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The Hunt for AI

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Just before Christmas, I witnessed something extraordinary. I stood in

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a room with two robots that were starting to do something distinctly

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human. They were learning like young children do, when they

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discover the world for the first time. They were developing a

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language, one that I didn't understand. It's quite scary. Oh,

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gosh, it's asked me to do something. More and more of the things we once

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thought made us human are now being taken on by machines. I want to

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find out how close we are to creating artificial intelligence.

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And what that might mean for us as human. As a mathtition, should I be

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worried that they may put me out of a job, or could they extend human

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intelligence in unimaginable new ways? What are these machines? Or,

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In February 2011, New York was a seen for a very public test of how

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clever machines have become. A showdown watched by millions, took

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place between two men and a supercomputer. The arena for this

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battle was a quiz show called Jeopardy. It's one of the most

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popular and long-running quiz shows in America. It's famous for its

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Fortunately, those in mankind's corner had no problem with these

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questions. One of the two human contestants was Brad Rutter, the

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show's biggest-ever money winner. He became a five-time champion,

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earning a record $3.2 million. long as I can remember, I grew up

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with Jeopardy and by the time I was in high school I started realising

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I knew the answers to most questions and if I ever get a

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chance to try out, I think I can do pretty well. If wow have told me

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how well I would actually do I would have said you were crazy, but

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$3.2 million later here I am. the newcomer. This is Watson.

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Watson was one of the most sophisticated AI computers ever

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built. Sitting in the audience was his creator. We wanted to do this

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kind of experiment, this illustration of what would it take

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for a computer to play Jeopardy against a human. A brain that fits

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in a shoebox powered on a glass of water and sandwich then you have a

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computer 80 kilowatts of electricity and 20 tonnes of air

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conditioning. This is what it took. In the lead-up to the match, Watson

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had to learn how to play the game. In effect, it had to be coached.

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One of the most difficult tasks it faced was to get to grips with the

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cryptic format of the game, where the contestants are first given the

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answer and have to come up with the question. This queen fled to London.

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It had to go through the 200 million pages of information it had

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been programmed with and find the answer in just three seconds.

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famous clown or any incompetent fool? Who is Bozzo. After four

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years of development, Watson was considered ready for the challenge.

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Let's play Jeopardy. Here we go. was a really, really tense moment.

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If I was going to fail we were going to fail in front of - This

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was real jeopardy? This is real. You were putting yourself on the

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line. This was real and live. People are going to route for the

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people rather than the machine, so we felt a little bit of pressure,

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but also a great opportunity to kind of represent humanity against

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encroaching technology, shall we say? Four-letter word for a vantage

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point or belief? What is a view. You have adrenaline going. It was

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an open playing field. Anything could happen. Who can Grendell

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is the 1930s? After the first round of the first game, Watson was tied

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with Brad at about $5,000. Into a tie for the lead. I think everyone

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sat there and thought at least we didn't lose. In other words, at

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least at this point, the awedients is understanding -- audience is

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understanding that the system is good to tie a grand champion.

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close for comfort. I remember that moment when I first heard about

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Watson. I thought what a key moment for AI. We have already programmed

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a computer to beat humans at chess, but chess is a vertical thought

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process, where you are making deep, logical connections. Watson is

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doing something completely different and thinking horizontally,

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accessing a huge database. This is machine intelligence taken in a new

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direction. The computer is starting to think in a multi-dimensional way.

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How the game would unfold was to change the way many people think

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about artificial intelligence, including me. But in order to

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understand the real significance of Watson, I need to know where this

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dream to replicate our intelligence For thousands of years, we have

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invented machines to do things for us. But there was one moment in

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history where we realised they weren't just slaves, but had the

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potential to be our rivals. Or even I've come to the place where this

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realisation first came to life. It was in this building, that the

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search for artificial intelligence began. What happened here still

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resonates today. The idea that machines could be intelligent like

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us was sparked by one man. He was the brilliant man, Alan Turing and

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he's a real hero of mine. When he was a young man in his 20s he came

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to work here at Bletchley Park, as a code breaker. This was the home

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of British intelligence at the time. It was the 1930s and Britain was

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gearing up to war with Germany. As well as needing the brute force of

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the military, the Government were gathering a crack team hear. And

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his genius was precisely what was needed at this time of national

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The main problem that he had to wrestle with whilst here, was this.

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It's an enigma machine and it's an incredibly clever piece of kit. It

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was used by the German military to scramble secret messages into code

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that nobody could understand. Cracking this code was incredibly

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difficult for humans. The Germans believed that the code was

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uncrackable. It was up to human minds to decipher the cryptic

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communication that was picked up on the airwaves. This was an

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impossibly long task. We were on the brink of war and didn't have

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time to waist. -- waste. The machine consists of a key board

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that I will type my message on to try to encode it. If I press an R,

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then the wires in the machine go through and light up a letter at

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the top here, so the R was encoded by theler N, but the incredible

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thing is, about the machine, if I press R again it doesn't get

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encoded by N the next time, because these rotas have kicked on one step

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and so the wires are connecting up the letters in a completely

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different way now. So when I now press R it is F. The operators had

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different ways that they could set up the machine, so they can make a

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choice, for example, of which rotas they were using, so there were five

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different rotas and they would choose three and they could put

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them inside in different orders. Then they would set each of the

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rotas on one of 26 different settings and then not only that,

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they had some plugs at the front here, which would also scramble up

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all of the letters, so the combined effect is that there are over 150

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Slobodan Milosevic different ways that the machine can be -- million,

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million, million, different ways that the machine can be set up. It

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really was human against machine. Alan Turing and his team realised

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the human mind wasn't fast enough to do this and the only hope they

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had of cracking the code was to create another machine to do it.

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This is the incredible machine that he and his colleagues created. It's

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called the Bomb. It was from this mesh of cogs and switches held

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together by over ten miles of wire that finally came the ability to

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crack the code on a daily basis. Something that no human had come

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close to being able to do. What this machine did changed the course

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of the war and many regard as crucial to the allied victory over

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In one sense, this machine was just a collection of cogs and wheels.

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But it could do something that some of the finest finds in Britain

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couldn't. It was an unsettling moment. Was this machine cleverer

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than a human? That is an incredibly satisfying sound. It must have been

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an amazing moment when they switched it on for the first time,

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seeing the cogs clicking through all the different possibilities,

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getting a possible setting in ten minutes what would have taken

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humans weeks to try to find. To see this machine solve a problem that

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many regarded as impossible, it must have really fired Alan

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Turing's imagination. This was built to crack the enigma code, but

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he must have begun to think about what was the limit of what machines

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could do? Once the war was over, his ideas about machines that could

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mimic the human mind really started to take shape. But it was when he

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asked the question, can a machine think in a paper in 1950, that the

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idea finally sparked people's imaginations. For many, it conjured

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up terrifying visions of the future and sparked furious debate. But it

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was this idea, above any other, than inspired scientists in labs

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across the world to try to re- create what is essentially us. The

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hunt for artificial intelligence has become tied up with one

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technology, the computer. At the heart of it is the simple idea that

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humans and computers aren't so very different. The central thesis of AI

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is that actually all human thought is just computation, that the mind

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and the brain are just a sophisticated sort of machine. So,

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if you could make a big enough computer, with the best software,

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you can make a copy of the human So riveted by the man versus

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machine Jeopardy game. Because at estheart, it was so much more than

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just a TV showdown. Hoare was a machine that could grapple with

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natural language, as well as any human. -- here. Including pun,

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riddles and complex questions across a broad range of knowledge.

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Please welcome Watson. At the end of the first round, the

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supercomputer Watson was neck and neck with human Brad. But as the

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second round began, the computer started to show what it was really

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capable of. Watson who is... Frans list. Who is the church lady. It

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all fell apart from Ken and me the second day. Watson went on a tear.

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What is violin. It ex exceeded expectations Watson dominated the

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entire thing. The final blow for human kind was when Watson went on

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to win a bonus question. Answer... Which doubleded his score. When

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Watson hit the daily double that was pretty much the end of the road

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ch you could see the look on Ken's face, we knew, the guys in the team,

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that "It was over, we've won." Watson's landslide victory made

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headlines across America. I would have thought technology like this

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was years away but it is here now. I have the bruised eco-to prove it.

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For days after the interhet was alive with chatter about the demise

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of humanity. Here was a computer that was displaying uniquely human

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qualities. This machine could understand all the complexities,

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puns and riddles much faster than any human. But was this true

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artificial intelligence? Was Watson really thinking like us? The 1980s

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the American philosopher John Searle challenged the idea whether

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a machine could ever really think. He came up with an interesting

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thought experiment to try and prove his idea. It is called the Chinese

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Room. I am going to try and put that into practise to explore his

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He imagined a non-Chinese speaking person like me, in a bare room,

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with an instruction manual, and a selection of Chinese characters. He

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then imagined someone who could speak Chinese, to be outside the

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room, communicating with me, by a series of messages written using

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Chinese characters. Can you speak Chinese? The messages are posted to

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me through a door. And I have to come up with a response, even

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though I don't understand a word of Chinese. OK I haven't got a clue

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what this says. But if I follow the instructions in this book, I should

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be able to give some sort of response to this question, which

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will make some sort of sense, so let's have a look. What do I have

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to find? There is a little one over X symbol. The book tells me to find

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an exact match of the message on the top half of the page. And then

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to reply by copying the Chinese characters from the bottom half of

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the page. That is interesting. Some of the characters appear in the

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answer, which makes some sense. Gosh, I don't know whether these

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are the right way round. That looks like a little house with a roof on

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the top. I need this character here. Oh yes. Bingo! Who knows what I

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have just said! Can you speak Chinese? He compared the person in

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the room making up the Chinese message, to a computer reading a

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bit of code. Have you been to No I haven't been to China but

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would one day like to. I am just faking I can speak Chinese. So just

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how convincing was I? You your Chinese was perfect. If we were on

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line having a chat like that, you would have been convinced you were

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talking to... I really really would. I would have thought you were

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someone who spoke Chinese. Chinese Room I think is an

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important thought experiment. Not for the answers it offers but more

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for the questions it raise, because I certainly didn't understand a

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word of what I was writing down there, yet I seem to be able to

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fake the fact I was speaking Mandarin, so if a computer is

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following instructions, isn't really thinking, is it? But then

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again, what is my mind doing when I am actually articulating words now?

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It is following a set of instructions, so it raises the

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question, where is the threshold? What point can you they a machine

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is really understanding and thinking? So, our bigger and faster

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machines going to bring us closer to this threshold? Since John

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Searle came one the Chinese Room experiment computer power has been

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doubling every two years. I am off to see one of the most powerful

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computers that exists in the world today. We enter the lab I need to

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warn you it will be very noisy. You will need some ear protection.

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What is the noise? So that is all cold air blowing through the racks,

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to cool off the computer chip. Because they are working so hard.

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Because they are working so hard. This is the first time I have done

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an interview with ear plugs in. This beast of a machine called Blue

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Gene is a phenomenal piece of engineering. It is used to perform

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the kind of calculations that my brain could never do, and it never

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has to sleep. Fred Mintzer is the naers inventor and Cray to have

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Blue Gene. -- master. So this is first generation Blue Gene rack.

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Its commutational performance is 5.6 Terra nops a second. A teraflop

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is a million million operations per second. Faster than I can go.

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Faster than you can go. How many have you got. We have 16 Rab rack,

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they all work in parallel. That is 60,000 processors and a performance

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of 91 teraflops a second. Blue Gene is solving complex calculations

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about biological processes. It would take me the rest of my life

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to do what this computer can do in an hour. But is it really being

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intelligent? Or is it just doing what it is told? The real

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intelligence comes from the programmer. It is totally

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programmed intelligence. Would you say it's a big number cruncher.. It

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is an enormous number cruncher. But it the doing the things our brains

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could never get anywhere near. computers are good at computation,

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brains do much more. It is impressive to see just how powerful

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they have become. This firepower you have with Blue Gene is

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something my brain could get nowhere near, but then again, with

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when I am doing maths it is not just doing number crunching. I am

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doing more than that. The brain is working, making leap, a lot of

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imagination, there is a lot of choices involved, which often

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involve a kind of aesthetic sensibility. I am not saying a ma

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sheen like that might not be able do that but at the moment it is a

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sheer brute force crunching. It hasn't captured what my brain can

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do. These other qualities that inare inhe want hernt in how man

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intelligence, like creativity and imagine nation, are what make us so

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unique. And they are the reason that the quest for artificial

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intelligence is such an interesting and difficult challenge. There is

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no reason to think that a computer couldn't one day develop these

:25:34.:25:40.

qualities too. But to do this, scientists have got to get o grips

:25:40.:25:50.
:25:50.:26:01.

with exactly how well develop them At the heart of human intelligence

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lies ourable to learn to do things we have never done before. Which is

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what has borough me here. And that is not me by the way. I have joined

:26:14.:26:20.

the circus an my challenge is to walk the tightrope. Not something

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mathematicians normally do. My instructor for the day is Lila.

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are nervous. It is not that. There we go. This is a training wire.

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So... This isn't the real thing. is. It is the same you will feel on

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the high wire. You don't have the fear fabg - factor because you can

:26:42.:26:49.

stand over it safely. The other one there is a bit of jeopardy. No, it

:26:49.:26:59.
:26:59.:27:00.

is just... You could get a body bubble edouble. He is wearing a red

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:27:10.:27:11.

shirt! See, no movement. So once upon a time he couldn't do that. He

:27:11.:27:14.

wasn't born being able do that. That is something he learned.

:27:14.:27:19.

I should have the confidence that I can learn this new skill.. You know

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the younger you learn something... What are you saying? I am saying it

:27:25.:27:33.

is easier. You saying I am too old. No, not at all. I am going to do

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this. Lila can't programme me with a set of rules. Unlike a computer,

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my brain and my body have to master this by themselves. As a

:27:47.:27:52.

mathematician I know there is a formula to describe what is going

:27:52.:27:56.

on here. But I can't calculate my way out of this. I just have to do

:27:56.:28:06.
:28:06.:28:13.

Let your muscles do the thinking. Turn your brain off a bit. I have o

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do things with my body and I have to think and counter act certain

:28:18.:28:23.

things which I shouldn't do. There is a guy over there, who can just

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sort of skip and dance across. Hands up. Hands up. Hands up!

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trying to concentrate on the wall over there and keep my hand up, I

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am trying to keep this leg straight, and maybe I am thinking too much.

:28:46.:28:50.

think, we have to get some of it into muscle memory so you don't

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think about that, that it comes naturally. As I flail round on the

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tightrope, I can see the complicates physics I am asking my

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brain and body do to master this new skill. It is this idea that the

:29:04.:29:08.

brain and body work together. That is taking the hunt for AI in a

:29:08.:29:18.
:29:18.:29:31.

I have come to meet a scientist who has built the world's first

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anthropomimetic robot. Which means it mimics the hue -- human body.

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Because he believes that our fiscal form actually helps shape the way

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we think. Without a body, artificial intelligence cannot

:29:51.:29:57.

exist. 6. What we are interested in is how the brain controls body and

:29:57.:30:01.

having built the body we have realised what a serious problem

:30:01.:30:07.

that. Can I interact with it? you like to shake hands with it?

:30:07.:30:15.

Yes, provided it doesn't clonking me anywhere sensitive. Give its

:30:15.:30:25.
:30:25.:30:29.

hand a good squeeze and a shake. It's squeezing back. It's really

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responding to contact with me, picking up something. It gives

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people the sensation of interacting with something that in a way of

:30:37.:30:47.
:30:47.:30:47.

really like themselves. This robot called Ecce Robot has no blood or

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skin or flesh, but ha bones, joints, muscles and tendons that move like

:30:56.:31:00.

ours, so it's able to have human interactions. This is an

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extraordinary piece of engineering, but how does it help in getting

:31:03.:31:09.

insight into intelligence? We are interested in what is known as

:31:09.:31:14.

embodied intelligence. This is the idea that your intelligence is not

:31:14.:31:18.

an abstract free intelligence, but tied very much to having a body and

:31:18.:31:22.

in particular to having a human body. In order to investigate this,

:31:22.:31:29.

what we have to do was build a robot that had as near to a human

:31:29.:31:33.

body as we can manage and investigate the way it interacts

:31:33.:31:37.

with the world and enables it to develop a particular sort of

:31:37.:31:41.

intelligence for dealing with objects and so on. Once we get on

:31:41.:31:48.

that track we'll be able to see how that intelligence, how that is

:31:48.:31:52.

actually determined and conditioned by its body. You think it will be

:31:52.:31:58.

distinct from the sort of intelligence that - does it know

:31:58.:32:04.

I'm talking about it? The intelligence that before they were

:32:04.:32:07.

doing things in robots, computers and that doesn't have a sense of a

:32:07.:32:11.

body, it's a box? What happened when people started trying to make

:32:11.:32:15.

robots that were controlled by AI and computers, is that they found

:32:15.:32:19.

that the things that we thought would be difficult like playing

:32:19.:32:24.

drafts or something like that were extremely easy. What was difficult

:32:24.:32:28.

was moving the pieces. The interaction with the world is very,

:32:28.:32:31.

very difficult and requires a lot of computation, but we are

:32:31.:32:37.

completely unaware of this. What about the ultimate goal of things

:32:37.:32:40.

like imagination or consciousness, this would actually become

:32:40.:32:45.

conscious of its own body? I like to think it's the most interesting

:32:45.:32:48.

question of all, but there are lots of problems to overcome before we

:32:48.:32:52.

get to that stage. I happen to believe it will be possible one day

:32:52.:32:57.

to build a machine that will have a kind of consciousness, whether it's

:32:58.:33:02.

the same consciousness as ours, whatever that means, will be an

:33:02.:33:06.

issue. I don't really believe we are going to arrive at

:33:06.:33:14.

consciousness with a disembodied computer. This robot suggests that

:33:14.:33:17.

human intelligence simply cannot be divorced from the human body that

:33:17.:33:27.

created it. The hunt for AI has ended up on an alternative path.

:33:27.:33:30.

It's not about hardware and software, but about trying to

:33:30.:33:40.
:33:40.:33:41.

replicate human biology. Back at the circus school, I'm still having

:33:41.:33:44.

problems with my own body as I continue to struggle to get across

:33:44.:33:50.

the tightrope. Take that leg off right away. Good. Up, up. Come on,

:33:50.:33:57.

fight it. It's a battle. You think this isn't going to work, but the

:33:57.:34:02.

minute you reach up you are pulling yourself back in line. It doesn't

:34:02.:34:10.

seem logical. It really didn't. I was thinking that will do that. I

:34:10.:34:14.

feel there are lots of instructions going on here and I've got to try

:34:14.:34:19.

to put them together and maybe I'm - well, I don't know, whether I'm

:34:19.:34:27.

ever going to be able to do this. This is something -- is this

:34:27.:34:33.

something instinctive I'll be able to learn, or whatever? Move, move,

:34:33.:34:36.

move. If you are doing maths, you are thinking, thinking, you need to

:34:36.:34:45.

clear that. This feels really alien. But when I take a step back I

:34:45.:34:49.

realise that I've done something very similar to this before. That's

:34:49.:34:55.

when I learnt to walk as a child. Really relax your brain. Hands up.

:34:55.:35:01.

Step. Come on. I need to get mean now. Hands up. What's truly

:35:01.:35:04.

remarkable about our brains is that everything we learn to do from

:35:04.:35:08.

seeing, thinking, walking and talking, becomes automatic when

:35:08.:35:18.
:35:18.:35:19.

practice. Go, go, go. Don't rush. Hands up. Hands up! We'll try a

:35:19.:35:26.

little bit of military technique. Use a whip now! This ability to

:35:26.:35:30.

learn to do something instinctively, without even thinking about it, is

:35:30.:35:35.

what makes us human. Getting a machine to do this is one hell of a

:35:35.:35:45.
:35:45.:35:51.

task. Getting a computer to see has become one of the most difficult

:35:51.:35:59.

challenges in AI. Our vision is key to our intelligence. Without it how

:35:59.:36:09.
:36:09.:36:16.

would we perceive the world around us? The one scientist -- for one

:36:16.:36:20.

scientist, the challenge has been to get a computer to be able to

:36:20.:36:29.

monitor and interpret CCTV footage, like a human does. You would think

:36:29.:36:32.

with the power of computers and a mainframe in here scanning all the

:36:32.:36:36.

programmes we can write, you would be able to pick up We haven't

:36:36.:36:40.

written the programme that can do this sort of thing. It's so

:36:40.:36:45.

difficult for a computer to pick out the needles in the haystacks.

:36:45.:36:49.

Our brains have learnt to control our eye movements effortlessly,

:36:49.:36:54.

taking in the whole seen. We instantly combine this with our

:36:54.:36:59.

previous knowledge of what a car, bus, person or building looks like.

:36:59.:37:03.

In less than seconds we can make sense of all the activity that's

:37:03.:37:09.

going on in busy Piccadilly Circus. For a computer to recognise just

:37:09.:37:14.

one of these elements, is almost impossible. James wants to show me

:37:14.:37:22.

why this is. This is a simple programme to demonstrate how

:37:22.:37:30.

difficult vision is. What it does is remove our hardware, our vision

:37:30.:37:33.

processing hardware from the problem, so we have to process each

:37:34.:37:38.

bit of the image piece by piece, rather like a computer has to.

:37:38.:37:43.

challenge is to try to recognise a few familiar faces. It's through a

:37:43.:37:48.

tiny dot on the screen. Is this somebody I should know? Yep, you

:37:48.:37:55.

should know all of these. OK. I've found the eye. OK. I've got a bit

:37:55.:38:00.

of clothing there. Check shirt. Exactly. You can put all this

:38:00.:38:04.

together, Marcus. Then I should get it? Yeah. I don't know. Give in on

:38:05.:38:12.

that one. Stephen Hawking. again. Another face. It's weird, I

:38:12.:38:22.
:38:22.:38:22.

can pick out eyes and nose and mouth. Putting that all together in

:38:22.:38:27.

bits, but it doesn't have enough hair for Einstein. It's relevant

:38:27.:38:37.
:38:37.:38:38.

for this programme. OK. Alan Turing. We'll have one more. Beyond picking

:38:38.:38:46.

out that this is a face, I think just the little pieces kind of

:38:46.:38:54.

piece by piece - all right, it's me. I don't spend ages looking at me.

:38:54.:38:59.

It really demonstrates how difficult it is. A computer can

:38:59.:39:04.

only interpret one small business of the image at a time. But we have

:39:04.:39:06.

learnt to piece all the pixels together and see the whole thing

:39:06.:39:10.

with no effort at all. James has been wrestling with the problems

:39:10.:39:15.

for years. He has done something really interesting, I think. He's

:39:16.:39:20.

developed a piece of software that uses ground-breaking technology to

:39:20.:39:26.

recognise individuals in a crowd. He wants to use me as a guinea pig.

:39:26.:39:33.

What we'll do now is take you out on to the streets and track you out

:39:33.:39:37.

and about. This means tracking you as you go within a camera and then

:39:37.:39:40.

when you move on to a different view, we'll track you into the new

:39:40.:39:44.

camera and try to find you there and carry on to see how far we can

:39:44.:39:54.
:39:54.:39:54.

take you. I'll be passing under the gaze of the Dons of CCTV cameras in

:39:54.:40:00.

and around Piccadilly Circus. -- dozens. It's difficult for a

:40:00.:40:04.

computer to pick out, because as I walk through the different camera

:40:04.:40:08.

shots my size and position changes all the time. Whereas we can

:40:08.:40:16.

separate what is me and what isn't without even thinking about it.

:40:16.:40:20.

James' computer system gives each person a sequence of numbers and

:40:20.:40:22.

then attempts to recognise individuals by matching their

:40:22.:40:28.

number between all the different camera views. On the right-hand

:40:28.:40:31.

side of the screen we can see where the computer has successfully

:40:31.:40:36.

picked me out of the crowd on my journey around central London. In

:40:36.:40:40.

the end, I was tracked across five views as I walked a quarter of a

:40:40.:40:50.
:40:50.:40:51.

mile. James is making real progress in getting a computer to recognise

:40:51.:40:58.

what it's looking at. But there's a deeper question at stake here. --

:40:58.:41:03.

steak here. It's not just about -- stake here. It's not just about

:41:03.:41:07.

assigning objects, but what we see. It's something that humans working

:41:07.:41:12.

here don't have a problem with. don't think we'll get rid of the

:41:13.:41:17.

human intervention. On a Friday or Saturday night, we see lots of

:41:17.:41:21.

people, the ones who have had a drink will mess about and play

:41:21.:41:24.

fight. I don't think a computer would be able to tell the

:41:24.:41:26.

difference between a real and play fight, because we can look at the

:41:26.:41:31.

expressions on faces and see if they're smiling or if they're angry,

:41:31.:41:35.

so I don't think we're at that stage. The chaos of the urban

:41:35.:41:41.

jungle. Exactly. The human eye is still better. We are programmed to

:41:41.:41:46.

pick out something unusual. That's weird. What are you found there?

:41:46.:41:53.

pair of shoes on a seat. I was curious as to why they are there.

:41:53.:41:56.

It's kind of strange. I've looked around to see who they might belong

:41:56.:42:00.

to why they might be there, a couple of people have sat down next

:42:00.:42:05.

to them and walked away, so you know they're not theirs, but I

:42:05.:42:09.

don't think a computer can be programmed with the level of

:42:09.:42:16.

curiosity and that's a natural human emotion. This desire to

:42:16.:42:19.

understand meaning is absolutely at the forefront of artificial

:42:19.:42:25.

intelligence right now. Our brains do it by learning complue

:42:25.:42:35.
:42:35.:42:39.

experience and it's become a really -- through experience and it's a --

:42:39.:42:43.

it's become a really complex experience. I'm being reminded

:42:44.:42:47.

today what a great human thing it is to be able to learn something

:42:48.:42:55.

through experience. You would learn this pretty quick if you thought a

:42:55.:43:01.

crocodile was under you. Trying to get across a wire encapsulates all

:43:01.:43:06.

the problems involved in getting a machine to do a new task. Through

:43:06.:43:09.

the process of learning by experience we are able to combine

:43:09.:43:16.

all the different abilities that are needed. Like body control,

:43:16.:43:22.

reasoning and instinct. I've tried everything to get across this wire.

:43:22.:43:30.

Nothing seems to be working. But then Leila comes up with one little

:43:30.:43:37.

thing. Try a little song. It releases any little tune. I'm

:43:37.:43:47.
:43:47.:43:52.

chatting to you now to try to relax you a bit. La, la, la, la. Keep

:43:53.:43:59.

coming, don't stop. Keep the leg down. Keep coming. Keep it small.

:43:59.:44:08.

Hands up. Hands up. Come on! That was so easy. You were there.

:44:08.:44:13.

close and then my hand went down. What you just did there, right to

:44:13.:44:17.

that point, looked really easy. I thought you can cracked this. I

:44:17.:44:20.

don't know, sometimes the last, the anticipation, it's the last step or

:44:20.:44:25.

something. This is the last bit. You've got it, so keep that

:44:25.:44:29.

momentum going. We can't fully explain how our brains learn in

:44:29.:44:39.
:44:39.:44:39.

this way. I don't know where the huming is helping, but it is.

:44:40.:44:49.
:44:50.:45:09.

rush. Oh, yes, I'm rushing. Mm, mm, Hand up. Leg off, hand up. Hand up.

:45:09.:45:19.
:45:19.:45:21.

Little steps. Hands up. Hand up. Hand up. Yes! It wasn't elegant.

:45:21.:45:25.

Frpbts it was pretty elegant. Perfect. Well done. That is an

:45:25.:45:29.

amazing feeling to be able to get from one end of the tightwire to

:45:29.:45:32.

the other. But that is the amazing thing about human intelligence,

:45:32.:45:38.

just how adaptable it is, how I can go from nothing to learning a new

:45:38.:45:41.

skill like doing the tightwire. If you think about a computer, you

:45:41.:45:44.

would have to programme it especially to be able to do such a

:45:44.:45:50.

something different and for it to be lost again. Perhaps we can try

:45:50.:46:00.
:46:00.:46:01.

the trapeze now? Yes, sure, great! One other human quality we like to

:46:01.:46:09.

celebrate, and often link to intelligence, is creativity. I have

:46:09.:46:18.

come to Paris, where the streets are steeped in artistic culture. I

:46:18.:46:25.

am here to visit an unusual artist's studio. Run by computer

:46:25.:46:28.

scientist Simon Colton, who is normally based at Imperial College

:46:29.:46:37.

London. All these immap -- images have been created not by him but by

:46:37.:46:42.

a computer. Because Simon wants to find out if it is possible to break

:46:42.:46:48.

down the creative process into a set of instructions. That is why I

:46:48.:46:51.

started simulating paint strokes. And this is a good example of that.

:46:51.:46:55.

As well as programming it with painting techniques, Simon gives

:46:55.:47:00.

the computer rules about different artistic styles. We can choose a

:47:00.:47:04.

painting style according to the emotion of the sitter. So it is

:47:05.:47:10.

kind of feeding off its suant and trying to tap in and appreciate its

:47:10.:47:15.

mood. His of her mood and express that through the art. I can see

:47:15.:47:21.

what a difficult task this is. But there is one image that has really

:47:21.:47:26.

caught my eye. I love this one. Lots of comments from people.

:47:26.:47:31.

is what dancers? That is right. But these people don't really exist.

:47:31.:47:38.

This isn't a photo that, or a video it has looked at. It is taking some

:47:38.:47:42.

concept of people fed into it. you watch it being painted it

:47:42.:47:46.

paints it all in one stroke. This is a single line. Is that is what

:47:46.:47:50.

is giving it this sense of energy and motion? That is right. Yes.

:47:50.:47:54.

That is a classic example of where you put in a rule, where you have

:47:54.:47:58.

more out of it than you put in I had no idea that I would get this

:47:58.:48:05.

kind of effect. I had no idea it would give such a dynamism. I say

:48:05.:48:08.

that real creativity makes you think more, so the software we are

:48:08.:48:12.

developing, that will make us think more not less. Just like every

:48:12.:48:18.

artist or musician or poet wants us to think more. So, I see a

:48:18.:48:21.

different future for AI which is somewhere software is out there to

:48:21.:48:28.

challenge you. So if these creations are meant to challenge us,

:48:28.:48:36.

what do the city's artists make of them? There is something sexual. In

:48:36.:48:45.

this artwork. Interesting. Like bacon, like monkey. You are seeing

:48:45.:48:52.

bacon and monkey? That is interesting. Is it a man or ma heen

:48:52.:48:57.

who does the paintings. Why do you think? Was a it is done

:48:57.:49:02.

automatically. How can you tell, you are right, but, I am interested.

:49:02.:49:09.

You can see it. For me I am not an artist, so I would be hard pushed.

:49:09.:49:13.

What gives it away? There is no sensibility. Really? You can pick

:49:13.:49:18.

that up. That is interesting. I quite like the image, or some of

:49:18.:49:23.

them any way. It is clear that trying to mimic something as

:49:23.:49:28.

elusive as raytivety remains a huge challenge. -- creativity. But

:49:28.:49:38.
:49:38.:49:39.

scientists are starting to put down the first important building blocks.

:49:39.:49:42.

100 years the birth of the visionary mathematician Alan Turing,

:49:42.:49:49.

we are discover hag the hunt for AI is a more difficult problem than he

:49:49.:49:55.

or we ever imagined. Machines have surpassed us as specific tasks, but

:49:55.:49:59.

the human brain sets the bar for artificial intelligence extremely

:49:59.:50:06.

high, in its ability to constantly learn new skills. Hard wear and

:50:06.:50:16.
:50:16.:50:18.

software, are struggling to mimic our biology. But maybe there is

:50:18.:50:25.

another way to create a thinking machine. That can capture the

:50:25.:50:29.

versatility of the human brain. I have come to Berlin, for the

:50:29.:50:35.

highlight of my journey. To meet a scientist who believes that for

:50:35.:50:39.

something to be truly intelligent, it needs to develop and evolve like

:50:39.:50:45.

we did at the very beginning of our lives and as a species. If you want

:50:45.:50:49.

to understand how to create or understand intelligence in some way,

:50:49.:50:55.

we have to capture this evolutionary adaptive aspect of it.

:50:55.:50:59.

So we cannot programme intelligence? We cannot hope to

:50:59.:51:03.

give it the purpose and do the learning and, a lot of people think

:51:03.:51:11.

that today, but it, we have to understand this continuous adaptive

:51:11.:51:17.

nature, this flexibility, also this motivation to continue to learn, to

:51:17.:51:25.

be excited about new thing, to seek out new things. Luc is taking me to

:51:25.:51:30.

a lab on the outskirts of the city. To show me some robots that are

:51:30.:51:40.
:51:40.:51:40.

starting to do something remarkable. Three of them. They are punch

:51:40.:51:45.

larger than I thought they were going to be. -- much. These robots

:51:45.:51:55.
:51:55.:51:58.

have been set up to develop much They are beginning to understand

:51:58.:52:03.

how their bodies work, by looking at a reflection of themselves. You

:52:03.:52:08.

have a mirror, so what is the.... Well the experiment is about that

:52:08.:52:12.

the robot would learn something about its own body, because in

:52:12.:52:18.

order to move in the world, in order to control it, in order to

:52:18.:52:22.

also recognise the movements of another, you need to have some sort

:52:22.:52:26.

of model of your own body. Right. The way that the model is going to

:52:26.:52:33.

be built up is that the robot is doing actions, and watching itself.

:52:33.:52:39.

Performing the actions. So to get a relation between the visual image,

:52:39.:52:44.

and movement of the motor for. So here you see this looking at the

:52:44.:52:50.

its hand. You also see very much how I was trying to keep balance.

:52:50.:52:55.

It is very impressive. How all the motor commands are in an early

:52:55.:53:04.

phase, right. Well caught! It has just woken up. I know that feeling.

:53:04.:53:11.

I think this was a beautiful example of feedback, and of finding

:53:11.:53:15.

balance. It is extraordinary. It really does look like it is

:53:15.:53:23.

encountering itself for the first time. But what is even more

:53:23.:53:28.

remarkable about Luc's robots, is that once they are able to

:53:28.:53:33.

recognise themselves, they start to evolve their own language and

:53:33.:53:40.

communicate with each other. So what will happen now? Well, one of

:53:40.:53:45.

them is going to speak, then he is going o to ask the other one to do

:53:45.:53:48.

an action. He is going to invent a word, because he doesn't have yet

:53:48.:53:54.

the word to maim that action. OK, so then he says the word and

:53:54.:53:58.

this one isn't sure whether what it could mean, this is a brand-new

:53:58.:54:05.

word, so he will make a guess, and if the guess is OK, totally by luck,

:54:05.:54:08.

well they both know this word and they know for the future they can

:54:08.:54:12.

use this word to communicate with each other. What if he gets it

:54:12.:54:17.

wrong? If it gets it wrong this one will say this is not what I had in

:54:17.:54:24.

mind. He will show it. OK. I mean I don't know which one is going to

:54:24.:54:33.

speak first. OK, he is speaking first. He is doing the action.

:54:33.:54:40.

is fantastic. OK. Notice how he looked. So he is... He is doing the

:54:40.:54:43.

real action. So now there is another interaction going to happen

:54:43.:54:50.

again. I don't know which one is going to speak. OK. Is that the

:54:50.:54:57.

word it just learned. Kimato? doing it. He will say yes,

:54:57.:55:03.

presumably. Yes. So would they interact with me, to learn this

:55:03.:55:10.

language? Yes, we could try it. Do you remember any one of the

:55:10.:55:19.

words. We will see. In is scary. Gosh it has asked me to do

:55:19.:55:24.

something. Kimato. What was that? Was it that, have I got it right.

:55:24.:55:34.
:55:34.:55:35.

OK, you tell me what Kimato is. OK, that is right. All right. Tomima.

:55:36.:55:41.

Tomima. I think that was lifting my right arm. Did it get it right. Yes.

:55:41.:55:49.

I am learning robot! What would you say to people that would suggest

:55:49.:55:53.

that, well this looks simple. They are doing a few action, swapping

:55:53.:55:57.

words what is the big deal? first thing I would say to people,

:55:57.:56:03.

just try it yourself, OK, just try to build a robot that stands up.

:56:03.:56:08.

And then, you already will see its extraordinary complicated. How fast

:56:08.:56:12.

do you think the development will be in of the intelligence of these

:56:12.:56:16.

robot, such they are beginning to create a new language between

:56:16.:56:20.

themselves, but how long will it be before they are, I don't know,

:56:20.:56:25.

creating great works of art, the sort of things that we can do?

:56:25.:56:30.

know, if we have these two robots and we let them alone. We come back

:56:30.:56:36.

at the end o of the day and they will have developed a vocabulary

:56:36.:56:39.

for co-ordinating their action, once they start talking about

:56:39.:56:44.

shapes and colours and we don't know when they say Bobita do they

:56:44.:56:48.

mean a colour or a position in space. You will be going through

:56:49.:56:53.

the same process that the robot did to acquire that language, I mean...

:56:53.:57:01.

Yeah. The exciting thing about the robots, is they have their own

:57:01.:57:05.

evolutionary journey to go on. Tomima. Which could happen much

:57:05.:57:11.

quicker than ours. And they open up the possibility for intelligences

:57:11.:57:18.

very different from our own to emerge. That was a striking example

:57:18.:57:22.

of a merging intelligence, something which wasn't

:57:22.:57:25.

preprogrammed. Those robots really started with a pretty blank sheet,

:57:25.:57:30.

and by the end of the day just talking to each other, they had

:57:30.:57:35.

evolved their own language. A Laing cadge I couldn't really understand.

:57:35.:57:39.

Turing asked can machines think? I think he would be excited by what I

:57:39.:57:49.
:57:49.:57:50.

-- a language I couldn't understand. I have discovered that computers

:57:50.:57:54.

have far surpassed humans in many fields but are unlikely to put us

:57:54.:58:00.

out of business yet. Because these super machines haven't come close

:58:00.:58:07.

to capturing the true nature of our intelligence. But I also learned

:58:07.:58:11.

how closely our intelligence is linked to our biology. And that

:58:11.:58:15.

perhaps the secret to creating thinking machines lies in letting

:58:15.:58:25.
:58:25.:58:33.

But the one thought that has been creeping up on me throughout all of

:58:33.:58:39.

this, is that maybe we have become obsessed with our own abilities.

:58:39.:58:44.

And are blinded by being human. It does raise the question about

:58:44.:58:48.

whether we should be bothering to try and recreate what is us, when

:58:48.:58:54.

machines can do something so different to what human beings can.

:58:54.:58:59.

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