Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcomed up Forward Thinking, the
Speaker 1: podcast that looks at the future and says, Oh, dream Weaver,
Speaker 1: I believe you can help me through the night. I'm Jonathan,
Speaker 1: I'm Lauren PoCA, and I'm Joe McCormick. And today I
Speaker 1: would begin by asking both of you if you had
Speaker 1: seen images produced by Google Deep Dream. But I already
Speaker 1: know the answer because we spent part of this afternoon
Speaker 1: looking at pictures of Jonathan in his Renaissance festival costume
Speaker 1: with caterpillars growing out of his arm sockets and so
Speaker 1: many dogs, yeah, like bison puppies in his head. It
Speaker 1: was kind of like It's kind of like the American
Speaker 1: Kennel Club mixed with lovecrafty and horror, thrown on top
Speaker 1: of a Renaissance festival thrown on top of a giant
Speaker 1: pile of acid. So now you you listeners at home
Speaker 1: or wherever you are, whether you're at home or standing
Speaker 1: in line somewhere, or standing on top of a giant
Speaker 1: statue of a naked Greek hero, someone is I guarantee
Speaker 1: it probably waiting to hear the end of the Senates.
Speaker 1: You are now in one of two camps. You're either going,
Speaker 1: oh man, Google deep dream, this is crazy, or you're saying,
Speaker 1: what are they talking about? If you're in the latter camp,
Speaker 1: the people saying what are they talking about? Pause this
Speaker 1: right now, Stop what you're doing. Go look up these images.
Speaker 1: And there are numerous images for you to look at,
Speaker 1: and uh, and ultimately what you need to know is
Speaker 1: that the images are all pictures that have been altered
Speaker 1: by essentially artificial intelligence. Yeah, and in case you're like
Speaker 1: driving or otherwise visually indisposed, allow me to paint a
Speaker 1: brief word share for you. You put in a normal
Speaker 1: old photograph and and what comes out of these algorithms
Speaker 1: is recognizably your subject and your background. But the subject
Speaker 1: might have extra faces in places where faces usually are not,
Speaker 1: and and the background is maybe dripping with tentacles and eyes,
Speaker 1: and the edges of things are feathered out, as though
Speaker 1: technicolor anonomy hats have suddenly become all the rage for animals,
Speaker 1: vegetables and minerals. It looks you, guys like like like
Speaker 1: a gritty post Frank Miller reboot of Yellow Submarine. I
Speaker 1: was going to say it looks like everything is taking
Speaker 1: place in the sacred halls of Lord Dagon, and just
Speaker 1: like dripping tentacled creatures, I go with Lovecraft plus impressionist painters. Yeah,
Speaker 1: all of these descriptions are valid descriptions. Yeah. I Actually
Speaker 1: the other night I found someone who had created an
Speaker 1: app that makes use of the of Google's deep dream
Speaker 1: algorithm and allows you to submit your own photos and
Speaker 1: get them deep dreamed. So I sent a picture of
Speaker 1: my dog, Charles Darwin, a little Charlie sitting on the couch,
Speaker 1: and he's so cute. But in the deep dream photo,
Speaker 1: what's going on with him? Well, the turquoise pillow that
Speaker 1: he was laying on in the original picture has turned
Speaker 1: into a giant caterpillar with lots of strange eyes. I
Speaker 1: see more like a parrot, like a tentacle parrot. Okay,
Speaker 1: it's kind of a tentacle parrot. Yeah. Charlie's face has
Speaker 1: turned into a sort of bifurcated evil sweet dog to
Speaker 1: face face. And then he's also got a face in
Speaker 1: his butt which which appears to be a very similar face,
Speaker 1: almost exactly. There's another weird dog face in his butt.
Speaker 1: His leg has tentacles and antennae, and like it looks
Speaker 1: like a fish baby on one foot and then and
Speaker 1: then a bird where his tail would be. His tail
Speaker 1: is a bird peeking over the top of the couch.
Speaker 1: I was trying to figure out what this was. In
Speaker 1: the original image. It's just some details against the wall.
Speaker 1: It's like the top of my co feemaker and stuff.
Speaker 1: But that has turned into a creepy teddy bear head
Speaker 1: peeking over the back of the couch cushion angry ewalk.
Speaker 1: I think it's I think you're essentially making the box
Speaker 1: art for the next five nights at Freddie's video game. Yeah,
Speaker 1: so what is going on with these images that you
Speaker 1: may have seen going around the internet with all these
Speaker 1: animal faces or they're not all animal faces. That's my
Speaker 1: favorite version, but we'll explain how you get these different
Speaker 1: filters coming through. But there are also some that have
Speaker 1: just strange accents on curves and corners, or that have
Speaker 1: geometrical patterns emerging from the figures in the in the picture. Yeah,
Speaker 1: so what is going on? I mean, has Google just
Speaker 1: decided to make a super trippy weird art project? Is
Speaker 1: that the purpose of deep Dream? No, Deep Dream actually
Speaker 1: is an extension of research that's been going on at
Speaker 1: Google about image processing that I think is mainly based
Speaker 1: in the idea of image recognition. Uh. And this is
Speaker 1: done through something we've talked about on the podcast before,
Speaker 1: but we'll go into more detail about today, which is
Speaker 1: artificial neural networks. UH. And in this case, the the
Speaker 1: application you could see applications for this beyond you know,
Speaker 1: making trippy images for for practical purposes, doing things like
Speaker 1: let's say you've got a picture that has some blurry
Speaker 1: elements to it and you've already taken the picture. You
Speaker 1: can't unless you're using uh, you know, like a light
Speaker 1: field capture camera. You can't change the focus after you've
Speaker 1: taken the picture. But you might be able to use
Speaker 1: algorithms to to recognize elements within a photo and bring
Speaker 1: it into focus after the picture has already been taken,
Speaker 1: assuming the algorithms are good enough to do that reliably
Speaker 1: and not turn it into a nightmarish experience. Yeah. That
Speaker 1: is one of the weird outcomes of this type of
Speaker 1: artificial neural network and image processing is that it could
Speaker 1: actually lead to the idea of zoom and enhanced I mean,
Speaker 1: it wouldn't be perfect, but it might be better than
Speaker 1: anything we've ever had in this fake idea of zoom
Speaker 1: and enhanced today. Yeah. Yeah, so these beautiful trippy pictures
Speaker 1: are kind of a mid step between what we have today,
Speaker 1: which is not zoom and enhanced and and and really
Speaker 1: amazing artificial intelligence. Yeah. So let's get into the mechanisms
Speaker 1: behind what's going on to produce these crazy trippy pictures.
Speaker 1: And the main thing to talk about is what is
Speaker 1: going on with artificial neural networks. And I have to
Speaker 1: admit I have had a lot of trouble like actually
Speaker 1: visualizing and understanding artificial neural networks. And I've read about
Speaker 1: them plenty of times before, but they're they're one of
Speaker 1: those abstract concepts where it's it's tough to fit it
Speaker 1: to a real world example that makes it make sense
Speaker 1: to people who have a i don't know, more intuitive
Speaker 1: kind of kinetic grasp on things. After after a while
Speaker 1: reading about them, my and kind of goes, yeah, I'm
Speaker 1: gonna go get some sushi, and like that's it. It's
Speaker 1: it's tricky, largely because there is such a difference between
Speaker 1: the way our brains work and the way computer processors work. Right,
Speaker 1: So Artificial neural networks are problem solving systems that are
Speaker 1: designed to work like our brains. Actually, they're trying to
Speaker 1: take computer hardware. Well, actually you could create an artificial
Speaker 1: neural network that was hardware based, but I think we're
Speaker 1: talking usually about using software within a traditional computer architecture
Speaker 1: to mimic the cells inside a biological brain. So if
Speaker 1: they solve problems by directing data through these layers of
Speaker 1: nodes that form information exchanging connections. So let me walk
Speaker 1: you through, and I'll explain how computer processors at a
Speaker 1: high level work, and then the difference between that and
Speaker 1: an organic brain, and then how this artificial neural network
Speaker 1: is attempting to simulate what's going on with a brain. So,
Speaker 1: your typical computer process or has transistors, right, They have transistors,
Speaker 1: all of them, and transistors are serially linked. So typically
Speaker 1: you would find a transistor that's linked at most to
Speaker 1: two other transistors, and these are forming logic gates collectively
Speaker 1: which direct the ones and zeros based upon very simple rules,
Speaker 1: and then collectively, when you get lots of them together,
Speaker 1: you can do neat complex stuff. But they're still linking
Speaker 1: just to one or two other transistors. Brains however, have
Speaker 1: neurons along with a lot of other types of cells.
Speaker 1: But neurons are interconnected with each other in super complex ways.
Speaker 1: They're not serially linked, they're linked in parallel, so a
Speaker 1: single neuron could have connections to as many as ten
Speaker 1: thousand other neurons. And also, while you look at the
Speaker 1: number of transistors that are on a microprocessor, we keep
Speaker 1: on increasing that number by decreasing the size of those
Speaker 1: discrete elements. So you're talking around two billion or so
Speaker 1: on a microprocessor, which that's a lot, but our brains
Speaker 1: have somewhere around eighty two hundred billion neurons. So we
Speaker 1: have way more neurons in our brains with much more
Speaker 1: sophisticated interconnectivity than you would find in a microprocessor. So
Speaker 1: it's no big surprise that our brains work in a
Speaker 1: very different way. Now, one of the cool things about
Speaker 1: our brains is that we can innovate, we can be creative,
Speaker 1: we can learn things. It takes time for us to
Speaker 1: learn stuff, but once we learn things, we can then
Speaker 1: extrapolate from what we've learned and create new things. And
Speaker 1: this is where we get everything from. Hey, maybe this
Speaker 1: would work better if we try it. This way to
Speaker 1: a genius like Mozart. I mean that's sure. Yeah, this
Speaker 1: is the basis of imagination and engineering and invention and
Speaker 1: everything that we kind of when it really comes down
Speaker 1: to it, talk about as being human, Like what it
Speaker 1: is to be human is these qualities and there you
Speaker 1: know other elements as well that may play a part
Speaker 1: in this. But our understanding of the brain is still
Speaker 1: so limited we cannot say definitively like how much of
Speaker 1: this is what is required for consciousness for example, But
Speaker 1: that that we've we talked about that in previous episodes,
Speaker 1: so I'm not gonna I'm not gonna go over that again.
Speaker 1: So artificial neural networks attempt to capture some of that
Speaker 1: complexity and sophistication found in the brain, usually through a
Speaker 1: software virtualization as opposed to let's hook up these finding
Speaker 1: eighty billion computers just laying around and trying to connect
Speaker 1: them together probably not your best use of time. So
Speaker 1: you're usually going to be creating this through software. Uh.
Speaker 1: And they have these units, they call them units that
Speaker 1: are interconnected um. And you want to try and use
Speaker 1: these simulations to teach a computer something, for example, pattern
Speaker 1: recognition or the one that we've talked about before what
Speaker 1: a cat is, even if you don't tell it that
Speaker 1: this is a cat. If you feed enough pictures of
Speaker 1: cats uh to an artificial neural network, and you use
Speaker 1: a feedback system so that it is able to different
Speaker 1: differentiate between cat and things that are not a cat,
Speaker 1: it then understands that a cat is a thing, even
Speaker 1: if it's seeing different pictures of different catsum it starts
Speaker 1: to pick out the common elements to all of these
Speaker 1: all of these data points that are being fed through it.
Speaker 1: Right now, the important part is the training process, because
Speaker 1: without that training process and feedback, it never learns, right
Speaker 1: you would, it's meaningless to the artificial neural network. So
Speaker 1: in in this artificial neural network, each artificial neuron is
Speaker 1: a unit. There are three types their input units. This
Speaker 1: is what accepts the incoming information, so that kiddy cat picture,
Speaker 1: for example. Then you have on the other side of
Speaker 1: this network you've at the output units. That's what ends
Speaker 1: up being the information that says, yes, that is a
Speaker 1: picture of a kittie cat, or no, that is most
Speaker 1: certainly not a kittie cat. In between the the input
Speaker 1: and output you have the hidden units. These are the
Speaker 1: layers of neurons that represent the various parts of the
Speaker 1: brain that the inter connections that would happen. Um. And
Speaker 1: essentially all these units are connected to all the other units. Uh,
Speaker 1: And those connections are weighted. By weighted, I mean they
Speaker 1: have a specific relationship from one unit to the next unit.
Speaker 1: And it helps to visualize this as thinking of it
Speaker 1: being from left to right, with left most being input units,
Speaker 1: right most being output units, and everything in between being
Speaker 1: the hidden units. So the connections between each unit as
Speaker 1: you move from left or right are weighted. If it's
Speaker 1: a positive weight it means that the unit on the
Speaker 1: left can excite the unit on the right. All right,
Speaker 1: So input coming into the unit on the left, it
Speaker 1: excites the connection to the next unit on the right.
Speaker 1: Is is weighted positively, it excites the unit on the right.
Speaker 1: If it's negative, it means it suppresses the next unit
Speaker 1: that it's connected to that. And keep in mind that
Speaker 1: each of these hidden units is connected to lots of
Speaker 1: other units. It's not it's not serial, so it's not
Speaker 1: just you know, a straight line left right, it's an
Speaker 1: interconnected network of these connections. UM, i'm usn't connect a lot.
Speaker 1: Sorry about that. But anyway, the bigger the way to number,
Speaker 1: the greater the influence one unit will have on the
Speaker 1: next one. And a single unit might have all these
Speaker 1: multiple connections. Some of them are weighted positively, some of
Speaker 1: them are weighted negatively. The whole point of it is
Speaker 1: this represents a single sort of think of it almost
Speaker 1: like a decision or a perception. So in the case
Speaker 1: of the kenycat picture, the first wave might be very
Speaker 1: general shapes that would be associated with cats, and then
Speaker 1: the next wave might be more particular details, and the
Speaker 1: next wave more particular details. And uh as units pick
Speaker 1: up on those details and send the message on further
Speaker 1: down the line, it starts to refine it and refine
Speaker 1: it until it finally comes to the decision of yes,
Speaker 1: kitty cat or no, not a kitty cat. I'm way
Speaker 1: over simple but but yeah yeah. So so so there's there's
Speaker 1: a bunch of layers in the middle here where the
Speaker 1: machine is going like, yeah, this is probably a kitty cat. Yeah, yeah,
Speaker 1: probably or probably not until you get to the very
Speaker 1: end and and generally you have like a threshold and
Speaker 1: if the data at the end of it meets that
Speaker 1: threshold or exceeds it. Then it's one result, and if
Speaker 1: it doesn't, it's a different result. It's a negative result.
Speaker 1: So you can almost think of it as the probabilistic
Speaker 1: approach that a system like Watson goes through when it's
Speaker 1: trying to determine if an answer to a jeopardy question,
Speaker 1: or rather the question to a jeopardy answer is the
Speaker 1: appropriate one, where it says, all right, as long as
Speaker 1: it meets this level of of of being sure this
Speaker 1: is the correct one, we're going with it. It'll push
Speaker 1: the button and yeah, give give an answer to the
Speaker 1: form of question. And maybe when a copy of the
Speaker 1: home game. Uh so, all of these, all of all
Speaker 1: of this is going in what is called a feed
Speaker 1: forward network, which is just one type of artificial neural network.
Speaker 1: I'm using the feed forward network because it's one of
Speaker 1: the easiest ones to explain. Uh there are others that
Speaker 1: get way more complicated than this, and it requires an
Speaker 1: understanding of artificial neural networks that goes beyond my surface
Speaker 1: shallow level of understanding. Now, one of the ways that
Speaker 1: artificial neural networks have become most significant is in the
Speaker 1: field of machine learning, where you're not just coming up
Speaker 1: with a logical process for a machine. But you're showing
Speaker 1: a machine how it can refine its own decision making.
Speaker 1: And that comes in with the feedback that I was
Speaker 1: talking about earlier. You have to have feedback. You have
Speaker 1: to tell the machine. You have to be able to
Speaker 1: communicate to the machine when it has made a success
Speaker 1: versus when it has failed. And you have to be
Speaker 1: able to tweak the machine. And by machine, i'm talking
Speaker 1: about software in this case, you have to you have
Speaker 1: to tweak that design so that you get the outcome
Speaker 1: you want. Now, this is where we get a little meta.
Speaker 1: We as people know if a picture is of a
Speaker 1: kittie cat or not when we look at it and
Speaker 1: we recognize it whether or not it's kittie cat, well
Speaker 1: as an adult human who has experienced cats. Yes, yes,
Speaker 1: don't over generalized, Jonathan. Sorry, my my one year old
Speaker 1: niece knows what a kitty cat horse. All right, Let's
Speaker 1: let's say that we have determined ourselves that this this
Speaker 1: photograph we hold and on multile hands is that of
Speaker 1: a kitty catch And I'm sorry to want you know
Speaker 1: what we're gonna We're gonna title this episode after a
Speaker 1: Hamlet quote. It's going to be called very like a whale, uh,
Speaker 1: because it makes sense in that context. So anyway, you've
Speaker 1: got a picture of a kid cat. Now you you
Speaker 1: feed it to the computer and the computer output comes
Speaker 1: out and it says it's not a kiddy cat. And
Speaker 1: you know that's the wrong answer. So you have to
Speaker 1: look at how to fix the system so that it
Speaker 1: recognizes the picture you're showing it is in fact a
Speaker 1: kitty cat, right, And that might require you to to
Speaker 1: dig down back through those layers and and pick out
Speaker 1: the one that kind of said like, noah, well it's
Speaker 1: more triangular, so it's obviously not a cat, right or whatever. Exactly,
Speaker 1: you have to figure out where in this, in this
Speaker 1: stage of interconnections, did that one decision or maybe multiple
Speaker 1: decisions lead to the conclusion that it was not a
Speaker 1: kitty cat. The one one way of doing this is
Speaker 1: called back propagation, where you start with the output and
Speaker 1: you work your way backwards and you say, all right,
Speaker 1: for in order for this to say yes, this is
Speaker 1: a kitty cat, we need to have this result at
Speaker 1: this stage. Do we have that result? Yes? All right,
Speaker 1: let's go one step you know actually probably no, no, Well,
Speaker 1: then what is going on the step before it, that
Speaker 1: sort of thing, and you work your way back and
Speaker 1: you start tweaking those waitings. I was talking about the
Speaker 1: connect connections, and you say, all right, well, maybe this
Speaker 1: connection is actually waited too much. It's too far in
Speaker 1: the positive. We need to bring that down. Or maybe
Speaker 1: it's in the negative and we need to switch it
Speaker 1: to positive. So you start making these adjustments in the
Speaker 1: software to those weighted connections in the neural network, and
Speaker 1: that might end up allowing you to pass that same
Speaker 1: kitty cat picture through and now it says, oh, that's
Speaker 1: a kitty cat. Like Yeah. You do this a lot,
Speaker 1: with lots of different examples, and eventually you get to
Speaker 1: a point where you feel confident that it is doing
Speaker 1: what you intended it to do, that it is in
Speaker 1: fact able to recognize the picture of the kitty cat
Speaker 1: at a high enough percentage that you're that it's you
Speaker 1: that you say, this can recognize a kitty cat. Then
Speaker 1: you can start feeding it pictures you have never shown
Speaker 1: it before, including pictures of stuff that looks like a
Speaker 1: kitty cat but isn't, and kitty cats that maybe slightly
Speaker 1: outside the norm of what it had experienced before, and
Speaker 1: see how it does in that case, and once you
Speaker 1: get to a certain point, the device is able to
Speaker 1: maintain its ability to recognize things without you having to
Speaker 1: go in there and tweak stuff in between the training
Speaker 1: sessions it has been trained. Yeah. So so that's what
Speaker 1: they were working on, was the idea of image recognition.
Speaker 1: But one of the things that comes out in the
Speaker 1: Google research blog post where they were first describing the
Speaker 1: the idea and the genesis of the Google Deep Dream
Speaker 1: was that the researchers found that and I'm gonna have
Speaker 1: to quote this here, neural networks that were trained to
Speaker 1: discriminate between different kinds of images have quite a bit
Speaker 1: of the information needed to generate images too. So that
Speaker 1: in training, and that was the end of the quote,
Speaker 1: So that in training, what they had done in teaching
Speaker 1: these neural networks how to recognize images was also sort
Speaker 1: of teach them how to make images of things. Right.
Speaker 1: If you say, here are the shapes that you would
Speaker 1: see in Japanese architecture, and these are the these are
Speaker 1: the shapes commonly used in that architecture. So this is
Speaker 1: how you can recognize you're shown an image of a
Speaker 1: building from historic region of Japan. Then it knows it
Speaker 1: being you know, knowing and is being generous here, but
Speaker 1: it recognizes those features. Those are the features that define
Speaker 1: what that thing is. It can now generate those same features.
Speaker 1: And so if it sees quote unquote sees patterns within
Speaker 1: an image that resemble that, it could generate those images,
Speaker 1: kind of kind of tweaking and shaping the the fed
Speaker 1: image and producing something new. Yeah. So they had some
Speaker 1: wonderful examples on this Google research blog post where one
Speaker 1: of the things they were doing was just refining images
Speaker 1: based on white noise until they started to show the
Speaker 1: image that was desired. So you would start off with static,
Speaker 1: just pure static in an edge, and then tell the
Speaker 1: algorithm to constantly tweak that static to enhance it to
Speaker 1: become more like an image of a banana, and eventually,
Speaker 1: for example, yeah, yeah, and eventually the static evolved into
Speaker 1: a banana or a cluster of bananas kind of I
Speaker 1: wouldn't say, like like not like a group of banana,
Speaker 1: but a banana pile, like some sort of weird minion
Speaker 1: slash banana box made out of bananas. And I thought
Speaker 1: this was funny because this is the digital equivalent of apophenia.
Speaker 1: Do you know the process of apophenia. It's in psychology
Speaker 1: where we see significance in random patterns, sort of like
Speaker 1: paradolia being a very specific version of that, where you
Speaker 1: can you see faces in in shapes, like seeing something
Speaker 1: in a cloud, very like a whale, very much. But yeah,
Speaker 1: so we were essentially here teaching computers how too, Well,
Speaker 1: just keep trying at all of this random noise until
Speaker 1: you can find the banana there. Yeah, it's almost like
Speaker 1: pointing a sculpture at a block of marble and say,
Speaker 1: just keep cutting away until the masterpiece, like David emerges,
Speaker 1: until you find the banana. I'm sure that is what
Speaker 1: someone told Michael Angelo is a very young boy. Pretty
Speaker 1: sure that was. That was in one of his famous paintings.
Speaker 1: It's just there's a little thing at the very end,
Speaker 1: like have fun, find a banana. But yeah, obviously the
Speaker 1: actual goal of the research is image recognition. I mean
Speaker 1: we've actually done podcasts about image recognition of various forms before.
Speaker 1: Oh yeah, well that that in speech recognition and and
Speaker 1: facial recognition, which is very uh kind of creepy and
Speaker 1: important in our daily Internet lives. And if you if
Speaker 1: you want to, you know, we've talked about this so often.
Speaker 1: If you want to do a really deep dive on
Speaker 1: these topics, you can check out episodes such as can
Speaker 1: computers describe what they see? From November? I know that
Speaker 1: face from October zoom and enhanced from August, and speech
Speaker 1: recognition from April. Man, we talk about computers learning a lot.
Speaker 1: It's well, it is the future and and there's still
Speaker 1: a lot of challenges to this field, right. I mean,
Speaker 1: it's not so easy to make a computer see and
Speaker 1: recognize what it sees the way a human or an
Speaker 1: animal would well, and a lot of these involve uh
Speaker 1: systems that are tweaked for very specific types of recognition.
Speaker 1: It's not like you have one neural network that recognizes everything.
Speaker 1: If you looks really just the cat network or the
Speaker 1: banana network. Yeah, by the way, I get the banana
Speaker 1: work network. It's really appealing. So how are you guys
Speaker 1: just released acknowledge that entirely? I think we keep that
Speaker 1: whole part. I think we do. I think we should
Speaker 1: continue right now. Okay, but but so if you if
Speaker 1: you want to go on the teap dive, So if
Speaker 1: you want to go on a deep dive about all
Speaker 1: of this stuff, you certainly can. But let's go over
Speaker 1: like like a basic overview of why it's so difficult
Speaker 1: to get computers to to see and hear the way
Speaker 1: that we do well. The big one being that architecture,
Speaker 1: you know, the difference between computer architecture and the way
Speaker 1: our brains work. That's that's the biggest, right, that's just
Speaker 1: fundamentally they work in very different ways. And we have
Speaker 1: expressed this in multiple episodes too, especially dealing with things
Speaker 1: like how computer memory is so different from our memory.
Speaker 1: That's just one easy way of pointing at this. So
Speaker 1: that's a big one even And the software simulations are
Speaker 1: incredibly limited because they require a great deal of processing
Speaker 1: power to work properly. Um, and uh, you know, we're
Speaker 1: still learning how the brain works, and so to create
Speaker 1: simulation of it while we have only a partial understanding
Speaker 1: is really tough. In fact, a lot of people, I
Speaker 1: saw one person say, uh, the simulation the artificial neural
Speaker 1: network is similar in a way, like you wouldn't say
Speaker 1: it's a brain, the same way you wouldn't say a
Speaker 1: weather simulation is an actual weather front. It's you know,
Speaker 1: it's a It's as close as we can get right now,
Speaker 1: based upon our understanding and our technological sophistication, and that's
Speaker 1: going to only improve as time goes on. But we're
Speaker 1: still at the very early stages of that, sure. And
Speaker 1: it's also just a major difference in the approach of
Speaker 1: problem solving. I mean, typically, computers as they exist today
Speaker 1: are good at learning by explicit instructions to get the
Speaker 1: right answer. Yes, and assuming that everything in the computer
Speaker 1: is working properly, then they will reliably execute those instructions
Speaker 1: precisely every single time. Yeah, And our brains are exactly
Speaker 1: the opposite. They're not getting the perfect answer, but they're
Speaker 1: very good at something computers aren't at approximating. They're good
Speaker 1: at approximating based on a lot of inputs. So we
Speaker 1: learn what a chair is not by reading a definition
Speaker 1: of the key features of a chair, and then a
Speaker 1: list of all the possible exceptions, including every variation on
Speaker 1: a chair there could be, I mean, like, why would
Speaker 1: we do that? Instead we just see a bunch of chairs.
Speaker 1: Notice that people identify all of these things as chairs.
Speaker 1: We can generally yeah, and then we get an approximate
Speaker 1: idea of Okay, here's basically what a chair is. So
Speaker 1: we have a sort of fuzzy feel for what constitutes
Speaker 1: that object. And that's what the neural networks are trying
Speaker 1: to do. They they have large samples of data and
Speaker 1: they try to get a feel for it. And it
Speaker 1: it takes the the the work of actual human beings
Speaker 1: to make certain that that early stage of training is
Speaker 1: actually working. It's not like, it's not like we have
Speaker 1: a computer that you can just turn on and it
Speaker 1: just automatically starts to learn and it knows when it's
Speaker 1: right and knows when it's wrong, and it can thus
Speaker 1: start to learn everything. We're nowhere close to that. We
Speaker 1: are to the point where you turn a computer on,
Speaker 1: you feed it some information, you see what comes out,
Speaker 1: and then you either say, all right, looks like this
Speaker 1: particular uh go through work. Fine, let's try something else,
Speaker 1: or you say, oh, this didn't work. Let's find out
Speaker 1: what's wrong and fix it so we can try it
Speaker 1: again before you ever get to a point where you
Speaker 1: can start showing it new stuff, right and kind of that.
Speaker 1: The way that you do that, that you build a
Speaker 1: better neural network is that you you check in on
Speaker 1: what it's doing, and by by asking a layer to
Speaker 1: create a visualization, a layer of these these artificial neurals
Speaker 1: to create a visualization of what it's working through. Is
Speaker 1: one way that you have of checking in interesting. As
Speaker 1: an example from this Google research blog, you might have
Speaker 1: set your network to figure out what a dumbbell is,
Speaker 1: but until you ask it for an image, you might
Speaker 1: not realize that all the pictures of dumbbells that it's
Speaker 1: found so far include beefy human arms. So it's obviously
Speaker 1: searching through our stock image libraries that we use. Sure, sure,
Speaker 1: but you know so. So once you see it's images
Speaker 1: of these really weird arm dumbbell hybrids, you can help
Speaker 1: it correct by telling it to enhance the bits that
Speaker 1: are dumbbells, you know, the shapes and the colors that
Speaker 1: go with dumbells, and to ignore the beef arm bits. Yeah,
Speaker 1: it actually is very interesting because as much as I
Speaker 1: joke about the stock stock image thing, it really does
Speaker 1: tell you that when we choose certain images to represent concepts,
Speaker 1: we often will go to very similar ones, and to
Speaker 1: the point where as humans, we know we can differentiate
Speaker 1: the thing in that image that actually represents the concept
Speaker 1: versus some other supplemental thing. But a computer doesn't know
Speaker 1: that unless Yeah, and it's so loaded with context. I mean,
Speaker 1: you wouldn't know why a bf arm and a dumbbell
Speaker 1: would go together, and you understood culturally speaking that that
Speaker 1: sometimes when you work with a lot of dumbbells, you
Speaker 1: get big BPRM. So all of this is extranees information
Speaker 1: that a computer can't possibly be asked to automatically know
Speaker 1: the way that a human person would. Uh And okay,
Speaker 1: So so extrapolating out a little bit further from this concept,
Speaker 1: the end goal is really for your neural network to
Speaker 1: be able to auto correct, so you can program it
Speaker 1: to enhance whatever it thinks is important, and it will
Speaker 1: look for patterns in the visual data of an image
Speaker 1: and enhance those, then evaluate the resulting image and find
Speaker 1: more patterns and enhance them, and so on. It's a
Speaker 1: little like like asking a child to tell you what
Speaker 1: shapes she sees in the clouds. You can get a
Speaker 1: decent sense based on her answers of her abilities to
Speaker 1: think abstract lee and to extrapolate visually interesting. So yeah, yeah,
Speaker 1: because I think of ties where uh, me and my
Speaker 1: friends would be looking at the clouds and we would
Speaker 1: sit there and talk about what shapes we saw. And
Speaker 1: I remember like a friend might say, oh that I
Speaker 1: see a dog, and other guy says, I see a man,
Speaker 1: and they'd say, what do you see? As I see
Speaker 1: it looks like it's going to rain, and m hm
Speaker 1: explains why I don't have friends. But but yeah, you know,
Speaker 1: so so you get to you get to evaluate this
Speaker 1: kid's conceptualizations, and you also probably get to have a
Speaker 1: really rad, trippy conversation. And these are the two things
Speaker 1: that we are getting out of deep dream. Yeah, yeah, certainly.
Speaker 1: And to bring it back to this sort of byproduct
Speaker 1: these deep dream images, how you actually get these is
Speaker 1: that refining feedback process. So at a certain layer of analysis,
Speaker 1: you tell the neural network, okay, what whatever you found here,
Speaker 1: focus in on that and and pay a lot of
Speaker 1: attention to it, and then look at it again and
Speaker 1: then pay more attention. Yeah, and and it does. Paying
Speaker 1: attention goes beyond just focusing. It goes to the addition
Speaker 1: of information, right if relation of information Yeah, yeah, Like
Speaker 1: if you think that you see a pagoda in those clouds,
Speaker 1: then really really enhance that pagoda tendency, right, And this
Speaker 1: is how you end up with Jonathan having eyes all
Speaker 1: over his shoulders and caterpillars for arms. It might be
Speaker 1: looking for images that it has recognized before in biological life,
Speaker 1: in pictures of animals, pictures of insects, and say, yeah, okay,
Speaker 1: that arms kind of you know, kind of tube shaped.
Speaker 1: So maybe we can make that a little bit more
Speaker 1: like a caterpillar. Oh now it's looking a lot like
Speaker 1: a caterpillar. Make it look more like a caterpillar. Even, Yeah, totally.
Speaker 1: By the way, you can get to that same destination
Speaker 1: just by hanging out with me on a Saturday night,
Speaker 1: Same same result to me. This actually raises a pretty
Speaker 1: weird and possibly interesting, possibly superficial question about artificial intelligence.
Speaker 1: If we're teaching our computers to seem or like us,
Speaker 1: does that mean they'll eventually learn to hallucinate like us?
Speaker 1: Like Is hallucinating a natural consequence of human levels of
Speaker 1: vision and object recognition? Well, I would ask, how how
Speaker 1: do we know that they're not already hallucinating? There's a
Speaker 1: theory Follow me here, Follow me here. There's there's a
Speaker 1: theory of baby brain growth that suggests that infant sensory
Speaker 1: processing is on a level similar to adults that are
Speaker 1: using hallucinogens, because there's there's less or or even no
Speaker 1: awareen awareness of context, and there's less separation between internal
Speaker 1: and external stimuli. So everything feels and looks and sounds
Speaker 1: real and immediate, including things that are artifacts of brains
Speaker 1: inner processings like associations and fragments of memories and misunderstandings
Speaker 1: of what you're seeing and hearing. So h so are
Speaker 1: our computers all tripping all the time? Is the question
Speaker 1: I posed? Do androids dream of electric sheep or are
Speaker 1: the electric sheep there for them always? I don't know. Well,
Speaker 1: they don't dream of electric sheep standing in a pasture.
Speaker 1: They dream of electric cheap emerging out of your pectoral
Speaker 1: muscles again Saturday night. Yeah, this is ah to me?
Speaker 1: This is this isn't a great A great way of
Speaker 1: appreciating how weird and amazing artificial intelligence as a discipline is.
Speaker 1: And while while this is almost like a byproduct, like
Speaker 1: its just an interesting byproduct of something that was intended
Speaker 1: to improve upon image recognition software, it also has created
Speaker 1: some truly remarkable images. I mean, it's it's you could
Speaker 1: argue it's a new form of art, and you start
Speaker 1: by feeding it an image that you think is already
Speaker 1: interesting or maybe not interesting. Because either way it works
Speaker 1: and in you see what comes out of it, and
Speaker 1: U we've even seen some pretty you know, mostly it's
Speaker 1: done for laughs, but there have been some that I
Speaker 1: think it just really striking images that make me think
Speaker 1: of Impressionism and some other and more even more abstract
Speaker 1: and surreal approaches to art as well. Some of the
Speaker 1: ones I've seen very much have a Salvador Dolly kind
Speaker 1: of the ones with like a dripping architecture where there
Speaker 1: seemed to be you see, like arches and things that
Speaker 1: you would recognize from buildings, except they seem to be
Speaker 1: made of liquids somehow. Yeah. Yeah. And there's also an
Speaker 1: element like Dolly of that, of that kind of of
Speaker 1: that kind of Escher sort of influence in there too,
Speaker 1: because of the way that the shapes repeat and twist
Speaker 1: on each other. And oh it's fascinating, like impossible perspective.
Speaker 1: Yeah yeah, yeah. So we're getting some some phenomenal pictures, uh,
Speaker 1: and maybe we'll even post some from the Forward Thinking
Speaker 1: crew later on. We've talked about the possibility of of
Speaker 1: doing a photo shoot just just to feed it through
Speaker 1: here and find out what kind of fresh horror awaits us.
Speaker 1: But I really think that this was cool also just
Speaker 1: to kind of get a look at how artificial neural
Speaker 1: networks work and and the process that they tend to
Speaker 1: use in order for machines to be able to learn stuff,
Speaker 1: because we've talked about learning so much without really getting
Speaker 1: into the process that's going on, just you know, to
Speaker 1: say that, hey, this machine can learn. It's it's it's
Speaker 1: not it's doing it a disservice. So this was great
Speaker 1: to get into the nuts and bolts of that. I'm
Speaker 1: going to go home and lay on a caterparrot. Okay,
Speaker 1: well you you own one. We've seen it. So guys,
Speaker 1: if you have any suggestions for future episodes of forward Thinking,
Speaker 1: maybe there's something you've always wanted to know more about,
Speaker 1: like what's that going to be like in the future,
Speaker 1: Or maybe there's even a topic we've covered in the
Speaker 1: past that you want to have us focus on the
Speaker 1: very specific part of that, or whatever it may be.
Speaker 1: You should write us, and maybe you just have comments
Speaker 1: about the the goal project or artificial intelligence of machine learning.
Speaker 1: Send us a message. Our email is FW thinking at
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