicone retina. <v Narrator>A computer chip model of the early stages of visual processing. <v Narrator>His goal is to understand better how our sensors work. <v Carver Mead>Your nervous system takes in almost unlimited amount of stuff <v Carver Mead>and sifts it and sifts it, and sifts it down until only <v Carver Mead>the relevant salient features are the ones that finally come through. <v Carver Mead>So it's wonderful at discarding garbage and <v Carver Mead>winnowing out the gems of information that are in this
<v Carver Mead>babbling confusion of stuff that comes in through your senses. <v Narrator>And what does the brain do with the information? <v Narrator>It makes patterns. As the Nobel Prize winning physiologist Sir Charles <v Narrator>Sherrington wrote, the brain is an enchanted loom where millions of <v Narrator>flashing shuttles weave a dissolving pattern, always a meaningful pattern, <v Narrator>though never an abiding one. <v Narrator>Poetic, perhaps, but it highlights an important difference between our brains <v Narrator>and the kinds of computers most of us are familiar with. <v Narrator>Caltech neuroscientist John Hopfield. <v John Hopfield>A given computer looks very good at some problems and very bad at other problems, <v John Hopfield>and it depends on what the designer designed it for. <v John Hopfield>In the case of digital machines that were primarily designed to do arithmetic. <v John Hopfield>And they're just brilliant there. And when you get down to it, biology <v John Hopfield>has no real designer. But evolution emphasized pattern
<v John Hopfield>recognition was a way to survival and the evolution designed the brain, which is <v John Hopfield>tremendous at pattern recognition and very poor at logic. <v John Hopfield>Logic doesn't help us survive very much. <v Narrator>When we make machines, we aim for precision. <v Narrator>We try to avoid the spontaneous, the haphazard evolution is a different <v Narrator>kind of watchmaker, as UC San Diego vision scientist V.S. <v Narrator>Ramachandran explains. <v V.S. Ramachandran>It's common to assume that the brain is in some <v V.S. Ramachandran>sense perfect, that it looks at the world and records <v V.S. Ramachandran>what's really out there. But we know that the brain is full of imperfections. <v V.S. Ramachandran>Like any other biological system, like the liver or heart or pancreas, <v V.S. Ramachandran>it's beautifully engineered. <v V.S. Ramachandran>But it's certainly not optimal or perfect. <v V.S. Ramachandran>In fact, I often point this out to my colleagues that the brain is <v V.S. Ramachandran>really a bag of tricks. <v V.S. Ramachandran>And it's interesting to compare the functions of the brain, especially the human brain,
<v V.S. Ramachandran>or indeed of any biological system with a computer. <v V.S. Ramachandran>And the key difference, I think, is that a computer <v V.S. Ramachandran>can be designed from scratch. <v V.S. Ramachandran>There's a master plan or an engineer who says, I want to get this job done. <v V.S. Ramachandran>What's the best way to do it? The brain, on the other hand, is really a patchwork <v V.S. Ramachandran>job for millions of years of evolution. <v V.S. Ramachandran>What seems to have happened is that the brain seems to have tried <v V.S. Ramachandran>different tricks, if you like, or shortcuts and picked the ones that work best. <v V.S. Ramachandran>So what you end up with is an enormous patchwork job rather than a perfect machine or <v V.S. Ramachandran>a perfect computer. <v Narrator>Here's an example of how our patchwork brains can make mistakes. <v Narrator>Most fans and coaches, too, will swear that players have hot shooting <v Narrator>streaks. But after sinking a few shots in a row, the chance of <v Narrator>hitting the next one is particularly good.
<v Narrator>Well, it's not. <v Narrator>The chance is just the same as on any other shot. <v Narrator>The hot hand is an illusion. <v Narrator>In the random sequence of hits and misses, we see patterns that aren't really there. <v Speaker>We have main engine start. <v Narrator>Here's another example of a thought illusion. <v Narrator>At one point, NASA engineers estimated the chance of space shuttle failure at one <v Narrator>in 100000. <v Narrator>Even the booster rockets had failed once every 57 firings. <v Speaker>Challenger go at throttle up. <v Narrator>The engineers were overconfident about their knowledge of the system. <v Speaker>Obviously a major malfunction. <v Narrator>NASA experts knew a great deal about the shuttle, but they had illusions about how <v Narrator>complete that knowledge was. <v Narrator>Political pressures might explain NASA's overconfidence in this particular case. <v Narrator>But psychological studies show again and again that most people, <v Narrator>technical experts included, overrate the reliability of their own knowledge.
<v Narrator>The fact is, we don't know much about how much we know. <v Narrator>How well do we perform when our powers of judgment are put on trial? <v Narrator>Members of the jury, your duty today is to determine the guilt or <v Narrator>innocence of the accused. <v Narrator>Now, you have heard instructions from the judge. <v Narrator>You've listened to testimony from witnesses. <v Narrator>And you've seen exhibits presented by the prosecution and the defense. <v Narrator>Your task now is to weigh the evidence and deliver your verdict. <v Speaker>If you are impressed with the gravity of this situation, the fact that someone's <v Speaker>fate may be determined by your power of judgment. <v Speaker>Well, I put it to members of the jury. <v Speaker>That's the case every day of your lives. <v Narrator>Every day you make decisions by combining the best information
<v Narrator>you have available, just as in a courtroom and in a complex society like <v Narrator>ours, the judgment of other people can affect your life. <v Narrator>So how good are we at making judgments? <v Narrator>Take the case of experts whose job it is to answer questions like <v Narrator>which prisoners should be paroled. <v Narrator>Who should be admitted to graduate school. <v Narrator>Which heart patients are likely to survive the next three years? <v Narrator>Well, in over 100 studies of cases like these there wasn't one <v Narrator>in which the judgment of an expert produced significantly better results than a computer <v Narrator>combining the same information according to a simple equation. <v Narrator>You see, we humans are good at deciding which information is relevant <v Narrator>to a problem, but we're not so good at combining that information to make <v Narrator>accurate judgments. <v Narrator>How do we see ourselves? <v Narrator>How accurate is the information we collect to form our self images?
<v Narrator>UCLA psychologist Shelly Taylor. <v Shelley Taylor>People see themselves as the central character and indeed <v Shelley Taylor>the hero of their own lives. <v Shelley Taylor>We tend to interpret our behavior in very benign terms. <v Shelley Taylor>We see a behavior that other people might interpret as self centered <v Shelley Taylor>or tedious, as witty and engaging. <v Shelley Taylor>We we make very self aggrandizing interpretations of what we <v Shelley Taylor>do, and then we encode the information that way in memory. <v Shelley Taylor>So when we come to retrieve instances of our behavior, we have all <v Shelley Taylor>this wonderful data stored away off of our talents <v Shelley Taylor>and relatively little data stored away regarding our faults. <v Narrator>We edit our experiences, especially those that concern ourselves.
<v Narrator>We throw out the bad takes, keep the good ones. <v Shelley Taylor>This is not altogether a self-deception. <v Shelley Taylor>It's a self-deception with one eye open. <v Shelley Taylor>I'll give you an example from one of our cancer patients that we talked with. <v Shelley Taylor>This was a woman who had battled very hard against an initial and very severe bout <v Shelley Taylor>of cancer. And she said to me during the course of the interview, I will never, <v Shelley Taylor>never get cancer again. <v Shelley Taylor>I can keep it from coming back. <v Shelley Taylor>And then later in the interview, she said, and if it does come back, I'll just fight it <v Shelley Taylor>as hard as I did the first time. So she knew in the back of her mind that <v Shelley Taylor>there was a very good chance that she would have a recurrence. <v Narrator>According to Taylor, getting through tough times can be easier when we <v Narrator>put the best face on a situation. <v Narrator>Coping with the drama of life requires the right mental moves.
<v Narrator>And we've been training for that for our entire evolutionary history. <v Narrator>Chess is a kind of mental aerobics. <v Narrator>This game can be a real workout, and if current trends in computer <v Narrator>chess continue, there'll be more to sweat about. <v Narrator>The world's computer chess champion, which is a complex program, <v Narrator>has already started to beat human grandmasters at their own game. <v Narrator>And yet hardest chance is there are much harder things that we humans <v Narrator>do without a thought. <v Narrator>Just seeing these pieces is, in a sense, a much more difficult <v Narrator>problem than actually playing the game. <v Narrator>Computers can beat most of us at chess, but we can still beat every computer at <v Narrator>seeing, at processing sensory information. <v Carver Mead>I think probably the biggest single contribution to our understanding
<v Carver Mead>of the brain that's been made by computers is our inability <v Carver Mead>to do sensory processing with them. <v Carver Mead>We've just totally failed to be able to do real-time sensory processing <v Carver Mead>with the highest horsepower digital technology we have. <v Carver Mead>Remember, this is a technology that has evolved over a factor of about one hundred <v Carver Mead>million since we built the first computer. <v Carver Mead>And we're still not close to being able to do the simplest things that <v Carver Mead>the brain does. <v Narrator>The first computers were just turbo charged adding machines. <v Narrator>We built them to do things that were difficult for us to do by hand or by <v Narrator>brain, things like precise, elaborate calculations. <v Narrator>They were flashy and fast, but none too bright. <v Narrator>The question was, could they solve more interesting problems? <v Narrator>Do things that people do well? <v Narrator>Could an artificial intelligence be kindled within a computer? <v Narrator>In January 1956,
<v Narrator>Herbert Simon, who would later win the Nobel Prize in economics, walked <v Narrator>into his classroom and announced to his students that during the Christmas vacation, <v Narrator>he and a colleague had invented a thinking machine. <v Narrator>They'd actually written a computer program that went on to prove mathematical <v Narrator>theorems like this one, not just arithmetic, not just routine number <v Narrator>crunching. Proving a theorem like this is one of the hardest things that we do. <v Narrator>And so some scientists thought if a computer can do that, maybe <v Narrator>simulating some of the other things that people do will be just a mopping up operation. <v Narrator>Well, they're still mopping. <v Narrator>Thinking of the mind as a computer has taught us some important lessons. <v Narrator>The things we expected to be the most difficult to program, like playing <v Narrator>chess or proving theorems, turned out to be much easier <v Narrator>than the everyday skills we take for granted.
<v Narrator>Vision is a good example. <v Narrator>We make sense of the patterns of light that come into our eyes. <v Narrator>We see colors and shapes, recognize objects, judge their distance and direction. <v Narrator>All this is fast, subconscious and accurate. <v Narrator>Our perceptions are usually on the mark. <v Narrator>Let me tell you a story about survival, about <v Narrator>Mother Nature and Mrs. Winchester. <v Narrator>This is the house in San Jose, California, that Mrs. Winchester built. <v Narrator>Her name was Sarah, and she was heiress to the Winchester family <v Narrator>fortune. When she moved here in 1884, there were just eight <v Narrator>rooms when she'd finished.
<v Narrator>There were over 160. <v Narrator>Why did she do it? Well, it seems that a spirit medium had told her that she could <v Narrator>avoid death and perhaps escape the curse placed on her <v Narrator>by the spirits of all those people killed by Winchester rifles if she moved <v Narrator>west and kept building. <v Narrator>So she hired a crew of carpenters and kept them busy around the clock <v Narrator>for 38 years building, well, strange things like <v Narrator>a window that opens onto an elevator shaft. <v Narrator>A door that opens onto an eight foot drop into the kitchen. <v Narrator>There are cupboards that are a bit on the <v Narrator>small side. <v Narrator>And some cupboards that aren't cupboards at all. <v Narrator>Nobody building this house was working from a master blueprint because there simply
<v Narrator>wasn't one. What you see here is the result of a series of spontaneous <v Narrator>additions. In a sense, you could say the same thing <v Narrator>about the human brain. <v Narrator>There was no master blueprint, no designer. <v Narrator>It evolved. Mother Nature's handiwork, not Mrs. Winchester's. <v Narrator>Over millions of years, bits and pieces were added to our patchwork brains. <v Narrator>The strange architecture of this house is a record of Mrs. Winchester's <v Narrator>struggle to survive. <v Narrator>And the strange architecture of your brain is a record of the human struggle <v Narrator>to survive. <v Narrator>Here's the kind of curious design that evolution produces. <v Narrator>It's a map of the areas in a monkey's brain that process visual information, <v Narrator>32 distinct brain regions are involved, communicating over three hundred and <v Narrator>five pathways. <v Narrator>It's a patchwork, but it gets the job done beautifully.
<v Narrator>In fact, visual systems are so good that you have to go to great lengths to fool <v Narrator>them. <v Narrator>Take a look at this. At first glance, it seems to be a perfectly ordinary <v Narrator>triangle, but if you study the joints, you'll see that you could not build <v Narrator>a triangle like this. <v Narrator>Watch. <v Narrator>There's something very strange going on here. <v Narrator>It's your brain trying to tell the best story it can with the available <v Narrator>information. And this time it was fooled. <v Narrator>Keep your eyes on the red mark at the bottom of the screen, but pay attention to the <v Narrator>moving shapes. You should see the yellow patch on the left moving up
<v Narrator>and down. <v Narrator>Now, look at it. It wasn't moving at all. <v Narrator>Our brains used a gimmick that in this case led to the wrong answer about what was <v Narrator>moving. <v Narrator>Seems like a perfectly ordinary room, but there's <v Narrator>more to it than meets the eye. <v Narrator>Watch. <v Narrator>You see this room is in the Exploratorium in San Francisco. <v Narrator>And no, this is not earthquake damage. <v Narrator>It was designed this way. <v Narrator>It's a visual illusion and it's based on the fact that our brains <v Narrator>make assumptions. They make assumptions about the angles in this <v Narrator>room, that they're 90 degrees, right angles. <v Narrator>Except that in this case, those are the wrong angles. <v Narrator>If they were right angles, I'd be the same distance away in each corner.
<v Narrator>But the walls and ceiling are actually askew. <v Narrator>I'm really closer and so appear bigger on the right. <v Narrator>We don't get much insight into vision from our intuitions. <v V.S. Ramachandran>Typically, we ask a man in the street what goes on in your head when you perceive the <v V.S. Ramachandran>world, He will tell you there's an image in the eyeball, the retina, <v V.S. Ramachandran>and there's images transmitted faithfully through the optic nerve. <v V.S. Ramachandran>And you have a screen in the brain, a screen called the visual cortex, <v V.S. Ramachandran>where images reproduced. <v V.S. Ramachandran>That's how you perceive the world. Well, a moment's reflection will reveal that this is <v V.S. Ramachandran>complete nonsense. <v V.S. Ramachandran>There's a basic logical fallacy here, because if you create an image inside the <v V.S. Ramachandran>brain, inside the head, then you need another person in the head <v V.S. Ramachandran>looking at that image. <v Narrator>But how does this little person see? <v Narrator>Well, that's the problem we started with. <v Narrator>He must have an even smaller person in his head. <v Narrator>And it goes on and on. <v Narrator>This way of thinking about vision doesn't explain a thing.
<v V.S. Ramachandran>There is no replica of the world inside the head. <v V.S. Ramachandran>What you have instead is a very abstract, symbolic description of the world. <v V.S. Ramachandran>What we're all trying to do as scientists studying the brain is we're trying to <v V.S. Ramachandran>understand what the nature of that symbolic description is, what you're trying to - <v V.S. Ramachandran>you're cryptographers, if you like, trying to crack the code that the brain uses <v V.S. Ramachandran>when perceiving the external world. <v Narrator>What is a code? It's a system for expressing information in the symbols. <v Narrator>Here's a kind of code. This is the talking drum once used in Africa <v Narrator>to send messages from town to town. Musician Francis <v Narrator>Harway can translate my speech into a pattern of drum tones. <v Narrator>This is a telecommunication system, <v Narrator>a long distance carrier. <v Woman>You called me the other night.
<v Woman>Oh, a few weeks ago on the telephone. <v Woman>Yes. And I was so surprised. <v Narrator>Here's another way to reach out and touch someone. <v Narrator>We're most familiar with the sight and sound of language. <v Narrator>But for Rick Joy of Santa Rosa, California. <v Narrator>Blind and deaf from an early age. <v Narrator>Language has another dimension. <v Narrator>Using a system called Tadoma, he catches up on old times with a former <v Narrator>teacher. For Rick, the flow of air and vibrations of her jaw <v Narrator>and lips are a tactile code that allow him to understand speech. <v Woman>When, what year? <v Narrator>Inn the brain, <v Narrator>the activity of neurons is a code for representing information. <v Narrator>A neuron receives messages from other neurons, processes them and sometimes <v Narrator>fires, transmitting an electrical pulse. <v Narrator>Our picture of reality is sketched in neural codes.
<v Narrator>Here's a way to see that we construct reality rather than somehow grasp it directly. <v Narrator>We see this as a walking person because our brains connect the dots in a certain way <v Narrator>like this. <v Narrator>But we could have connected the dots in other ways like this. <v Narrator>Or like this. <v Narrator>There are many ways to interpret these moving dots. <v Narrator>Our brains pick this one. <v Narrator>To perceive. Is to make choices among interpretations. <v Narrator>And we make those choices with computation. <v Narrator>Here's another example of computing reality color. <v Narrator>The colors we see are the result of a mental algorithm. <v John Hopfield>When you first think about color and you learn as a student about light, you say <v John Hopfield>something is green because green light is coming to be it for that object. <v John Hopfield>And in a certain sense, that's true.
<v John Hopfield>On the other hand, you know that by candle light and by sunlight, <v John Hopfield>you know, somebody has red and yellow shirt, looks red and yellow, and the light <v John Hopfield>coming from that object is just radically different of the two circumstances. <v John Hopfield>And you do have to ask, why don't you see the change? <v John Hopfield>And you don't understand why that should be the case until you go <v John Hopfield>back and say, what's the purpose of having color vision? <v John Hopfield>We ought to be able to recognize objects. <v John Hopfield>Now, what's the same about that object between candlelight and sunlight? <v John Hopfield>Is the fact that the dyes in it haven't changed. <v John Hopfield>And so what you really want to recognize is what dyes are present at, not what light is <v John Hopfield>coming from it. <v Narrator>The mental algorithm for computing color isn't yet known. <v Narrator>But researchers think that the color of an object is based on comparing the light <v Narrator>from that object with the light from neighboring objects. <v V.S. Ramachandran>Looking at the brain as an information processing device says that you've not <v V.S. Ramachandran>completely understood everything yet.
<v V.S. Ramachandran>I mean that what we think of as the world, what do you think of <v V.S. Ramachandran>as the self is really convenient fiction, which happens <v V.S. Ramachandran>to work for the time being. <v V.S. Ramachandran>But it gives you highly - it gives you a clear idea that <v V.S. Ramachandran>our conception of reality and our conception of ourselves is highly tentative <v V.S. Ramachandran>in nature, that it can change any any time when there's new information available. <v V.S. Ramachandran>And I think this gives you ultimately gives you a sense of humility. <v Narrator>Let's go back to the drawing board. <v Narrator>Our brains are continuously computing what we call reality, making sketches <v Narrator>of the world. <v Narrator>We're not aware of most of that mental activity, that computation. <v Narrator>It seems to us as if we're aware just of the end results.
<v Narrator>And as we've seen, although the models we make of the world are far from perfect. <v Narrator>Most of the time we do just fine. <v Narrator>It's some measure of how little we know about the brain that we're always comparing it to <v Narrator>something. You know, it's like an enchanted loom or a telephone switchboard. <v Narrator>How about it's like a mail sorting office. <v Narrator>Messages are constantly arriving from the far corners of our sensory world. <v Narrator>They get sorted according to destination. <v Narrator>It's a fast, efficient system. <v Narrator>Mostly.
<v Narrator>It may not be perfect, but it gets the job done. <v Narrator>And here it is. <v Narrator>Message received. <v Narrator>But of course, the world isn't delivered right to us. <v Narrator>We have the impression that we take in our surroundings at a glance, <v Narrator>but that's an illusion. <v Narrator>We construct our picture of the world by moving our eyes several times a second. <v Narrator>Each glance takes in a part of the scene. <v Carver Mead>There's a place in your brain. There must be a place, nobody knows where it is, where <v Carver Mead>this collage is put together into a single unified percept of <v Carver Mead>the outside world. But that's a computed construct. <v Carver Mead>It's not something you see. <v Carver Mead>You piece it together from these individual little vignettes. <v Narrator>Sampling a glass of wine is a practical demonstration of how
<v Narrator>the brain works as an information processor. <v Narrator>Wine is literally a message in a bottle. <v Narrator>And to read it, without cheating by looking at the label, <v Narrator>we use our senses. <v Narrator>The first clue is visual. <v Narrator>The color, the appearance, but clarity. <v Narrator>And in fact, there are some experts who can tell you the year the grapes were harvested. <v Narrator>The vintage of the wine just by looking at its color. <v Narrator>Next, the smell, what professionals call the bouquet. <v Narrator>And again, there are experts who can pinpoint the vineyard just by sniffing <v Narrator>the wine. Finally. <v Narrator>Taste. Now, those three different sensory <v Narrator>mechanisms developed during our evolutionary history to solve <v Narrator>different problems that were essential to our survival. <v Narrator>We have different mechanisms for collecting the sensory information and different
<v Narrator>specialized parts of our brains for processing it. <v Narrator>So in a sense, a glass of wine reveals the compartments, <v Narrator>the modules of the mind. <v Narrator>A film editor works with two different kinds of information, picture <v Narrator>and sound. The sound is stored in a magnetic stripe. <v Narrator>One part of this machine is specialized for decoding it. <v Narrator>There's a division of labor in the brain as well. <v V.S. Ramachandran>Different parts of the brain are specialized for different jobs, and they carry out these <v V.S. Ramachandran>functions relatively independent of other parts of the brain. <v V.S. Ramachandran>Of course, it doesn't mean that these different parts of the brain don't communicate with <v V.S. Ramachandran>each other. They do. And that's why you are one person. <v V.S. Ramachandran>But there is a surprising degree of autonomy and function. <v V.S. Ramachandran>And this is the I think, the new insight into how the brain works.
<v Narrator>This clinical recording by neurologist Barbara File <v Narrator>shows evidence for this kind of design. In this patient, a stroke <v Narrator>has cut off vision from other compartments of the mind. <v Narrator>She sees well enough to read, but she's unable to name objects by looking <v Narrator>at them. Her other senses, however, work fine. <v Narrator>The syndrome is called visual agnosia. <v Barbara File>What is this? <v Patient>That looks like, uh... <v Narrator>Looking at a clothespin, she's baffled. <v Narrator>But she recognizes it quickly with another sense. <v Barbara File>You can touch it then tell me what it is. <v Patient>Oh I can touch it? Now I can see it already. It's a clothespin. <v Barbara File>Okay. What's this here? <v Narrator>Here, she can't recognize a candle by looking at it. <v Narrator>Her sense of touch lets her make a good guess. <v Narrator>But this time, it's her sense of smell that provides the answer. <v Patient>Oh,
<v Patient>it's a candle. <v Narrator>Researchers are now finding that the brain's compartments are even further subdivided. <v Narrator>Take vision, for example, different kinds of visual information <v Narrator>seemed to be processed separately. <v Narrator>Some of the evidence for that comes from studying patients with brain damage. <v Narrator>And there's a classic case of a woman with a disorder called motion blindness. <v Narrator>For her, even pouring a drink became a guessing game. <v Narrator>The sub compartment of her brain that handles visual motion was damaged. <v Narrator>She had no trouble seeing cars, but she couldn't judge their speed. <v Narrator>So crossing the street became an ordeal. <v Narrator>She avoided parties because of the way people suddenly seemed <v Narrator>to appear in front of her. <v Narrator>And how do you follow a conversation when you can't see the motion of someone's <v Narrator>lips? <v Narrator>Motion is only one of several visual sub compartments in the brain.
<v Narrator>You know, those days when life seems colorless? <v Narrator>Well, for some people suffering from a disorder called achromatopsia it really <v Narrator>is. Their retinas work fine but the part of the brain <v Narrator>that processes color is damaged. <v Narrator>So their world really is black and white. <v Narrator>If the mind is made of modules, how do they act together? <v Narrator>In fact, how do billions of neurons act together to produce art, <v Narrator>poetry, flights of the imagination? <v Narrator>Here's a simple analogy. <v Narrator>A single bird can do many things. <v Narrator>It can soar. Build a nest and perch. <v Narrator>But one thing it can't do is flock. <v Narrator>How do individual birds fly together as if they were of one mind?
<v Narrator>An answer comes from computer simulations of flocks. <v Narrator>In this animation, every bird independently follows a few simple rules. <v Narrator>So this isn't a cartoon where every motion is specified by an animator. <v Narrator>It's more of an experiment within a computer. <v Narrator>Intuition might lead you to expect an air traffic controllers nightmare. <v Narrator>And yet, out of these rules spread throughout the group, a flock emerges. <v Narrator>This is a computer reconstruction of a neuron from a human brain. <v Narrator>A cell like this can receive process and transmit information, <v Narrator>but it can't see or think. A collection of billions of <v Narrator>neurons - a brain c- an see and think.Tthe neurons <v Narrator>somehow flock together to form a mind. <v Narrator>If it looks like a duck, walks like a duck and quacks like a duck,
<v Narrator>it's probably a duckreBut these different kinds of information, sound <v Narrator>motion appearance, are processed in different modules of the mind. <v Narrator>What ties them together to give us a unified experience of a duck? <v Narrator>Researchers are beginning to speculate about a possible answer. <v Narrator>It seems that when neurons are carrying information about different objects, they <v Narrator>fire independently of one another. <v Narrator>But when they carry information about a single object, they seem to fire together about <v Narrator>40 times a second. <v Narrator>Being in sync may turn out to be the glue that binds the pieces of our <v Narrator>perceptual world together. <v Narrator>This isn't something that could have been discovered by studying individual neurons. <v Carver Mead>It isn't the pieces that carry the secret of thought. <v Carver Mead>It's the way the whole system works together. <v Carver Mead>It's a much more holistic view of what the question is. <v Carver Mead>How does the system work? <v Carver Mead>And that's been hard because science did so well for so long, being reductionist,
<v Carver Mead>going down and getting the one problem at the bottom and solving <v Carver Mead>that. And then everything flowed from there. <v Carver Mead>It's hard for us to get our heads around the fact that that <v Carver Mead>isn't the question in neurobiology. <v Carver Mead>The question is, how is the system organized? <v Carver Mead>How can it carry out these fantastic computations? <v Carver Mead>How would you even imagine organizing a system that could do that? <v Narrator>For years, researchers have studied the minds' computations with serial <v Narrator>computers, machines that carry out instructions one step at a time. <v Narrator>Some scientists are saying this approach may be a dead end.
<v Narrator>The possibilities of parallel computing are now being explored using <v Narrator>neural networks inspired by the architecture of the brain. <v Narrator>Each computing unit of the network sends messages to other units which process <v Narrator>the information and relay new messages. <v Narrator>The strength of their connections can be changed to fine tune the performance of the <v Narrator>network. The idea is simple. <v Narrator>Instead of one powerful central computer, there are many more modest processors <v Narrator>linked together. <v Narrator>Of course, real neurons are much more complicated than the units in these networks. <v Narrator>And yet researchers hope that these connectionist models may offer clues <v Narrator>to the way minds work. <v Narrator>For example, different memories in a single network may have processing <v Narrator>units in common. <v Narrator>Jeff Hinton is a psychologist and computer scientist with the Canadian Institute <v Narrator>for Advanced Research. <v Geoff Hinton>If I, for example, tell you a fact that you probably didn't know - that chimpanzees just
<v Geoff Hinton>love eating onions - and now I ask you, do you think gorillas like like eating <v Geoff Hinton>onions? Well, you don't know, but you think it's a bit more likely than you thought it <v Geoff Hinton>was before. That will happen automatically within your own network because internally <v Geoff Hinton>it's representation for a chimpanzee. <v Geoff Hinton>We'll share a lot of neurons in common with his representation for a gorilla. <v Geoff Hinton>And when you learn that chimpanzees like onions, you'll change some of the connection <v Geoff Hinton>strengths coming from the neurons that are active in the representation of chimpanzee. <v Geoff Hinton>And that will automatically change some of the connections strengths coming from the <v Geoff Hinton>neurons that are active in the representation of gorilla. <v Geoff Hinton>So you'll get this automatic generalization to similar things. <v Narrator>Parallel computation seems to be the right approach for many of the problems that animals <v Narrator>need to solve. <v Narrator>A beehive can be considered a parallel information processor. <v Narrator>Individual bees are the processers, collecting information and distributing it. <v Narrator>The result? An efficient society. <v Narrator>A free market is another case of information processing spread over many individuals.
<v Narrator>Every sale can be seen as an exchange of information, and the result <v Narrator>of these parallel computations, according to some economists, is an efficient <v Narrator>distribution of goods. <v Narrator>Carver Mead is an advocate of parallel computation, and he is putting his ideas <v Narrator>to the test. Trying to copy some of the brains decentralized computing <v Narrator>power in a silicon chip. <v Carver Mead>What you're looking at is not a television picture. <v Carver Mead>It's the result of an image processed by a silicon retina. <v Carver Mead>The first stage in the processing of your visual system <v Carver Mead>is done by a set of cells just behind your eyeball that <v Carver Mead>extract the first kinds of salient information from <v Carver Mead>the image coming in through your lens. <v Carver Mead>The silicon retina does a similar kind of computation. <v Carver Mead>It's extracting the salient information and discarding
<v Carver Mead>less interesting information. <v Narrator>The word retina comes from the Latin word for net, and that's a good way to think <v Narrator>about Mead's silicon version. <v Narrator>It's a net for catching information. <v Narrator>Like the human eye, the chip is a surprise detector. <v Narrator>It finds places in an image that are darker, brighter or moving <v Narrator>compared to their neighbors. <v Narrator>This image, remember, is not produced by a camera. <v Narrator>A camera simply records. <v Narrator>The silicon retina, like the human eye, interprets, computes. <v Narrator>The silicon retina uses parallel computation. <v Narrator>Knitting a sweater is a stepwise or serial algorithm. <v Narrator>Different styles of computation fit different problems. <v Narrator>This is the end product of a knitting algorithm. <v Narrator>It's one of the things you can get from following a set of instructions.
<v Narrator>I'm another. <v Narrator>My DNA is an algorithm. <v Narrator>It contains the instructions for building me. <v Narrator>Incidentally, the lady who carried out the sweater algorithm also <v Narrator>helped create my algorithm. <v Narrator>She's my mother. <v Narrator>Part of her DNA became my DNA. <v Narrator>This molecule contains an algorithm for building a living thing. <v Narrator>I'm the Three-Dimensional output of my DNA program, constructing <v Narrator>an organism from its genes m aybe the first information processing problem <v Narrator>solved on Earth. <v Richard Dawkins>Nowadays, genetics is an intensely digital process. <v Richard Dawkins>I would almost go as far as to say genetics is just a branch of computer science or vice <v Richard Dawkins>versa. There's there's very little difference between the way you can think about genetic <v Richard Dawkins>information and the way you think about information in a computer. <v Narrator>DNA - primordial information - has spawned an information
<v Narrator>processor, the human brain. <v Narrator>And this computer has culture. <v Narrator>Culture is information that is learned. <v Narrator>Information like beliefs, ceremonies and traditions. <v Narrator>Culture is a second evolutionary path and it's faster than biological <v Narrator>evolution, where information must cross a generation gap as it travels from gene <v Narrator>to gene. Cultural information travels from brain to brain. <v Narrator>Christianity is cultural information. <v Narrator>So is architecture and music and the stories we tell about where we <v Narrator>came from and where we're going. <v Narrator>And so is science. <v Narrator>And what is the culture of science doing? <v Narrator>It's creating more and more sophisticated computers and programs, programs
<v Narrator>that are now beginning to simulate minds. <v Narrator>But they may go farther than that. <v Narrator>Some researchers imagine a day when computer programs will create their own successes. <v Narrator>The computer, our brainchild, may then head off on yet another evolutionary <v Narrator>path. The journey that began with DNA is leading to unexpected <v Narrator>destinations. <v Richard Dawkins>Genetic evolution got brains up to the point where they were complicated <v Richard Dawkins>enough to start developing language and start developing culture. <v Richard Dawkins>And that enabled a new kind of evolution to start. <v Richard Dawkins>So there's been a kind of takeoff and it runs a lot faster than genetic evolution. <v Richard Dawkins>After a few centuries of this cultural evolution - or a few millennia of this cultural <v Richard Dawkins>evolution - brains started to develop artifacts so <v Richard Dawkins>complicated - computers I'm talking about - that they themselves were potentially <v Richard Dawkins>capable of yet another takeoff point. <v Richard Dawkins>And that may be what we're now about to see.
<v Richard Dawkins>There may be yet another kind of cultural evolution which leaves humans <v Richard Dawkins>out, which ultimately leaves humans out. <v Richard Dawkins>I suppose this is yet another blow to our vanity, if finally, <v Richard Dawkins>we become superfluous to civilization. <v Narrator>Might these be the first steps towards a silicon civilization? <v Narrator>This is a computer animation by Michael McKenna of M.I.T.'s Media <v Narrator>Lab. Unlike traditional animation, the exact motions <v Narrator>in this film aren't scripted in advance. <v Narrator>There's a program in this bug. <v Narrator>The animator has set out the path and speed of a cockroach. <v Narrator>But an algorithm automatically computes the motion of the legs. <v Narrator>This could be a path that leads towards artificial life, a pattern of activity
<v Narrator>within a computer so complex and adaptive we might be tempted to think <v Narrator>of it as a alive. <v Narrator>We are sometimes made uneasy by the achievements of science. <v Narrator>We feel perhaps that with each discovery that opens us to closer investigation <v Narrator>and strips away some of the mystery of how we think or the mechanisms of creativity, <v Narrator>that we're somehow diminished. <v Narrator>But something is lost. <v Narrator>And that prospect is fundamentally disturbing. <v Narrator>Comparing the brain to an enchanted loom was really an admission of our ignorance. <v Narrator>It was just a metaphor, not a working model. <v Narrator>No scientists installed looms in their laboratories to study the warp and weft <v Narrator>of the fabric of mind. <v Narrator>But the computer is different. <v Narrator>We now routinely use the invention to study the inventor.
<v Narrator>The notion that we are information processes that we perform computations <v Narrator>to assemble, pictures of reality has yielded new insights. <v Narrator>How has this model changed our image of ourselves? <v Narrator>Well, for one thing, we've learned that our brains are far more sophisticated than we <v Narrator>could have imagined before we tried to reproduce our performance in silicon. <v Narrator>But we've also discovered that our brains are patchwork, not perfect. <v Narrator>It's perhaps ironic that the computer, for some a symbol of the dehumanizing <v Narrator>power of technology, is becoming our collaborator in our search for self-knowledge. <v Narrator>The information processing model of mind can even be thought of as liberating. <v Narrator>Focusing on what human beings have in common as thinking machines <v Narrator>is helping us to appreciate more clearly what it is to be human. <v Narrator>You do find it an exciting time, don't you? <v Carver Mead>It's fantastic. There's never been a time like this in human history.
<v Carver Mead>This is the greatest intellectual quest of all time. <v Carver Mead>Hands down.
Inside Information
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KCET (Television station : Los Angeles, Calif.)
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The Walter J. Brown Media Archives & Peabody Awards Collection at the University of Georgia (Athens, Georgia)
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"Some say the Information Age began in the late 40's, when digital computers were invented. But if researchers in biology and the brain sciences have it right, the Information Age is some three billion years older than that. It began when life of Earth appeared. Living things are, in a sense, the three-dimensional read-out of the information contained in their DNA. And part of that genetic read-out is the brain, itself an information processor. "INSIDE INFORMATION, a one-hour special from the KCET Science and Society Unit, explores how brains make sense of the world. In the last half century, a powerful metaphor of mind has emerged. The mind as computer. And just as steam engine was the defining image of the Industrial Revolution, the computer is the driving engine and totem of the Information Revolution. This product of human intelligence has now become a tool for exploring and modeling the human brains that brought it into being. Our future sense of ourselves is now intimately bound up with the evolution of our brain-child. "This program goes behind the scenes in the mind to ask: How do we know what we know? How accurate are our impressions of ourselves? What kind of computer is the human brain? And if computer games can already beat chess grandmasters, will the next chapter in the story of human evolution leave humans out' "We believe INSIDE INFORMATION merits Peabody consideration because of its ability--at a time of diminishing scientific literacy--to convey complex information in an unusually engaging and entertaining manner."--1990 Peabody Awards entry form.
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Producing Organization: KCET (Television station : Los Angeles, Calif.)
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Chicago: “Inside Information,” 1990-11-29, The Walter J. Brown Media Archives & Peabody Awards Collection at the University of Georgia, American Archive of Public Broadcasting (GBH and the Library of Congress), Boston, MA and Washington, DC, accessed June 26, 2022,
MLA: “Inside Information.” 1990-11-29. The Walter J. Brown Media Archives & Peabody Awards Collection at the University of Georgia, American Archive of Public Broadcasting (GBH and the Library of Congress), Boston, MA and Washington, DC. Web. June 26, 2022. <>.
APA: Inside Information. Boston, MA: The Walter J. Brown Media Archives & Peabody Awards Collection at the University of Georgia, American Archive of Public Broadcasting (GBH and the Library of Congress), Boston, MA and Washington, DC. Retrieved from