Speaker 1: Get in touch with technology with tech Stuff from half
Speaker 1: stuff works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: how Stuff Works in a love all things tech, and
Speaker 1: today we're going to tackle a story that recently unfolded recently.
Speaker 1: As of the recording of this show, I'm sitting in
Speaker 1: the recording studio on October two, thousand eighteen. It's not
Speaker 1: my normal studio either, So if you hear other noises,
Speaker 1: that's because we've got noisy people walking around the office
Speaker 1: and I'm in a different studio. That's commentary. But this
Speaker 1: story unfolded just at the very end of October. That
Speaker 1: was when the auction house Christie's, put a special item
Speaker 1: up on the auctioning block. It was a somewhat blurry
Speaker 1: portrait of a man dressed in antiquated clothing. It looked
Speaker 1: like a painting that could have come from the eighteenth
Speaker 1: century from one of any number of artists, but it
Speaker 1: was in fact a much more recent painting. The artist
Speaker 1: was not a famous painter. In fact, the artist wasn't
Speaker 1: a person. It was an artificially intelligent algorithm that created
Speaker 1: the portrait through the process of machine learning. And what's more,
Speaker 1: the group of human artists who supplied the AI generated
Speaker 1: portrait had taken a great deal of direction, let's say,
Speaker 1: from a different computer programmer, but perhaps did not do
Speaker 1: as much to attribute that coder's work to the creation
Speaker 1: of this portrait that they should have done. So what
Speaker 1: we have here sounds a bit like a twenty first
Speaker 1: century futuristic art heist, only this isn't about stealing a
Speaker 1: work of art, but rather a means of generating art itself,
Speaker 1: and it's creating a lot of interesting conversations about concepts,
Speaker 1: ranging from what is art in the first place, to
Speaker 1: the practical applications of machine learning to the nature of
Speaker 1: open source code. So let's dive down into this, because
Speaker 1: when it comes to discussing our how technology interacts with
Speaker 1: our lives, this is a doozy of a story. It
Speaker 1: highlights not just technological issues but human ones that just
Speaker 1: happened to intersect with technology. So to begin with, let's
Speaker 1: talk about the tech behind generating this portrait in the
Speaker 1: first place. It is an application of machine learning. That's
Speaker 1: one of those topics we've talked about a lot on
Speaker 1: tech stuff, especially recently. But basically, machine learning is all
Speaker 1: about designing processes that allow machines to parse data in
Speaker 1: some useful way and then apply the results of those
Speaker 1: operations to future problems. But that's pretty darn vague, right,
Speaker 1: that's not that doesn't really tell you anything useful if
Speaker 1: you dive down a bit further, it's about creating a
Speaker 1: framework within which machines can learn to perform a task
Speaker 1: without having to be programmed to do it. So let's
Speaker 1: use an example, and it's one I've talked about a
Speaker 1: lot because it was one of the early examples of
Speaker 1: what machine learning could do once it reached a certain
Speaker 1: level of sophistication. Back in two thousand twelve, Google showed
Speaker 1: how their computer scientists teams had taught an AI algorithm
Speaker 1: or neural network to recognize images of cats. Now, this
Speaker 1: was perhaps a funny way of showing an approach to
Speaker 1: a difficult problem. So if you want a computer to
Speaker 1: recognize an image of a cat, if it's a specific
Speaker 1: image of a cat, you have a couple of different options.
Speaker 1: One is, you can program the computer so that when
Speaker 1: it encounters a specific arrangement of pixels for this particular image,
Speaker 1: it recognizes that as the image of a cat, and
Speaker 1: that you have programmed the computer to say, when you
Speaker 1: see this arrangement of pixels, then that means this is
Speaker 1: a cat. The computer doesn't understand what a cat is,
Speaker 1: it doesn't have any context. It doesn't understand what any
Speaker 1: other picture of a cat might be because that would
Speaker 1: be a different arrangement of pixels. So you could program
Speaker 1: a computer to do this and it would be able
Speaker 1: to do it with that one image. But if you
Speaker 1: gave it a different image of a cat, or even
Speaker 1: an image of the same cat, but it's a different picture,
Speaker 1: the computer would not be able to identify it. You
Speaker 1: would have to repeat the entire process from beginning to
Speaker 1: end to get the same result. And once you start
Speaker 1: adding up images, you realize this is not really an
Speaker 1: efficient means of teaching a computer anything. Or you could
Speaker 1: create an artificial neural network that examines the pixels in
Speaker 1: an image, and each neuron might be looking at a
Speaker 1: different element of the data to determine if that data
Speaker 1: was consistent with images of cat pictures. So we've talked
Speaker 1: about this recently too, and artificial neuron can take in
Speaker 1: multiple bin binary points of data's euros and ones and
Speaker 1: then create a single binary output. So it might be
Speaker 1: looking at specific features that might have to do with ears,
Speaker 1: for example, and if it detects that the ears are
Speaker 1: consistent with those of a cat, it might pass a
Speaker 1: positive response further down the neural network, and a full
Speaker 1: collection of all these looking at multiple points of data
Speaker 1: would allow the computer to come to a decision does
Speaker 1: this image represent a cat or does it represent something else. So,
Speaker 1: in this way, by feeding thousands or tens of thousands
Speaker 1: or hundreds of thousands of images to a computer, you
Speaker 1: can train it to recognize cats. And the more you
Speaker 1: train it and the more closely you're able to tweak
Speaker 1: the network so that it waits certain elements more than others,
Speaker 1: the better it gets. So the tweaking makes the network
Speaker 1: more capable and eventually get to a point where it
Speaker 1: can identify a picture as either being a cat or
Speaker 1: not a cat with pretty good results. Um Back in
Speaker 1: two thousand twelve when Google was talking about this, it
Speaker 1: was still a little jankie. It could sometimes recognize a cat,
Speaker 1: and sometimes it would think that a person was a
Speaker 1: cat or that a cat was a person, So it
Speaker 1: was not infallible, but it was pretty good. Now, because
Speaker 1: I've covered artificial neural networks in recent episodes of tech Stuff,
Speaker 1: I'm not gonna go through the whole thing all over again.
Speaker 1: That high level I just gave you that's a pretty
Speaker 1: good starting point. It's just important to remember that the
Speaker 1: general output here is through training and network using that
Speaker 1: input data set in this case or in the case
Speaker 1: of that example, hundreds of thousands of images of cats.
Speaker 1: Machine learning can actually take a few different approaches. The
Speaker 1: one that I sort of outlined earlier would kind of
Speaker 1: fall into the category of supervised machine learning. See in
Speaker 1: that approach, we human beings are trying to teach a
Speaker 1: machine through algorithms and data sets two recognize something that
Speaker 1: we already know the answer for. Right, you can look
Speaker 1: get a picture, and you can recognize whether that picture
Speaker 1: is of a cat or not, so you already know
Speaker 1: the answer. You're not asking the computer to give you
Speaker 1: new information. You're trying to teach the computer to do
Speaker 1: something that you already can do. So we human beings
Speaker 1: are able to supervise the machine as it is learning
Speaker 1: this process and make those minor adjust adjustments that are
Speaker 1: needed throughout the system in order for it to get
Speaker 1: better at its job. That is supervised machine learning. We
Speaker 1: can keep working with it until it reaches what we
Speaker 1: consider to be an acceptable level of success, which doesn't
Speaker 1: mean it has to be perfect. It just has to
Speaker 1: be good enough for whatever it is we're building it for.
Speaker 1: But there's another approach called unsupervised machine learning, and as
Speaker 1: you might imagine, this is different from the previous one.
Speaker 1: On this approach, you only have input data and your
Speaker 1: goal as a human is to learn more about that
Speaker 1: data itself. So you don't have a correct answer in mind.
Speaker 1: You don't already know that the data represents, say a
Speaker 1: cat in a photo. It's a different type of problem
Speaker 1: you're looking at. Uh. The machine is learning about the
Speaker 1: nature of the information itself, including how different points of
Speaker 1: data relate to one another or correspond with other data,
Speaker 1: and you in turn can learn more about the information
Speaker 1: as well. So within this category you have a couple
Speaker 1: of subcategories. There are clustering problems. With a clustering problem,
Speaker 1: you're learning about the groupings within data. So one example
Speaker 1: might be that you have a population of customers. Let's
Speaker 1: say you own a business. You've got customers. You have
Speaker 1: data that represents all these different customers, and you're using
Speaker 1: the collective behaviors of those customers to sort them into
Speaker 1: meaningful groups so that you can better serve each of
Speaker 1: those groups. Maybe you learn that there are four basic
Speaker 1: types of customers, and that helps you plan out your
Speaker 1: business so that you can cater it to those four types.
Speaker 1: But another type of problem in unsupervised machine learning is
Speaker 1: called an association problem. Now, in those problems, you want
Speaker 1: to learn rules that describe large parts of the data
Speaker 1: that you're feeding into the system. So, for example, let's
Speaker 1: go back to you run a business. You've got this
Speaker 1: big pool of customers, and you're feeding all the customer
Speaker 1: behavior data into your system. It might tell you that, hey,
Speaker 1: it turns out that of the customers who are buying
Speaker 1: widgets go on to buy sprockets. So that would tell you, hey,
Speaker 1: now I know more information. I know that if I
Speaker 1: sell a widget to someone, there's a good chance I
Speaker 1: can upsell that and include a Sprocket as well. So
Speaker 1: I'm going to tailor my business approach to try and
Speaker 1: take advantage of that. Now, the reason I went through
Speaker 1: all of this is to explain that the type of
Speaker 1: artificial intelligence algorithm that was used to produce the painting
Speaker 1: I was talking about at the top of the show,
Speaker 1: falls into a group called generative adversarial networks or g
Speaker 1: a N or a GAN. These are used in unsupervised
Speaker 1: machine learning applications. So it's in that second category I
Speaker 1: was just talking about. So what is with this name?
Speaker 1: What is a generative adversarial network? Well, for one thing,
Speaker 1: it actually uses a pair of deep neural net architecture networks.
Speaker 1: These two nets are in competition with one another. That's
Speaker 1: why it's called an adversarial network. You have these two
Speaker 1: different constructs that are working against each other. The approach
Speaker 1: was first proposed by researchers at the University of Montreal,
Speaker 1: and we chiefly associate the concept with a guy named
Speaker 1: Ian Goodfellow. Ian Goodfellow wrote the definitive paper on the
Speaker 1: subject back in two thousand and fourteen, and it is fascinating.
Speaker 1: So from a very high level, what's happening is that
Speaker 1: you have a neural network called the generator and you
Speaker 1: have a second year old network called the discriminator. So
Speaker 1: you're feeding the discriminator your input data. Let's again go
Speaker 1: with pictures of cats, So actual pictures of cats photographs
Speaker 1: of cats. If you will, you're you're feeding photographs of
Speaker 1: cats to the discriminator. The generator's job is to create
Speaker 1: a an image that fools the discriminator into thinking that
Speaker 1: that's a legitimate photograph of a cat, but in fact
Speaker 1: it was created or generated by the generator. So you've
Speaker 1: got two processes going on at the same time. The
Speaker 1: generator is trying to create essentially a forgery or a counterfeit.
Speaker 1: It's it's creating something from scratch to fool the discriminator
Speaker 1: into thinking this is a legitimate piece of data from
Speaker 1: the training data set. The discriminator is looking at each
Speaker 1: image and thinking, all right, now does this represent a
Speaker 1: real picture or is this something that is coming from
Speaker 1: the generator that's designed to fool me, And the two
Speaker 1: are working against each other. Both networks learn as this
Speaker 1: goes on. If the discriminator gets an image and rejects it,
Speaker 1: that becomes a feedback to the generator and the messages. Essentially,
Speaker 1: this was not good enough, and the generator starts to
Speaker 1: try again, taking a slightly different approach. If the discriminator
Speaker 1: accepts it, the generator says, ah ha, you're onto something.
Speaker 1: But then you can tweak the discriminator and say this
Speaker 1: was wrong. You you got this part wrong, and it
Speaker 1: can start to try and look for signs that might
Speaker 1: otherwise fool it. The goal here is that you are
Speaker 1: going to have a generator producing better and better versions
Speaker 1: of whatever it is you're trying to create. And that
Speaker 1: could be a picture, it could be text, it could
Speaker 1: be music. You could feed any sort of data to
Speaker 1: both of these systems in an effort to deuce a
Speaker 1: computer generated version of that thing, and as long as
Speaker 1: it reached a certain level of quality, the discriminator won't
Speaker 1: be able to tell the difference, and then you've got
Speaker 1: yourself a computer generated whatever it might be, in this case,
Speaker 1: a painting. I'll explain more about the specifics of this
Speaker 1: case in just a moment, but first let's take a
Speaker 1: quick break to thank our sponsor. So a couple of
Speaker 1: years ago, there were computer scientists at Microsoft as well
Speaker 1: as tu Deft University, and they were working together with
Speaker 1: a banking company I n G to create a brand
Speaker 1: new painting in the style of the painter Rembrandt. This
Speaker 1: project involved processing high resolution digital scans of three hundred
Speaker 1: forty six different images of Rembrandt's works, specifically portraits of men.
Speaker 1: That information was fed to a deep learning algorithm that
Speaker 1: analyzed Rembrandt's style and also the techniques that were common
Speaker 1: across all the images. What were the common elements that
Speaker 1: were found in those numerous paintings, And eventually this machine
Speaker 1: was told, or this system was told to produce a
Speaker 1: new painting based on those uh those common factors. And
Speaker 1: so it narrowed down the approach to be a portrait
Speaker 1: of a Caucasian white male because that's what most of
Speaker 1: Rembrandt's portraits were of, somewhere between the ages of thirty
Speaker 1: and forty, wearing white and black clothing, because again that
Speaker 1: was the vast majority of the portraits that Rembrandt created,
Speaker 1: and the focus of the subject was off to the right,
Speaker 1: like looking slightly off to the right, because a lot
Speaker 1: of the subjects in the other paintings were doing the same.
Speaker 1: The algorithm also analyzed the faces of all those portraits
Speaker 1: and came up was sort of a kind of a
Speaker 1: mishmash average of them to produce the face of the
Speaker 1: fictional Dutch gentleman in the new painting. To go a
Speaker 1: step further, the team then added depth to this painting.
Speaker 1: It was a two dimensional image, and then they decided
Speaker 1: to add some depth. They included some ridges and some
Speaker 1: bumps that would have been created from brush strokes onto
Speaker 1: a two dimensional surface. So if you're using paint, then
Speaker 1: it's actually a three dimensional image. You know, if you
Speaker 1: get super close enough, you can see raised areas and
Speaker 1: dips and trenches and stuff like that that the brush leaves.
Speaker 1: And it all depends upon your painting technique how these
Speaker 1: get laid out on canvas. So the team added those
Speaker 1: details in to make it look even more authentic. Ultimately,
Speaker 1: the design was printed using thirteen layers of ultra violet
Speaker 1: based inc and the result is a work that looks
Speaker 1: like it could have come from Rembrandt, complete with techniques
Speaker 1: Rembrandt used in actually making his brushstrokes. And that's just
Speaker 1: one high profile example of computers generating paintings after being
Speaker 1: fed information about works that human artists have created. Now,
Speaker 1: as get back to the story of the recently auctioned painting. Now,
Speaker 1: to do that, we have to talk about a young
Speaker 1: man named Robbie Barrett. Barrett is nineteen years old and
Speaker 1: is attending Stanford and has been doing some really interesting
Speaker 1: work in machine learning. It was his code that would
Speaker 1: be the basis for the computer generated portrait that was
Speaker 1: recently auctioned off. Barrett's work was going a step further
Speaker 1: than copying the style of an established artist. Barrett's algorithms
Speaker 1: would work to create new images after having analyzed numerous
Speaker 1: real world examples. So just a couple of years ago,
Speaker 1: the state of the art in GAN networks or GN
Speaker 1: networks might produce some really disturbing images, like there are
Speaker 1: early pictures of GAN attempts at making realistic human faces
Speaker 1: that were not terribly successful, and that's because those networks
Speaker 1: were able to recognize certain basic visual elements and images,
Speaker 1: but not understand the reation ships between multiple elements within
Speaker 1: an image, so you could end up with a face
Speaker 1: with really extreme features like pronounced asymmetry. But over just
Speaker 1: a short amount of time, people have developed much more
Speaker 1: sophisticated GAN algorithms and performance has improved, and there of
Speaker 1: course artists who have gone in a different approach, specifically
Speaker 1: emphasizing some of these more absurd elements in order to
Speaker 1: get that kind of a result when you're actually producing art.
Speaker 1: Verrett created GAN algorithms that could generate all sorts of
Speaker 1: interesting images. He was enabling computers to make art themselves.
Speaker 1: And sure, these computers were learning to create art after
Speaker 1: being fed numerous paintings and images from human artists. But
Speaker 1: you could argue that if you want to become a
Speaker 1: human artist, you have to do the same thing. You
Speaker 1: have to study art that was created by other people.
Speaker 1: So computers are no different. The computers weren't replicating specific works,
Speaker 1: they weren't trying to make a copy. They were learning
Speaker 1: various styles. Barrett would frequently put these images and also
Speaker 1: the algorithms he used to create those images up on
Speaker 1: get hub for free and open source. He also had
Speaker 1: uh people download these and upload their own art, and
Speaker 1: it was all in the spirit of this open source community.
Speaker 1: This way, not only could people use the tools that
Speaker 1: Barrett had created, they could understand how those tools worked,
Speaker 1: and perhaps in the future they can make their own tools,
Speaker 1: tweaking the approach the Barrett had used, maybe making art
Speaker 1: that was even more indistinguishable from human art, or perhaps
Speaker 1: going in a totally different direction, making something truly new
Speaker 1: and alien. By the way, some of the images created
Speaker 1: by Barrett's algorithms are a little unsettling. They can be
Speaker 1: surreal and absurd, and some of them even come across
Speaker 1: a little sinister to me. But that's my own interpretation.
Speaker 1: I mean, that is what art is all about, is
Speaker 1: the interpretation of the person looking at art. But they
Speaker 1: remind me of some of the horror movie effects you
Speaker 1: might see where the visual effects artists will distort a
Speaker 1: person's face for the effect of horror, like in the
Speaker 1: movie The Ring. Anyway, Barrett created several GAN algorithms and
Speaker 1: put them up online for others to use, and this
Speaker 1: in itself was not unusual. There are many in the
Speaker 1: digital art field who work on AI who have done
Speaker 1: similar things. Now he creates this code, Let's take a
Speaker 1: trip across the world from Stanford over to France. That's
Speaker 1: where three artists in their mid twenties were working in
Speaker 1: a group they had called Obvious and their stated goal
Speaker 1: is to promote ganism, that is, the art that has
Speaker 1: been generated through AI algorithms running on this GAN approach. Now,
Speaker 1: according to an article on Medium written by one of
Speaker 1: these artists, they quote want to send out an update
Speaker 1: of the state of the research and AI end quote.
Speaker 1: They want to do this they want to tell the
Speaker 1: world what is going on in the world of AI
Speaker 1: research through showing off artwork made by AI, so kind
Speaker 1: of a creative artistic way of talking about artificial intelligence.
Speaker 1: The group says that the value of the art may
Speaker 1: not be in the art itself, but rather the discussions
Speaker 1: that the art inspires, like what is it that makes
Speaker 1: art art? Can machines be creative? Who ultimately would you
Speaker 1: say is the artist in a work that was created
Speaker 1: by a machine? What does that art mean? Who does
Speaker 1: it belong to? That's a big one. So the artists
Speaker 1: reached out to Barrett when they were tackling this project.
Speaker 1: They wanted to use a gain algorithm to generate a
Speaker 1: portrait in a style similar to what you see in
Speaker 1: eighteenth century paintings out of Europe. The students have made
Speaker 1: it clear that Barrett had been a big part of
Speaker 1: their inspiration. More on that in just a second now.
Speaker 1: Members of Obvious began using gan code to generate portraits,
Speaker 1: and they created several of them, eleven in fact of
Speaker 1: a fictional noble family they named the Bellamy family B. E. L. A. M. Y.
Speaker 1: The name Bellamy itself was a bit of a pun
Speaker 1: and a reference to Ian Goodfellow, the guy who wrote
Speaker 1: that main paper on gangs. In the first place, Bellamy
Speaker 1: can be broken down into bell and Amy. That would
Speaker 1: mean all the different spellings. It would mean good friend
Speaker 1: or good fellow, which is kind of cute. Right. Well,
Speaker 1: the artists produced these portraits, and they are all of
Speaker 1: hollow eyed nobles that will stare right into the void
Speaker 1: in a way that actually that's getting off track. Never
Speaker 1: mind it. It creates me out a little bit. But
Speaker 1: the last in the line of portraits would be Edmund
Speaker 1: do Bellamy, the fictional noble whose portrait would go up
Speaker 1: on auction in October and fetched way more money than
Speaker 1: was anticipated and so obvious had fed to the algorithms
Speaker 1: numerous paintings from the eighteenth century to guide its efforts,
Speaker 1: and once they started producing these, they had each one
Speaker 1: signed with a line of code referencing the algorithm. They
Speaker 1: framed the machine generated portraits in golden frames, and when
Speaker 1: Edmund de Bellamy went up for auction, the best guess
Speaker 1: was that it would probably fetch between seven thousand and
Speaker 1: eleven thousand dollars. Instead, the winning bid was for more
Speaker 1: than four hundred thirty thousand dollars. So that raises a
Speaker 1: good question who the heck should get that money. Who
Speaker 1: was responsible for this painting and that would become something
Speaker 1: of a controversy. I'll explain more in just a second,
Speaker 1: but first let's take another quick break to thank our sponsor.
Speaker 1: So as the group Obvious was getting press coverage for
Speaker 1: the AI produced Bellamy portraits, this is before they had
Speaker 1: even put one up for auction, some people, including Barratt,
Speaker 1: express some disappointment with the group. They said that it
Speaker 1: looked like they had used Barrett's code to produce these portraits,
Speaker 1: and yet they weren't quick to attribute him. They didn't
Speaker 1: give him credit, at least not readily and not visibly
Speaker 1: in a lot of locations. And so his code, while
Speaker 1: it was open source and he didn't begrudge anyone from
Speaker 1: being able to use it, would have usually meant that
Speaker 1: people would give him credit. Typically in the open source community,
Speaker 1: it's considered bad form or even ghosh if you prefer
Speaker 1: to not give credit where credit is due. As to
Speaker 1: how much of the code was actually used unaltered, that
Speaker 1: is a bit of an open question. The artists that
Speaker 1: Obvious have admitted that they did use his code and
Speaker 1: they changed it a little bit. Some other artists say
Speaker 1: they believe that or more of the code was unaltered.
Speaker 1: One such artist, a New Zealander named Tom White, said
Speaker 1: he downloaded Barrett's code and ran it unaltered to see
Speaker 1: if he could produce images similar to those that Obvious
Speaker 1: had generated, and he said they look pretty close. So
Speaker 1: I took a look at as well. I would say
Speaker 1: that the ones that that White had created with that
Speaker 1: AI have a little bit more of the weird facial
Speaker 1: distortion thing going on than the ones that were made
Speaker 1: by Obvious, but they are fairly similar. Throughout the project,
Speaker 1: members of Obvious reached out to brot to for for
Speaker 1: help and getting the GAN algorithms to run properly on computers.
Speaker 1: Those communications are up on geth hubs, so I mean
Speaker 1: they definitely happened. Anyone can see them. So that's definitely
Speaker 1: a sign that a significant portion of the code used
Speaker 1: to create the expensive painting came from ROT. So we
Speaker 1: get into that tricky question who owns the art before
Speaker 1: it gets purchased at auction? Obviously, so does the computer
Speaker 1: scientist who created the code own anything that the code produces.
Speaker 1: I mean, the code has to have a programmer. Without
Speaker 1: a programmer, there's no code. So without the code, you
Speaker 1: get no artistic output. But then again, you could say
Speaker 1: that human artists learn from their teachers. There's a long
Speaker 1: history of artists taking on apprentices, and those apprentices later
Speaker 1: on go on to become great artists of their own.
Speaker 1: So maybe you could argue that Brought was a teacher
Speaker 1: and the AI was the student, and therefore Brought wouldn't
Speaker 1: own the art. He didn't make it. He just taught
Speaker 1: the student how to make art, not in a traditional sense,
Speaker 1: but that's how it happened. But here's another problem. AI
Speaker 1: cannot own stuff. Artificial intelligence can't have property. We have
Speaker 1: no legal means to assign ownership, so that a program,
Speaker 1: or an algorithm or an artificial neural network could own property.
Speaker 1: And even if we did, what good would it do.
Speaker 1: The AI doesn't want or need anything. It doesn't even
Speaker 1: have will or self awareness. So maybe Obvious could claim
Speaker 1: ownership because they were the ones who fed the information
Speaker 1: to the algorithm. They're the ones who gave the algorithm
Speaker 1: the access to all the different portraits. They made some
Speaker 1: changes to the code, and the algorithms ran on computers
Speaker 1: that they controlled, so if the code was using their assets,
Speaker 1: maybe they own the output. But this is also complicated.
Speaker 1: They didn't build the algorithm. They made use of it,
Speaker 1: but they didn't design it from the ground up. But
Speaker 1: if someone else could have run the code and use
Speaker 1: the same general pool of images and train the code,
Speaker 1: they might have seen similar results, which means someone else
Speaker 1: could have done the exact same thing that obvious did,
Speaker 1: and so that raises questions as well. Maybe there's nothing
Speaker 1: special about owning the machine. In other words, in the
Speaker 1: digital world, using open source code to make something new
Speaker 1: and then profit from it sell it. That happens regularly,
Speaker 1: but again it's all on how you do it. If
Speaker 1: you follow the general rules of etiquette, you're typically pretty good.
Speaker 1: But if not, people think of that as being kind
Speaker 1: of a jerk face. So it's not it's it's frowned
Speaker 1: upon in the open source community. Broad is quoted in
Speaker 1: a piece on The Verge as saying, quote, I'm more
Speaker 1: concerned about the fact that actual artists using AI are
Speaker 1: being deprived of the spotlight. It's a very bad first
Speaker 1: impression for the field to have end quote. So he's
Speaker 1: not saying he's upset and missing out on money, but
Speaker 1: rather that the the whole field is getting is represented
Speaker 1: The Verge piece also does a great job pointing out
Speaker 1: how many in the AI digital art field feel that
Speaker 1: Obvious is painting a misleading picture to use a pun
Speaker 1: that if you were to look at the press release
Speaker 1: that the group has put out and the way that
Speaker 1: they've presented the art, it would seem as if these
Speaker 1: programs were largely undirected or even fully autonomous, and they aren't.
Speaker 1: Just because it's called unsupervised machine learning doesn't mean that
Speaker 1: there's no human component. So there's a debate going on
Speaker 1: within the digital art world on where in the spectrum
Speaker 1: these algorithms should fall. Are they closer to being tools
Speaker 1: like what a paint brush would be to a traditional painter,
Speaker 1: or are they more closely connected to a collaborator, maybe
Speaker 1: someone who's assisting a painter. But they certainly are not
Speaker 1: fully autonomous robots. Now. In a way, this question of
Speaker 1: ownership actually makes me think of an earlier incident involving
Speaker 1: a different art form. It involved a monkey, a digital camera,
Speaker 1: and a lawsuit. So back in two thousand and eleven,
Speaker 1: a photographer named David Slater was working on an assignment
Speaker 1: in Indonesia and that's where he met Naruto Naruto was
Speaker 1: a seven year old crested macaque, so Naruto was a
Speaker 1: monkey now. On this assignment, Naruto at one point grabbed
Speaker 1: Slater's camera, and while handling Slater's camera, Naruto took a
Speaker 1: photo of himself. So it's a monkey selfie, and it's
Speaker 1: a great photo. If you've not seen it, you've got
Speaker 1: to look up monkey selfie because it is amazing. The
Speaker 1: monkey obviously didn't understand what it was doing, but the
Speaker 1: selfie is just about perfect. So then this image goes
Speaker 1: up online and it goes viral. It gets posted all
Speaker 1: over the place, including on Wikipedia, and David Slater would
Speaker 1: reach out to Wikipedia and say, hey, you can't just
Speaker 1: put my photograph up on your site without asking for
Speaker 1: permission or paying a licensing fee. The Wikipedia said, dude,
Speaker 1: you didn't take the photograph. It doesn't belong to you.
Speaker 1: It was taken on your camera, but you didn't snap
Speaker 1: the picture. A monkey took the photos, so you don't
Speaker 1: have copyright to that image. In fact, no one has
Speaker 1: copyright to that image because news flash, animals can't hold
Speaker 1: copyrights to any work. But then Peter ak, a People
Speaker 1: for the Ethical Treatment of Animals, would sue David Slater
Speaker 1: and a publishing company called Blurb for copyright infringement, saying, Hey,
Speaker 1: Naruto took that photo, so Naruto should hold the copyright.
Speaker 1: The judge in that case would ultimately say that animals
Speaker 1: can't hold copyright, backing up what Wikipedia had said, and
Speaker 1: that this whole argument was invalid. Peter appealed the decision
Speaker 1: it went to or it was scheduled to go to
Speaker 1: a higher court, but ultimately the various parties came to
Speaker 1: a settlement out of court. And this is where I
Speaker 1: kind of roll my eyes at Peter. But this situation,
Speaker 1: while silly on the surface, raises questions that also applied
Speaker 1: to artificial intelligence. In a case like this, who has
Speaker 1: the right to use or exploit a work? Now, I
Speaker 1: would argue than the case with artificial intelligence, it gets
Speaker 1: even thornier than that. Right now, we're talking about paintings.
Speaker 1: But as I said earlier, gain algorithms could produce all
Speaker 1: sorts of different stuff, including text. So we could have
Speaker 1: a computer generated novel or a screenplay in the future,
Speaker 1: and sure, the first versions of those will probably be terrible,
Speaker 1: And to be fair, we already have a surplus of
Speaker 1: terrible books and terrible movies and terrible TV shows that
Speaker 1: are made by real human beings. We don't we don't
Speaker 1: need robots to make more of those, but we could
Speaker 1: also end up with some that are interesting or that
Speaker 1: say something surprising that people will value. In those cases,
Speaker 1: who has a claim to that intellectual property? Who should
Speaker 1: profit from it? Maybe it should be the person who
Speaker 1: wrote the code in the first place. But if that's
Speaker 1: the case, let's take this thought experiment in another direction.
Speaker 1: Let's say someone creates code for an AI that does
Speaker 1: something entirely different. There it's not generating any content. Let's
Speaker 1: say it's the artificial intelligence you would need to power
Speaker 1: an autonomous car. Now, let's say one of those cars
Speaker 1: is found to have caused a really bad accident. So
Speaker 1: should the person who wrote the code be held responsible?
Speaker 1: What if the scenario that led up to the accident
Speaker 1: was so unusual that no one would have ever predicted it.
Speaker 1: Because it's one thing to overlook a common event, Like
Speaker 1: if someone were to program an autonomous car and say, oh, crap,
Speaker 1: I totally forgot about stop signs, that would be demonstrably bad,
Speaker 1: And you could say, well, that is that is endangerment,
Speaker 1: That is definitely not cool. But it's a totally different
Speaker 1: thing if you just don't predict an accident that involves
Speaker 1: a lot of unique factors, because those happen too. There's
Speaker 1: stuff that happens on the road every single day that
Speaker 1: happens in a way that nobody anticipated. And because we
Speaker 1: have so many people driving so many cars on so
Speaker 1: many roads under so many conditions on a daily basis,
Speaker 1: it's inevitable that we're going to have moments where those
Speaker 1: unique situations pop up and it would be impossible to
Speaker 1: identify or predict them. So in those cases, would you
Speaker 1: still hold hold someone who made the code responsible that
Speaker 1: they weren't able to predict something that nobody could predict?
Speaker 1: Or does that put them at an unreasonable standard? Is
Speaker 1: it the fault of the car manufacturer? Is it the
Speaker 1: fault of the person who designed the road. I mean,
Speaker 1: there's so many different questions and we don't have all
Speaker 1: the answers, But I think in this case, with the painting,
Speaker 1: we have this high profile example of AI producing something.
Speaker 1: It leads us to get into a deeper conversation about
Speaker 1: those ideas, and my guess is we will ultimately come
Speaker 1: up with answers that are not entirely satisfactory for all situations,
Speaker 1: but maybe some people will even go so far as
Speaker 1: to to vehemently disagree with him. But more importantly, we
Speaker 1: will actually have maybe answers right So, yeah, it might
Speaker 1: be answers that not everyone is happy with, but at
Speaker 1: least they would be answers right now we have nothing.
Speaker 1: So this is a good case study for us to say,
Speaker 1: we've got to start thinking about this stuff because the
Speaker 1: era of AI playing a more pivotal role in our
Speaker 1: lives is right around the corner, and it would be
Speaker 1: better for us to figure this out now rather than
Speaker 1: have to react to it when it's too late later.
Speaker 1: I'm curious to hear what you guys have to say
Speaker 1: about this subject. Why don't you pop on over to
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Speaker 1: touch with me and let me know what you think.
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