Jeff Hawkins: Thousand Brains Theory of Intelligence | Lex Fridman Podcast #25

1. 0:00 – 2:01:45

   Intro

   1. LFLex Fridman0:00

      The following is a conversation with Jeff Hawkins. He's the founder of the Redwood Center for Theoretical Neuroscience in 2002, and Pneumenta in 2005. In his 2004 book, titled On Intelligence, and in the research before and after, he and his team have worked to reverse engineer the neocortex and propose artificial intelligence architectures, approaches, and ideas that are inspired by the human brain. These ideas include hierarchical temporal memory, HTM, from 2004, and new work, the Thousands Brains Theory of Intelligence from 2017, '18, and '19. Jeff's ideas have been an inspiration to many who have looked for progress beyond the current machine learning approaches, but they have also received criticism for lacking a body of empirical evidence supporting the models. This is always a challenge when seeking more than small incremental steps forward in AI. Jeff is a brilliant mind and many of the ideas he has developed and aggregated from neuroscience are worth understanding and thinking about. There are limits to deep learning as it is currently defined. Forward progress in AI is shrouded in mystery. My hope is that conversations like this can help provide an inspiring spark for new ideas. This is the Artificial Intelligence podcast. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at Lex Fridman, spelled F-R-I-D. And now here's my conversation with Jeff Hawkins. Are you more interested in understanding the human brain or in creating artificial systems that have many of the same qualities but don't necessarily require that you actually understand the underpinning workings of our mind?

   2. JHJeff Hawkins1:41

      So, there's a clear answer to that question: My primary interest is understanding the human brain. No question about it. But, um, I also firmly believe that we will not be able to create fully intelligent machines until we understand how the human brain works. So I don't see those as separate problems. Um, I think there's limits to what can be done with machine intelligence if you don't understand the principles by which the brain works, and so I actually believe that studying the brain is actually the frast- the fastest way (laughs) to get to machine intelligence.

   3. LFLex Fridman2:12

      And within that, let me ask the impossible question: How do you not define, but at least think about what it means to be intelligent?

   4. JHJeff Hawkins2:19

      So, I didn't try to answer that question first. We said, "Let's just talk about how the brain works and let's figure out how c- certain parts of the brain," mostly the neocortex, but some other parts too. The parts of the brain most associated with intelligence. And let's discover the principles by how they work, 'cause i- i- intelligence isn't just like some mechanism and it's not just some capabilities. It's like, okay, we don't even have, know where to begin on this stuff. And so now that we've made a lot of progress on this, after we've made a lot of progress on how the neocortex works, and we can talk about that, I now have a very good idea what's gonna be required to make intelligent machines. I g- I can tell you today, we know some of the things are gonna be necessary, I believe, to create intelligent machines.

   5. LFLex Fridman3:03

      Well, so, we'll, we'll get there. We'll get to the neocortex and some of the theories of how the whole thing works, and you're saying as we understand more and more, uh, about the neocortex, about our own human mind, we'll be able to start to more specifically define what it means to be intelligent. It's not useful to really talk about that until-

   6. JHJeff Hawkins3:22

      I don't know if it's not useful. You know, look, th- there's a long history of AI, as you know.

   7. LFLex Fridman3:25

      Right.

   8. JHJeff Hawkins3:26

      And there's been different approaches taken to it. And who knows? Maybe they're all useful.

   9. LFLex Fridman3:31

      Right.

   10. JHJeff Hawkins3:32

       Right? So-

   11. LFLex Fridman3:32

       In the end.

   12. JHJeff Hawkins3:32

       ... uh, you know, the good old-fashioned AI, the, uh, expert systems, uh, current convolutional neural networks, they all have their utility. They all have a value in the world. Uh, but I would think almost everyone agree that none of them are really intelligent in, in a sort of a deep way that, that humans are. And so, um, it's, it's just the question is how do you get from where those systems were or are today-

   13. LFLex Fridman3:56

       Mm-hmm.

   14. JHJeff Hawkins3:56

       ... to where a lot of people think we're gonna go?

   15. LFLex Fridman3:58

       Right.

   16. JHJeff Hawkins3:59

       And there's a big, big gap there, a huge gap. And I think the quickest way of, of bridging that gap is to figure out how the brain does that. And then we can sit back and look and say, "Oh, which of these principles that the brain works on are necessary and which ones are not?"

   17. LFLex Fridman4:14

       Right.

   18. JHJeff Hawkins4:15

       Clearly we don't have to build this in, in intelligent machines aren't gonna be built out of, uh, um, you know, organic living cells. Um, but there's a lot of stuff that goes on in the brain that's gonna be necessary.

   19. LFLex Fridman4:25

       So let me ask maybe before we get into the fun details, uh, let me ask maybe a depressing or difficult question. D- do you think it's possible that we will never be able to understand how our brain works, that maybe there's aspects to the human mind, like, we ourselves cannot introspectively get to the core, that there's a wall you eventually hit?

   20. JHJeff Hawkins4:48

       Yeah. Yeah. I don't believe that's the case. I have never believed that's the case. There's n- not been a single thing we've ever, humans have ever put their minds to that we've said, "Oh, we reached the wall. We can't go any further." It just people keep saying that. People used to believe that about life, you know, elan vital, right? There's like, what's the difference between living matter and nonliving matter? Something special we never understand. We no longer think that. So there's, there's no, uh, historical evidence to suggest this is the case, and I just never even consider that's a possibility. I would also say, uh, today, uh, we understand so much about the neocortex, we've made tremendous progress in the last few years, that I no longer think of it as, um, uh, uh, an open question. Um, the answers are very clear to me. Uh, the pieces we know, we don't know are clear to me, but the framework is all there and, and it's like, oh, okay, we're gonna be able to do this. Uh, this is not a problem anymore, just takes time and effort, but it- there's no mystery, uh, big mystery anymore.

   21. LFLex Fridman5:44

       So then let's get in- into it for, for people like myself who are not very well versed in the human brain, except my own.

   22. JHJeff Hawkins5:54

       (laughs)

   23. LFLex Fridman5:54

       Uh, can you describe to me at the highest level what are the different parts of the human brain, and then zooming in on the neocortex, the parts of the neocortex and so on-

   24. JHJeff Hawkins6:04

       Yeah.

   25. LFLex Fridman6:04

       ... a, a quick overview.

   26. JHJeff Hawkins6:05

       Yeah, sure.A human brain, uh, we can divide it roughly into two parts. There's the old parts, lots of pieces, and then there's the new part. The new part is the neocortex. Um, it's new because it didn't exist before mammals. Only mammals have a neocortex, and in humans, in primates, it's very large. In the human brain, the neocortex occupies about 70% to 75% of the volume of the brain. It's huge. And the old parts of the brain are, are... there's lots of pieces there. There's a spinal cord and there's the brain stem and the cerebellum and the different parts of the basal ganglia and so on. In the old parts of the brain, you have autonomic regulation, like breathing and heart rate. You have basic behaviors, so like walking and running are controlled by the old parts of the brain. All the emotional centers of the brain are in the old part of the brain, so when you feel anger or hungry, lust or things like that, those are all in the old parts of the brain. And, uh, and we associate with the neocortex all the things we think about as sort of high-level perception and cognitive functions. Anything from seeing and hearing and touching things, to language to mathematics and engineering and science and so on. Those are all associated with the neocortex, and they're certainly correlated. Uh, our abilities in those regards are correlated with the relative size of our neocortex compared to other mammals. So that's like the rough division. And you obviously can't understand the neocortex completely isolated, but you can understand a lot of it with just a few interfaces to the old parts of the brain, and so it, it gives you, um, a system to study. The other remarkable thing about the neocortex, uh, compared to the old parts of the brain, is the neocortex is extremely uniform. It's not visibly or anatomically or, uh, it's very, it's like a sh- I always like to say it's like the size of a dinner napkin, about two and a half millimeters thick, and it looks remarkably the same everywhere. Everywhere you look in that two and a half millimeters is this detailed architecture, and it looks remarkably the same everywhere. And that's across species, a mouse versus a cat and a dog and a human. Where if you look at the old parts of the brain, there's lots of little pieces do specific things. So it's, it's like the old parts of a brain involved, like, this is the part that controls heart rate, and this is the part that controls this, and this is this kind of thing, and that's this kind of thing. And these evolved for eons, a long, long time, and they have their specific functions, and all of a sudden, mammals come along and they got this thing called the neocortex, and it got large by just replicating the same thing over and over and over again. This is like, wow, this is incredible. Um, so all the evidence we have, um, and this is an idea that was first, uh, articulated, um, in a very cogent and beautiful argument by a guy named Vernon Mountcastle in 1978, I think it was, um, that the, the neocortex all works on the same principle. So language, hearing, touch, vision, engineering, all these things, are basically underlying or all built in the same computational substrate. They're really all the same problem.

   27. LFLex Fridman9:11

       So at the low level, the building blocks all look similar?

   28. JHJeff Hawkins9:14

       Yeah, and they're not even that low level. We're not talking about like, like neurons. We're talking about this very complex circuit that exists throughout the neocortex is remarkably similar. It is, it's like, yes, you see variations of it here and there, more of this cell, less, and that's not all, and so on. But, uh, what Mountcastle argued was, he says, you know, if you take a section of neocortex, why is one a visual area and one is a auditory area? Or why is... And his answer was, it's because one is connected to eyes and one is connected to ears.

   29. LFLex Fridman9:45

       Literally, you mean just it's most closest in terms of number of connections to-

   30. JHJeff Hawkins9:49

       Literally-

Episode duration: 2:09:41