The Creativity Code - Marcus du Sautoy

[Music]

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 lineup of jokes how many mathematicians do we have in the room just to get me a sense of who I'm talking to okay great so the mathematicians in the room you probably have the similar experience that I do when I go to parties and you get this question about what you do I don't pass you all 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 at you always before they go they always tell me what they've got in their GCSE or o-level which is I'm not really quite sure what that's about but some 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

oh 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 said we're all a little bit threatened at the moment about this advancing AI 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 nineties actually when 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 Meuse you can make with the pieces there's a kind of end game that you're after at the end of the proof the QED you a you sort of win the game or you win the proof and so a lot of people said to me during the 90s well come on you must be next

but I never felt felt particularly threatened by chess and actually there was always another game that we mathematicians always use as our kind of protective shield against the idea that computers could do our 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 grain 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 or doing chess and 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 the tree of possibilities through go was always said to be a game that no computer would have ever be able to play and certainly any attempt of somebody 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 go so you certainly won't yet we will 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 dennis has

abyss who went to see elect he went to Cambridge to do his study and he was told this a old adage that you can't complete a program a computer to play go and this was 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 challenged Lisa Dahl a korean grandmaster and Lisa Dahl was totally dismissive of this algorithm that wouldn't be able to get anywhere near the level that he could play at he said I'm going to demolish this thing 5-nil they were gonna play over five games but he got a little bit of a shock i sat and watched these games obsessively on YouTube because I realized that my life was probably under threat and as I watched I saw Lisa dole get more and more depressed throughout the games he lost the first game he lost the second he lost the third he'd lost the match already after three he won the fourth game and he now regards that as 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 4-1 what had changed in the last two years the style of coding has changed and we've got new sort of code on the block which is able to do things the 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 past used to be written in a very top-down manner you really have to know what the thing what you was you 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 cuz it was told to implement this thing it could go deeper it could analyze more situations than a human but the human was still telling the program 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 a the parents DNA would give birth

to a child but the child would be still attached to the DNA of the parent it wouldn't learn and I think knew but suddenly we've got code that can adapt and change and mutate and re parameterize 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 use to actual trained alphago to to learn how to play this game by playing games and failing back they started with some other games to start with some some simpler games so in fact they started with Atari games claims I used to be obsessed with actually when I was a kid and one of the ones I really loved was this one called breakout and where you have a little ball which ping pongs up and down you've got a paddle and you've got to knock these out new score points the Bloor just one point up to read to 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 score go up so it Reap parameterised itself a turn and said i'm going to prioritize moving towards where the ball came 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 deep mind actually you learnt to play the Atari game it learnt the same hack it is you know not just humans but the computer is also lazy doesn't want to move this thing around and so shoots it back up again this is extraordinary after 600 games it had learned just by randomly moving the paddle and seeing that what the moves were that made that the score 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 to first take all the human games that are on the Internet a lot of games encoded on the Internet it learned how humans lost and won the games and that was its first material 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 if it lost a game it would understand which were the moves that meant that our that it actually was failing to play at a high level and it would reap ramit rise itself and so after a while it got to this high level quite amazing than it did suchly it could challenge lease at all now at first sight you say ok so the community's just got very good it can analyze it's gained very deeply but I think that there was something more amazing that happened during these games the first game interestingly Lisa Dahl decided that if it had done learning on how humans play it maybe the best way to beat this computer was to play 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 may turn out to be quite weak moves and he lost the game because he was not playing his standard game so in the second game least had all decided to play a much more standard a high level game that he knew very well very early on in the game your go master teaches you a few things strategies one is that you should play on the kind of age of the board so you're encouraged to pray on the first second third and fourth rows in because there's a kind of competition early on for the edges 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 doll on the 36 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's that there and it's not for a while and then it asked the human player 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 AI to actually pick a stone up and place it on the board but it told the human player to place a stone on the fifth row in so I've circled it he was playing he or Bernie already anthropomorphize the AI the AI is playing black and it put this stone or in on the fifth row in which to put a little white circle on all the commentators I remember this on YouTube they all gassed and went whoa Wow alphago has made a huge mistake never play that sort of move early on in the game this is a Lisa door will be in it'll be one one after this and they were all very complacent and Lisa it all came down after his cigarette break looked at what alphago and you can see you should watch this bag is so funny because it just Lisa don't like cannot

believe what this AI is just suggested what a stupid mood but he's he's a bit more suspicious things you know what that why is he done that move I still said it why did it do that move um 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 and more and more pieces were put on the board and territory was building up from the bottom right hand corner and it turned out that alphago's move at move 37 that black stone meant that it was it that won that territory rather than Lisa doll it was an incredibly inspired move it won alphago the second match that decision on move 37 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 kind of ten years Dennis was on the committee and there was also a philosopher Margaret Bowden 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 could be creative 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 and we can objectively judge whether something is new but you have two 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 as something we as humans believe is creative it's going to learn to to know what we think is surprising and has value what I think so nice about games it's of course you can judge this kind of these qualities quite quickly surprise yes the commentators all went or it's made a mistake value yes this move one 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 creative can it be creative in mathematics actually I sat next to demos and I I joke to him we just become both FRS is and I said well could you get alphago to become an FRS and part of the story of my book is about demo said yeah we're already on the case so they're already a deep mind looking at big making a creative a I mathematician but so the the journey that what I think is exciting about this kind of new AI is that they're 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 of for throw-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 at a local maximum actually this was like Snowdonia and there was actually a much higher amount in an 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 an hour ago is Nell 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 AI that disappearing 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 in other realms not just creative in a game the fact one of the first people to think of the idea of code was already suggesting that Koh 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 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 realize 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 that this could do maybe things which are a little bit

more interesting than just sort of scientific calculations then 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 connection with mathematics the idea of patterns getting the machine to to kind of run out patterns perhaps it could Bank music and we'll come to that a little bit later on the challenge of whether a 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 to the powers of the analytic engine it has no pretensions whatever 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 created if it is 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 our new artistic data music that is starting to become code that the original coder doesn't quite know how it's performing so this machine learning is producing programs which now perhaps disconnect itself from the original coder so here's the challenge can this new ie AI 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 there's you've probably heard of the Turing test kind of computer part itself off in an interaction online as I landed you know what would you be convinced that that was a human talking or is it just an AI computer so touring put this down as quite a good big challenge can it process natural language and respond real-time so here's a new challenge that has been offered connected to the artistic realm is called the Lovelace test so the test is can a machine

originate a creative work of art some sort of the process is repeatable so it shouldn't just be some sort of glitch in the hardware somehow the code should know what it's doing um it shouldn't be some sort of randomness which is put in there such that the code wouldn't be able to reproduce what it's done but here's the challenge yet the programmer the person who wrote the code that has now learns a mutated is unable to explain actually how the algorithm produced its output so this is the challenge that I want to explore with you how good has AI been in the last couple of years and 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 our AI is going to be driving our cars or maybe even be our doctors although a little nervous of that you know if it suggests move 37 in the game to of go and suggest you take a pill that looks incredibly dangerous do you do that is it mistake or is it

incredibly insightful move that's gonna 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 in art in poetry and novels so if AI can get close to doing something that reward isn't uniquely human I think this is a very exciting moment so so how good is it well I think I made a program for the BBC a horizon of about six years ago it was a during anniversary about AI and six years ago I was pretty disappointed in the state of AI that point and there was one hurdle that seemed AI seem to be finding really difficult to achieve and that was vision recognition our brain is very good at taking in a huge onslaught of information you know lots of different colors here people faces but I'm able to integrate this into a single story about seeing an audience it's come to the talk I'm giving so vision was one of the great hurdles for AI at the time

and this is one of the things that it's been able to do with this idea of machine learning and so actually we're going to do we've used a bit of machine learning to do and little few experiments during this lecture so we have a camera here so the point is that a machine learning that it gets 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 two we're going to be doing some tests here so you'll see you've got some some cards in front of you you're going to be doing I'm gonna give you some challenges some AI are art kind of Turing test Lovelace tests and you're gonna have to decide what you think is made by a human and what do you think is made doesn't have a soul and you know that's that's an AI 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 - ah okay that's interesting

it worked in there so exactly but when you're doing AI and tech it's always asking for trouble so okay well I do you think you can sort that out it's not a disaster but okay so um this is how we trained our AI so what we did was we want when you throw up your cards we want the AI to be able to recognize 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 AI on pictures so what we did was - he just took a random load of pictures and then we put the things that we were trying to get the AI to recognize and so there'll 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 learns over those six hundred faces to be able to distinguish these and 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 a kind of action so what I'm going to do is to offer you some challenges so here you are this is um okay so you you've have to improvise okay so I'm gonna say that's fine cuz I'll just swap over between the HDMI and that'll come bring you up so that's why okay so here you are so these this is a project that was done so I'm going to start with visual art because vision has been the place where our AI creativity AI is being very successful so you can probably recognize the artist here this is Rembrandt and there was a team in Hollands that decided to see whether they could get their AI vision recognition to sort of understand Rembrandt's 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 use to actually train our AI and but you know the in the region of 300 portraits and of course one of the things Rembrandt has is a very special use of light so it was going to learn how to put light on a

portrait a very particular style of address 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 to first of all to see whether our cards are working so let's switch over to show you so here you are so I'd like you to have all put up your blue cards so let's see whether and you can see it's 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 okay 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 there so that's 100% red now turn back to blue and you'll see the bar shoot back to the to the blue side okay 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 just show you the pictures again so

I'm you know ask you about one of these so let me flip my coin so okay so I'm going to ask you about the painting on the Left I want you to boat where do you think the painting on the Left is done by a AI or is it done by Rembrandt so if you think the painting on the Left is by AI I want you to show your red faces to me and if you think it was a human then I want you to show your blue faces and okay 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 is edging over more to red I'm saying probably about 60 30 okay so good let's see whether how good you we're at this one so we'll go back to here so which one was the AI is in fact so you are pretty good already so you can feel good about yourselves so as an audience at least you voted correctly so yes the one on the left is in fact the the AI Rembrandt um 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 analyzing the kind of height of the paint and so they 3d printed interestingly and when they asked Rembrandt expert to come along and review their their result and of course the Rembrandt Expo it was incredibly snooty and dismissive about the whole project but the only thing that he could find to criticize it about was well 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 okay so but you might say well what's the point about another Rembrandt we've got wonderful Rembrandt's why do we need any more Rembrandt so certainly my favorite 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

that is creative in human nature when technology is used for things it never should be used for but frankly anyone who wears out sort of shirt as an art critic I really not quite sure I trust very much in their their critique but but to a certain 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 ai is it starting to recognize 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 analyze and kind of judge a kind of the fractal the greenness of this a dimension of these paintings so a is giving us new insight 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 our 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 on our likes and dislikes which is kind of expressing our common feelings for a film in ways that we didn't really have a aim for that genre it was almost as if the AI had 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 AI to do new things to to break them all 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 jerk

judge which is which so we'll do the same since I've flipped we will 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 AI so let's turn you turn it over to you so your chance to vote those four paintings on the left are they okay so now all look there's a brexit vote going on there yeah so you seem to be still going for the lip it's much less convinced by that but there's it's just edging a little bit over to the oh yeah still changing all right but I think that's you think that one's the AI one okay let's go back and see what I think it's an awesome supporter over there okay so which one was which no in fact those were for blue so a few people yes yes I knew that yes so in fact so you find that one a bit more difficult it's interesting because in some ways I would say that the four which are produced by AI 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 AI one so they were people were much more emotionally engaged with the AI ones than they were with the human ones and then of course you see when you then tell somebody well in fact that was created by a computer really upsets people I think you know oh my gosh I had intimate but that's you know there's no emotional world going on inside there I think this is really interesting the reaction one has to to experiencing something then finding out it's done by AI and I think most of you but I mean I also feel like I've been cheated somehow um but if I tell you a joke you know Ellen's jokes were all actually made by an AI and you laughed at them all but then if I tell you now no they actually just ran do the AI joke app does that invalidate your laughter I don't think it does but why I don't think we should get too threatened by

this is because this AI is learning on our emotional world to produce its next step so it's not disconnected it has got an emotional word it's representing 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 learned how to classify art in particular styles it understood when something was Cuba start or pointillist art by doing a machine learning process or 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 mold it had to make something that couldn't be classified given the parameters that it had learned but it was also tasked with creating something that we as humans would recognize as art so it had already learned from the all all the art last 1500 years what we regarded as art and so it knew kind of an upper limit of where how much it could push the idea the second algorithm was tasked it was the discriminator algorithm was tasked with either saying look that I think that's still stuck you're still stuck in Cuba start there or else was saying you've gone wait fahren that is an art at all and it was the competition between these two that ultimately led to these images now 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 his Paul clay talking about the act of creation already at the very beginning of the productive act shortly after the initial motion to create occurs a thirst counter motion the initial movement of receptivity this means that creator controls where the water is produced so far is good there's always that you know you do something and then you just is that I'm not sure I'm gonna throw it away I'll do something again here's Paul valéry a French poet talking about the idea of these two kind of mind sets um it takes two to invent anything the one makes up combinations the other one chooses and I certainly 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 whilst a collaborator here in the Middle East that I have he's the kind of mad creator and I'm the discriminator in that case and it's by doing that kind of

combination that we actually make progress together okay so we done the visual world what about the written word and the written word we already heard how text a kind of prediction produces some strange effects the written word interestingly a is having quite a lot of difficulty with but then again it's one of the first things that a I was actually interested in in trying to do this is the Manchester Universal computer after touring left Bletchley Park he went up to try and realize 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 and 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 a program for the computer which was a template and he was using a Rand number generator that touring had 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 AI 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 ah actually you know as an audience you bring a lot of your own creativity when you work look at a piece of art don't you I mean yeah I think that's the point an artist leaves room for your own will to fill things with this and ambiguity to things and so you know poetry has a slight nomic quality that I think especially is something that you bring a lot of your creativity to when you read it um so here are your challenges now I've got some poems for you I want you to vote whether you think these poems are precious guys already on oh god poetry now so whether these poems are by AI or are they by humans so but or not okay 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 and I won't read it also mortal my mates burying my rocker

heart warm beats with cold beats company shall i earlier or you fail at our force and lie the ruins of rifles once a world of art okay no stop yeah so do you think that is sport or not so now you're just voting row at red for robots blue for okay I think okay so it's a massive boat for red they're a few thinking it's human and blue okay so that was your first challenge so let's go back to your next challenge I won't reveal them yet okay I'll reveal I've got three poems for you okay so here's your next one this is quite different even to read there are small nurses of pesticide reaction of real time of packs of displaced exclusionary heart hurt of powerlessness of magazine fired non dignified as head fatty employed internalized violence a frozen helpless this is off-white Cholula did okay um so that is AI or do you think that's human okay so let's over to you to vote am i messing with you or you know it was good okay so you think yeah that's so you that's quite a little yeah a little bow

for human there but still some people thinking okay is this a double bluff because that's clearly code okay so you I think you've gone for human there okay so right this is working okay so um here's your next challenge ports or not imagine now the dark smoke awakened to fly all these years to another day notions of tangled pries the other side of water I see it is already here sequences of her face series shared and old friends passed their dreams but or not okay so over to you you think they're all bought yeah okay so you're going for bought on that one so so that's right so you think they're all being they're all a I that's a sort of thing I would do isn't it you know okay so let's see so you've gone for the for the bots on that one Sagan oh there you into human yes that's right they sorry you're absolutely right yeah thank you very keen that one up yes so let's go back let me give you the answers then so the first one you or pretty convinced that was AI paroled Jarrell Manley Hopkins will be turning in his grave

what do you think honorable I chose Gerald Manny Hopkins because I've never understood any poem that Gerald Manny Hopkins has ever written so didn't sniff that one out second one yes you did sniff out that okay that's that's too much like code it can't be an AI 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 actually we've had two humans so that leaves the last one is the human did I really mess with you no the last one you sniffed out actually it was the only one that made any sense at all was in fact the only one crazy 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 Eliot and then the the bot was kind of tasked with creating something was a kind of fusion

of Yeats and Eliot for example so of course Rico as well is one of the people who's talking about the idea of the singularity the moment when computers might actually be more intelligent than humans and so that moment the singularity and so poetry energy is not doing too bad it's a kind of longer scale writing that AI is having real difficulty at it so it can generate quite interesting sort of text right 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 Bach botnik they decided their big Harry Potter fans and they decided they were very disappointed there are only seven volumes of Harry Potter and they wanted an eighth they want to know what happens next um so what they did was they got the machine learning to take all of JK Rowling's writing learnt kind of ideas that she's interested in her style of writing and decided they would create an

algorithm to create an eighth book so here is the beginning of this eighth book I actually love the title this is called Harry Potter and the portrait of what looked like a large part of Ashe I'd read that I'd read that so that 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 no bad leathery sheets of rain lashed at Harry's ghost as he wore leather he sheets of rain I think that's a beautiful image leathery she's the rain as he walked across the grounds towards the castle after this it began to lose the plot a little bit um ron was standing there and doing a kind of frenzy tap-dance he saw Harry and immediately began to eat Hermione's family very much good Ron's Ron shirt was just as bad it's Ron himself so AI is having it's very good a kind of local generation of things which have some sort of meaning but it doesn't have many very good sense of a long term structure okay what about music

lovely Seder gave us this challenge of whether a 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 particularly the sort of sound world that they have can the AI learn that and be able to produce something at a particularly good level to to replicate or do something new so AI always starts on Bach Bach is where AI always us 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 he wrote for Duke Ferdinand as as I've kind of puzzled to solved there was a little algorithm you had to show which you had to expand and and see what the music actually meant so so actually Bach is a very good place actually for AI to start and something you may remember a few weeks ago and the Google Doodle did you have a go on the Google doodle it was celebrating box and birthday and this

Google doodle you could put in a line of music and then it would harmonize the other three voices so the machine learning it to take this is based on something called magenta it had taken all the corrals that bark had written Corral's are very good because they kind of they don't change a key very often they're very sort of boring and but it can learn a lot about the way that a crawl and a coronis Phinney in the harmony is a bit like filling in a Sudoku you've got just learn the rules so this would actually the little Google doodle would fill in with your tune so I actually put in the a bit of the musical offerings so this is my attempt on it because the musical offering is actually quite a difficult challenge it 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 you could then say I thought that is rubbish and the thing would learn and actually say okay that harmonizing 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 we privatize it so okay I'll do more of that sort of harmony so very nice so what we've done and I've got a actually set up a Center in the Royal Northern College of Music with a composer Emily Howard it's a called prism which stands to practice and research in science and music and what we're here any 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 quartet representing mathematical proofs and together but I've now got a PhD student who's a composer which is really exciting Rob laid low he actually had his quartet premiered last week at the Wigmore Hall he was going to be here tonight unfortunately he's ill which is a real shame but he's been working alongside me and various other people and she here in the university as well and to produce a piece using some of the best software we have around at the moment to create a

kind of hybrid AI bark piece and 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 in there we've just been doing very straight AI human tests this is a slightly different challenge you've now got a piece which sometimes is AI and sometimes is bark 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 where the joins are can you tell oh gosh no that's horrible that's gone AI or oh yeah that's bark so we're going to run this twice the piece is about four minutes long and so we're going to record your thoughts on this and then hopefully if we can do this I'm gonna play this back to you oh gosh because now we don't have two screens now it's gonna be quite okay no III know what I can do okay yeah I've thought of an improvised way of doing this I'm going to do it very analog

so you'll see what my solution in a minute okay so so perhaps we can give a big round and pause to Kobe who's going to come and play this piece we have a page-turner as well and so I 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 by bark you turn to the red face and if you think it's gone back to bark you move to the blue vase okay it's very simple okay so now in order to sync these things I'm going to have to count this down so we go hopefully we can show these two things again so I've got you ready ready combing okay so I'll go 4 3 2 1 [Music] [Applause] [Music]

I was raising I think there are moments when bar will be turning in his grave as you'll see but it's inching because I think when it was barks there was much more confidence in your knowledge of that he could feel it was right but when it wasn't it was really kind of edgy and there were moments when his sunny surge read which they were give away moments so so we're going to replay this so you can actually see what what's your answers were this was actually so what we did was we took one of the English sweets so this is the fourth English sweet and what we did was to take these bits out of the piece of music and then ask 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 clora it's developed now it's developed by open AI this team which is trying to make AI very open to the world something that Elon Musk has helped set up and so this is actually buy a piece of software written by Christine Payne as part of the open AI

project and it's growing so we're going to keep on working on this to to make it even better with something called muse net but what's interesting is that this is kind of predictive in the sense of it here's what's happened up to date and then makes a decision about what will be next so some of the AI is working very cleverly in 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 in its decisions as it goes forward and one of the things I talked about with Cobie beforehand he could feel the AI very clearly because the AI is not embodied so it has no trouble with giving really awkward fingerings whilst Bach was writing something that would fit very nicely under the fingers um so I think is one of the challenges of AI very generally is the idea of it not being

embodied and you could really feel that that it just did I mean when I first playthrough it was just like wow this is just really gooey um okay so let's play that back so what I was hoping to do is have things on one screen and the other but I think what I would do so we're going to play oh yeah exactly so let's say we'll get the thing up here so we're going to get poorer Kobey's got to play this again the painful the bits the bark is great fun and what I will do analog wise and so what's going to do this on the screen but I will just show you as we're going along which bits are AI and which are not and so you'll be able to see what you voted and I'll tell you what the answers are okay all right okay so as you I sort of slightly bias 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 gonna count this down so we get the sinking right hopefully so

there you all are ready so uh four three two one [Music] you pricky you picked that up quite quickly that that was bark I think you can hear that it's got some sort of direction to it [Music]

robach [Music]

oh now you can be the Cabal [Music] [Applause]

[Music] [Applause] [Music] and thank you very much to Kobe now one of the things I was very strict about with Rob the composer was that it had to be AI and it was not allowed any chance to try and improve the AI so we were very strict about because very often when you look at projects and the many in the book although it's saying it's an involvement of AI you can see there's a lot of human inputs it makes a much better story if you just says the AI and the human isn't involved at all so it's very strict with this that the portions that where AI had to be only AI and interestingly we asked 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 actually it was pretty convincing and there were few given way moments but actually quite hard to pick out which was the AI I think it were pretty good on what was bark although there are a few horrific moments when you thought it was AI and bark will be poor turning in

his grave so um and I think that 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 AI that have been trained to play jazz and it's called the jazz continue ater something constructed in sunny labs in Paris by Francois fouché and his team and they got the AI to learn in a very way to a jazz musician learning what the probability is of the kind of next move after a certain sequence of notes um and they then did a concert where people found it very difficult to tell when it was the human playing when it was the AI but what struck me was the jazz musician who at this a I've been trained on his response to hearing the AI play back to him and he said this is Bernhard lumad the system shows me ideas I could have developed but that would have taken me

years to actually develop it is years ahead of me yet everything it plays is unquestionably me another thing 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 because like Bernard lubob was in a room the spotlight was on him he didn't realize there was so much more to play with his in within his sound world so the exciting thing for me is that this movement into an AI 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 behaving less like machines and actually become more creative again as humans thank you [Applause]

you