He's becoming quite a hard act to follow these days. Actually, I think he's going to put me out of a job with all his line up of jokes. How many mathematicians do we have in the room just to give me a sense of who I'm talking to? OK, great. So the mathematicians in the room, you probably have a similar experience that I do when I go to parties and you get this question about what you do.
I don't. Perhaps you will fake it. I'm starting to fake it, actually, and say I'm an international spy or something. But because most of the time I get a kind of stock set of reactions. One of them is that they just flee to the other end of the party and I'm abandoned. But actually, you always before they go, they always tell me what they got in their GCSE or A-level, which is I'm not really quite sure what that's about.
But but if they do stick around, one of the things they often say is he's trying to work out the tech here, which is, yeah, doing a talk about tech is always a disaster, actually using the tech. But so one of the questions I often get or statements is, come on, surely you must have been put out of a job by a computer by now? I think it's mostly because people think that what I do in my office up here is long division to lots of decimal places.
And if that were true, certainly my computer would have put me out of a job. But you know, we're all as Alan saying, we're all a little bit threatened at the moment about this advancing A.I. that it seems to be very powerful doing lots of interesting things. And surely aren't computers all about kind of logic and mathematics?
So wouldn't my job be one of the first to be threatened? I got this a lot during the 90s, actually, when a deep, deep blue beat Kasparov because often people used to compare the idea of playing a game of chess to doing mathematics. There are certain logical moves you can make with the pieces. There's a kind of endgame that you're off to the end of the prove the QED when you sort of win the game or you win the proof.
And so a lot of people say to me during the 90s, Well, come on, you must be next. But I never felt particularly threatened by chess. And actually, there was always another game that we mathematicians always used as all kind of protective shield against the idea that computers could do on subject because there's another game and that's the ancient game of Go.
This Chinese game played on a 19 by 19 grid where you put black and white stones down, you try to engulf the other person's territory before they engulf yours. And this is a green which has a high degree of complexity, much more complex, actually, than chess. There's a lot of pattern recognition that needs to go on to be able to play this game, and that's something that really mathematics is about.
It's about spotting patterns, underlying patterns. But quite often when you're playing go and especially when you're doing mathematics, to be able to quite know where you're going requires a lot of intuition, a lot of creativity, not quite sure why you're making moves. You spend a lot of time in this world and you build up kind of kind of a feel for playing go or doing chess.
I'm very traditionally in computer science lectures. They would always say, Yeah, chess is something we can automate because we can understand the kind of logical implications of playing particular moves we can follow. The tree of Possibility through Go was always said to be a game that no computer would ever be able to play. And certainly any attempt somebody's trying to encode the way that a human plays this game always failed.
The attempts to try and encode playing go in some sort of algorithm wouldn't even beat an amateur at this game. So I felt pretty safe because computer science say you can't play games, so you certainly won't be able to play the complex game of mathematics. So I got a little bit of a shock a couple of years ago, and you're probably aware of this story. When a team in London declared that they had got an algorithm that they believed could compete at not just a high level but the highest level.
This is DeepMind in London and actually was Demis Hassabis. We went to see it. He went to Cambridge to do his study, and he was told this old adage that you can't complete programme a computer to play go. And this was sort of like a red rag to Dennis. And so he went away and set up this company, and they devised this algorithm that they thought could play the best and they challenge. Lee Sedol, a Korean grandmaster, and Lisa Dolan was totally dismissive of this algorithm.
It wouldn't be able to get anywhere near the level that he could play at. He said, I'm going to demolish this thing five nil. They were going to play over five games, but he got a little bit of a shock. I sat and watched these games obsessively on YouTube because I realised that my life was probably under threat and as I watched, I saw, Lee said. I'll get more and more depressed throughout the games. He lost the first game, he lost the second.
He lost the third. He lost the match already. After three, he won the fourth game and he now regards that. It's the greatest game that he's ever played, that he was able to beat this algorithm in one game and he lost the fifth game, so he lost four one. What had changed in the last few years? The style of coding has changed and we've got new sort of code on the block which is able to do things that code in the past couldn't do.
And it's something that we're very interested here in Oxford. This idea of deep learning or machine learning. So code in the pass used to be written in a very top down manner. You really have to know what the thing is, how the thing was going to behave. You told it the rules of how it was going to play. You had to understand the setting and the machine just implemented that. Yeah, sure, it could play chess because it was told to implement this thing. It could go deeper.
It could analyse more situations than a human. But the human was still tallying the programme. What to do What has changed is that the code is now written in a very bottom up manner. We've got a sort of code which is learning very much like a child learns. In the past, it was like the parent's DNA would give birth to a child, but the child would be so still attached to the DNA of the parent it wouldn't learn anything new.
But suddenly we've got code that can adapt and change and mutate, and we parameter AIS itself as it encounters a new environment. There's almost like a meta code, which is telling the code how to change if it gets something wrong and change and mutate. And this is what they used to actually train AlphaGo to to learn how to play this game. By playing games and failing that, they started with some other games to start with some, some simple games.
So they started with Atari games, games I used to be obsessed with actually, when I was a kid and one of the ones I really love with, this one called out where you have a little a ball, which ping pong is up and down. You've got a paddle and you've got to knock these out. You score points, the blue out just one point up to read the higher points. The machine was only given the pixels on the screen and the score it had to learn how to play.
This game wasn't told anything about the fact that you had to hit this ball, but it was randomly moving the paddle and every time it hit the ball, it saw the school go up. So it rip parameterised itself and said, I'm going to prioritise moving towards the where the ball came from.
Now, when I was with my mate and we played this, we were very pleased when we found a fantastic hack because you can create a little tunnel on the left hand side and then if you get the ball to go up there, then you don't have to do any work at all because the ball just bounces backwards and forwards.
What I was absolutely staggered. When you see how this DeepMind actually learnt to play, the Atari gave it the same hack is, you know, not just humans, but the computer is also lazy, doesn't want to move this thing around, so shoots it back up again. This is extraordinary. After 600 games, it had learnt just by randomly moving the paddle and seeing that what the moves were that made that the school go up fast.
It had learnt how to do this hack. So this is how it then went on to play the game of GO. So what it did was the first take all the human games that are on the internet, a lot of games encoded on the internet and learnt how humans lost and won the games, and that was its first material that it learnt on. Then it started to create synthetic data. It started to play itself. Different versions of itself would play a game and then it lost a game.
It would understand which were the moves that meant it, that it actually was failing to play at a high level and it would reprioritize itself. And so after a while, it got to this high level quite amazing that it did such that it could challenge release at all. Now, at first I say, OK, so the computers just got very good. It can analyse its game very deeply, but I think that there was something more amazing that happened during these games the first game.
Interestingly, Lisa Donald decided that if it had done learning on how humans played, maybe the best way to beat this computer was to play it rather unlike a human. So he actually played a very disruptive game, but at the AlphaGo was smart enough to actually just cope with the moves that he was making. And they turned out to be quite weak moves, and he lost the game because he was not playing his standard game.
So in the second game, at least, it all decided to play a much more standard, high level game that he knew very well. Very early on in the game, your game master teaches you a few things strategies. One is that you should play on the kind of edge of the board, so you're encouraged to pray on the first, second, third and fourth rows end because there's a kind of competition early on for the ages and the kind of internal part of the board.
And if you play too far into the middle of the board early on, it's considered a weak move because you're not really establishing important territory at that point. So Lisa, all on the 36th move of this game, decided that he needed a cigarette break and he went up to the top of the hotel, had a cigarette.
AlphaGo didn't need to smoke in order to get stimulation. So. It sat there and it felt for a while, and then it asked the human plant because this wasn't an exercise in robotics, this was an exercise in just pure thought, so there was still human. This is actually still quite difficult for an eye to actually pick a stone up and place it on the board. But it told the human plant to place a stone on the fifth row in. So I've circled this. He was playing. He already anthropomorphise the A.I.
The A.I. is playing black, and it put this stone in on the fifth row in which I put it a little white circle on all the commentators. I remember this on YouTube. They all gasped and went, Wow, wow, AlphaGo has made a huge mistake. Never play that sort of move early on in the game. This is at least it all will be, and it'll be one one after this. And they were all very complacent.
And at least it all came down after a cigarette break looked at what AlphaGo and you can say you should watch this back. It's so funny because it just literally like, cannot believe what this is just suggesting. What a stupid move. But he's he's a bit more suspicious things. You know that why has he done that move? Why did it do that move?
It turned out that as the game built up, and there's something rather different about chess and go because chess gets simpler as the game goes on because pieces get taken off. But Go gets more and more complex because more and more pieces get put on. As the game built up, a more and more pieces were put on the board. Territory was building up from the bottom right hand corner, and it turned out that AlphaGo is move at move thirty seven.
At that, Blackstone meant that it was it that won that territory rather than lease at all. It was an incredibly inspired move. It won AlphaGo the second match that decision on move thirty seven to put the stone there and break the tradition of how humans thought we should play the game. And for me, this was really exciting because I believe that this is an example of what we should call a creative act by artificial intelligence. I spent some time on a committee at the Royal Society.
Over the last few years, we've been looking at the impact that machine learning is having on society. Over the next ten years, Dempsey was on the committee and there was also a philosopher, Margaret Boden, and I talked to her quite a bit about the idea of creativity. She's been very interested in what she calls these tin cans can do computers and the idea of whether they can be created. And she had a very nice working definition of what we should call creative.
I'm not sure it's the best one, and we can argue there's lots of philosophical debate over the idea of what we mean by creativity. But I think this is going to be quite useful working definition as we go forward tonight. So creativity is something which should be new. Well, computers can make new things quite easily. We can objectively judge whether something is new budget to other qualities. It should also have an element of surprise. Now that's a little bit more subjective and also value.
That's also quite subjective. So a computer, if it's going to be creative, is something we as humans believe. It's creative, it's going to learn to, to know what we think is surprising and has value. One of the things I notice about games is, of course, you can judge this kind of these qualities quite quickly. Surprise, yes, the commentators all went, Oh, it's made a mistake. Value? Yes, this move won AlphaGo the game.
And so what I've been interested in is to look at if it can be creative in this very close environment of a game, where else can it be created? Can it be creative in mathematics? Actually, I sat next to Dennis and I joked to him, We just become both our friends, and I said, Well, could you get AlphaGo to become an f r s? And part of the story of my book is about Demi said, Yeah, we're already on the case. So they were already a deep mind looking at making a creative AI mathematician.
But so the journey that what I think is exciting about this kind of new A.I. is disappearing is that in the case of AlphaGo, it not only played the game at a high level, but it taught us how to play the game in a new way. We thought we'd reached a kind of peak of playing with these kind of rules that we had about playing and the ones the fourth wrote in. And, you know, there was a kind of optimal way to play the game.
What AlphaGo has shown us in these games is that although we thought we were at the peak of a performance in playing this game, actually this was only what we mathematicians call a local maximum. Actually, this was like Snowdonia, and there was actually a much higher mountain and Everest, a new way to play the game that AlphaGo had experimented by taking risks gone down this kind of adaptive valley and found a much better way to play.
And AlphaGo is now taught us a new way to play this game, new strategies that are helping us to play the game at a much higher level. And so the journey of this book, which is called the creativity code, is to look at, well, this ain't only that it's a pairing that seems to be able to. To learn about through its interaction with the kind of digital world around it, could it perhaps be creative and other realms, not just creative in a game?
France, one of the first people to think of the idea of code, was already suggesting that code might be able to do things of an artistic nature. So we celebrate Ada Lovelace Day, Ada Lovelace every year. Ada Lovelace was taken by her mother to see a Babbage's analytical engine. Her mother used to like to expose her to lots of different ideas, scientific ideas.
And when she saw this machine, she already began to realise that this could do more than just the long division or the multiplication that it could do something a little bit more exciting. And she started to write down code to make the analytic engine do interesting things. And that's why we sort of celebrate Ada Lovelace, the notes that she wrote for a paper about the analytic engine we regard as the first idea of code to make machines do interesting things already.
Then she was thinking about the fact this could do maybe things which are a little bit more interesting than just sort of scientific calculations. She wrote the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent. So she's already thinking about music, a place, of course, which has quite a lot of connexion with mathematics, the idea of patterns getting the machine to, to kind of run out patterns.
Perhaps it could make music, and we'll come to that a little bit later on the challenge of whether I can write music. But she offered a word of caution when she wrote this. She said it is desirable to guard against the possibility of exaggerated ideas that might arise as though the powers of the analytic engine it has no pretensions won't ever to originate anything. It can do whatever we order it to perform.
And I think that's what we always felt in the past is that this kind of top-down coding, well, it's the human that's telling the computer what to do. So if the computer is being creative, that's because the human has been creating images encoded that in just a set of rules that the computer is just implementing.
But I think something has changed now. The code is beginning to change mutate as it interacts with, say, new artistic data music that is starting to become code that the original code doesn't quite know how it's performing. So this machine learning is producing programmes, which now perhaps disconnect itself from the original coder.
So here's the challenge can this new only A.I. that's appearing, which seems to be moving on from the original code written by the coder kind of put some distance between the code and the coder. So you probably heard of the Turing Test kind of computer pass itself off and an interaction online, as Alan did. You know, would you be convinced that that was a human talking? Or is it just an AI computer?
So Turing put this down as quite a big challenge kind of process natural language and responds real time. So here's a new challenge that is being offered connected to the autistic realm. It's called the Lovelace test. So the test is kind of machine originate a creative work of art such that the process is repeatable. So it shouldn't just be some sort of glitch in hardware. Somehow, the code should know what it's doing.
It shouldn't be some sort of randomness which is put in there, such as the code wouldn't be able to reproduce what it's done. But here's the challenge the programmer, the person who wrote the code that is now learns and mutated, is unable to explain actually how the algorithm produced its output. So this is a challenge that I want to explore with you how good has been in the last couple of years.
It's really the story of the book in understanding what we regard as art and creativity and being able to produce its own version of that. And I think, you know, I think we're quite happy that A.I. is going to be driving our cars or maybe even beyond doctors, although are a little nervous about, you know, suggest move thirty seven in the game to have go and suggest you take a pill that looks incredibly dangerous.
Do you do that? Is that mistake or is it incredibly insightful mood? It's going to save your life. So I think we are quite happy in certain realms, but I think the one thing that we regard as uniquely human is our own creativity, our artistic output. That's what it means to be human. We express it in music and art, in poetry and novels. So if I only can get close to doing something that craft is uniquely human, I think this is a very exciting moment.
So. So how good is it? Well, I think I made a programme for the BBC a horizon of about six years ago. It was a touring anniversary about A.I., and six years ago I was pretty disappointed in the state of A.I. at that point. And there was one hurdle that seemed, I only seem to be finding really difficult to achieve, and that was beyond recognition. Ryan is very good at taking in a huge onslaught of information, you know, lots of different colours here, people faces.
But I'm able to integrate this into a single story about seeing an audience that's come to the talk I'm giving. So Vision was one of the great hurdles for A.I. at the time, and this is one of the things that it's been able to do with this idea of machine learning. So actually, we're going to do it. We've used a bit of machine learning to do a few experiments during this lecture, so we have a camera here.
So the point is machine learning. It's shown some images of cats and dogs, and it has to distinguish them and it gets it wrong to start with. But it starts to ask more and more questions, which helps it to get it more and more, right? So we've got to we're going to be doing some tests here, so you'll see you've got some SIM cards in front of you, you're going to be doing.
I'm going to give you some challenges, some AI art kind of Turing test, Lovelace tests, and you're going to have to decide what you think is made by a human. And what do you think is made? Doesn't have a soul and that's that's an A.I. So in order to do this right, so we're going to get you up on your display one and we want the HDMI. So that's interesting. So display to.
Oh, OK, that's interesting. It worked in their rehearsal, exactly, but when you're doing air and tank, it's always asking for the trouble. So OK, well, do you think you can sort it out? Possibly not. It's not a disaster, but OK, so this is how we trained our air. So what we did was we want when you throw up your cards, we want the air to be able to recognise, recognise what you're putting up, that it's a blue which will be for human and a red for robot.
But he doesn't want to get confused by a red jacket over there or a blue shirt. And so we had to train this eye on pictures. So what we did was we just took a random load of pictures and then we put the things that we were trying to get the A.I. to recognise. And so there will be a training image. So we had about 600 training images. And basically, it would be told there are four red robots and three human faces.
And gradually it learnt over those 600 faces to be able to distinguish these such that now when we show it an image, it can count quite quickly and effectively. How many robots and how many humans there are? So we're going to get to you. So. So this is really machine learning in kind of action. So what I'm going to do is to offer you some challenges. So here you are. This is OK. So. You. Separate so you can only show on it.
Oh, that's OK, right? I shall have to improvise. OK. So I'm going to that's fine because I'll just hop over between the HMO and that will bring you upset. That's fine. OK, so here you are. So this this is a project that was done. So I'm going to start with visual art because vision has been the place where creativity is being very successful. So you can only recognise the artist here.
This is Rembrandt, and there was a team in Holland that decided to see whether they could get their A.I. vision recognition to sort of understand Rembrandt very particular style of painting. Rembrandt did quite a lot of portraits, so it was quite a bit of data, not as much data as we used to actually train or A.I. But, you know, in the region of 300 portraits. And of course, one of the things Rembrandt has is a very special use of light.
So we're going to learn how to put light on a portrait at a very particular style of dress at that particular time. So one of these images is a Rembrandt. The other one is the product of the artificial intelligence learning on Rembrandt style and producing a new Rembrandt. So the challenge for you is, can you tell which of these is the real Rembrandt? Now I'm going to just do a little experiment. First of all, to see whether all cards are working, so let's switch over to show you.
So here you are. So I want you to open up your blue cards. So let's see where. And you can see it starting to pick out some of you a little bit edgy. I'm sorry, you'll just have to feel like your vote does count. And OK, now turn them over to red. Let's see. And you'll see the bar at the top is recording, so we'll be able to test the proportion that say that's 100 percent red now, turn back to blue and you'll see the bar shoot back to the to the blue side.
OK, so good. So it seems to be working. So now not to prejudice the thing. So I'm going to let's go back. I'll just show you the pictures again. So I'm going to ask you about one of these. So let me flip my coin. So, OK, so I'm going to ask you about the painting on the left. I want you to vote. When did you think the painting on the left is done by a? I or is it done by Rembrandt, so if you think the painting on the left is my art, I want you to show your rates faces to me.
And if you think it was a human, then I want you to show your blue faces. And OK, so here we're starting to get so quite a lot of you voting red. And we're seeing, although some of you are also voting blue, so it's edging over. More to read, I'm saying is probably about 60 30. OK, so good. Let's see whether how good you were at this one. So we're going back to here. So which one was the I? In fact. So you were pretty good already, so you can feel good about yourselves. That's as an audience.
At least you voted correctly. So yes, the one on the left is, in fact, the Rembrandts. Now, it's interesting that not only did they do this as a 2D image, but you know if you've seen a Rembrandt, it has a very his use of paint is very special. It's very kind of 3D effect. And they even went to the extent of analysing the the kind of height of the paint.
And so they 3D printed this interestingly. And when they asked a Rembrandt expert to come along and review that, their result and of course, the Rembrandt expert was incredibly snooty and dismissive about the whole project. But the only thing that he could point to criticise it about was while the use of paint is 20 years earlier than the style of the actual portrait. So the team felt actually quite good that they'd managed to just, you know that if that was all that was wrong with it, it's OK.
So but you might say, Well, what's the point about another Rembrandt? We've got wonderful Rembrandt. Why do we need any more Rembrandt? So certainly my favourite art critic Jonathan Jones in The Guardian. I love reading Jonathan Jones because he's always totally dismissive about anything to do with A.I. This is what he wrote about this Rembrandt project. What a horrible, tasteless, insensitive and soulless travesty of all.
It is created in human nature when technology is used for things it never should be used for. But frankly, anyone who wins that sort of shirt is an art critic. I'm really not quite sure I trust very much in that critique, but but to some extent he has a point. You know, what is the point about creating other Rembrandt? Well, I do think there is a point because the wonderful thing about this A.I. is it's starting to recognise things that we,
as humans have missed in the data. So not so much in Rembrandt. I haven't seen anything new in size in Rembrandt. But, for example, something like Jackson Pollock, a kind of algorithmic analysis of Jackson Pollock has revealed the Pollock is doing something very special when he splatters paint around that he's creating a very special mathematical shape that we can actually analyse and kind of judge a kind of the fractal,
the greenness of this dimension of these paintings. So aliens giving us new insights. There's a wonderful story I tell in the book about the Netflix algorithm that just took our likes and dislikes of films, and the numbers of the films didn't know anything about the films. But just from all likes and dislikes is able to clump them together into films of a similar sort of genre. So you can see, Oh yeah, look, these are all comedy films.
These are all thrillers, but every now and again, it would clump films together because of all likes and dislikes, which is kind of expressing our common feelings for film in ways that we didn't really have a name for that genre. It was almost as if the alien spotted through our likes and dislikes that there was a kind of way of clumping films together that deserved a new name. It had spotted a new sort of structure in the films that we hadn't kind of named.
So I think there is a point about looking backwards, but I think the most exciting thing is looking forward. Can we get the A.I. to do new things to, to break the mould, to do exciting new things? So here's your next challenge Four of these paintings are done by a human. Four of these paintings are done by an AI. You have to now judge, which is which. So we'll do the same since I flipped. We'll do the left hand one again.
So the left hand one is the one you're going to be voting on. Do you think the four paintings on the left are by the human or by the A.I.? So let's turn you turn it over to you. So your chance to vote those four paintings on the left, are they? OK, so now much oh, look, this Brexit vote going on there? So you seem to be still going for it. It's much less convinced by that, but there's just edging a little bit over to the end, still changing.
But I think that you think that once the AI won, OK, let's go back and see what I think is an awesome supporter over that good or something. OK, so which one was which? No. In fact, those were for only a few people. Yes. Yes, I knew that. Yeah. So in fact, so you find that one a bit more difficult. It's interesting because in some ways I would say that's the. Four, which are produced by I have much greater complexity to them.
And these were actually shown at Basel Art Fair a couple of years ago, I think this is 2016. These were and nobody was told there was any AI involved in this. They were just asked to give their feedback on the paintings and the feedback on the A1. So they were people were much more emotionally engaged with the AI ones than they were with human ones. And then, of course, you see when you then tell somebody, well, in fact, that was created by a computer.
It really upsets people. And I think, Oh my gosh, I had it. But that's, you know, there's no emotional world going on inside that. I think this is really interesting. The reaction one has to experiencing something, then finding out it's done by A.I. And I think most of you, I mean, I also feel like I've been cheated somehow. But if I tell you a joke, you know, Alan's jokes were all actually made by an A.I. and you laughed at them all.
But then if I tell you now, no, no, he actually just ran the the AI joke app. Does that invalidate your laughter? I don't think it does. But why I don't think we should get to threatened by this is because this only is learning on our emotional world to produce its next step. So it's not disconnected. It has got an emotional what? It's representing all our emotional world, but in a sort of new filter.
What's interesting about this project, I think especially, is the way that these paintings were created because these four paintings on the right were not created by one algorithm, but two algorithms almost working in competition against each other. They almost made it into a game, something called a creative or generative, sometimes adversarial network.
So the first algorithm was tasked with creating the art, and what it did was to learn on all of the art of the past, and it learnt it became a kind of art historian. It learnt how to classify art in particular styles, and understood when something was Cubist art or pointless art. By doing a machine learning process on the images and being told what was in which particular style, then it was tasked with creating something that didn't fit into any of those styles.
So it was really trying to break the mould. It had to make something that couldn't be classified given the parameters that it had learnt. But it was also tasked with creating something that we, as humans would recognise as art. So we did already learnt from the all in all, the art lost one thousand five hundred years what we recorded as art. And so it knew kind of an upper limit of where how much it could push the idea.
The second algorithm was tossed. It was the discriminator algorithm was tasked with either saying, Look, that I think that still stuck. You're still stuck in Cubist art there or else was saying you going way too far and that isn't art at all. And it was the competition between these two that ultimately led to these images. Now, I think that's what's exciting with this particular idea is because very often algorithms can just churn out loads of things.
But the challenge is choosing which ones are interesting. So we had a second algorithm which was doing some choosing and discriminating. And this is very close, I think, to actually how humans work creatively. Here's Paul Clay talking about the act of creation already at the very beginning of the productive act. Shortly after the initial motion to create a the first counter motion, the initial movement of receptivity. This means the creator controls whether what he has produced so far is good.
There's always that you do something and then you. Is that good? I'm not sure I'm going to throw it away. I'll do something again. Here's Paul Valery, a French poet, talking about the idea of these two kind of mindsets. It takes two to invent anything. The one makes up combinations the other one chooses. And I suddenly find that as a mathematician that I have collaborators around the world where we kind of play these two roles.
So I have a collaborator in Germany where I'm the kind of mad creator, and he's the discriminator kind of knocking things down. Now, while it's a collaborator in the Middle East that I have, he's the kind of man creator and I'm the discriminator in that case. And by doing that kind of combination that we actually make progress together. OK, so we don't the visual world. What about the written word, the written word? We already heard how a text kind of prediction produces some strange effects.
The written word, interestingly, is having quite a lot of difficulty with. But then again, it's one of the first things that I was actually interested in trying to do. This is the Manchester Universal computer after Turing left Bletchley Park. He went up to try and realise some of his ideas in Manchester, and the team there were rather perplexed when letters started appearing around the lab, which were kind of love letters written by the Manchester Universal computer.
And they were sort of perplexed by this until one of the team admitted that in fact, he'd written a programme for the computer, which was a template, and he was using a random number generator that Turing. It just created for the computer, which was randomly filling in the template with words, amorous words. So after a while, you'd spot the template. So not very good, but poetry is somewhere where ale has been quite successful.
I think partly because it's again nice closed form. It's not asking sort of too much large scale structure. Also, I think that oh, actually, you know, as an audience, you bring a lot of your own creativity when you look at a piece of art stone, you I mean, I think that's the point. An artist leaves room for your own world to fill things with a certain ambiguity to things.
And so, you know, poetry has it's like gnomic quality that I think especially is something that you bring a lot of your creativity to when you read it. So here are your challenges. Now I've got some poems for you. I want you to vote, whether you think these poems are because these guys already know poetry now. So whether these poems are by AI or are they by humans so bought or not? OK, so here's your first poem. So I'll read you the poem, and then we'll go to see what you think about it.
I won't read it also. Mortal mind makes burying my rock a heart warm beat with cold beats company, shall I earlier or you fail at our force and lie the ruins of rifles once a world of art? OK, let's stop there. So do you think that is bought or not? So now you just voting, wrote red for robots. Blue for. Yeah. Oh gosh, I didn't get that one. OK. So a massive vote for red. There are a few thinking it's human and blue.
OK, so you know, if that was your first challenge. So let's go back to your next challenge. I won't reveal them yet. I'll reveal I've got three poems for you. OK, so here's your next one. This is quite different. Even three. There are smaller pieces of plastic side reaction of real time of packs of displaced exclusionary heart hurt of powerlessness. The magazine fired and undignified as head fatty implied internalised violence. A frozen helplessness is off white chocolate, a two tiered OK.
So what do you think? You think that is I? Or do you think that's human? OK, so let's give it to you to vote. Am I messing with you or you know? OK, so you think, yeah, that's so that's quite a little, yeah, a little bit for human there, but still some people thinking, OK, is this a double bluff? Because that's clearly code? OK, so I think you're going for human there. OK, so right. This is working. OK, so here's your next challenge bolt or not.
Imagine now the dark smoke awakened to fly all these years to another day. Notions of tangled trees, the other side of water. I see it is already here. Sequences of a face sea. The shared an old friends past their dreams. Boats or not? OK, so over to you. You think they're all bought? OK, so you're going for bought on that one, so. So that's right. So you think they're all using, they're all. I yeah, that's the sort of thing I would do, isn't it?
OK, so let's see. So you've gone for the for the bolt on that one? So again. Oh, they went to human, yes, that's right, they sorry, you were absolutely right, yeah. Thank you for picking that one up. Yeah. So let's go back. Let me give you the answers then. So the first one, you were pretty convinced that was I parole Gerard Manley Hopkins will be turning in his grave. And which you think honourable?
I chose Gerard Manley Hopkins because I've never understood any poem the Gerard Manning Hopkins has ever written, so didn't sniff that one out. Second one. Yes, you did sniff out that. OK, that's that's too much like code. It can't be an A.I. It's actually a young Australian poet called Miss Breeze, and she's very interested in this kind of interplay between computer code having its own kind of poetry and rhythm to it, yet having meaning that might have something to say to us.
And so she's very interested in this kind of weird interface between the two. So actually, we've had two humans, so that leaves the last one is that human that I really mess with, you know, the last one you sniffed out, actually, it was the only one that made any sense at all was in fact, the only one created by an AI. This is actually Ray Kurzweil created something called the cybernetic poet.
And this is a machine learning process where he took poems of Yeats, Keats, Elliott and then the the bot was kind of tasked with creating something which is a kind of fusion of Yeats and Elliott, for example. So, of course, Ray Kurzweil is one of the people talking about the idea of the singularity, the moment when computers might actually be more intelligent than humans. And so that moment the singularity. So poetry actually is not doing too bad.
It's a kind of longer scale writing that A.I. is having real difficulty on it, so you can generate quite interesting sort of text generation or sort of small scale. But it's kind of the idea of writing a novel is still way beyond it. Although there have been some attempts to write novels, so there was a very interesting case by a team called Book Bottleneck. They decided the big Harry Potter fans, and they decided they were very disappointed.
There were only seven volumes of Harry Potter and they wanted an eighth. They wanted to know what happens next now. So what they did was they got the machine learning to take all of J.K. Rowling's writing learns kind of ideas that she's interested in, in her style of writing and decided they would create an algorithm to create an eighth book. So here is the beginning of this latest book. I actually love the title.
This is called Harry Potter and the portrait of what looked like a large part of Ash. I'd read that. I'd read that. So it starts off pretty well. Magic. It was something that Harry Potter thought was very good. So good. It's already picked up that these books are about magic, you know? But leathery sheets of rain lashed at Harry's ghost as he leathery, She's right, rhino.
I think that's a beautiful image leathery sheets of rain as he walked across the grounds towards the castle after they began to lose the plot a little bit. Ron was standing there and doing a kind of frenzied tap dance. He saw Harry and immediately began to eat him on his family. A of much good that runs run. It was just as bad as Ron himself. So I was having a very good kind of local generation of things, which had some sort of meaning,
but it doesn't have my very good sense of a long term structure. OK, what about music? Lovelace either gave us this challenge of whether I could produce music and music has full of lots of patterns. A composer when you hear a piece of music on the radio, you can probably very quickly pick out what the composer is because they have particular styles, particular sort of sound world that they have.
Can the I learn that and be able to produce something at a particularly good level to try to replicate or do something new? So I always starts on Bach. Bach is where I always thought because Bach has a lot of algorithms at work. If you do look at something like the musical offerings, Bach wrote, the musical offerings, these little pieces that he wrote for Duke Ferdinand as a as a kind of puzzle dissolved.
There was a little algorithm you had to show which you had to expand in and see what the music actually meant. So. So actually, Bach is a very good place, actually for I just thought and some of you may remember a few weeks ago the Google Doodle, did you have a go on the Google Doodle celebrating Bach's birthday and this Google Doodle? You could put in a line of music and then it would harmonise the other three voices. So the machine learning take this is based on something called magenta.
It it's taken all the carols that Bach had written. Carol is very good because they kind of they don't change key very often. They're very, sort of boring, and they can learn a lot about the way that a crawl and a Koran is filling in the harmonies. A bit like filling in a Sudoku. You've got to learn the rules. So this would actually the the the little Google Doodle would fill in.
With your tune, so as you put it, in the pit of the musical offerings, so this is my attempt to win it because the musical offerings is actually quite a difficult challenge. She doesn't seem to have any key to it at all. And when I played it back, it was really rubbish, actually. But what was very nice about the Google Doodle is that you could then say I thought that was rubbish and the thing would learn and actually say,
OK, that harmonising didn't work. And so you were training the algorithm as you gave your feedback. And if you thought it was good, it would kind of reprioritize itself like, I do more of that sort of harmony. So very nice. So what we've done and I've got to actually set up a centre in the Royal Northern College of Music with a composer, Emily Howard. It's a called PRISM, which stands so practise and research in science and music.
And what we're already interested in is this kind of interplay between the scientific and mathematical world and the world of composition. So we've done various projects. We wrote a string quartette representing mathematical proofs together, but I'm now what I now got a Ph.D. student who's a composer, which is really exciting. Robledo, who actually had his quartette, premiered last week at the Wigmore Hall. He was going to be here tonight, so I'm sure he's ill, which is a real shame.
But he's been working alongside me and various other people as she here in the university as well to produce a piece using some of the best software we have around at the moment to create a kind of hybrid A.I. Bach piece. And this is what we want to play you. So the challenge here is so we've got a fantastic musicians in the department here, and Kobe is going to come up and play this piece for you, which is so we just been doing very straight up human tests.
This is a slightly different challenge. You've now got a piece which sometimes is AI and sometimes is ball, and I'm not going to tell you how many times it moves between one and the other. And what I want you to do is to see whether you can see whether joins or can you tell, Oh, gosh, that's horrible. That's going AI or oh yeah, that's boring. So we're going to run this twice.
The piece is about four minutes long, and I say, we're going to record your thoughts on this and then hopefully, if we can do this, I'm going to play this back to you. Oh gosh, because now we don't have two screens, that's going to be quite a. OK, no, I know what I can do. OK, yeah. I've thought of an improvised way of doing this. I'm going to do it very analogue. So you'll see what my solution in a minute.
OK, so so perhaps we can give a big round of applause to Coby, who's going to come and play this piece. We have a page turner as well. And so just to set this thing off, can you all show your blue faces? So we're going to just start you off all on blue. So the idea is as soon as you think that the music has gone into something which is not my ball, you turn to the red face. And if you think it's gone back to Bach, you move to the blue face, OK?
It's very simple. OK, so now in order to think these things, I'm going to have to count this down. So we going to hopefully we can show these two things again. So you ready? Ready timing. OK, so I'll go for three to one. Well, I was raising I think there are moments when Bach will be turning in his grave, as you'll see. But it's think because I think when it was Bach, there was much more confidence in your knowledge of that.
You could feel it was right. But when it wasn't, it was really kind of edgy. And there were moments when it's sunny surge red, which there were give away moments. So. So we're going to replay this so you can actually see what what your answers were. This was actually so what we did was we took one of the English suites. So this is the fourth English suite, and what we did was to take these bits out of the piece of music and then asked the AI to fill in the gaps.
And the machine learning was. It's interesting because we actually use quite a simple piece of machine learning. It's called Clara. It's developed now. It's developed by open air, by this team, which is trying to make A.I. very open to the world, something that Elon Musk has helped set up. And so this is actually by a piece of software written by Christine Payne as part of the open A.I. project.
And it's growing. So we're going to keep on working on this to to make it even better with something called News Net. But what's interesting is that this is kind of predictive in the sense of here's what's happened up to date and then make the decision about what will be next. So some of the A.I. is working very cleverly and working backwards as well. So knowing where the piece is going, it can make some prediction.
But this piece of software does not have a long term memory, and this is one of the challenges to create a piece of music software that can actually know about what it's done in the past and exploit that and its decisions as it goes forward. One of the things I talked about with Kobe beforehand, he could feel the AI very clearly because the AI is not important.
So it has no trouble with giving really awkward fingerings whilst Bork was writing something that would fit very nicely under the fingers. So I think this is one of the challenges of A.I. very generally is the idea of it not being embodied and you could really feel that it just did. I mean, when I first played through it, it was just like, Well, this is just really goofy.
OK, so let's play that back. So what I was hoping to do is have things on one screen on the other, but I think what I would do. So we're going to yeah. Yeah, exactly. So let's we'll get the the thing up here. So we're going to get Paul. Kobe's going to play this again. The painful bits, the bark is great fun and what I will do analogue wise. So I was going to do this on the screen. But I will just show you as we're going along, which bits are I and which are not.
And so you'll be able to see what you voted and I'll tell you what the answers are. OK. OK. All right. OK. So as I sort of slightly biased things by asking you to put all up the human face, I wanted to see how long it would take until you actually spotted that the opening wasn't bark at all. It was, in fact, so it took quite a long time. So we're going to start. So we're going to count this down. So we get the thinking right, hopefully. So they all already. So four, three two one.
You pick you pick that up quite quickly, that that was back, I think you can hear that it's got some sort of direction to it. You know, I did it. Know. Or a bar? Oh, cheer. Now you can be this gun ball. Right. And thank you very much to Katie. Now, one of the things I was very strict about with Rob and the composer was that it had to be air and he was not allowed any chance to try and improve the air.
So we were very strict about because very often when you look at projects and the many in the book, all that saying it's an involvement of air, you can see this a lot of human input because it makes a much better story if you just say it's the eye and the human isn't involved at all. So he's very strict with this that the portions that were air had to be only air. And interestingly, we all it just to fill in the last chord, and it missed out one crucial note which made the whole thing resolved.
So. But you can see from that, that's actually it was pretty convincing, and there were few given away moments, but actually quite hard to pick out, which was the air. I think you were pretty good on what was Bach, although there were a few horrific moments when you thought it was II and Bach will be poor turning in his grave, so. And I think that's again, what's the point of this? Well, I think there is a point for creative artists, and I think this is not about competition.
This is about collaboration. This is a new tool to push our own human creativity. We've always already seen that in the realm of art. But in music, one of the most interesting stories I saw was the idea of an A.I. that had been trained to play jazz. It's called the Jazz Continuato, something constructed in Sony Labs in Paris by Francois Paci and his team.
And they got the A.I. to learn in a very similar way to a jazz musician, learning what the probability is of the kind of next move after a certain sequence of notes. And they then did a concert where people found it very difficult to tell when it was the human playing or when it was the A.I. But what struck me was the jazz musician who this A.I. been trained on his response to hearing the AI play back to him.
And he said This has been a clue that the system shows me ideas I could have developed, but would have taken me years to actually develop. It is years ahead of me. Yet everything it plays is unquestionably me. And I think this is what's exciting because I think that we, as humans often end up behaving very much like machines. We get stuck in our ways of thinking. We just perform the same ideas over and over again, especially in creativity.
I know that in my own mathematics, I try the same things over and over again. And sometimes I need something to push me out of the way I've been thinking and see that my world of possibilities is much richer. Like, blown out, I was in a room. The spotlight was on him. He didn't realise there was so much more to play with his and within his sound world. So the exciting thing for me is that this movement into an A.I. that might be creative. It's not about a threat. It's about an opportunity.
It's about the fact that this thing could push us to be behave less like machines and actually become more creative again as humans. Thank you.