Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says rewritten by
Speaker 1: a machine with new technology. I'm John and I'm Joe McCormick. So, guys,
Speaker 1: you know, we've talked a little bit about AI and
Speaker 1: its potential role in creativity a few times. Back in September,
Speaker 1: we recorded an episode called the Future of Music Composition,
Speaker 1: and we talked about electronically aided composition as well as
Speaker 1: the idea of AI being able to compose music all
Speaker 1: by itself. You know. One of the funny things is
Speaker 1: on that episode, I think we we talked about, you know,
Speaker 1: will we ever have something that and and then it's
Speaker 1: stuff we already have right now. Yeah. Yeah, to be fair,
Speaker 1: some of it may not have been as as publicly
Speaker 1: available in as it is. No, I mean now today,
Speaker 1: not necessarily now, back then we had earlier versions of them. Yeah. Oh,
Speaker 1: the base base technology that we're going to be talking
Speaker 1: about today, really deep learning didn't kind of was not
Speaker 1: announced to the people. Yeah, we can. We cannot be blamed,
Speaker 1: is I think what we're trying to say here, But
Speaker 1: we're futurists, not psychics. We're not even really futurists. Reporters, Yeah,
Speaker 1: we're not even really reporters. Kind of sit in front
Speaker 1: of microphone. We're just gonna keep stepping back. We don't
Speaker 1: know what we are, but we know what we're excited about.
Speaker 1: We're excited about this idea of AI being able to
Speaker 1: engage in works of creativity. And before, just before we
Speaker 1: jumped into the studio, Joe, you came across an example
Speaker 1: of AI that works with music. Yeah, so I I
Speaker 1: was wondering if you guys had listened to any of
Speaker 1: the creations of this app called juke deck, And before
Speaker 1: you asked us, I had not, But Lauren and I
Speaker 1: listened to a track. I decided to generate a music
Speaker 1: track that was in the electronica genre with a mood
Speaker 1: of aggressive. Was it aggressive, No, it was kind of.
Speaker 1: It was kind of a little bouncy yeah, yeah, aggressively bouncy. Yeah.
Speaker 1: What juke Dick does is it will let you specify
Speaker 1: a mood and specify a genre from like, you know,
Speaker 1: four different choices. You can pick a piano thing, a
Speaker 1: folk thing, an electronic thing, or something else. I can't
Speaker 1: remember the last one, and then it'll let you pick
Speaker 1: a mood to go with that and say how long
Speaker 1: of a track you want, and then it'll just generate
Speaker 1: for you an original track of music that is royalty
Speaker 1: free that was created by a I. So I first
Speaker 1: told it I wanted a ninety second original track of
Speaker 1: electronic music for a chilled mood, and it gave me
Speaker 1: a track called Holistic Adventure. Ours was called Sweltering Seas.
Speaker 1: That's pretty good. And then it also made me a
Speaker 1: sixty second original track of folk style music for a
Speaker 1: melancholic mood, which was called Infinite Atoms. And beyond that,
Speaker 1: if you were to go further into it, they actually
Speaker 1: have other genres available as well, including cinematic, and they're uh,
Speaker 1: just to pay up for those. Yeah, it's it's when
Speaker 1: you've subscribed to the service. And one thing we should
Speaker 1: point out is that not every mood is available with
Speaker 1: every style of music, So you know, you can't really
Speaker 1: necessarily get a lot of aggressive folk necessarily. Yeah, So
Speaker 1: I'm not exactly sure how this works. So the way
Speaker 1: they explain it is, quote, our AI uses machine learning
Speaker 1: to understand how to write music chord by chord and
Speaker 1: note by note, this means that every track you create
Speaker 1: using juke Deck is truly unique, and so what they're
Speaker 1: saying is that it truly is ground up auto generated. Um,
Speaker 1: you know, artificial intelligence created music and they're not just
Speaker 1: like working from templates or something like that. But then again,
Speaker 1: I mean, it's it's hard to know what what's really
Speaker 1: going on on the back end, but at least if
Speaker 1: it works as advertised, I'm impressed. Neither one of the
Speaker 1: tracks I heard was like great music it you know,
Speaker 1: it wasn't blowing my mind. I wasn't just amazed, oh man,
Speaker 1: that's so good, but it was absolutely passable for its genre,
Speaker 1: totally good enough to be elevator music or the kind
Speaker 1: of music you'd playing a store or something like that.
Speaker 1: I was telling Lauren that it reminded me of the
Speaker 1: kind of music you would encounter in like a small
Speaker 1: independent video games, Like you're playing a little video game
Speaker 1: where clearly it's maybe one or two people who have
Speaker 1: worked on that, and it totally was something that you
Speaker 1: would hear from that. Not bad, not like a spectacular
Speaker 1: entry into the field, as you were saying, Joe, but
Speaker 1: not bad. Um. It's also I don't know for a
Speaker 1: fact that you can do this, But I was playing
Speaker 1: with it and it looked like there might even be
Speaker 1: the possibility of combining genres. So I wanted to find
Speaker 1: out what would happen if I did folk electronic with
Speaker 1: a really kind of action oriented music. But I didn't
Speaker 1: get a chance to get far enough into it to
Speaker 1: find out if, in fact, that is possible. Well, we
Speaker 1: have that little example there. And I don't mean to
Speaker 1: say little as in like I'm I'm dismissing it. I
Speaker 1: mean it's it's a it's a relatively um modest approach
Speaker 1: to it, and there are some really intelligent people behind it.
Speaker 1: It's a it's a group of folks from originally Yeah,
Speaker 1: so smart folks. Meanwhile, we also covered another related topic
Speaker 1: in August two thousand and fourteen, we did a show
Speaker 1: called the Future of Art, and we talked about the
Speaker 1: merger of technology and art. And today we're gonna talk
Speaker 1: about a project that's really trying to push this entire
Speaker 1: idea forward, Google Magenta, And of course it's by Google.
Speaker 1: When you're wearing your Google Fiber is coming, sir, I
Speaker 1: am that was not I didn't think about it when
Speaker 1: I put it on this morning. But yeah, I'm wearing
Speaker 1: my my Google Fiber Georgia shirt. Uh and and I
Speaker 1: understand they're laying Google Fiber along North Avenue right now
Speaker 1: as we speak. Yeah, which is which is right outside
Speaker 1: our office building. In fact, nor that all what all
Speaker 1: those police cars were escorting this morning, the Google Fiber
Speaker 1: escorting the fiber down pondstantly in Avalue right like it
Speaker 1: gets like a presidential motorcade level of welcome. One can
Speaker 1: only hope, right, I'm still I'm really holding out hope
Speaker 1: that I get Google Fiber before too long. At any rate,
Speaker 1: you might be wondering what exactly is Google Magenta. Well,
Speaker 1: it's a project that falls under the Google Brain team.
Speaker 1: That's the department within Google dedicated to using machine intelligence
Speaker 1: focused on deep learning, and in deep learning, engineers program
Speaker 1: networks of like virtual neurons that can look at data.
Speaker 1: So so let's use like images for example. Okay, the
Speaker 1: neurons can look at a picture and assess some factor
Speaker 1: of it, the shapes or the colors maybe, and then
Speaker 1: the neurons can decide what those shapes or colors were
Speaker 1: minded of and assign that decision of probability. Um, probably
Speaker 1: this is a boat. Maybe it's a banana. Most likely
Speaker 1: it's not a centipede, but it's slightly sent a petish
Speaker 1: udmule recognized boats that don't have sales. I mean, you know,
Speaker 1: it all depends on what you fed it to tell it.
Speaker 1: It was about, remember this is This is very similar
Speaker 1: to that idea we talked about, where feeding all those
Speaker 1: pictures of cats to machine learning, so that eventually the computer,
Speaker 1: without being told this is a cat, starts to learn
Speaker 1: what a cat is. It knows that it's got certain
Speaker 1: features it's really aloof, and it really doesn't care if
Speaker 1: you live or die exactly. So, so the neurons, the
Speaker 1: turons passed this information around to other neurons in their layer,
Speaker 1: and then that layer kind of compiles and passes its
Speaker 1: information up through other layers, and and the system makes
Speaker 1: increasingly educated guesses about what exactly is going on in
Speaker 1: this picture. And as you feed the system more and
Speaker 1: more pictures, it makes more and more associations with particular
Speaker 1: shapes and colors, so it learns right. And we also
Speaker 1: talked about a related uh products that came out of
Speaker 1: this group Deep dream. Oh, this is the project that
Speaker 1: turned my dog into a big old mess of caterpillars.
Speaker 1: It turned me into a big old mess of dogs. Yeah.
Speaker 1: This is This also came out of the Brain Team group.
Speaker 1: That's the Artificial Neural Network project that teaches computers how
Speaker 1: to recognize patterns out of visual data, even when the
Speaker 1: patterns aren't necessarily there. It's not it's not too different
Speaker 1: from when you look up at the clouds and you say, oh,
Speaker 1: there's a it's very like a whale. Right, So we
Speaker 1: were teaching computer programs how to hallucinate. Yeah, pretty much.
Speaker 1: And we talked about that in an episode called deep
Speaker 1: Dreaming with Google and that published in July two thousand fifteen.
Speaker 1: And and basically what is going on with deep dream
Speaker 1: here is that instead of just making a guess that
Speaker 1: a picture contained a dog, for example, um, it changed
Speaker 1: the image so that everything that appeared to be a
Speaker 1: dog in it would look more dog like. So like
Speaker 1: a layer would say, well, this this is probably a
Speaker 1: dog based on the shapes in it, So let's enhance
Speaker 1: the doggie shapes to really emphasize the dogginess. And and
Speaker 1: then as that extrapolated image is passed through Deep Dreams layers,
Speaker 1: each one emphasizes whatever it guesses is going on in
Speaker 1: the photo, and yes, frequently what it guesses is going
Speaker 1: on in the photo is a nightmareescape of dog faces
Speaker 1: as far as the eye can see. Right. So well,
Speaker 1: I think the most popular ones were definitely the animal
Speaker 1: recognition ones, but you could tweak the algorithm to recognize
Speaker 1: all kinds of stuff. And it was because that they
Speaker 1: had started with just feeding so many different animal images.
Speaker 1: That was like that was sort of their their starting
Speaker 1: point over at Google when they were training this how
Speaker 1: to recognize different visual patterns. So if a fold in
Speaker 1: your clothing looked even remotely similar to a dog, guess
Speaker 1: what you're wearing? Dogs? Now, according to arms, your arms
Speaker 1: have bugs in them, your your shirt, and your shoulders
Speaker 1: or dogheads, your dog actually is not a dog but
Speaker 1: a bunch of cats taped together. Yeah. I like that.
Speaker 1: Everything became very dog lee for a while. But one
Speaker 1: of the other projects under the Brain team is one
Speaker 1: we're going to mention a little bit later called TensorFlow,
Speaker 1: which is a machine learning engine, and it's an open
Speaker 1: source project, meaning lots of people anyone really can access
Speaker 1: those tools and not just access them, but tweak them,
Speaker 1: improve them, evolve them, and and grow them, and that's
Speaker 1: really an interesting and potentially exciting development in machine learning. Yeah,
Speaker 1: but you can see how pro projects like this might
Speaker 1: be sort of evolving toward ultimately generative powers in AI,
Speaker 1: not just recognition and modification of visual images and other
Speaker 1: types of sensory input and data, but but actually building
Speaker 1: things from the ground up, recognizing patterns and saying I've
Speaker 1: got enough sense of what the patterns are that i
Speaker 1: can make one on my own, right, exactly like you,
Speaker 1: You no longer are taking a a uh, you know,
Speaker 1: an image or a concept and then saying, here, enhance
Speaker 1: this or a this in some way or recognize it.
Speaker 1: And you're now saying, hey, you know what one of
Speaker 1: those happens to look like? Make one? Yeah, And that's
Speaker 1: that's a big leap, right, that's a huge leap. And
Speaker 1: you can do the same thing you somebody could say,
Speaker 1: tell me the plot of a James Bond movie that
Speaker 1: doesn't exist. You've seen enough of them. You know, there's
Speaker 1: a standard pattern. You gotta have the gadgets, you've got
Speaker 1: to have the you know the yeah, yeah, yeah, you
Speaker 1: gotta have a pit of sharks or something like. You
Speaker 1: you can put the pieces together. You gotta have at
Speaker 1: least one one lady who is pretty much a good
Speaker 1: guy and one lady who's pretty much a bad guy,
Speaker 1: and Bond's gotta mess with both of them. You know,
Speaker 1: there's there's certain rules that you probably both what's that
Speaker 1: messed with? Hey, I'm being very uh, you know, being
Speaker 1: very family friendly here, so like James Bond, Yes, exactly,
Speaker 1: James Bond, so family friendly. So the Magenta project is
Speaker 1: aimed at developing artificial intelligence capable of actually creating art,
Speaker 1: not not just altering something so that it looks like art,
Speaker 1: but to create art, both visual art and music. Now,
Speaker 1: the official launch date of Magenta is June one, two
Speaker 1: thousand sixteen. We are recording this on May twenty six,
Speaker 1: two thousand sixteen, so it has not launched as of
Speaker 1: the time we're recording this, UM, but we wanted to
Speaker 1: kind of talk about it, and it was just recently
Speaker 1: announced to the world, although not officially unveiled. Douglas k
Speaker 1: who's working on the project, announced Magenta at Mogue Fest
Speaker 1: I assume named after the Mogue synthesizer which everybody lives
Speaker 1: and um that's actually a music and technology festival that
Speaker 1: takes place in North Carolina, so it was not too
Speaker 1: far away, and also stressed that while they're working on
Speaker 1: this project and while they have high hopes for it,
Speaker 1: he says, AI is still a very long way from
Speaker 1: creating long narrative arcs. So it's not like this is
Speaker 1: going to be, you know, within a year, we're going
Speaker 1: to have computers righting the next great American novel or
Speaker 1: anything along those lines, but that this is the first
Speaker 1: step to word computers making a creative problem solving and
Speaker 1: moving to a point where that problem solving isn't about
Speaker 1: tackling a question, but about creating something new, like music
Speaker 1: or a painting or anything along those lines, or even video. Well,
Speaker 1: I mean, I think something literary and long and coherent
Speaker 1: like a novel would would be one of the most
Speaker 1: difficult things because that involves just the most uh what
Speaker 1: might you call it, semantically diverse array of things that
Speaker 1: you're working with. I mean, in a lot of moving parts,
Speaker 1: not just not just the words that have to make
Speaker 1: sense and sentences and in paragraphs and in chapters, but
Speaker 1: also character development, character motivation. I mean, it's not even
Speaker 1: that doesn't even make sense to a computer, world building, etcetera. Right, Yeah, exactly.
Speaker 1: There are a lot of things, a lot of elements
Speaker 1: in creating a long narrative that are would take a
Speaker 1: long long time to teach a computer. What does this mean?
Speaker 1: I mean you can imagine that a book written by
Speaker 1: a computer before it has a full grasp on that,
Speaker 1: it could end up being incredibly dull. You're just you're
Speaker 1: reading a very mundane account of a person or or
Speaker 1: it could be the opposite. It could be like well,
Speaker 1: it could be like a Dan Brown novel where every
Speaker 1: page ends with a cliffhanger and you think I need
Speaker 1: to break you know. I was looking up Douglas X
Speaker 1: so I was reading a little about him on his
Speaker 1: Google research page, and one of the things he had
Speaker 1: previously worked on was was content. What would you call it?
Speaker 1: Uh oh, I'm losing the word for it. He worked
Speaker 1: on Google Play music delivering you the kind of music
Speaker 1: you would want. There's a term for that, curation. Curation sure, yeah,
Speaker 1: based on yeah, yeah, yeah, based on what you like
Speaker 1: to listen to. Figuring out, Okay, what's the other type
Speaker 1: of music you haven't heard yet that fits in with
Speaker 1: the profile of the stuff that you like, which is,
Speaker 1: you know, that's kind of a tough job, because how
Speaker 1: does the computer know that one song? Oh you know,
Speaker 1: if Jonathan really likes this song by they might be giants.
Speaker 1: He'll probably really also like this song by Slayer. Yeah
Speaker 1: I think that. Yeah, they're like, hey, like like you
Speaker 1: like rock and roll Joe, so probably you like this
Speaker 1: Nickelback song which is rock and roll. Yes, yeah, yes,
Speaker 1: So at least with the Music Genome Project, which is
Speaker 1: what Pandora is based off of, the way that works
Speaker 1: is they have human beings who meta tag every song
Speaker 1: with every kind of descriptor that would be relevant to
Speaker 1: that particular song, and then the algorithm starts looking for
Speaker 1: other songs that have several of those same meta tags
Speaker 1: associated with it and say yeah, yeah, and so it's
Speaker 1: going like you like melancholy guitar solos because you listened
Speaker 1: to the Decembrists, so probably you're going to like the
Speaker 1: dashboard confessionals, right, So that that may be similar to
Speaker 1: the way Google Play Music does it. I don't know,
Speaker 1: because I don't know. He made it sound more like
Speaker 1: that this was a more automated process. That's really interesting,
Speaker 1: more difficult. Yeah, yeah, you're not having a human hold
Speaker 1: your hand taking you through Okay, now this is what
Speaker 1: moody sounds like. This is melancholy, right, that's really interesting. Well.
Speaker 1: At at the mog Fest conference, Magenta team member Adam
Speaker 1: Roberts showed off a digital synthesizer program where he could
Speaker 1: feed a few musical notes into it. I think it
Speaker 1: was a sequence of four notes, and then allowed the
Speaker 1: program to build a melody off those basic notes. And
Speaker 1: what I'm picturing this kind of going off the deep
Speaker 1: dream concept, is you know like, Okay, well these notes
Speaker 1: sound like the beginning of a big band slow dance,
Speaker 1: So I'll just add more notes to make it look
Speaker 1: more like that. Sound more like that. Yeah. The the
Speaker 1: example that they showed in the in the actual festival,
Speaker 1: when I when I watched it, I thought, ah, they
Speaker 1: picked up bad for initial notes. It just because you
Speaker 1: listen to it and you're like, to me, that sounds
Speaker 1: like just someone aimlessly plunking on a synthesizer keyboard. It
Speaker 1: didn't sound like someone actually creating melody. But I would
Speaker 1: argue that you're really in this case, the way this works,
Speaker 1: the the final tune is really only going to be
Speaker 1: as good as the initial input you give to the computer.
Speaker 1: Because it's not creating it out of whole cloth. It's
Speaker 1: taking a foundation and then building upon it. If the
Speaker 1: foundation is faulty, then you can't really expect the rest
Speaker 1: of it to be awesome. Um So, but maybe that
Speaker 1: was just me. I also don't have the best ear
Speaker 1: so perhaps I'm being particularly harsh. Also, this this is
Speaker 1: an early prototype that he's showing off, right, Yeah, and
Speaker 1: maybe that the what we see on June one will
Speaker 1: be a more comprehensive demonstration of the abilities of Magenta.
Speaker 1: And keep in mind the Magenta we had it composed
Speaker 1: this song and destroy these four countries? What what did
Speaker 1: you do? Now? Um? We think? Uh. For one thing,
Speaker 1: Magenta is supposed to be an ongoing project, right. This
Speaker 1: is not something that's a fully fleshed out product and
Speaker 1: they're going to reveal it on June one for people
Speaker 1: to play with. It's more like, here's the concept behind
Speaker 1: the project, here's how we're going to try and accomplish
Speaker 1: our goals. Here's where we are now. That's more likely
Speaker 1: to be the announcement on June one. So uh. Ex
Speaker 1: hope is that by feeding enough musical information to Magenta,
Speaker 1: enough songs, in other words, it will be able to
Speaker 1: produce its own music that is esthetically pleasing to human
Speaker 1: like people persons, and that those yeah, I mean that
Speaker 1: my list is pretty small, but I know a couple.
Speaker 1: So the program first has to learn what makes music work?
Speaker 1: What are the rules of music? So what are the
Speaker 1: sort of things that we like to listen to? And
Speaker 1: once you know those rules, when is it okay or
Speaker 1: even preferable to break those rules? When are when is
Speaker 1: it all right to stray from the conventions of any
Speaker 1: particular musical genre and do so in a way that's
Speaker 1: interesting and and maybe it's pleasing, maybe it's maybe it's
Speaker 1: not pleasing, but it's the sort of thing that catches
Speaker 1: your attention and that's what makes the music really stand
Speaker 1: out to you. Right, So these are not easy concepts,
Speaker 1: even for musicians, like human musicians it you know, it
Speaker 1: can take years of study to really understand musical theory
Speaker 1: and be able to craft something that is most likely
Speaker 1: to evoke the reaction your hope to get from your audience.
Speaker 1: You know, I mean, I know, I've written a couple
Speaker 1: of songs. They're terrible, uh, rather than any rate. So
Speaker 1: music is just the first type of art the Magenta
Speaker 1: is going to tackle. They're going to actually use the
Speaker 1: same sort of approach to generate images and perhaps even
Speaker 1: video in the future that they mentioned eventually text too. Yeah,
Speaker 1: so again getting to that point where maybe we can
Speaker 1: get to that narrative arc. And this also kind of
Speaker 1: feeds into when we were talking of our I don't
Speaker 1: know if we've ever talked about on the podcast, but
Speaker 1: Google recently in the news is uh, a lot of
Speaker 1: people poked fun at Google because it was feeding romance
Speaker 1: novels too. It's um digital assistant, so it would learn
Speaker 1: better about how people converse. The important thing being that
Speaker 1: romance novels tend to follow a very similar uh uh
Speaker 1: pattern right, you're very they're very formulaic, but different romance
Speaker 1: novels say the same thing in different ways. And so
Speaker 1: the hope is that by feeding this kind of information,
Speaker 1: and romance novels were just one genre that we're fed
Speaker 1: to it, but everyone focused on it because of course
Speaker 1: it's funny. You know, this idea that your digital assistant
Speaker 1: is going to be making some very sassy recommendations to
Speaker 1: you or explain the weather in ways that are probably inappropriate.
Speaker 1: But the idea being that by feeding all this information
Speaker 1: using machine learning, that you would be able to get
Speaker 1: your finished product to be more capable of interacting with
Speaker 1: people using natural language, very similar to what Magenta is doing,
Speaker 1: except in that case it's music and art. Right. You're
Speaker 1: feeding more and more music in to Magenta so that
Speaker 1: it has a quote unquote understanding of what music is
Speaker 1: and can more likely produce something that is similar to
Speaker 1: what a human would make without it actually just copying
Speaker 1: something that a human has already made. Yeah, I mean
Speaker 1: I wonder sort of what the what the end goal, like,
Speaker 1: what the expectation is here, because like, on one hand,
Speaker 1: if we're to believe what's supposedly going on in the
Speaker 1: back end that creates these tracks we listen to a
Speaker 1: juke deck, I feel like, here's the system that is
Speaker 1: already creating perfectly passable music. Again, like I said, it's
Speaker 1: it's not amazing. So I'm wondering, is is Magenta aiming
Speaker 1: to create music that's really going to be amazing and
Speaker 1: people will be like, wow, I love that song. I
Speaker 1: think Magenta's not to put words into the mouths of
Speaker 1: the people in the project, but I think Magenta, by
Speaker 1: using a very specific goal, is really out driving machine
Speaker 1: learning further. Yeah, so I agree. I think that the
Speaker 1: art is absolutely secondary and kind of the headline grabber,
Speaker 1: right um, and and that right the other applications like
Speaker 1: in a voice recognition or something like that, or what
Speaker 1: they're really aiming for, right So. So for a great
Speaker 1: example of this would be looking at the private space
Speaker 1: industry and saying, you know, you've got some pretty big,
Speaker 1: big ideas, how do you focus that in a way
Speaker 1: where you can actually engineer toward a solution? And then
Speaker 1: you just start taking specific questions like how do we
Speaker 1: make sure that astronauts can can breathe in space? And
Speaker 1: you take that first question and you start trying to
Speaker 1: solve that and and it ends up being that it's
Speaker 1: one part of a much bigger picture. I think that
Speaker 1: that's what we're going to see when the GENTA. Yeah,
Speaker 1: i'd agree. I mean I just wondered about the art itself,
Speaker 1: like how what do they think it's gonna be? Like,
Speaker 1: I'm very curious about that too. And obviously since we're
Speaker 1: so early into the project, it's hard to say. I
Speaker 1: would love to be able to to revisit this. In fact,
Speaker 1: maybe we will be able to revisit this, uh sometime
Speaker 1: into the future and listen to some of the stuff
Speaker 1: Magenta has produced and say, does this sound like a
Speaker 1: human being made it? Or you know, the fact that
Speaker 1: we know a computer made it, does that change what
Speaker 1: we feel about it? And anyway you might wonder how
Speaker 1: the heck is this thing working well? To to kind
Speaker 1: of build upon what Lawrence point was with the neural networks. Uh,
Speaker 1: they're specifically using this set of tools called TensorFlow, which
Speaker 1: falls into that category. UM, that's the open source machine
Speaker 1: learning set of tools. UH. And generally speaking, first, it's
Speaker 1: going to accept midi files, that's a very common version
Speaker 1: of music files. UH. And that's going to be submitted
Speaker 1: by a community of contributors to teach itself the basic
Speaker 1: rules and concepts around music. But TensorFlow itself, according to
Speaker 1: its web page, is an open source software library for
Speaker 1: numerical computation using data flow graphs. Nodes in the graph
Speaker 1: represent mathematical operations, while the graph edges represent the multidimensional
Speaker 1: data arrays or tensors communicated between them. The flexible arc
Speaker 1: actually allows you to deploy computation to one or more
Speaker 1: CPUs or GPUs in a desktop, server, or mobile device
Speaker 1: with a single API found artistic to me, couldn't be
Speaker 1: more transparent or simple. So I mean, I think the
Speaker 1: simple version is just that TensorFlow is. They say it's
Speaker 1: open source software for machine intelligence. Yes, and it's and
Speaker 1: it's essentially modeled after, at least inspired by, rather the
Speaker 1: way brains work. Yeah. Now, tensor flows particularly well suited
Speaker 1: to process information for visual analysis and recognition as well
Speaker 1: as speech recognition. So that's that's what they were primarily
Speaker 1: intending it for when they made it open source and
Speaker 1: said that different developers could use this kind of tools
Speaker 1: set in order to give their various apps or projects
Speaker 1: the capabilities that they would need, uh, in order to
Speaker 1: process visual information or to do some sort of speech
Speaker 1: recognition or speech activation kind of process. Now, the ideas
Speaker 1: that developers can use these tools to create the stuff
Speaker 1: they need. They can test the stuff that they have
Speaker 1: planned and see if it works, and if it does work,
Speaker 1: they don't have to do any more new code. They
Speaker 1: can actually use the code from TensorFlow as and incorporate
Speaker 1: into their their project. Um And like I said, it's
Speaker 1: not a complete set. It's something that will continue to
Speaker 1: evolve over time as people find shortcuts or they might
Speaker 1: find a more elegant way to do something or a
Speaker 1: more robust way, which is part of why it's open source.
Speaker 1: I'm sure that they're hoping that the people who want
Speaker 1: to get in there and work with it will also
Speaker 1: want to improve it. Right, so uh and and again
Speaker 1: going back to Lawren's point about probabilities, it really is
Speaker 1: all about assigning probabilities, assessing those probabilities, refining them so
Speaker 1: that you start looking at your individual options and determining
Speaker 1: which option is the best out of all of them.
Speaker 1: This might sound familiar to you if you remember IBM S.
Speaker 1: Watson when it was on Jeopardy. There was a big
Speaker 1: bit about how does it come up with the answers?
Speaker 1: How does it know what the answer is? Well, IBM S.
Speaker 1: Watson would receive the clue and then it would come
Speaker 1: up with potential answers and assign each one a probability
Speaker 1: of how certain it is that's the correct answer. And
Speaker 1: if the probability was above a certain threshold, that is
Speaker 1: when Watson would buzz in and and send that message in.
Speaker 1: And I think it was something like an eight percent
Speaker 1: uh certainty, something along those lines. It was somewhere around there.
Speaker 1: And this is very similar to when we were talking
Speaker 1: about the Kepler space telescope and how if you had
Speaker 1: a probability greater than of a signal being an exo
Speaker 1: planet that was considered a verified exo planet. So TensorFlow
Speaker 1: does the same sort of thing. It looks through these
Speaker 1: probabilities and then it goes with the highest option. Uh.
Speaker 1: It kind of makes you wonder how that works with music.
Speaker 1: And I don't know the answer to that because we've
Speaker 1: reached the limit of my understanding of this particular approach
Speaker 1: to machine learning. But I wanted to talk a little
Speaker 1: bit about a related project, not directly related to the
Speaker 1: Google team, but one about a computer trying to generate art. Yeah,
Speaker 1: and this was one that you covered for our video show. Now. Yes,
Speaker 1: this would be a project that a group of art
Speaker 1: historians and computer scientists and researchers and developers tackled together.
Speaker 1: And it was in the efforts of creating a new
Speaker 1: painting in the style of Rembrandt, and the idea of
Speaker 1: being that this should be a painting that Rembrandt could
Speaker 1: have painted himself. But if only he'd had a three
Speaker 1: D printer, if only you had a three D printer. So, yeah,
Speaker 1: they specifically use machine learning, computer algorithms and a three
Speaker 1: D printer to create a new quote unquote new Rembrandt painting,
Speaker 1: at least in the style of Rembrandt. And it's not
Speaker 1: quite the same thing as generating completely new art because again,
Speaker 1: you're using Rembrandt's style as your starting point, that's your foundation.
Speaker 1: It's kind of like those intro notes when they were
Speaker 1: building out that melody. Yeah yeah. It also like a
Speaker 1: very specific type of Rembrandt painting in order to create
Speaker 1: a very specific type of ram Brant paints right right,
Speaker 1: instead of feeding it every single Rembrandt painting that ever
Speaker 1: was ever, they took a specific subtype of Rembrandt painting. Now,
Speaker 1: granted it was a specific subtype that rembrand did a
Speaker 1: whole lot of. Sure. It was of a dude, white white,
Speaker 1: an old white dude, well you know, old ish, not
Speaker 1: probably younger than I am actually, but at any rate,
Speaker 1: sitting sitting and not quite you know. It's a little
Speaker 1: bit of a profile shot, not a full profile um
Speaker 1: wearing black with a big old white collar and a
Speaker 1: big old black hat, looking somewhat pensive, as Rembrandts subjects
Speaker 1: often did, probably thinking is this guy gonna let me
Speaker 1: sneeze or something on those lines and it uh. The
Speaker 1: The way they did this was they actually had the
Speaker 1: computer analyze ice portrait after portrait after portrait in this style,
Speaker 1: and the computer began to take measurements of all the
Speaker 1: different little elements of these these portraits to determine what
Speaker 1: is the typical Rembrandt portrait, like, like, what is the
Speaker 1: spacing of the eyes right, how is the nose shaped
Speaker 1: in comparison to the eyes right when you get to
Speaker 1: the corner of the mouth, how does that look in
Speaker 1: a Rembrandt painting? All of these ideas, and of course,
Speaker 1: how do the brush strokes look. So they used very
Speaker 1: high tech scanning technology to get the texture of the
Speaker 1: brush strokes as well. Once they did all this, they
Speaker 1: then fed all that information and generated a Rembrandt style
Speaker 1: portrait using all of those points of data as kind
Speaker 1: of a roadmap, a guide, saying, make sure that the
Speaker 1: eyes are this far apart, make sure that they are
Speaker 1: this large, make sure that their space this far from
Speaker 1: the nose, all these little basic rules that they had
Speaker 1: established through the analysis of all those other portraits, And
Speaker 1: so the computer generated one and then they sent it
Speaker 1: to a three D printer which was able to replicate
Speaker 1: the ridges you would find from brushstrokes, and the end
Speaker 1: result was a painting that looked an awful lot like
Speaker 1: a Rembrandt portrait, enough so that if you put it
Speaker 1: in a gallery of Rembrandt portraits and you brought a
Speaker 1: non expert into the room who someone who was not
Speaker 1: familiar with every painting Rembrandt has ever done, and said,
Speaker 1: pick out the one that was done by a computer,
Speaker 1: I bet it would have been really hard to do
Speaker 1: because it looked pretty much like every other rem Breand
Speaker 1: but again, that was more about copying a specific style, right.
Speaker 1: It was a little It's incredibly impressive. I don't want
Speaker 1: to downplay how impressive the the achievement was. It took
Speaker 1: them two years to do this. But it's not the
Speaker 1: same as trying to teach a computer what art is
Speaker 1: and then tell the computer, now, make something right. So
Speaker 1: it's a little different because you're giving the computer way
Speaker 1: more of a roadmap in the rembrand approach. Now, assuming
Speaker 1: we get to a point where we actually are able
Speaker 1: to have a I produce music and art that we
Speaker 1: think has value to it, it doesn't just seem like
Speaker 1: a random representation of whatever, right, But we're not just
Speaker 1: tuning in for the novelty of it. What does that mean?
Speaker 1: I mean, that's a big question. What does it mean
Speaker 1: to us if AI is capable of of creating something
Speaker 1: that falls into the realm of art. Now, I would argue,
Speaker 1: at least in the foreseeable future, we wouldn't say that
Speaker 1: the computer is actually expressing itself. Uh no, no, And
Speaker 1: I mean for them more like the computer isn't really
Speaker 1: the artist, Like the programmers of the computer are kind
Speaker 1: of the artists. Yeah, just several stages removed. Yeah, the
Speaker 1: computer is the tool that they are using. I think
Speaker 1: of I think of it personally, and let me know
Speaker 1: if you guys disagree. I'm really curious to hear your
Speaker 1: thoughts on this. I think of it as the computer
Speaker 1: could be thought of as an early stage artist, someone
Speaker 1: who is learning their craft by copying the work of others,
Speaker 1: not directly copying their work, but copying the style and
Speaker 1: and not necessarily expressing themselves through that art, but rather
Speaker 1: just trying to master the tools of creation for that art,
Speaker 1: but has not made that next step where they are
Speaker 1: the ones who are being able to express something deeper,
Speaker 1: something new, something unique. To themselves, right, rather than rather
Speaker 1: than simply I now understand how this works. I think
Speaker 1: that the computers will be on that first step uh
Speaker 1: and not in the second one. But if you disagree,
Speaker 1: or if you feel like I am being way too
Speaker 1: uh or narrow minded of how to define this, or
Speaker 1: that you don't even think like I don't even think
Speaker 1: that's a You're looking at it far too utilitary in
Speaker 1: a way. I mean, that is perfectly fine. And I
Speaker 1: didn't put this in the note. So that's why I'm
Speaker 1: just springing it on you to find out what you
Speaker 1: think I mean. I mean, I guess the question is
Speaker 1: really like, do you think that uh, an artist needs
Speaker 1: for for for lack of a better term, a soul
Speaker 1: in order to actually create art? Or can a computer,
Speaker 1: not being truly self aware, actually create art? Right? Here's
Speaker 1: another question. If an artist accidentally spills a bunch of
Speaker 1: paint onto a canvas and it turns out to be
Speaker 1: something that people think is very beautiful and they want
Speaker 1: to look at, is that art, well, it depends what
Speaker 1: is the artist's opinion of this. They aren't feel that
Speaker 1: the act of accident was truly a moment of chaos
Speaker 1: or was it something that was instigated through I mean, like,
Speaker 1: I mean, these are questions people have been asking all
Speaker 1: of that, you know what, what actually counts is all right?
Speaker 1: In fact, this is actually one of the things I
Speaker 1: wanted to look at. So one of the back to
Speaker 1: Douglas x uh Google research page. One of the things
Speaker 1: he says on that page about Magenta is he wants
Speaker 1: to ask the question can machines make make music and art?
Speaker 1: If so, how? If not? Why not? And that very
Speaker 1: last sentence was actually the most interesting part to me.
Speaker 1: I like, I like the inclusion of this question why not,
Speaker 1: because it's it's an interesting way to phrase it. It
Speaker 1: illuminates one of the roles Magenta could play in the
Speaker 1: larger world of AI development. So the study of artificial intelligence,
Speaker 1: to me, it's not just about can I get a
Speaker 1: computer program to perform or X or y intelligent behavior?
Speaker 1: It's about understanding the nature of the behavior to begin
Speaker 1: with the nature of intelligence and intelligence based labor. And
Speaker 1: in this case that might mean that, you know, computers
Speaker 1: could help give us insights into questions like what doesn't
Speaker 1: mean to create a piece of art? What we're just
Speaker 1: talking about a minute ago? I mean, think about in
Speaker 1: the past, um all the for weird pieces of abstract
Speaker 1: art that people looked at they said, that doesn't that's
Speaker 1: not art, you know, Jackson Pollock, does that counts art?
Speaker 1: John Cage? Is this really music? He's just playing one
Speaker 1: note over and over or he's just like turning radios
Speaker 1: on and off? Is that really music? And it? I
Speaker 1: don't know. It makes me think what makes us want
Speaker 1: to pay special attention to a particular display of shapes
Speaker 1: and colors or a particular ordered sequence of sounds. And
Speaker 1: there's difficulty there because when we encounter art in our lives,
Speaker 1: it's not devoid of context. Like so when you encounter
Speaker 1: a particular sequence of sounds or group of shapes and colors,
Speaker 1: it might come with social pressures, right, like people saying, like,
Speaker 1: you know, this thing here is a piece of art.
Speaker 1: You should know and the mere fact that a piece
Speaker 1: of art is hanging in a museum gives it weight exactly,
Speaker 1: So people people are telling you to pay attention to
Speaker 1: this thing, and that might make you pay attention to
Speaker 1: something that you wouldn't pay attention to otherwise, or maybe
Speaker 1: you would who knows. But then again, this could also
Speaker 1: apply to artificial intelligence, because what if it's a an
Speaker 1: AI making a piece of music and you're saying, well,
Speaker 1: maybe I should listen to this because I want to
Speaker 1: hear what artificial intelligence can come up with, and you wouldn't. Really,
Speaker 1: it wouldn't be all that interesting to you. Otherwise what
Speaker 1: you were talking about earlier actually is it? Is it
Speaker 1: just the novelty of it? Sure? Um, which I I
Speaker 1: guess really, I mean, I don't know. I would suppose that, uh,
Speaker 1: post postmodernism, I would define art as a thing that
Speaker 1: makes you think or feel h and therefore a machine
Speaker 1: can totally create absolutely by that definition. I would say
Speaker 1: that if you were to have your your AI create
Speaker 1: some music and that made you feel something, and you
Speaker 1: define that as music that makes me feel something, is
Speaker 1: is art, whether it's you know, happy or sad or
Speaker 1: energetic or whatever, then it would by that definition you
Speaker 1: have to say that the puter was able to generate art.
Speaker 1: And also, let's not forget that there are a lot
Speaker 1: of pieces of music out there throughout the ages where
Speaker 1: musicians were taking very calculated decisions on how to craft
Speaker 1: that music to get a specific kind of feeling for it,
Speaker 1: to the point where you could even be cynical about
Speaker 1: it and be like that song was manufactured day one
Speaker 1: to be a single and get play on like the
Speaker 1: pop charts. And first of all, I don't think there's
Speaker 1: anything wrong with that. I I don't look down at
Speaker 1: my nose at the idea of a manufactured piece of music.
Speaker 1: If it makes someone happy, that's awesome, and that's all
Speaker 1: that really matters in my eyes. I know a lot
Speaker 1: of people have other opinions about it, um and that's
Speaker 1: not bad either. But if a computer were capable of
Speaker 1: doing that, I would I would agree with you, Lauren,
Speaker 1: I'd say, well that that counts is art. H On
Speaker 1: a related note, so let's imagine that we've got computers
Speaker 1: capable of making music. And they talked about the work
Speaker 1: of trying to create text. One wonders if then you
Speaker 1: could actually have computers capable of creating entire songs, like,
Speaker 1: not just music, but music with lyrics. We talked about
Speaker 1: this last time. We were saying that, you know, I
Speaker 1: can easily imagine computers creating perfectly passable instrumental music. Not
Speaker 1: so much music with vocals. That seems a lot more
Speaker 1: difficult to me. So let me give you some poetry
Speaker 1: written by machine. Remember when I told you that Google
Speaker 1: had fed it's artificial intelligence all these novels. It started
Speaker 1: to try and create sentences, and it wasn't attempting to
Speaker 1: create poetry. It was creating a series of sentences that
Speaker 1: other people have looked at and said, this is there's
Speaker 1: something interesting here. Now there's not an intent behind it, necessarily,
Speaker 1: but there is something that feels like really morose poetry.
Speaker 1: So here's here's a poem from Google's AI. It made
Speaker 1: me want to cry. No one had seen him since
Speaker 1: it made me feel uneasy. No one had seen him.
Speaker 1: The thought made me smile. The pain was unbearable. The
Speaker 1: crowd was silent. The man called out, the old man said.
Speaker 1: The man asked, it's a poem by Google AI. Yeah,
Speaker 1: I can dig that. I mean, it's just it's it's interesting, though,
Speaker 1: because this is just this is just AI trying to
Speaker 1: suss out the meaning or or the intent behind words
Speaker 1: so that it can better understand when we communicate to
Speaker 1: it and ask for something using different language, how it
Speaker 1: should respond. It's not attempting to create anything of meaning,
Speaker 1: but because we're humans, we find meaning where perhaps there
Speaker 1: was none intended. And I thought that you know, you
Speaker 1: could argue that that's maybe it's maybe the artists created
Speaker 1: not through the computer right get down, but through us
Speaker 1: reading it. Maybe that's where the artist created. And I mean,
Speaker 1: and I've definitely read some some like like found poetry
Speaker 1: from from spam emails for example. You know, back before
Speaker 1: all of our email filters were so good that they
Speaker 1: didn't don't let spam through anymore. That often um that
Speaker 1: the like just terrific keyword salad that you would get
Speaker 1: that was beautiful, not intending to be. So the funny
Speaker 1: thing we found poetry is who gets the byline on
Speaker 1: found poetry. It's the person who put it together. I mean,
Speaker 1: it's not the where the your text originally came from.
Speaker 1: It's the person who well, I I edited together this
Speaker 1: found poem out of some text I put this in
Speaker 1: this order. So so so is the suggestion here that
Speaker 1: the artist, when we read works by AI is the reader.
Speaker 1: I mean, that's a possibility. It's it's a question that
Speaker 1: I think has has merited. Uh yeah, I don't know. Well,
Speaker 1: I mean, in the case of yours, Jonathan, I suspect
Speaker 1: that somebody was that just it was that a sequence
Speaker 1: fully generated by the AI itself or somebody pull pieces.
Speaker 1: That's an excellent question. I suspect it was the latter,
Speaker 1: And in that case, I would say that the poet
Speaker 1: is the is the person who pulled those pieces together. Actually,
Speaker 1: here it is. Here's the way it works. The team.
Speaker 1: Now that I'm reading this more because when I first
Speaker 1: saw this, I just saw the examples and I didn't
Speaker 1: see how they were generating it. What they were doing
Speaker 1: was they gave the computer a starting word or sentence
Speaker 1: and an ending word or sentence, and then the computer
Speaker 1: had to generate a series of uh sentences to link
Speaker 1: the two together. And they began they began to say
Speaker 1: that these were telling stories. Now they were sometimes abstract stories. Um,
Speaker 1: some of them are some of them are really weird.
Speaker 1: I wish we could find the one that I that
Speaker 1: I saw where it was really really sad, and then
Speaker 1: it became about horses at the end. It was amazing.
Speaker 1: I was like it was like Tina Belcher from Bob's
Speaker 1: Burgers had written a poem. But it was a pretty
Speaker 1: phenomenal and and you know it's I bring it up
Speaker 1: mainly just to say that we're seeing some really interesting
Speaker 1: work in this field of machine learning and creation that
Speaker 1: could ultimately lead to things that perhaps don't replace any
Speaker 1: sort of human creativity, but in either enhance something. Maybe
Speaker 1: you do a partnership quote unquote with a computer to
Speaker 1: create something, just to put that out there like this
Speaker 1: is part of me, part machine. There's something interesting with
Speaker 1: that as well, or maybe just it'll be you know,
Speaker 1: another another option. One of the things that the team
Speaker 1: talked about or or that people have chatted about as
Speaker 1: far as the prospect of computer generating music is using
Speaker 1: it to enhance or suppress certain moods. So, for example,
Speaker 1: you're wearing a smart watch. It's got a uh activity
Speaker 1: tracker on it exactly, and it detects perhaps that you're
Speaker 1: being stressed out. It knows that you're not moving around,
Speaker 1: but the text, because of your physiological changes, you're getting
Speaker 1: stressed out. And so the headphones you're wearing you start
Speaker 1: listening hearing music that's more soothing to you, and it's
Speaker 1: generated on the fly. It's unique music. It's not something
Speaker 1: that you're gonna listen to and then just tune out
Speaker 1: because you've heard it a billion times before, or maybe
Speaker 1: you're it detects that you're working out, and it says, Oh,
Speaker 1: we need to generate some nice, fun up temposts type
Speaker 1: of stuff to keep the activity going at the right level.
Speaker 1: And it starts to create that on the fly. So
Speaker 1: that's a possible application for this, and that that would
Speaker 1: be fascinating because there's all this research into if you
Speaker 1: you listen to music that has the similar beats per
Speaker 1: minute to your active heart rate, then you will keep
Speaker 1: going at that active heart rate for longer. I've found
Speaker 1: I've found just anecdotally, yeah, I think I think by
Speaker 1: your research, I mean, like sports blog right right, Anecdotally,
Speaker 1: I have certainly found that to be the case. Like
Speaker 1: if you know, I walked to and from the office
Speaker 1: and if I'm listening to music and I'm listening to
Speaker 1: a podcast, I'm just strolling. If I'm listening to music
Speaker 1: and and something with a beat goes on there, if
Speaker 1: I'm not paying it, if I do suddenly pay attention,
Speaker 1: I realize I'm stepping on the beach. You're staying alive,
Speaker 1: absolutely exactly, Yeah, doing that CPR. So it's it's pretty cool.
Speaker 1: And also, you know, to kind of conclude this discussion, really,
Speaker 1: I think ultimately what this is going to do is,
Speaker 1: uh make a more robust machine learning system for problem solving.
Speaker 1: In general, you could think of creating a piece of
Speaker 1: art as a problem, not a problem in the sense of, oh, gosh,
Speaker 1: I've got a problem, but like an engineering problem, right,
Speaker 1: but imagine that you're able to create machine learning so
Speaker 1: that you could present a computer with a problem in
Speaker 1: the more colloquial sense, the more like I've got a problem,
Speaker 1: I don't know how to fix this, and the computer,
Speaker 1: because it's studied all of the analysis, you've got a problem.
Speaker 1: You I'll solve it, and then it takes your problem
Speaker 1: and gives you the problem the solution that is most
Speaker 1: probably the right one according to its machine learning algorithm.
Speaker 1: And ultimately we could get to a point where we
Speaker 1: consult the great oracle, perhaps made by oracle that tells
Speaker 1: us what we should do for some questions that are
Speaker 1: particularly tricky, where you've got lots of different variables, and
Speaker 1: the suggestion is always have a huge party, like I
Speaker 1: got the dance mix right here, get going, and it's
Speaker 1: all generated, has really morose poetry in it. Um, who knows,
Speaker 1: Maybe that's the answer. I will not say no to
Speaker 1: a party butt. I'm just throwing that out there, but
Speaker 1: I think that this particular project is really interesting. I
Speaker 1: think the the possible outcomes could be really cool, not
Speaker 1: just for the art that it creates, but how it
Speaker 1: advances machine learning in general. And like I said, maybe
Speaker 1: in a year will come back to this and take
Speaker 1: a look and see if the magenta has produced anything
Speaker 1: you know, no worthy. Yeah that's a pun musical nets
Speaker 1: all right, So I'm gonna wrap this up. Guys. If
Speaker 1: you have any suggestions for future episodes of forward Thinking,
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