Dileep George: Brain-Inspired AI | Lex Fridman Podcast #115

1. 0:00 – 4:50

   Introduction

   1. LFLex Fridman0:00

      The following is a conversation with Daleep George, a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious with Scott Phoenix, and formerly co-founder of Numenta with Jeff Hawkins, who's been on this podcast, and Donna Dubinsky. From his early work on hierarchical temporal memory to recursive cortical networks to today, Daleep's always sought to engineer intelligence that is closely inspired by the human brain. As a side note, I think we understand very little about the fundamental principles underlying the function of the human brain, but the little we do know gives hints that may be more useful for engineering intelligence than any idea in mathematics, computer science, physics, and scientific fields outside of biology. And so the brain is a kind of existence proof that says it's possible, keep at it. I should also say that brain-inspired AI is often overhyped and used as fodder just as quantum computing for, uh, marketing speak. But I'm not afraid of exploring these sometimes overhyped areas since where there's smoke, there's sometimes fire. Quick summary of the ads. Three sponsors, Babbel, Raycon earbuds, and MasterClass. Please consider supporting this podcast by clicking the special links in the description to get the discount. It really is the best way to support this podcast. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcasts, support on Patreon, or connect with me on Twitter @LexFridman. As usual, I'll do a few minutes of ads now and never any ads in the middle that can break the flow of the conversation. This show is sponsored by Babbel, an app and website that gets you speaking in a new language within weeks. Go to babbel.com and use code LEX to get three months free. They offer 14 languages, including Spanish, French, Italian, German, and yes, Russian. Daily lessons are 10 to 15 minutes, super easy, effective, designed by over 100 language experts. Let me read a few lines from the Russian poem, (Russian) by Alexander Blok that you'll start to understand if you sign up to Babbel. (Russian) . Now, I say that you'll only start to understand this poem because Russian starts with language and ends with vodka. Now, the latter part is definitely not endorsed or provided by Babbel and will probably lose me the sponsorship, but once you graduate from Babbel, you can enroll in my advanced course of late night Russian conversation over vodka. I have not yet developed an app for that. It's in progress. So get started by visiting babbel.com and use code LEX to get three months free. This show is sponsored by Raycon earbuds. Get them at buyraycon.com/lex. They've become my main method of listening to podcasts, audiobooks, and music when I run, do push-ups and pull-ups, or just living life. In fact, I often listen to brown noise with them when I'm thinking deeply about something. It helps me focus. They're super comfortable, pair easily, great sound, great bass, six hours of playtime. I've been putting in a lot of miles to get ready for a potential ultra-marathon and listening to audiobooks on World War II. The sound is rich and really comes in clear. So again, get them at buyraycon.com/lex. This show is sponsored by MasterClass. Sign up at masterclass.com/lex to get a discount and to support this podcast. When I first heard about MasterClass, I thought it was too good to be true. I still think it's too good to be true. For 180 bucks a year, you get an all-access pass to watch courses from, to list some of my favorites, Chris Hadfield on space exploration, Neil deGrasse Tyson on scientific thinking and communication, Will Wright, creator of SimCity and Sims, on game design. Every time I do this read (laughs) , I really want to play a city builder game. Carlos Santana on guitar, Garry Kasparov on chess, Daniel Negreanu on poker, and many more. Chris Hadfield explaining how rockets work and the experience of being launched into space alone is worth the money. By the way, you can watch it on basically any device. Once again, sign up at masterclass.com to get a discount and to support this podcast. And now, here's my conversation with Daleep George.

2. 4:50 – 17:11

   Building a model of the brain

   1. LFLex Fridman4:50

      Do you think we need to understand a brain in order to build it?

   2. DGDileep George4:54

      Yes, if you want to build a brain, we definitely need to understand how it works. So Blue Brain or Henry Markram's project, uh, is trying to build a brain without understanding it. Like, you know, just trying to, uh, put details of the brain from neuroscience experiments into a giant simulation, uh, by putting more and more neurons, more and more details. Uh, but that is not going to work, uh, because when it doesn't perform as, uh, what you expect it to do, then what do you do? Do you just keep adding more details? How do you debug it? So it's a... So unless you understand, unless you have a theory about how the system is supposed to work, how the pieces are supposed to fit together, what they're going to contribute, you can't, you can't build it.

   3. LFLex Fridman5:42

      At the functional level. Understand. So can you actually linger on and describe the Blue Brain project? It's kinda fascinating, uh, principle and idea to try to simulate the brain. As we're talking about the human brain, right?

   4. DGDileep George5:57

      Right. Human brains and rat brains or cat brains have lots in common. The cortex, uh, the neocortex structure is very similar. So initially they were trying to just simulate a cat brain. Uh, and, uh-

   5. LFLex Fridman6:14

      To understand the nature of evil.

   6. DGDileep George6:16

      (laughs) And understand the nature of evil. Uh- Yeah. ... or, uh, as it happens in most of these simulations, uh, you, you easily get one thing out, which is oscillations, you know? Yeah. If you, you sim- if you simulate a large number of neurons, they oscillate, uh, and, uh, you can adjust the parameters and say that, "Oh, oscillations match the rhythm that we see in the brain," et cetera. But, uh-

   7. LFLex Fridman6:40

      Oh, I see. So like, uh, so the idea is, uh, is the simulation at the level of, uh, individual neurons?

   8. DGDileep George6:47

      Yeah. So the Blue Brain Project, the original idea as proposed was, um, you, you put very detailed bio physical neurons, uh, bio physical models of neurons, and you interconnect them according to the statistics of connections that we have found from real neuroscience experiments. And then, uh, turn it on and, uh, see what happens. Uh, and, and these neural models are, you know, incredibly complicated in themselves, right, because these neurons are, uh, modeled using, uh, this, uh, idea called Hodgkin-Huxley models, which are about how signals propagate in a cable. And there are a- active dendrites, all those phenomena which those phenomena themselves we don't understand that well. Uh, and then, uh, we put in connectivity, which is part guesswork, part, you know, observed. And of course, if you do not have any theory about how it is supposed to work, uh, we, you, you know, we just have to take whatever comes out of it as, "Okay, this is something interesting." You know?

   9. LFLex Fridman7:55

      But in your sense, like these models of the way signal travels along... or like with the axons and all the basic models, that's... they're too crude?

   10. DGDileep George8:04

       Oh. Well, actually they are pretty detailed and pretty sophisticated, and they do replicate the neural dynamics. If you take a single neuron and, uh, you, you try to, uh, turn on the different channels, the calcium channels and, uh, uh, the different receptors, uh, and see what effect of, uh, turning on or off those channels are in the neurons' spike output, people have built pretty sophisticated models of that. And, and they are, I, I would say, um, you know, in the regime of correct.

   11. LFLex Fridman8:41

       Well, see, the correctness... That's interesting because you've mentioned at several levels, uh, the correctness is measured by looking at some kind of aggregate statistics.

   12. DGDileep George8:49

       It, it would be more, uh, the, the spiking dynamics of a single neuron.

   13. LFLex Fridman8:53

       Spiking dynamics of a single neuron. Okay.

   14. DGDileep George8:55

       Yeah. Uh, and, and yeah, these models, because they are, they are going to the level of mechanism, right?

   15. LFLex Fridman9:00

       Mm-hmm.

   16. DGDileep George9:00

       So they are basically looking at, uh, okay, what, what is the effect of turning on an ion channel? Uh, and, um, and you can, you can model that using electric circuits, you know? And then so th- their model... S- so it is not just a, uh, function fitting. It is... People are looking at the mechanism underlying it and, uh, putting that in terms of electric circuit, uh, theory, signal propagation theory and, and modeling that. And so those models are sophisticated, but getting a single neuron's model 99% right does not still tell you how to... You know, it would be the analog of getting a transistor model right and now trying to build a microprocessor. Uh, and if you, if you just, uh, observe... you know, if you did not understand how a microprocessor works, uh, but you say, "Oh, I have... I now can model one transistor well, and now I will just try to interconnect, uh, the transistors according to whatever I could, you know, guess from the, the experiments and try to simulate it," um, then it is very unlikely that you will produce a functioning microprocessor. Um, you want to... you know, when you want to, uh, produce a functioning microprocessor, you want to understand Boolean logic, how does... how do the, the gates work, all those things, and then, you know, understand how do those gates get implemented using transistors.

   17. LFLex Fridman10:24

       Yeah. There's actually... I remember... This reminds me, there's a paper, maybe you're familiar with it, that I remember going through in a reading group that approaches a microprocessor from a perspective of a neuroscientist.

   18. DGDileep George10:36

       Yeah.

   19. LFLex Fridman10:36

       I think it, it basically... it, uh, uses all the tools that we have of neuroscience to try to understand like as if we just aliens showed up to study computers, uh-

   20. DGDileep George10:47

       Yeah.

   21. LFLex Fridman10:48

       ... and, and to see if, if those tools can be used to get any kind of sense of how the microprocessor works. And I think the final... the takeaway from th- at least this initial, uh, exploration is that (laughs) we're screwed. There's no way that the tools of neuroscience would be able to get us to anything, like not even Boolean logic. I mean, it's just a- a- any aspect of the architecture, of the, uh, function, of the processes involved, uh, the, the clocks, the, the timing, all that, you can't figure that out from the tools of neuroscience.

   22. DGDileep George11:24

       Yeah. So I'm very familiar with this, this particular paper.

   23. LFLex Fridman11:27

       Yeah.

   24. DGDileep George11:27

       I think it was, uh, called, um, Can, uh, a Neuroscientist Understand a Microprocessor or-

   25. LFLex Fridman11:34

       Yeah.

   26. DGDileep George11:34

       ... something like that. Following the methodology in that paper, even ele- electrical engineer would not understand microprocessors. So I couldn't have so... (laughs)

   27. LFLex Fridman11:43

       (laughs)

   28. DGDileep George11:43

       (laughs) So I co- so I don't think it is that bad in the sense of saying, um, neuroscientists do find valuable things, uh, by observing the brain. Uh, they, they do find good insights. Um, but those insight cannot be put together just as a simulation. You have to, you have to investigate what are the computational underpinnings- pinnings of those findings? How do, do all of them fit together from an information processing perspective? You have to, you have to... Somebody has to, uh, painstakingly put those things together and build hypotheses. Um, so I don't want to diss all of neuroscience as saying, "Oh, they're not finding anything." No, that, you know, that, uh, that paper almost went to that level of, uh, uh, neuroscientists will never understand. Uh, no, that- that's not true. I think they do find lots of useful things, but it has to be put together in a, in a computational framework.

   29. LFLex Fridman12:40

       Yeah. I mean, but, you know, just...... the AI systems will be listening to this podcast a hundred years from now and, and they will probably, uh, th- there's some non-zero probability they'll find your words laughable. It's like, "Oh, remember-"

   30. DGDileep George12:54

       (laughs) .
3. 17:11 – 27:50

   Visual cortex

   1. DGDileep George17:11

      cycle.

   2. LFLex Fridman17:12

      So what aspect of the brain are useful in this whole endeavor? Which, by the way, I should say you're, you're both a neuroscientist and an AI person. Uh, I guess the dream is to both understand the brain and to build AGI systems. So you're, it's like an engineer's perspective of trying to understand the brain. So what aspects of the brain, uh, functionally speaking, like you said, do you find interesting?

   3. DGDileep George17:38

      Yeah. Quite a lot of things. All right. So one is, um, you know, if you look at the visual cortex, uh, uh, and, and, you, you know, the, the, uh, visual cortex is, is a large part of the brain. Uh, I forgot the ex- exact fraction, but it is, it's a, it- a huge part of our brain area is, uh, occupied by just, just vision. Um, so vision, visual cortex is not just a feedforward cascade of neurons. Um, uh, there are a lot more feedback connections in the brain compared to the feedforward connections. And, and it is surprising to the level of detail neuroscientists have actually studied this. If you, if you go into neuroscience literature and poke around and ask, you know, have they studied what will be the effect of poking a neuron in, uh, level IT, uh, in level V1 and, uh, um, have they studied that? Uh, and you'll say, "Yes, they have studied that." (laughs) .

   4. LFLex Fridman18:35

      (laughs) . So every pos- every possible combination (laughs) -

   5. DGDileep George18:37

      Yeah. I mean, it's, it's, uh, it's not a random exploration at all. It's, uh, very hypothesis-driven, right?

   6. LFLex Fridman18:43

      Yeah.

   7. DGDileep George18:43

      They, they, they are very, uh, experimental neuroscientists are very, very systematic in how they probe the brain.

   8. LFLex Fridman18:48

      Yeah.

   9. DGDileep George18:49

      Uh, because experiments are very costly to conduct. They take a lot of preparation, they, they need a lot of control. So they, they are very hypothesis-driven-

   10. LFLex Fridman18:57

       Yeah.

   11. DGDileep George18:57

       ... in how they probe the brain. And, um, often what I find is that when we have a question in, um, in AI, uh, about have, has anybody probed, uh, probed how lateral connections in the brain works? And when you go and read the literature, yes, people have probed it and people have probed it very systematically. And, and they have hypotheses about how those lateral connections are-... supposedly contributing to visual processing. Uh, but, of course, they haven't built very, very functional detail models of it.

   12. LFLex Fridman19:28

       By the way, how do they... In those studies, sorry to interrupt, uh, do they, do they stimulate, like, a neuron in one particular area of the visual cortex and then see how the travel, uh, the signal travels kind of thing?

   13. DGDileep George19:39

       Fascinate, very, very fascinating experiments, right? You know, so I can, I can give you one example I was impressed with. Um, this is, um... So before going to that, let me, like, let me give you a, uh, you know, a, uh, overview of how the, the layers in the cortex are organized, right?

   14. LFLex Fridman19:54

       Sure.

   15. DGDileep George19:54

       Uh, visual cortex is organized into roughly four hierarchical levels. Okay. So, uh, V1, V2, V4, IT and in V1-

   16. LFLex Fridman20:03

       What happened to V3?

   17. DGDileep George20:04

       Uh, well, yeah, there is another pathway. Uh-

   18. LFLex Fridman20:06

       Okay.

   19. DGDileep George20:07

       Okay. So there is a... This is-

   20. LFLex Fridman20:08

       (laughs)

   21. DGDileep George20:08

       This, I'm, I'm talking about just object recognition pathway.

   22. LFLex Fridman20:10

       All right, cool.

   23. DGDileep George20:11

       Okay. Uh, and then, um, in V1 itself, um, i- uh, so it's, there is a very detailed micro circuit in V1 itself. There is, there is organization within a level itself. Um, the cortical sheet is organized into, uh, you know, multiple layers and there are columnar structure. And, and this, this l- layer wise and columnar structure is repeated in V1, V2, V4, uh, uh, IT, all of them, right? Uh, and, and the connections between these layers within a level w- you know, in V1 itself, there are six layers, roughly. And the connections between them, there is a particular structure to them. Uh, and, um, now... So one example of, uh, an experiment, uh, uh, people did is, when I, when you present a stimulus, uh, which is, um, let's say requires, um, separating the foreground from the background of an object. So it is a, it's a textured triangle on a textured background. Uh, and, um, you can check, does the surface settle first or does the contour settle first?

   24. LFLex Fridman21:19

       Settle?

   25. DGDileep George21:20

       Settle in the sense that the... So w- when you fi- finally form the percept of the, of the, uh, triangle-

   26. LFLex Fridman21:27

       Mm-hmm.

   27. DGDileep George21:28

       ... you understand where the contours of the triangle are, and you also know where the inside of the triangle is, right? That's when you form the final percept. Um, now you can ask, what is the dynamics of forming that final percept?

   28. LFLex Fridman21:42

       Mm-hmm.

   29. DGDileep George21:42

       Um, do the, uh, d- do the neurons, um, first find the edges and converge on where the edges are-

   30. LFLex Fridman21:51

       Mm-hmm.
4. 27:50 – 31:35

   Probabilistic graphical models

   1. LFLex Fridman27:50

   2. DGDileep George27:50

      So this is a classic example in, uh, uh, graphical models, probabilistic models. Um, so if you-

   3. LFLex Fridman27:56

      Uh, what are those?

   4. DGDileep George27:57

      Uh, okay. Um... (laughs)

   5. LFLex Fridman28:00

      (laughs) Oh, I think it's useful to mention because we'll talk about them more.

   6. DGDileep George28:04

      Yeah. Yeah. So neural networks are one class of machine learning models. Um, uh, you know, you have distributed set of, uh, nodes, which are called the neurons. A- you know, each one is doing a dot product. And you can- you can approximate any function using this, uh, multilevel, uh, network of neurons. So that's, uh, uh, a class of models which are used for- useful for function approximation. There is another class of models in machine learning, uh, called probabilistic graphical models. And you can think of them as each node in that model is variable, which is- which is talking about something. You know, it- it can be a variable representing, is- is an edge present in the input or not? Uh, and at the top of the, uh, uh, network, a node can be, uh, representing, is there an object present in the, uh, world or not? And- and then... So it can... It is- it is another way of encoding knowledge.

   7. LFLex Fridman29:03

      Mm-hmm.

   8. DGDileep George29:05

      And, uh, um, and then you... Once you encode the knowledge, you can, uh, do inference in the right way, you know. How... What is the best way to, uh, y- you know, explain some set of evidence using this model that you encoded? You know.

   9. LFLex Fridman29:19

      Mm-hmm.

   10. DGDileep George29:19

       So when you encode the model, you are encoding the relationship between these different variables. How is the edge connected to my, uh, the model of the object? How is the surface connected to the model of the object? Um, and then, um, of course, this is a very distributed, complicated model. And inference is how do you explain a piece of evidence when- when a set of stimulus comes in? If somebody tells me there is a 50% probability that there is an edge here in this part of the model, how does that affect my belief on whether I should think that there should be a... Is the square present in the image?

   11. LFLex Fridman29:53

       Mm-hmm.

   12. DGDileep George29:54

       So- so this is the process of inference. So one example of inference is having this explaining away effect between multiple causes. So, uh, graphical models can be used to represent causality in the world. Um, so let's say, um, y- you know, uh, your, uh, alarm, uh, the, uh, at home can be, uh, triggered by a, uh, burglar getting into your house, uh, or it can be triggered by an earthquake.

   13. LFLex Fridman30:24

       Yeah.

   14. DGDileep George30:24

       Both- both can be causes of the alarm going off. So now, you- you are dri- y- you know, y- you're in your office, you heard burglar alarm going off. You are heading, uh, home, uh, thinking that there's a burglar got in.

   15. LFLex Fridman30:37

       Mm-hmm.

   16. DGDileep George30:37

       But while driving home, if you hear on the radio that there was an earthquake in the vicinity, now your hype- you know, uh, strength of evidence for, uh, a burglar getting into your house is diminished.

   17. LFLex Fridman30:49

       Mm-hmm.

   18. DGDileep George30:50

       Because now that- that piece of evidence is explained by the earthquake in- being present. So if you- if you think about these two causes explaining a lower level, uh, variable, which is alarm, now w- w- what we are seeing is that increasing the evidence for some cause ex- you know, there is evidence coming in from below for alarm being present, and initially it was flowing to a burglar being present. But now since somebody, uh... Some- this evi- there is side evidence for this other cause, it explains away this evidence, and it- evidence will now flow to the other cause. Th- this is, you know, two competing causal, uh, things trying to explain the same evidence.

   19. LFLex Fridman31:28

       And- and the brain has a similar kind of mechanism-

   20. DGDileep George31:30

       Yes.

   21. LFLex Fridman31:31

       ... for, uh, for doing so.

   22. DGDileep George31:32

       Yes.

   23. LFLex Fridman31:32

       That's kind of interesting. I mean, and that-How's

5. 31:35 – 36:56

   Encoding information in the brain

   1. LFLex Fridman31:35

      that all encoded (sighs) in the brain? Like, where is the storage of information? Are we talking, just maybe to get it, uh, a little bit more specific, is it in the hardware of the actual connections? Is it in the chemical communication? Is it electrical communication? Do we, do we know?

   2. DGDileep George31:54

      So, so this is, you know, a, a paper that we are bringing out soon-

   3. LFLex Fridman31:57

      Which one is this?

   4. DGDileep George31:58

      Um, this is the cortical microcircuits paper that-

   5. LFLex Fridman32:01

      Got it.

   6. DGDileep George32:01

      ... I sent you a draft of. Of course, this is, uh, a lot of it is still hypothesis. One hypothesis that a, you can think of a cortical column as encoding a, a concept. A concept, you know, think of it as a, uh, a, um, conc- an example of a concept is, um, is an edge present or not? Or is, is an object present or not? Okay. So it can, you can think of it as a binary variable. A binary random variable. The presence of an edge or not, or the presence of an object or not. So, each cortical column can be thought of as representing that one concept, one variable. And then the connections between these cortical columns are basically encoding the relationship between these random variables.

   7. LFLex Fridman32:45

      Mm-hmm.

   8. DGDileep George32:45

      And then there are connections within the cortical column. There are... Each cortical column im- is implemented using multiple layers of neurons-

   9. LFLex Fridman32:53

      Mm-hmm.

   10. DGDileep George32:53

       ... with very, very, very rich, um, structure there, you know. There are thousands of neurons in a cortical column. Uh-

   11. LFLex Fridman33:00

       But, but that structure is similar across the different cortical columns.

   12. DGDileep George33:03

       Correct.

   13. LFLex Fridman33:04

       Yeah.

   14. DGDileep George33:04

       Correct. And also, these cortical columns collect, connect to a substructure called thalamus in the, uh, in... So all, all cortical columns pass through this substructure. So, our hypothesis is that, yeah, the connections between the cortical columns implement this, uh, you know, that's where the knowledge is stored about, you know, how these different connups- concepts connect to each other. And then the, the neurons inside this cortical column and in thalamus in combination implement this, um, actual computations needed for inference, which includes explaining a way and competing between the different, uh, hypotheses. Um, and it is all very... So what is amazing is that, um, neuroscientists have actually done ex- experiments to the tune of showing these things. Uh, they might not be putting it in the overall inference framework, but they will show things like if I poke this higher level neuron, uh, it will inhibit, through this complicated loop through the thalamus, it will inhibit this other column.

   15. LFLex Fridman34:09

       Mm-hmm.

   16. DGDileep George34:09

       Uh, so they will, they will do such (laughs) experiments.

   17. LFLex Fridman34:11

       But do they use terminology of concepts, for example? So, so you're-

   18. DGDileep George34:16

       No.

   19. LFLex Fridman34:16

       I mean, (sighs) uh, is it, uh, is it something where it's easy to anthropomorphize and think about concepts like, uh, you start moving into logic-based kind of reasoning systems. So, um, are we to think of concepts in that kind of way? Or is it s-, uh, is it a lot messier, a lot more gray area? You know, even, even more gray, even more messy than, uh, the artificial neural network kinds-

   20. DGDileep George34:47

       Um-

   21. LFLex Fridman34:47

       ... kinds of abstractions?

   22. DGDileep George34:48

       Easiest way to think of it as a variable, right? It's a binary variable.

   23. LFLex Fridman34:51

       Mm-hmm.

   24. DGDileep George34:52

       Which is showing the presence or absence of something.

   25. LFLex Fridman34:55

       S- but I guess what I'm asking is, is that something, uh, that... Are we supposed to think of something that's human interpretable of that something?

   26. DGDileep George35:04

       It doesn't need to be. It doesn't need to be human interpretable. There is no need for it to be human interpretable. Uh, but it's, it's almost like, um, you, you will be able to find some interpretation of it, uh, because it is connected to the other things-

   27. LFLex Fridman35:21

       Yeah. So it's-

   28. DGDileep George35:21

       ... that you know about.

   29. LFLex Fridman35:22

       And, and the, the point is it's useful somehow.

   30. DGDileep George35:25

       Yeah.
6. 36:56 – 51:09

   Recursive Cortical Network

   1. LFLex Fridman36:56

      uh, w- what can we say? What is the paper that, uh, you're working on, uh, propose about the ideas around these cortical microcircuits?

   2. DGDileep George37:04

      So this is a fully functional model for the microcircuits of the visual cortex.

   3. LFLex Fridman37:10

      So the, the paper focuses... and your idea and our discussions now is focusing on vision.

   4. DGDileep George37:15

      Yeah.

   5. LFLex Fridman37:16

      The, uh, visual cortex. Okay.

   6. DGDileep George37:18

      Yeah. I-

   7. LFLex Fridman37:19

      This is a model, this is a full model. This is-

   8. DGDileep George37:21

      Th- this-

   9. LFLex Fridman37:21

      This is how vision works.

   10. DGDileep George37:22

       Uh, well, this is, this is a, yeah, model of-

   11. LFLex Fridman37:25

       A hypothesis.

   12. DGDileep George37:25

       Yeah.

   13. LFLex Fridman37:25

       A hypothesis.

   14. DGDileep George37:26

       So, okay, so let me, let me step back, uh, a bit. Um, so we looked at neuroscience for insights on how to build a vision model.Right. And, and, and we synthesized all those insights into a computational model. This is called the recursive cortical network model that we, we used for breaking captchas and, uh, and we are using the same model for robotic picking and, uh, uh, tracking of objects.

   15. LFLex Fridman37:50

       And that, again, is a vision system.

   16. DGDileep George37:52

       That's a vision system.

   17. LFLex Fridman37:52

       Com- computer vision system.

   18. DGDileep George37:54

       That's a computer vision system.

   19. LFLex Fridman37:55

       Takes in images and outputs what?

   20. DGDileep George37:59

       On one side, it outputs the class of the image, uh, and also segments the image. Uh, and you can also ask it further queries, "Where is the edge of the object? Where is the interior of the object?" So-

   21. LFLex Fridman38:10

       Got it.

   22. DGDileep George38:11

       ... so it's a, it's a model that you build to answer multiple questions. So you're not trying to build a model for just classification or just recommendation et cetera. It's a, it's a, it's a joint model that can do multiple things. Um, and, um, so, so that's the model that we built using insights from neuroscience. And some of those insights are, what is the role of feedback connections? What is the role of lateral connections? Uh, so all those things went into the model. The m- the model actually uses feedback connections.

   23. LFLex Fridman38:40

       All these ideas from neu- from neuroscience.

   24. DGDileep George38:43

       Yeah.

   25. LFLex Fridman38:43

       Uh, so what, what, what the heck is a r- recursive cortical network? Like what, what are the architecture approaches, interesting aspects here, which is essentially a brain-inspired approach to computer vision?

   26. DGDileep George38:56

       Yeah. So there are multiple layers to this question. Again, go from the very, very top and then zoom in. Okay?

   27. LFLex Fridman39:03

       Mm-hmm.

   28. DGDileep George39:03

       So one important thing, constraint that went into the model is that you should not think vision, think of vision as something in isolation. We should not think perception as something, as a pre-processor for cognition.

   29. LFLex Fridman39:18

       Mm-hmm.

   30. DGDileep George39:18

       Perception and cognition are interconnected. And so you should not think of one problem in separation from the other problem. Um, and so that means if you finally want to have a system that understand concepts, uh, about the world and can learn a, you know, very conceptual model of the world, and can reason and connect to language, all of those things, you need to, you need to have, think all the way through and make sure that your perception system is compatible with your cognition system and language system and all of them. And one aspect of that is top-down controllability. Um-
7. 51:09 – 1:06:48

   Solving CAPTCHAs algorithmically

   1. DGDileep George51:09

      you know, so one of the first applications, uh, that we showed in the paper was to track, uh, text-based CAPTCHAs.

   2. LFLex Fridman51:17

      What are CAPTCHAs, by the way? I mean... (laughs)

   3. DGDileep George51:20

      Uh... Yeah. (laughs)

   4. LFLex Fridman51:20

      By the way, one of the most awesome, like, the people don't use this term anymore as human computation, I think. Uh, I love this term. The guy who created CAPTCHAs, I think, came up with this term.

   5. DGDileep George51:30

      Yeah.

   6. LFLex Fridman51:31

      I love it. Anyway, uh-

   7. DGDileep George51:33

      Yeah. Yeah.

   8. LFLex Fridman51:33

      Uh, wh- what, what are CAPTCHAs?

   9. DGDileep George51:35

      So CAPTCHAs are those strings that you fill in, uh, when you're, you know, when- if you're open- opening a new account in Google, they show you a picture. Uh, you know, usually, it used to be set of garbled letters, uh, that you have to kind of, uh, figure out what- what- what is that string of characters and type it. And the reason CAPTCHAs exist is because, you know, um, Google or Twitter do not want automatic creation of accounts. You can use a computer to create millions of accounts, uh, and, uh, use that for, you know, nefarious purposes. Uh, so you want to make sure that, to the extent possible, the interaction that, uh, their system is having is with a human. So it's a- it's called a human interaction proof. A CAPTCHA is a human interaction proof.

   10. LFLex Fridman52:22

       Yeah.

   11. DGDileep George52:23

       Um, so- so this is- CAPTCHAs are, by design, things that are easy for humans to solve, but hard for computers.

   12. LFLex Fridman52:30

       Hard for robots, yeah.

   13. DGDileep George52:31

       Yeah. Um, so- and text-based CAPTCHAs were- was the one which is prevalent until around 2014. Because at that time, text-based CAPTCHAs were hard for computers to crack. Even now, they are actually, in the sense of an arbitrary text-based CAPTCHA will be unsolvable even now. But with the techniques that we have developed, it can be, you know, you can quickly develop a mechanism that solves all the CAPTCHA, uh-

   14. LFLex Fridman52:58

       They- they've probably gotten a lot harder too. The people-

   15. DGDileep George53:01

       Correct.

   16. LFLex Fridman53:01

       (laughs) They've been getting cleverer and cleverer at generating these text CAPTCHAs.

   17. DGDileep George53:05

       Correct. Correct.

   18. LFLex Fridman53:06

       Yeah.

   19. DGDileep George53:06

       Right.

   20. LFLex Fridman53:06

       So, okay, so that was one of the things you've tested it on, is these kinds of CAPTCHAs-

   21. DGDileep George53:10

       Yeah.

   22. LFLex Fridman53:10

       ... in 2014, '15.

   23. DGDileep George53:12

       Correct.

   24. LFLex Fridman53:12

       That kind of stuff.

   25. DGDileep George53:13

       Right. Right.

   26. LFLex Fridman53:13

       So what, uh ... W- what I mean, why- by the way, why CAPTCHAs? Why?

   27. DGDileep George53:18

       Yeah. Yeah. Even now, I would say CAPTCHA is a very- very good challenge problem, uh, if you want to understand how human perception works and if you want to build, uh, systems that work like the human brain. Uh, and I wouldn't say CAPTCHA is a solved problem. We have cracked the fundamental defense of CAPTCHAs, but it is not solved in the way that humans solve it. Um, so I can give you an example. I can, um, take a five-year-old child who has just learned characters, uh, and, uh, show them any new CAPTCHA that we create.

   28. LFLex Fridman53:52

       Mm-hmm.

   29. DGDileep George53:53

       They will be able to solve it. Uh, I can show you pretty much any new CAPTCHA, uh, from any new website. You'll be able to solve it without getting any training examples from that particular style of CAPTCHA.

   30. LFLex Fridman54:05

       You're assuming I'm human, yeah.
8. 1:06:48 – 1:18:21

   Hype around brain-inspired AI

   1. DGDileep George1:06:48

      Uh-

   2. LFLex Fridman1:06:48

      Solving now. Well, let me ask you this kind of touchy question. I have to- I- I've spoken with, uh, your friend, colleague, Jeff Hawkins too. I mean, he's, uh... I ha- I have to kind of ask, there is a bit of... whenever you have brain-inspired stuff-

   3. DGDileep George1:07:04

      Yeah.

   4. LFLex Fridman1:07:04

      ... and you make big claims-

   5. DGDileep George1:07:06

      Yeah.

   6. LFLex Fridman1:07:06

      ... uh, big sexy claims-

   7. DGDileep George1:07:08

      Yeah.

   8. LFLex Fridman1:07:08

      ... there's a, you know, uh, there's critics. I mean, machine learning subreddit.

   9. DGDileep George1:07:14

      (laughs)

   10. LFLex Fridman1:07:14

       Don't get me started on those people. Uh, they're- they're har- I mean, criticism is good, but they're a bit- uh, they're a bit over the top. Um, there is quite a bit of sort of skepticism and criticism, you know, does this work really as good as it promises to be?

   11. DGDileep George1:07:30

       Yeah.

   12. LFLex Fridman1:07:30

       What... Do you have thoughts on that kind of skepticism? Do you have comments on the kind of criticism you might have received, uh, about, you know, is this approach legit? Is this- is this a promising approach?

   13. DGDileep George1:07:44

       Yeah.

   14. LFLex Fridman1:07:44

       Or at least as promising as it seems to be s- you know, advertised as?

   15. DGDileep George1:07:49

       Yeah. I- I can comment on it. Um, so you know, our- our RCN paper is published in Science, which I would argue is- is a very high quality journal, very hard to, uh, publish in. And us- you know, usually it is indicative of the- of the quality of the work. And, um, uh, I can- I can- I- I am very, very certain that the ideas that we brought together in that paper, uh, in terms of the importance of feedback connections, uh, recursive inference, lateral connections, uh, coming to best explanation of the scene as the problem to solve, trying to solve, um, recognition segmentation, uh, all jointly.... in a way that is compatible with higher level cognition, top-down attention, all those ideas that we brought together into something, you know, coherent and workable in the, uh, in the world, and solving a challenging, tackling a challenging problem. I think that will, that will stay and that, that contribution, I stand by, right?

   16. LFLex Fridman1:08:44

       Yeah.

   17. DGDileep George1:08:44

       Now, uh, I can, I can sh- uh, tell you a story, uh, which is funny in the, in the context of this, right? Um, so if you read the abstract of the paper and, like, you know, the argument we are putting in, we- you know, we are putting in, "Look, current deep learning systems take a lot of training data. Uh, they don't use these insights. And here is our new model, which is not a deep neural network, it's a graphical model. It does inference." This is wha- how the paper is, right? Now, once the paper was accepted and everything, um, it went to the press department in, in Science, you know, to play as science office. We, we didn't do any press release when it was published.

   18. LFLex Fridman1:09:17

       Yeah.

   19. DGDileep George1:09:17

       It was... it went to the press department. What did the, what was the press release that they wrote up? "A new deep learning model (laughs) so-

   20. LFLex Fridman1:09:25

       Solves CAPTCHAs.

   21. DGDileep George1:09:26

       Solves CAPTCHAs. And, uh, so, so you can see where was, you know, what, what was being hyped, uh, in that, uh, thing, right? So, so it's like, um, there is the, there is a dynamic in the, uh, in the community of, you know, so, uh, um, that's especially happens when there are lots of new people coming into the field and they get attracted to one thing, and some people are trying to think different, uh, compared to that. So there is, there is some, uh... I, I think skepticism in science is important and it is, eh, eh, um, you know, very much, uh, required, but it's also, it's not, uh, skepticism usually, it's mostly bandwagon effect that is happening, rather than inform-

   22. LFLex Fridman1:10:05

       Well, well, but that's not even that. I mean, I'll tell you what they react to, which is like, uh, I'm sensitive too as well. If you look, if you look at just companies, OpenAI, DeepMind...

   23. DGDileep George1:10:15

       Yeah.

   24. LFLex Fridman1:10:15

       Um, Vicarious. I mean, they just, uh, there's a little bit of a race to the top and hype, right?

   25. DGDileep George1:10:25

       Right.

   26. LFLex Fridman1:10:25

       It's, it's like it doesn't pay off to be humble.

   27. DGDileep George1:10:29

       (laughs) Right. (laughs)

   28. LFLex Fridman1:10:31

       We... so like, uh... and, and the press is just, uh, irresponsible often. They, they just... I mean, don't get me started on the state of journalism today. Like, it seems like the people who write articles about these things, they literally have not even spent an hour on the Wikipedia article about what is neural networks. Like-

   29. DGDileep George1:10:51

       Yeah.

   30. LFLex Fridman1:10:51

       ... they haven't, like, invested just even the language, the laziness. It's like, uh, robots beat humans. Like, they, they write this kind of stuff-
9. 1:18:21 – 1:21:32

   How does the brain learn?

   1. LFLex Fridman1:18:21

      don't really understand the full d- uh, the full, uh, v- biophysics or whatever of how the brain learns.

   2. DGDileep George1:18:27

      E- exactly. Exactly. So-

   3. LFLex Fridman1:18:28

      But l- let me ask, and I'm sorry to interrupt. Like, eh, d- what's up- what's your sense, what's our best understanding of how the brain learns?

   4. DGDileep George1:18:36

      So things like backpropagation, credit assignment. So, so many of these algorithms-

   5. LFLex Fridman1:18:42

      Yeah.

   6. DGDileep George1:18:42

      ... have- learning algorithms have things in common, right? It is- uh, yeah, backpropagation is one. We have credit assignment. There is another algorithm called expectation maximization, which is, you know, a- another b- weight adjustment algorithm.

   7. LFLex Fridman1:18:56

      But is th- your sense the brain does something like this?

   8. DGDileep George1:18:59

      Has to. There is no way around it, in the sense of saying that you do have to adjust the- the connections.

   9. LFLex Fridman1:19:06

      You- so, yeah, and you're saying credit assignment, you have to reward the connections that were useful in making a correct prediction, and not... Yeah, I guess, what el- but, yeah, it doesn't have to be (laughs) differentiable. I mean... (laughs)

   10. DGDileep George1:19:17

       Yeah, it doesn't have to be differentiable.

   11. LFLex Fridman1:19:19

       Yeah.

   12. DGDileep George1:19:19

       Yeah. But you have to have a, uh, you know, you have a model that you start with, you ve- you have data comes in, and you have to have a way of adjusting the model such that it better fits the data.

   13. LFLex Fridman1:19:31

       Yeah.

   14. DGDileep George1:19:32

       So that, that is all of learning. Right? And-

   15. LFLex Fridman1:19:34

       Yep.

   16. DGDileep George1:19:34

       ... and some of them can be using backprop to do that. Some of it can be using, uh, you know, very local, uh, g- graph changes to do that. Uh, there can b- you know, uh, many of these learning algorithms have similar update properties locally, i- in terms of what the neurons need to do locally.

   17. LFLex Fridman1:19:57

       I wonder if small differences in learning algorithms can have huge differences in the actual effect. So the dynamics of, I mean, uh, sort of the rev- the reverse like spiking, like, the, uh, i- if credit assignment is like, uh, a lightning versus like, uh, a rainstorm or something. Like, uh, whether, whether there's a f- like a looping local type of situation with the credit assignment.

   18. DGDileep George1:20:24

       Yeah.

   19. LFLex Fridman1:20:25

       Uh, whether there is, uh, like regularization, like how, how, um, how it injects robustness into the whole thing.... like, whether it's chemical or electrical or mechanical-

   20. DGDileep George1:20:40

       Yeah.

   21. LFLex Fridman1:20:41

       ... uh, all those kinds of things.

   22. DGDileep George1:20:42

       Yes. (laughs)

   23. LFLex Fridman1:20:43

       Like that, I, I feel like it, it that, yeah, I feel like those differences could be essential, right?

   24. DGDileep George1:20:49

       It could be. It's just that you don't know enough to, on the learning side, you don't know, uh, enough to say, "That is definitely not the way the brain does it."

   25. LFLex Fridman1:21:01

       Right. Got it.

   26. DGDileep George1:21:01

       Um-

   27. LFLex Fridman1:21:01

       So you don't wanna be stuck to it.

   28. DGDileep George1:21:03

       Right.

   29. LFLex Fridman1:21:03

       So that, yeah, so you, you've been open-minded on that side of things.

   30. DGDileep George1:21:07

       Correct. On the inference side, on the recognition side, I am much more, uh, amenable to being constrained, because I- it's much easier to do experiments because, you know, it's like, okay, here is a stimulus, you know, wha- how many steps did it get to take the answer?
10. 1:21:32 – 1:25:43

    Perception and cognition

    1. DGDileep George1:21:32

    2. LFLex Fridman1:21:33

       So let, let's, let's go right into cortical microcircuits right back. So what, uh, what are these ideas beyond recursive cortical network that, uh, you're looking at now?

    3. DGDileep George1:21:45

       So we have made a, uh, you know, pass through all, you know, multiple of the steps that we, uh, you know, as I, as I mentioned earlier, you know, we were looking at perception from the angle of cognition, right? It was not just perception for perception's sake. How do you, how do you connect it to cognition? Uh, how do you learn concepts and, uh, how do you learn abstract reasoning? Uh, similar to some of the things Francois, uh, uh, talked about, right? Um, so, um, so we have, uh, taken one pass through it basically saying, "What is the basic cognitive architecture that you need to have, which has a perceptual system, which has a system that learns dynamics of the world, and then has something like a routine program learning system on top of it, to learn concepts?" So we have, we have built one, the, you know, the version 0.1 of that system. Uh, this was another, uh, science robotics paper. Uh, it is, it's the title of that paper was, you know, something like Cognitive Programs. How do you build cognitive programs? Uh, I'm-

    4. LFLex Fridman1:22:48

       And the application there was on m- uh, manipulation, robotic manipulation?

    5. DGDileep George1:22:53

       It was, it was, um, so think of it like this. Suppose you, uh, wanted to tell, uh, a new person, uh, that you met, you don't know the language, uh, uh, that person uses. You want to communicate to that person, uh, to achieve some task.

    6. LFLex Fridman1:23:08

       Mm-hmm.

    7. DGDileep George1:23:08

       Right? So I want to say, "Hey, um, you need to pick up all the, the red cups from the kitchen counter and put it here."

    8. LFLex Fridman1:23:16

       Mm-hmm.

    9. DGDileep George1:23:16

       Right? Uh, how do you communicate that, right? You can show pictures. You can basically say, "Look, this is the starting state. Uh, the, the, the things are here, this is the ending state." And, and what does the person need to understand from that? The, the person need to understand what conceptually happened in those pictures from the input to the output, right?

    10. LFLex Fridman1:23:35

        Yeah. Right.

    11. DGDileep George1:23:35

        Um, so, um, so we are looking at pre-verbal conceptual understanding. Without language, how do you, how do you have a set of concepts that you can manipulate in your head? Uh, and from a s- you know, set of images of input and output, can you infer what is happening in those images?

    12. LFLex Fridman1:23:57

        Got it. With concepts that are pre-language. Okay.

    13. DGDileep George1:23:59

        Yeah.

    14. LFLex Fridman1:23:59

        So wha- what's it mean to, for a concept to be pre-language? Like-

    15. DGDileep George1:24:03

        Yeah.

    16. LFLex Fridman1:24:04

        ... why, why is so, why (laughs) why is language, uh, so important here?

    17. DGDileep George1:24:10

        So I, I want to make a distinction between concepts that are, are just learned from text-

    18. LFLex Fridman1:24:17

        Ah.

    19. DGDileep George1:24:17

        ... by, by just, just feeding brute force text. Uh, you can, you can start extracting things. Like, okay, a cow is likely to be on grass, uh, in a, in a-

    20. LFLex Fridman1:24:28

        Got it.

    21. DGDileep George1:24:28

        Uh, so those kinds of things you can extract purely from text. Um, uh, but that's kind of a simple association, uh, thing, rather than a concept as an abstraction of something that happens in the real world, uh, in a, in a, in a grounded way that I can, I can simulate it in my mind and connect it back to the real world.

Episode duration: 2:10:05