Speaker 1: Sleepwalkers is a production of our Heart Radio and unusual productions.
Speaker 1: So I'm here for a surprise poetry reading. It's about
Speaker 1: to start. The silence is hardly final. Somewhere in the street,
Speaker 1: I can see the trees begin to rise and fall
Speaker 1: for the light of the dark thing above me. The
Speaker 1: dream is like a shiny black hair, and the sun
Speaker 1: is like a dream. I stand up and watch the
Speaker 1: sun shine on a single day, and the sun has
Speaker 1: a chance to accomplish from the springs of my own delight.
Speaker 1: Kind of haunting, abstract, yes, but beautiful too. And crucially,
Speaker 1: when I read this, I felt, as you just did.
Speaker 1: I hope that it is beautiful. I found it evocative
Speaker 1: of experience that I've had it in the past. I
Speaker 1: found it nostalgic um and I'm like, oh my god,
Speaker 1: I'm having real human being emotions. That was filmmaker Oscar Sharp,
Speaker 1: and that poem wasn't written by him or by anyone else.
Speaker 1: It was written by a computer, a machine poet. We're
Speaker 1: more and more worried about robots coming to take our jobs.
Speaker 1: And though perhaps few would regret less hips to poets
Speaker 1: shambly around Brooklyn, somehow machines in the creative world are
Speaker 1: especially uncanny, even frightening, because poetry and music and humor
Speaker 1: are supposed to be the things that define our humanity,
Speaker 1: aren't they. In this episode, we look at how AI
Speaker 1: is being used in the creative arts, and in doing so,
Speaker 1: we understand a lot more about how this often intimidating
Speaker 1: technology actually works. I'm Moza Lush and welcome to Sleepwalkers. Hi,
Speaker 1: Hi Karen So did Oscar's poems spring your delight? I
Speaker 1: would have preferred it in your British act? Then I think, well,
Speaker 1: I can't blame you for that. It did remind me
Speaker 1: of Um. My friend is in a band called the
Speaker 1: x Ambassadors and they partnered with this producer called Alex
Speaker 1: the Kid who was asked by IBM to make a
Speaker 1: song using Watson. And the song is not bad. The
Speaker 1: song is not bad. Um, the sounds good. But basically,
Speaker 1: the way in which they used Watson was that they
Speaker 1: crunched the twenty six thousand songs from the top one charts.
Speaker 1: It's from over what time period, like over a few
Speaker 1: years presume, I don't know, But the point is that
Speaker 1: they used them to discover patterns in the songs. What
Speaker 1: makes the top hundred song. Basically that's right, and then
Speaker 1: that's reproduce it right, which is interesting because I think
Speaker 1: anybody could kind of tell you what makes the top
Speaker 1: on hundred song right. They're called earworms. But when you
Speaker 1: think about a data set of twenty six thousand, like,
Speaker 1: no human being can listen to that many songs and
Speaker 1: do any productive after they hear it. So in this episode,
Speaker 1: we're going to look at AI and art and different
Speaker 1: kinds of fields to really understand how computers crunched data
Speaker 1: to crack open this creative code. But first I want
Speaker 1: to go back to the algorithm who wrote the poem
Speaker 1: we heard at the beginning of the episode, because that
Speaker 1: algorithm also wrote the film that Steven Spielberg of the
Speaker 1: machine learning world, and the algorithm is called Benjamin. So
Speaker 1: we're going to meet Benjamin and a few people not
Speaker 1: named Benjamin. I was a speech trator for John Kerry,
Speaker 1: Tim Gaitner, and Barack Obama, um, not in that order,
Speaker 1: And I'm essentially a ghost writer and a photographer who
Speaker 1: learned to code one giant Frankenstein monster. That's Ross Goodwin
Speaker 1: and that Frankenstein monster it goes by Benjamin and it's
Speaker 1: the work of Ross Goodwin and Oscar Sharp, who you'll
Speaker 1: remember from that poetry reading, but neither of them actually
Speaker 1: named Benjamin. Well, they named itself, or rather, there was
Speaker 1: a piece of paper that came out of it that
Speaker 1: said my name is Benjamin on it. I read that
Speaker 1: in response to a question that was put to it,
Speaker 1: and a room full of people went, oh, a program
Speaker 1: that names itself is rather uncanny. And Oscar had been
Speaker 1: chasing that uncanny nous ever since he was at n
Speaker 1: y U for graduate school. Whenever I met anyone who
Speaker 1: good program, I would grab them by the pels and
Speaker 1: yell into the face. Can you can you build something
Speaker 1: that can write like people talk in some way? And
Speaker 1: one day in class Oscar notices there's this sneakerhead and
Speaker 1: he's sitting on his laptop and his laptop is writing
Speaker 1: without him touching it. And I'm like, oh, so we go,
Speaker 1: So we go for coffee, and that gut coffee was.
Speaker 1: It was a lengthy coffee. We're still having that coffee.
Speaker 1: We're still having such a cold coffee. By now, you
Speaker 1: might not believe it if it happened in a film,
Speaker 1: but Oscar had stumbled on exactly the person he was
Speaker 1: looking for. Oscar came to me and he said, I
Speaker 1: want to make a movie from a computer generated screenplay.
Speaker 1: And I said, you know, of course that sounds amazing.
Speaker 1: Let's do it. But let's figure out how we're going
Speaker 1: to generate the screenplay, because that's a nuanced process with
Speaker 1: lots of stabs, and we need to consider like every
Speaker 1: part it. So Oscar volunteered himself to teach me all
Speaker 1: the things about storytelling and narrative and filmmaking. He turned
Speaker 1: me onto like Vladimir prop Joseph campbell Um, all these
Speaker 1: theories of storytelling, and so they begin to experiment. I
Speaker 1: tried a bunch of prototypes that used like various structures
Speaker 1: that had been postulated by these theorists over time, and
Speaker 1: the output was not interesting. Despite following the rules laid
Speaker 1: out by narrative theorists, Ross couldn't get anything good just
Speaker 1: telling his programs what a story should contain. So a
Speaker 1: year passes, Oscar moves to l A and when I
Speaker 1: get this email from Ross and it's the results in
Speaker 1: one of those experiments that he wants me to read,
Speaker 1: and read it he did. Rossity mailed the poem from
Speaker 1: the beginning of the episode, the room is blown away
Speaker 1: from the door and the stones are beginning to shine.
Speaker 1: I immediately was like, oh my god, I don't know
Speaker 1: how he's doing this. But he said, I don't know
Speaker 1: what technology you're using right now, but can we it
Speaker 1: for screenplay? And so they did, and not just a screenplay,
Speaker 1: they actually produced a short film called Sunspring and they
Speaker 1: even got Thomas middle Ditch, the lead on Hbos Silaken Valley,
Speaker 1: to star in it. Principle is completely constructed. Of the
Speaker 1: same time, it's all about you. To be true, you
Speaker 1: didn't even watch the movie with the rest of the base.
Speaker 1: I don't know, I don't care. I know it's a
Speaker 1: consequence whatever you need to know about the presence of
Speaker 1: the story. I'm a little bit of a boy on
Speaker 1: the floor. So what do you think, Carol? It kind
Speaker 1: of reminds me of when my parents used to take
Speaker 1: me to like a bad production of Macbeth or as
Speaker 1: you like it. Traumatic. You're there and you're seven or eight,
Speaker 1: and you want to understand what's going on, and so
Speaker 1: you kind of pay as close attention as you possibly
Speaker 1: can to what the actors are doing because you have
Speaker 1: no idea what the dialogue means. Yeah, I mean that
Speaker 1: to me is quite impressive because a machine can create
Speaker 1: something which has enough of the elements in common the
Speaker 1: film that we can talk about a real film. You
Speaker 1: can't say it's not a film absolutely. Of course. What's
Speaker 1: different is it didn't take Benjamin very long at all
Speaker 1: to make it. Once you press the button fraction of
Speaker 1: a second there was a couple of seconds perpase, maybe
Speaker 1: maybe a couple of seconds total, actually a fraction of
Speaker 1: a second per page. That's right. After months of agonizing
Speaker 1: over centuries of storytelling theory, the final output only took
Speaker 1: a couple of seconds. So what was Ross's breakthrough? To
Speaker 1: understand we turned to one of the most famous AI
Speaker 1: scientists in the world, Sebastian Throne. Recently, something magical had
Speaker 1: happened recently. The feat has discovered was called machine learning.
Speaker 1: With AI, computers can now find their own rules. They
Speaker 1: are called neural networks. They're comprised of hundreds of millions
Speaker 1: of little vase sample processing units, and those units are
Speaker 1: modeled after what a neurons do in our physical brains.
Speaker 1: You just give them examples, very much like the way
Speaker 1: we we waste children. We don't give our children rules
Speaker 1: for every contingency in life. In the first eight years
Speaker 1: of education. We let them learn, They experience the world,
Speaker 1: and they loan behold. They make their own rules. And
Speaker 1: we are now in the world where computers can do
Speaker 1: the same thing. And this means machine learning can be
Speaker 1: used in all kinds of different fields. Sebastian himself applied
Speaker 1: the technology at Google, where he led the initial development
Speaker 1: of their self driving car. When you want to read
Speaker 1: a book, a book on like what the car should
Speaker 1: do in every situation, that rule book is really complicated
Speaker 1: and it can promise you no matter how many years
Speaker 1: you spent writing it, it's not gonna work. But when
Speaker 1: you give the machine the ability to learn its own rules,
Speaker 1: it is actually able to surpass how people can drive.
Speaker 1: We'll hear more from Sebastian later, but machine learning mL
Speaker 1: is the engine that drives almost all of the excitement
Speaker 1: about AI today, from identifying targets on the battlefield to
Speaker 1: understanding genetic diseases. And it's also what allowed Ross and
Speaker 1: Oscar to create a usable movie script. Rather than laying
Speaker 1: down storytelling rules, they simply showed Benjamin hundreds of examples
Speaker 1: and the algorithm found patterns and learned for itself more sleepwalkers.
Speaker 1: After the break, you're like, oh, did we essentially we
Speaker 1: teach this algorithm anything else about screenplay other than just
Speaker 1: putting in a bunch of screenplays, right, And that's the
Speaker 1: way that machine learning works. What is happening in a
Speaker 1: deep learning algorithm of this kind is it's building an
Speaker 1: extraordinarily complicated mathematical formula by reading all of this stuff
Speaker 1: over and over again, like the auto complete on your phone.
Speaker 1: The neural that is actually sampling from a probability distribution
Speaker 1: of which letters, bass or punctuation become next. So the
Speaker 1: script for sun Spring was essentially the most mathematically probable
Speaker 1: Sci Fi script except Ross and Oscar did have one
Speaker 1: important lever of creative control. The other of parameters that
Speaker 1: you're probably wondering about, there's one called the temperature is
Speaker 1: the riskiness of those next letter predictions. What Ross is
Speaker 1: describing is almost like a dial for creativity. Turn it
Speaker 1: up to a really high temperature, and the neural net
Speaker 1: is going to be extra creative and start making up words,
Speaker 1: babbling at a very high temperature. It's essentially drunk. Low temperature,
Speaker 1: it's going to be very repetitive and possible even begin
Speaker 1: to plagiarize its source material. So it'll be very repetitive.
Speaker 1: It'll be like the streets and the streets, and the
Speaker 1: streets and the streets. It's essentially went working for network television. Yeah, exactly.
Speaker 1: So we wanted it to be sort of in the middle.
Speaker 1: In the middle is where we found the best output
Speaker 1: and the most I think usable output, and Sunspring was born.
Speaker 1: So Benjamin Ross and Oscar right together now they write
Speaker 1: poetry and movies and sometimes what Benjamin spits out is good.
Speaker 1: Often they have to sift through it to find the
Speaker 1: best stuff. But he's prolific and he never ever suffers
Speaker 1: from writers. Look, so Kara was telling us earlier about
Speaker 1: Alex the kidd and using AI to make music, and
Speaker 1: that's something I want to understand a bit more about.
Speaker 1: So Julian went on a little bit of an expedition. Yes,
Speaker 1: I did. I've been seeing a lot of articles lately
Speaker 1: about AI and the arts, and I've been pretty curious
Speaker 1: about music specifically. We might take it for granted, but
Speaker 1: music is this primal emotional thing that's been with us forever.
Speaker 1: It might even predate language. But now Warner Music Group
Speaker 1: made history in April two nineteen, is the first major
Speaker 1: label to sign an AI to a record deal. Yeah,
Speaker 1: they signed this bot called Endel, which makes ambient noises
Speaker 1: based on where you are and what the weather is
Speaker 1: and what time of day it is. When I think
Speaker 1: of this kind of music, I think of those Spotify
Speaker 1: playlists like Peaceful Piano and Blissed Out Dinner Party, which
Speaker 1: would become extremely popular. It's not the same thing as Beyonce, No,
Speaker 1: definitely not. But Warner Music Group signed Endl to generate
Speaker 1: twenty albums of ambient music. And now that we live
Speaker 1: in a world where aies can get record deals, what
Speaker 1: does this mean for artists? What does this mean for
Speaker 1: even just music as we know it? Well, in my
Speaker 1: quest to find out, I visited this company called Amper.
Speaker 1: My name is Drew Silverstein. I am the co founder
Speaker 1: and CEO of Amper Music. Amper is an AI music
Speaker 1: company that Drew says will enable anyone to create music.
Speaker 1: In fact, the only things you need to know are
Speaker 1: the genre of music you want to create, the mood
Speaker 1: you'd like to convey, and the length of your piece
Speaker 1: of music. That's all you know. You can create a
Speaker 1: brand new, unique piece of music in a matter of seconds.
Speaker 1: So the big question is should musicians worry about computers
Speaker 1: taking their job? Well, let's try it and see. So
Speaker 1: what do you want to do? Cinematic, documentary, folk cinematic cinematic,
Speaker 1: minimal percussion or quirky percussion. It's rendering a song right now.
Speaker 1: And here we go. We've got something. I'm were deep
Speaker 1: in the forest of Nicaragua. There's a breed of jaguar.
Speaker 1: You might have heard of it. It's called the take
Speaker 1: a Killer panther. Look, here's the thing. I don't know
Speaker 1: what the difference between that and music is. I really don't. Yeah,
Speaker 1: so you're woud Yeah, all right, So there's that. And
Speaker 1: this isn't the only AI music app out there. Another
Speaker 1: major player is called Magenta, and big surprise, they're at Google.
Speaker 1: Magenta are using AI to create a ton of new tools.
Speaker 1: From a piano genie that makes it impossible to play
Speaker 1: bad notes to something that can generate drum loops, or
Speaker 1: something that can even play piano duets with you. You
Speaker 1: can even translate raw audio to a piano score. Raw audio,
Speaker 1: like if I play just something raw on the piano,
Speaker 1: raw audio like oh, literal raw audio, literal raw audio.
Speaker 1: And Magenta has also trained a neural network just like
Speaker 1: Ross and Oscar, only instead of sci fi scripts, they
Speaker 1: trained on over four hundred performances by skilled pianists. They
Speaker 1: fed it into the neural network and let me play
Speaker 1: one of the piano experts. First. This is a real
Speaker 1: piano player, all right, so nice? Right? Yeah, okay, ready
Speaker 1: for the AI. What that's a computer that was all
Speaker 1: a computer. I didn't ever play a human one. That's
Speaker 1: a computer that was trained by a human playing piano.
Speaker 1: And then how do you make a computer come up
Speaker 1: with that? Right? So even though it's not a screenplay,
Speaker 1: it's still data that you can feed a neural network
Speaker 1: with to find patterns. And in this case, Magenta used
Speaker 1: a data set from the Yamahai Piano competition. So human
Speaker 1: pianists played on these digital keyboards which recorded the nuances
Speaker 1: of their performance, like how long they hit notes, and
Speaker 1: it recorded all that information into a digital score that
Speaker 1: a computer could interpret. And we've actually had that technology
Speaker 1: for a while now, it's called MIDI. But training and
Speaker 1: on network on the data is new. See. The thing
Speaker 1: that I come back to is that a computer doesn't
Speaker 1: know it's playing music, so much of watching a musical
Speaker 1: performance is knowing that this is coming from someone who
Speaker 1: is emoting. Right, Yeah, there's actually there's an emotional communication happening, right,
Speaker 1: that's right. I do think though the future is not
Speaker 1: rejecting this. It's better to imagine what would Stravinsky have
Speaker 1: done with this kind of technology, because Stravinsky is still
Speaker 1: a musical genius. Right, Yeah, Definitely it's cool to listen
Speaker 1: to those musical examples of machine learning because you can
Speaker 1: really hear how the algorithm is reinterpreting existing material. Of course,
Speaker 1: listening to the output is one thing. Tasting it is
Speaker 1: quite another. The problem was that somebody had told me
Speaker 1: that they had made the recipe for Stan, that it
Speaker 1: was good, and what it was as a recipe called
Speaker 1: chocolate baked and serves. That's Janelle Shane. She's a research
Speaker 1: scientist and the author of a blog called AI Weirdness.
Speaker 1: She's talking about a recipe written by Ai that she
Speaker 1: actually cooked and eight. It starts out as a perfectly ordinary,
Speaker 1: flowerless chocolate brownie all the way until the very last ingredient,
Speaker 1: which is a cup of horse badish. I knew I
Speaker 1: was in trouble when I opened the oven door and
Speaker 1: my eyes just started watering. It was yeah, it was terrible.
Speaker 1: On her blog, Jenelle experiments with putting AI to a
Speaker 1: range of tasks, from writing new pickup lines to naming
Speaker 1: Halloween costumes, and often her experiments with machine learning are
Speaker 1: pretty revealing about us. It plays into this thought experiment,
Speaker 1: what would an alien think of our world? It takes
Speaker 1: something that's very ordinary and mixes it up into this
Speaker 1: thing that sounds like the original, but the meaning has
Speaker 1: been completely changed. Chopped whipping cream may be an ingredient
Speaker 1: and fold water, enrolled it into cubes, or spread the
Speaker 1: butter in the refrigerator. That's another direction that came up with.
Speaker 1: Remember Ross and Oscar playing with the creativity setting for
Speaker 1: their scripts. Janelle plays with herbot's temperature too, so I
Speaker 1: can turn it up and the neural net may choose
Speaker 1: its second best or third best guess as to what
Speaker 1: letter comes next. And if I turn the creativity all
Speaker 1: the way down, then everything maybe something like the the
Speaker 1: the or recipes may be just you know, one teaspoon
Speaker 1: of vanilla over and over and over again, because that's
Speaker 1: just a very likely ingredient. It's really interesting with the
Speaker 1: recipes to turn down the creativity and see what it
Speaker 1: comes up with as the most quintessential recipes. At the
Speaker 1: lowest setting, you may not get hole Strandish brownies, but
Speaker 1: you do get a clear picture of what we eat
Speaker 1: and who we are. I look at what kinds of
Speaker 1: recipe titles that comes up with. There are things like
Speaker 1: chocolate chicken chicken cake, and another one that's chocolate chocolate
Speaker 1: chocolate chocolate cake. And there was a lot of cheese
Speaker 1: in these recipes too, so it's kind of revealing about
Speaker 1: what sorts of things we cook with. Then we like chocolate,
Speaker 1: cheese and chicken apparently. But then I did the same
Speaker 1: experiment with recipes from Bone Appetite, and then the most
Speaker 1: common ingredients that kept using were cilantro and pomegranate juice.
Speaker 1: So these algorithms essentially hold up a mirror to the
Speaker 1: data sets that we give them. They do, yeah, they
Speaker 1: reflect the data sets back to us in really weird ways,
Speaker 1: and they can absolutely pick up whatever bias there is
Speaker 1: in a input data set. And I think what we're
Speaker 1: discovering is just how prevalent that bias is and how
Speaker 1: easy it is for neural networks to latch onto that
Speaker 1: bias and copy it as a handy tool toward copying
Speaker 1: whatever where the humans are doing. They say that the
Speaker 1: way to a person's heart is through their stomach. But
Speaker 1: Janelle didn't stop at chocolate, bakes and surfs. She's also
Speaker 1: turned AI onto some more direct roots. I really liked
Speaker 1: the pickup lines. And there are all these puns and
Speaker 1: all this wordplay that it didn't have any way to
Speaker 1: grab hold of and figure out how to use. But
Speaker 1: I think what it produced this sort of charming surrealism
Speaker 1: and kind of garble nonsensical. I think it's an improvement
Speaker 1: on every single one of the originals. My very favorite
Speaker 1: one is you look like a thing and I love you.
Speaker 1: You are so beautiful that you make me feel better
Speaker 1: to see you. Or you must be a tringle because
Speaker 1: you're the only thing here. Are you a camera? Because
Speaker 1: I want to see the most beautiful than you. Yeah,
Speaker 1: I'll definitely lie with you. No one's have a used
Speaker 1: real pickup line on me? Use one on you know?
Speaker 1: Do I look like someone who would receive a pickup Well,
Speaker 1: here's one of them. I don't know you. That's good
Speaker 1: a lot of girls are into that. Are you a candle?
Speaker 1: Because you're so hard of the looks with you. So
Speaker 1: in effect, what the algorithm is doing is highlighting patterns
Speaker 1: in the data. I mean, there's sound structural like pickup
Speaker 1: lines about the words themselves don't make any sense. The
Speaker 1: machines are reflecting their creators and spitting back something which
Speaker 1: resembles pick up lines and makes us think a little
Speaker 1: bit more carefully about what a pickup line is. And
Speaker 1: while training Benjamin Ross and Oscar found the same thing
Speaker 1: as the algorithm learned patterns revealed bias present in our cinema.
Speaker 1: When you train an algorithm like Benjamin on millions and
Speaker 1: millions in this case of synopsis from the Internet of
Speaker 1: the movies, the synopsis that come out have certain patterns
Speaker 1: in them. For example, they mentioned men full times more
Speaker 1: often than they mentioned women. But you you learn other
Speaker 1: things about it than that. You learned that the most
Speaker 1: common phrase in the in the output is a young
Speaker 1: man in a small town. So what does a filmmaker
Speaker 1: like Oscar learn from this? I used to call this
Speaker 1: project the average movie projects. And the reason I called
Speaker 1: it that is the theory was for me, if you
Speaker 1: could make the right kind of algorithm that the movie
Speaker 1: that you would make that would be the theoretically perfect
Speaker 1: movie would also be the most boring movie ever made,
Speaker 1: and that it would. It would it would be by
Speaker 1: definition all of the things that were the most clich
Speaker 1: because that's what cliche means, is the thing that that
Speaker 1: you can rely on to work. And why do that?
Speaker 1: Because the thing I'm most interested in is doing the
Speaker 1: thing that we haven't done yet. I want to move
Speaker 1: the form forward. Seeing all of these biases and assumptions
Speaker 1: that are baked into our movies and our snacks doesn't
Speaker 1: mean we're doomed to repeat them. In fact, the awareness
Speaker 1: can be liberating. That's what's helped me, I think, is
Speaker 1: seeing Benjamin's capacity to show me more directly what it
Speaker 1: is that are our patents, are our habits, and then
Speaker 1: I can ask more easily how to move forward from that.
Speaker 1: We'll get there after the break. So we've heard about
Speaker 1: Janelle Shane using AI to reveal bias, and Ross and
Speaker 1: Oscar using it to help them think more creatively about
Speaker 1: filmmaking as well as how it can be applied to music.
Speaker 1: And that's the great promise of AI. We may worry
Speaker 1: about replacing jobs, but it can augment our lives in
Speaker 1: so many ways. At least that's how Sebastian Throne sees it.
Speaker 1: I would say the term AI is a bit deceptive
Speaker 1: because it sets up computers to be on equal power
Speaker 1: with people. I see it to be stronger where we
Speaker 1: are weak, and weaker where you're strong. It's not a
Speaker 1: technology that will replace us, as it's not really empowers
Speaker 1: but what might that empowerment look like beyond bias detection
Speaker 1: and piano playing well. In seventeen, Sebastian published a paper
Speaker 1: in Nature on using AI to diagnose skin cancer using
Speaker 1: just an iPhone. So in medicine, you can think of
Speaker 1: your iPhone that can find skin cancer as turning regular
Speaker 1: physicians or anybody in the world into an expert on
Speaker 1: day one, because now they have the superpower to be
Speaker 1: able to distinguish something that previously would have taken tens
Speaker 1: of years to learn. The same is true for the
Speaker 1: self driving car. Now children can drive and and and
Speaker 1: blind people can drive, head blind people drive around and
Speaker 1: self driving cars. So for me, the real opportunities to
Speaker 1: use the eye to extract the knowledge from some human
Speaker 1: experts that are well trained and transpose this knowledge to
Speaker 1: other brains that people not so well trained. This is
Speaker 1: what I personally find so fascinating about AI and a
Speaker 1: big reason I wanted to do Sleepwalkers. The same technology
Speaker 1: underlies rossan Oscar's films, Janelle's recipes and self driving cars
Speaker 1: and cancer diagnostics and so much more. The training for
Speaker 1: skin cancer detection or cancer detection radeology and the training
Speaker 1: for the self dime car amazingly similar. In both cases,
Speaker 1: what you do is you compile the data set typically
Speaker 1: hundreds of thousands up to hundreds of millions of images.
Speaker 1: In skin cancer, we use biopsies. We had a database
Speaker 1: of a hundred twenty nine thousand images that a lab
Speaker 1: had biopsied and provided. In the self driving car, you
Speaker 1: could be as easy as having a human driver provide
Speaker 1: inputs with their student veel and the and the break
Speaker 1: as to what the right thing is to do, and
Speaker 1: then the network mimics human behavior, it mimics the diagnostics
Speaker 1: of a physician, or it mimics the style of a driver.
Speaker 1: The underlying albums are amazingly similar. What makes this moment
Speaker 1: all the more interesting is that AI is in the
Speaker 1: process of being consumerized like Sebastion said, your iPhone can
Speaker 1: diagnose skin cancer. Self driving cars are already on the roads,
Speaker 1: and as these tools become more and more accessible, society
Speaker 1: will start to change. These technologies become closer and closer
Speaker 1: to us. The fact that you carry yourself phone is
Speaker 1: a big deal. You might not see this way, but
Speaker 1: what it does it puts the computer seamlessly into your life.
Speaker 1: You're texting app, your SMS is so close to you
Speaker 1: that you can now talk to people thousands of miles
Speaker 1: of a on a button press or on a microphone.
Speaker 1: That makes you effectively super human without the actual physical implant.
Speaker 1: But when it comes to AI, for now, leading edge
Speaker 1: algorithms are off limits to those of us who can't
Speaker 1: code or who don't have the means to learn. Oscar
Speaker 1: wouldn't have been able to make Sunspring without Rosses technical expertise,
Speaker 1: but that's all starting to change, Julian, you spoke with
Speaker 1: somebody making AI more accessible. Yeah, while we were looking
Speaker 1: into AI and music, we came across Runway m L
Speaker 1: their lab based in Bushwick, and they feel strongly about
Speaker 1: letting more people into work with AI Creatively. I spoke
Speaker 1: with Christo bal Valence Weela, the co founder of Runway,
Speaker 1: which is basically like the Adobe Creative Suite for AI.
Speaker 1: So think of a program that looks like Photoshop. They're
Speaker 1: adding tons of different AI models to the Runway app,
Speaker 1: where instead of having to know how to code, you
Speaker 1: can just manipulate some sliders and dials and still have
Speaker 1: a I generate something. If we really think of this
Speaker 1: the game changing technology that will impact us for like
Speaker 1: years to come, we need to have more people from
Speaker 1: different backgrounds and disciplines jumping to that discussion and proposing
Speaker 1: ways of looking at algorithms that those researchers and those
Speaker 1: scientists are not thinking of. This is gonna impact us,
Speaker 1: and it's going to change the way we see not
Speaker 1: just the world, but ourselves. Not just ourselves, but how
Speaker 1: we think about our creativity. And the list of things
Speaker 1: that Runway can help you do is frankly crazy. Will
Speaker 1: post some on our Instagram at Sleepwalkers podcast, but just
Speaker 1: one example, you can take video of anyone adds and
Speaker 1: have their body copy the poses that you make in
Speaker 1: your webcam. So Krystoval actually tweeted one of him controlling
Speaker 1: Stephen Colbert's body on The Late Show just with his
Speaker 1: webcam moving his arms. Stephen moves his arms. It's nuts, right,
Speaker 1: And imagine that kind of technology in the hands of artists.
Speaker 1: Start thinking about them as not like something that's gonna
Speaker 1: destroy our creativity or gonna replace writers an artists or whatever.
Speaker 1: This is gonna be a typewriter, This is gonna be
Speaker 1: a paintbrush, and people will start building and using it
Speaker 1: to understand their own creativity in a new way. Of course,
Speaker 1: as always, it's up to us to make sure we
Speaker 1: use these new tools for good. If I build a shovel, okay,
Speaker 1: and you decide to go to the beach and digging sand,
Speaker 1: you're biased. You're digging in sand and guess what You're
Speaker 1: shoveled only to an up sand. The same as true
Speaker 1: for AI. If you give it a certain type of
Speaker 1: data set, I can promise you whatever I get out
Speaker 1: reflects the data you're put in. It's up to us,
Speaker 1: the people, to make responsible decisions. And as we want
Speaker 1: to create equal opportunity and evadicate certain biases society that
Speaker 1: exists today, is up to us to do it, and
Speaker 1: I promise you, if you work hard on this, technologies
Speaker 1: will reflect that. But even Sebastian, one of Silicon Valleys
Speaker 1: Great Optimists, recognizes the risks all technologies can harm people.
Speaker 1: In fact, technologies can be abused to harm people, Like
Speaker 1: my kitchen knife, which serves me a great purpose every
Speaker 1: time I've guests over in shopping. My produce can also
Speaker 1: be abused to harm people. In the next episode of Sleepwalkers,
Speaker 1: we dive deep into the ability of algorithms to cause harm.
Speaker 1: We traveled from China and the social credit system to
Speaker 1: a parole board in New York and we speak with
Speaker 1: people building technology they believe will make us safer. I'm
Speaker 1: as Vlachen, see you next time. Sleepwalkers is a production
Speaker 1: of our heart Radio and unusual productions. There's so much
Speaker 1: we don't have time for in our episodes, but that
Speaker 1: we'd love to share with you. So for the latest
Speaker 1: AI news, live interviews, and behind the scenes footage, find
Speaker 1: us on Instagram, at Sleepwalker's podcast or at sleepwalkers podcast
Speaker 1: dot com. Special thanks on this episode to paw Suris,
Speaker 1: who introduced us to Oscar Ross and Benjamin and to
Speaker 1: artificial intelligence, which composed over half of music in this episode.
Speaker 1: Could you tell which was which Sleepwalkers is hosted by
Speaker 1: me Ozveloshin and co hosted by me Kara Price. Were
Speaker 1: produced by Julian Weller with help from Jacopo Penzo and
Speaker 1: Taylor Chacog. Mixing by Tristan McNeil and Julian Weller. Our
Speaker 1: story editor is Matthew Riddle. Recording assistance. This episode from
Speaker 1: Joanne de Luna. Sleepwalkers is executive produced by me Ozveloshin
Speaker 1: and Mangesh hatt Together. For more podcasts from my heart Radio,
Speaker 1: visit the i heart Radio app, Apple Podcasts, or wherever
Speaker 1: you listen to your favorite shows,