#208 – Jeff Hawkins: The Thousand Brains Theory of Intelligence

Jeff Hawkins is a neuroscientist and cofounder of Numenta, a neuroscience research company. Please support this podcast by checking out our sponsors: – Codecademy: https://codecademy.com and use code LEX to get 15% off – BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free – Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings – Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium EPISODE LINKS: A Thousand Brain (book): https://amzn.to/3AmxJt7 Numenta’s Twitter: https://twitter.com/Numenta Numenta’s Website: https://numenta.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (10:35) – Collective intelligence (17:17) – The origin of intelligence in the human brain (30:31) – How intelligent life evolved on Earth (41:30) – Why humans are special in the universe (44:48) – Neurons (49:02) – A Thousand Brains theory of intelligence (57:42) – How to build superintelligent AI (1:15:41) – Sam Harris and existential risk of AI (1:27:43) – Neuralink (1:34:34) – Will AI prevent the self-destruction of human civilization? (1:40:05) – Communicating human knowledge to alien civilizations (1:50:22) – Devil’s advocate (1:55:16) – Human nature (2:03:39) – Hardware for AI (2:10:18) – Advice for young people

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Jeff Hawkins, a neuroscientist seeking to understand the structure, function, and the origin of intelligence in the human brain. He previously wrote a seminal book on the subject titled On Intelligence, and recently, a new book called A Thousand Brains, which presents a new theory of intelligence that Richard Dawkins, for example, has been raving about, calling the book, quote, "brilliant and exhilarating." I can't read those two words and not think of him saying it in his British accent. Quick mention of our sponsors: Codecademy, BioOptimizers, ExpressVPN, Aesleep, and Blinkist. Check them out in the description to support this podcast.

• 0:45Lex Fridman?

  As a side note, let me say that one small but powerful idea that Jeff Hawkins mentions in his new book is that if human civilization were to destroy itself, all of knowledge, all our creations, will go with us. He proposes that we should think about how to save that knowledge in a way that long outlives us, whether that's on Earth, in orbit or on Earth, or in deep space, and then to send messages that advertise this backup of human knowledge to other intelligent alien civilizations. The main message of this advertisement is not that we are here, but that we were once here. This little difference somehow was deeply humbling to me,

• 1:30Lex Fridman?

  that we may, with some non-zero likelihood, destroy ourselves, and that an alien civilization, thousands or millions of years from now, may come across this knowledge store, and they would only with some low probability even notice it, not to mention be able to interpret it. And the deeper question here for me is what information in all of human knowledge is even essential? Does Wikipedia capture it, or not at all? This thought experiment forces me to wonder, what are the things we've accomplished and are hoping to still accomplish that will outlive us? Is it things like complex buildings, bridges, cars, rockets? Is it ideas like science, physics, and mathematics? Is it music and art?

• 2:14Lex Fridman?

  Is it computers, computational systems, or even artificial intelligence systems? I personally can't imagine that aliens wouldn't already have all of these things. In fact, much more and much better. To me, the only unique thing we may have is consciousness itself and the actual subjective experience of suffering, of happiness, of hatred, of love. If we can record these experiences in the highest resolution directly from the human brain, such that aliens will be able to replay them, that is what we should store and send as a message. Not Wikipedia, but the extremes of conscious experiences, the most important of which, of course, is love.

• 2:58Lex Fridman?

  As usual, I'll do a few minutes of ads now. I try to make these interesting, but I give you timestamps, so if you skip, please still check out the sponsors by clicking the links in the description. It is actually the best way to support this podcast. I'm very fortunate to be able to be picky with the sponsors we take on. We have way more sponsors than we can take on, so we only take on the good ones. So hopefully, if you buy their stuff, you'll find value in it, just as I have. This show is brought to you by a new amazing sponsor called Codecademy. It's a website I highly recommend you go to if you want to learn to code. It doesn't matter if you're totally new or somewhat experienced, there's a course there for you.

• 3:41Lex Fridman?

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• 4:07Lex Fridman?

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• 4:46Lex Fridman?

  The next sponsor, also a new one, is BI Optimizers. B.I. Optimizers. They have a new magnesium breakthrough supplement that they want me to tell you about. And it is, in fact, the supplement I use. When I fast or I'm doing keto carnivore, which is what I've been doing a lot for the past several years now, actually. Getting the electrolytes is really important, and that means sodium, potassium, and magnesium. Those are essential. The amount and the type is also very important. And I think magnesium is the tricky one. It's difficult to get it right. That's why I use magnesium breakthrough supplement from BI Optimizers.

• 5:27Lex Fridman?

  Most supplements contain only one or two forms of magnesium, like glycinate or citrate, when in reality, there are at least seven that your body needs and benefits from. And of course, magnesium breakthrough supplement has all of them. You should definitely go to their website to check out all the different benefits. That's magbreakthrough.com/lex. If you go there, you get a special discount. That's mag as in M A G breakthrough.com/lex. This show is sponsored by ExpressVPN. I use them to protect my privacy on the internet. A lot of you probably go to a bunch of shady websites.

• 6:10Lex Fridman?

  So I should probably let you know that when you browse using incognito mode, that doesn't actually protect you. The ISP like Comcast or Verizon know every single website you visit still. And ISPs can sell this information legally to ad companies and tech giants who then use your data to target you. I think there's a lot of ways in which this can go wrong, from the book Brave New World to the book 1984. And I think the way to resist that is have tools that allow you to regain control over your data. A VPN, I think, like, is the most essential, the most basic tool everybody should be using.

• 6:55Lex Fridman?

  And my favorite VPN is ExpressVPN. I've been using it for many years. It started out with a sexy red button. It still has a big power on button, but it's no longer red. But it's still sexy. Anyway, go to expressvpn.com/lexpod to get an extra three months free. That's expressvpn.com/lexpod. This episode is also brought to you by Eight Sleep and its Pod Pro mattress. It controls temperature with an app, is packed with sensors, and can cool down to as low as 55 degrees on each side of the bed separately. Matt Walker, the sleep scientist, my new friend, just started a podcast you should definitely check out.

• 7:39Lex Fridman?

  He also did a podcast with Andrew Huberman, and he also did a conversation with me. I won't use this ad or the conversation with Matt to try to tease apart my philosophy on the pursuit of happiness in life. But let me say that once I do get into bed, I love a cold bed with a warm blanket, and that is something Eight Sleep provides, and it makes me look forward to naps, it makes me look forward to sleep, and I truly enjoy it. A good night's sleep in a cold bed is one of the best rewards in life after some hard-fought battles during a productive day. Anyway, they have a Pod Pro cover, so you can just add that to your mattress without

• 8:24Lex Fridman?

  having to buy theirs, but their mattress is nice, too, just so you know. I can track a bunch of metrics like heart rate variability, but cooling alone is honestly worth the money. Go to eightsleep.com/lex to get special savings. That's eightsleep.com/lex.

• 8:41Lex Fridman?

  This episode is supported by Blinkist, my favorite app for learning new things. Blinkist takes the key ideas from thousands of nonfiction books and condenses them down into just 15 minutes that you can read or listen to. I use it three ways. One, to pick which books I want to read next in full. Two, to review books I've already read, sort of to give me a very clean summary of what the book was about. And three, sometimes, you know, there's too many amazing books in this world you'll never get a chance to read, and it's good to sort of build up an intuition, build up a high-level understanding of the key insights in the book.

• 9:23Lex Fridman?

  I've actually been thinking about doing a project where I read a book a day and maybe make a video on each of those books, like especially powerful books that mean a lot to me, some of which I've already read. So rereading them, which I enjoy doing, and some of which I've always wanted to read and haven't gotten a chance. Part of me wants to return to that place of where I read basically full time for like a few weeks. I think that's a fascinating experiment I'd like to take on. Anyway, go to blinkist.com/lex to start your free seven day trial and get 25% off of a Blinkist Premium Membership.

• 10:01Lex Fridman?

  That's blinkist.com/lex, spelled B-L-I-N-K-I-S-T, blinkist.com/lex. This is the Lex Fridman Podcast, and here is my conversation with Jeff Hawkins.

• 10:35Lex Fridman?

  We previously talked over two years ago. Do you think there's still neurons in your brain that remember that conversation, that remember me and got excited? Like, there's a Lex neuron in your brain that just like finally has a purpose?

• 10:50Speaker 01

  I do remember our conversation. I have some memories of it, and I formed additional memories of you in the meantime. I wouldn't say there's a neuron or neurons in my brain that know you. There are synapses in my brain that have formed that reflect my knowledge of you and the model I have of you and the world. And whether the exact same synapses were formed two years ago, it's hard to say because these things come and go all the time. But we know from one thing to know about brains is that when you think of things, you often erase the memory and rewrite it again. So, yes, but I have a memory of you, and I have that's instantiated in synapses. There's a simpler way to think about it, Lex. You have We have a model of the world in your head. And that model is continually being updated. I updated this morning.

• 11:35Speaker 01

  You offered me this water. You said it was from the refrigerator. I remember these things. And so we, and so the model includes where we live, the places we know, the words, the objects in the world. It's just a monstrous model and it's constantly being updated. And people are just part of that model. So are animals, so are other physical objects, so are events we've done. So it's, it's no special, in my mind, special place for the memories of humans. I mean, obviously, I know, you know, I know a lot about my wife, but, and friends and so on, but it's, it's not like a, a special place for humans are over here. But we model everything and we model other people's behaviors too. So if I said yours is a copy of your mind in my mind, it's just because I know how

• 12:20Speaker 01

  humans, I've learned how humans behave and, and I've learned some things about you. And that's part of my world model.

• 12:28Lex Fridman?

  Well, I just also mean the collective intelligence of the human species. I wonder if there's something fundamental to the brain that enables that. So modeling other humans with their ideas.

• 12:43Speaker 01

  You're, you're, you're actually jumping into a lot of big topics. So collective intelligence is a separate topic that a lot of people like to talk about. We could talk about that. But, and so that's interesting. Like, you know, we're not just individuals. We live in society and so on. But from our research point of view, and so again, let's just talk, we studied the neurocortex, it's a sheet of neural tissue. It's about 75% of your brain. It runs on this very repetitive algorithm. It's a very repetitive circuit. And so you can apply that algorithm to lots of different problems, but it's all underneath, it's the same thing. We're just building this model. So from our point of view, we wouldn't look for these special circuits someplace buried in your brain that might be related to other, you know, understanding other humans.

• 13:28Speaker 01

  It's more like, you know, how do we build a model of anything? How do we understand anything in the world? And humans are just another part of the things we understand.

• 13:36Lex Fridman?

  So there's nothing, there's nothing to the brain that knows the emergent phenomenon of collective intelligence?

• 13:42Speaker 01

  Well, I certainly know about that. I've heard the terms, I've read.

• 13:46Lex Fridman?

  No, but that's.

• 13:46Speaker 01

  Right, well, okay.

• 13:47Lex Fridman?

  As an idea.

• 13:48Speaker 01

  Well, I think we have language, which is sort of built into our brains, and that's a key part of collective intelligence. So there are some, you know, prior assumptions about the world we're going to live in when we're born, we're not just a blank slate. And so, you know, did we evolve to take advantage of those situations? Yes. But again, we study only part of the brain, the neocortex. There's other parts of the brain are very much involved in societal interactions and human emotions and, and how we interact and even societal issues about, you know, how we are, how we interact with other people, when we support them, when we're greedy and things like that.

• 14:27Lex Fridman?

  I mean, certainly the brain is a great place where to study intelligence. I wonder if it's the fundamental atom of intelligence.

• 14:38Speaker 01

  Well, I would say it's absolutely an essential component, even if you believe in collective intelligence as, hey, that's where it's all happening, that's what we need to study, which I don't believe that, by the way. I think it's really important, but I don't think that is the thing. But even if you do believe that, then you have to understand how the brain works in doing that. It's, you know, it's more like we are intelligent, we are intelligent individuals and together we are much more magnified, our intelligence, we can do things which we couldn't do individually. But even as individuals, we're pretty damn smart and we can model things and understand the world and interact with it. So to me, if you're going to start someplace, you need to start with the brain. Then you could say, well, how do brains interact with each other? And what is the nature of language?

• 15:23Speaker 01

  And how do we share models that I've learned something about the world, how do I share it with you? Which is really what. You know, sort of communal intelligence is. I know something, you know, something. We've had different experiences in the world. I've learned something about brains. Maybe I can impart that to you. You've learned something about, you know, physics and you can impart that to me. But it all comes down to even just the epistemological question of, well, what is knowledge? And how do you represent it in the brain? Right? And it's not, that's where it's going to reside, right? Or in our writings.

• 15:54Lex Fridman?

  It's obvious that human collaboration, human interaction is how we build societies. Right? But some of the things you talk about and work on, some of those elements of what makes up an intelligent entity is there with a single person?

• 16:12Speaker 01

  Absolutely. I mean, we can't deny that the brain is the core element here in, in, at least I can't, I think it's obvious, the brain is the core element in all theories of intelligence. It's where knowledge is represented. It's where knowledge is created. We interact, we share, we build upon each other's work. But without a brain, you'd have nothing. You know, there would be no intelligence without brains. And so, so that's where we start. I got into this field because I just was curious as to who I am. You know, how, you know, how do I think? What's going on in my head when I'm, when I'm thinking? What does it mean to know something? You know, I can ask what it means for me to know something, independent of how I learned it from you or from someone else or from society. What does it mean for me to know that I have a model of you in my head?

• 16:57Speaker 01

  What does it mean to know I know what this microphone does and how it works physically, even when I can't see it right now? How do I know that? What does it mean? How the neurons do that at the fundamental level of neurons and synapses and so on? Those are really fascinating questions and I'm happy to, just happy to understand those if I could.

• 17:18Lex Fridman?

  So in your, in your new book, you talk about our brain, our mind as being made up of many brains. So the book is called A Thousand Brains, A Thousand Brain Theory of Intelligence. What is the key idea of this book?

• 17:34Speaker 01

  The book has three sections.

• 17:37Speaker 01

  And it has sort of maybe three big ideas. So the first section is all about what we've learned about the near cortex. And that's the thousand brains theory. Just to complete the picture, the second section is all about AI. And the third section is about the future of humanity. So the thousand brains theory, the big idea there, if you've, if I had to summarize into one big idea is that we think of the brain, the near cortex is learning this model of the world. But what we learned is actually there's tens of thousands of independent modeling systems going on. And so each, what we call a column in the cortex, there's about 150,000 of them, is a complete modeling system. So it's a collective intelligence in your head in some sense.

• 18:20Speaker 01

  So the thousand brains theory says, well, where do I have knowledge about, you know, this coffee cup or where's the model of this cellphone? It's not in one place. It's in thousands of separate models that are complementary and they communicate with each other through voting. So this idea that we have, we feel like we're one person. You know, that's our experience. We can explain that. But in reality, there's lots of these, like, it's almost like little brains, like, but they're, they're sophisticated modeling systems, about 150,000 of them in each of a human brain. And that's a total different way of thinking about how the near cortex is structured than we or anyone else thought of even just five years ago.

• 18:56Lex Fridman?

  So you mentioned you started this journey on just looking in the mirror and trying to understand who you are. So if you have many brains, who are you then?

• 19:07Speaker 01

  So it's interesting. We have a singular perception, right? You know, we think, oh, I'm just here, I'm looking at you. But it's, it's composed of all these things, like there's sounds and there's, and there's this vision and there's touch and all kinds of inputs, yet we have the singular perception. And what the thousand brain theory says, we have these models that are visual models. We have a lot of models, autotory models, models, autarkia models and so on, but they vote. And so these things in the cortex, you can think about these columns as about like little grains of rice, 150,000 of them stacked next to each other. And each one is its own little modeling system. But they have these long range connections that go between them. And we call those voting connections or voting neurons.

• 19:48Speaker 01

  And so the different columns try to reach a consensus, like, what am I looking at? Okay, you know, each one has some ambiguity, but they come to a consensus. Oh, there's a water bottle I'm looking at. We are only consciously able to perceive the voting. We're not able to perceive anything that goes on under the hood. So the voting is what we're aware of.

• 20:10Lex Fridman?

  The results of the vote?

• 20:11Speaker 01

  Yeah, the result, well, you can imagine it this way. We were just talking about eye movements a moment ago. So as I'm looking at something, my eyes are moving about three times a second. And with each movement, a completely new input is coming into the brain. It's not repetitive, it's not shifting it around, it's completely new. I'm totally unaware of it. I can't perceive it. But yet, if I looked at the neurons in your brain, they're going, I don't know, I don't know, I don't know. But the voting neurons are not. The voting neurons are saying, you know, we all agree, even though I'm looking at different parts of this, this is a water bottle right now. And that's not changing. And it's in some position and pose relative to me. So I have this perception of the water bottle about two feet away from me, it has a certain pose to me. That is not changing. That's the only part I'm aware of. I can't be aware of the fact that the inputs from the eyes are moving and changing and all this other is happening.

• 20:56Speaker 01

  So these long range connections are the part we can be conscious of. The individual activity in each column is, doesn't go anywhere else. It doesn't get shared anywhere else. It doesn't, there's no way to extract it and talk about it. Or extract it and even remember it to say, oh, yes, I can recall that. So, but these long range connections are the things that are accessible to language. And to our, it's like the hippocampus, our memories, you know, our short term memory systems and so on. So we're not aware of 95% or maybe it's even 98% of what's going on in your brain. We're only aware of this sort of stable, somewhat stable voting outcome of all these things that are going on underneath the hood.

• 21:41Lex Fridman?

  So what would you say is the basic element in the thousand brains theory of intelligence of intelligence? Like, what's the atom of intelligence when you think about it? Is it the individual brains? And then what is a brain?

• 21:56Speaker 01

  Well, let's, can we just talk about what intelligence is first? And then, and then we can talk about what the elements are. So in my, in my book, intelligence is the ability to learn a model of the world. To build internals to your head. A model that represents the structure of everything. You know, to know what this is a table and that's a coffee cup and this is a gooseneck lamp and all this thing. To know these things, I have to have a model in my head. I just don't look at them and go, what is that? I already have internal representations of these things in my head and I had to learn them. I wasn't born with any of that knowledge. You were, you know, we have some lights in the room here. I, you know, that's not part of my evolutionary heritage, right? It's not in my genes. So we have this incredible model and the model includes not only what things look like and feel like,

• 22:41Speaker 01

  but where they are relative to each other and how they behave. I've never picked up this water bottle before, but I know that if I took my hand on that blue thing and I turn it, it'll probably make a funny little sound as the little plastic things detach. And then it'll rotate and it'll rotate a certain way and it'll come off. How do I know that? Because I have this model in my head. So the essence of intelligence is our ability to learn a model. And the more sophisticated our model is, the smarter we are. Not that there is a single intelligence, because you can know about, you know a lot about things that I don't know and I know about things you don't know. And we can both be very smart, but we both learned a model of the world through interacting with it. So that is the essence of intelligence. Then we can ask ourselves, what are the mechanisms in the brain that allow us to do that? And what are the mechanisms of learning, not just the neural mechanisms? What are the general process for how we learn a model?

• 23:26Speaker 01

  So that was a big insight for us. It's like, what are the, what is the actual things that, how do you learn this stuff? It turns out you have to learn it through movement. You can't learn it just by, that's how we learn. We learn through movement. We learn. So you build up this model by observing things and touching them and moving them and walking around the world and so on.

• 23:43Lex Fridman?

  So either you move or the thing moves.

• 23:46Speaker 01

  Somehow, yeah. You, obviously you can learn things just by reading a book, something like that. But think about if I were to say, oh, here's a new house. I want you to learn, you know, what do you do? You have to walk, you have to walk from room to room. You have to open the doors, look around, see what's on the left, what's on the right. As you do this, you're building a model in your head. It's just, that's what you're doing. You can't just sit there and say, I'm going to grok the house. No, you know, or you could, you don't even want to just sit there and read some description of it, right?

• 24:11Lex Fridman?

  Yeah.

• 24:12Speaker 01

  You literally physically interact with them. The same with like a smartphone. If I'm going to learn a new app, I touch it and I move things around. I see what happens when I, when I do things with it. So that's the basic way we learn in the world.

• 24:22Lex Fridman?

  And by the way, when you say model, you mean something that can be used for prediction in the future?

• 24:28Speaker 01

  It's, it's used for prediction and for behavior and planning.

• 24:32Lex Fridman?

  And does a pretty good job at doing so.

• 24:36Speaker 01

  Yeah. Here's the way to think about the model. A lot of people get hung up on this. So you can imagine an architect making a model of a house. Right? So there's a physical model that's small. And why do they do that? Well, we do that because you can imagine what it would look like from different angles. Okay, look from here, look from there. And you can also say, well, how far would it get from the garage to the to the swimming pool or something like that, right? You can imagine looking at this. And so what would be the view from this location? So we build these physical models to let you imagine the future and imagine that behaviors. Now, we can take that same model and put it in a computer. So we now, today, they'll build models of houses in a computer. And they, and they do that using a set of, we'll come back to this term in a moment, reference frames.

• 25:20Speaker 01

  But basically, you assign a reference frame for the house and you assign different things for the house in different locations. And then the computer can generate an image and say, okay, this is what it looks like in this direction. The brain is doing something remarkably similar to this. It's surprising. It's using reference frames. It's building these, it's similar to a model on a computer, which has the same benefits of building a physical model. It allows me to say, what would this thing look like if it was in this orientation? What would likely happen if I pushed this button? I've never pushed this button before. Or how would I accomplish something? I want to, I want to convey a new idea I've learned. How would I do that? I can imagine in my head, well, I could talk about it. I could write a book. I could do some podcasts. I could, you know, maybe tell my neighbor, you know,

• 26:04Speaker 01

  and I can imagine the outcomes of all these things before I do any of them. That's what the model lets you do. Let's just plan the future and imagine the consequences of our actions. Prediction, you asked about prediction. Prediction is not the goal of the model. Prediction is an inherent property of it, and it's how the model corrects itself.

• 26:24Lex Fridman?

  So prediction is fundamental to intelligence.

• 26:26Speaker 01

  It's fundamental to building a model and the model's intelligent. And let me go back and be very precise about this. Prediction, you can think of prediction in two ways. One is like, hey, what would happen if I did this? That's a type of prediction. That's a key part of intelligence. But even prediction is like, oh, what's this? This is this water bottle gonna feel like when I pick it up? You know? And that doesn't seem very intelligent. But the way to think, one way to think about prediction is it's a way for us to learn where our model is wrong. So if I picked up this water bottle and it felt hot, I'd be very surprised. Or if I picked up was very light, it would be very, I'd be surprised. Or if I turned this top and it didn't, I had to turn it the other way, I'd be surprised. And so all those, I have a prediction like, okay, I'll do it. I'll drink some water.

• 27:11Speaker 01

  I'm okay, okay, do this. There it is, I feel opening, right? What if I had to turn it the other way? Or what if it's split in two? Then I say, oh, my gosh, I misunderstood this. I didn't have the right model of this thing. My attention would be drawn to, I'll be looking at it going, well, how the hell did that happen? Why did it open up that way? And I would update my model by doing it, just by looking at it and playing around with it. I'd update and say, this is a new type of water bottle.

• 27:31Lex Fridman?

  But you, so you're talking about sort of complicated things like a water bottle. But this also applies for just basic vision, just like seeing things. It's almost like a precondition of just perceiving the world is predicting.

• 27:46Speaker 01

  Hmm.

• 27:47Lex Fridman?

  It's just everything that you see is first passed through your prediction.

• 27:52Speaker 01

  Everything you see and feel, in fact, this was the insight I had back in the late eighties. And, excuse me, early eighties. And another people reached the same idea is that every sensory input you get, not just vision, but touch and hearing, you have an expectation about it.

• 28:10Lex Fridman?

  Yeah.

• 28:11Speaker 01

  And a prediction. Sometimes you can predict very accurately, sometimes you can't. I can't predict what next word is gonna come out of your mouth. But as you start talking, I'll have better and better predictions. And if you talk about some topics, I'd be very surprised. So I have this sort of background prediction that's going on all the time for all of my senses. Again, the way I think about that is this is how we learn. It's more about how we learn. It's a test of our understanding. Our predictions are a test. Is this really a water bottle? If it is, I shouldn't see, you know, a little finger sticking out the side. And if I saw a little finger sticking out, I'd say, oh, what the hell's going on? That's not normal.

• 28:51Lex Fridman?

  I mean, that's fascinating that let me linger on this for a second. It really honestly feels that prediction is fundamental to everything, to the way our mind operates, to intelligence. So like, it's just a different way to see intelligence, which is like, everything starts at prediction.

• 29:11Speaker 01

  And prediction requires a model. You can't predict something unless you have a model of it.

• 29:16Lex Fridman?

  Right. But the action is prediction. So like, the thing the model does is prediction.

• 29:22Speaker 01

  And but it also, yeah, but you can then extend it to things like, oh, what would happen if I took this today? I went and did this. What would be likely, or how you can extend prediction to like, oh, I want to get a promotion at work. What action should I take? And you can say, if I did this, I predict what might happen. If I spoke to someone, I predict what might happen. So it's not just low level predictions.

• 29:45Lex Fridman?

  Yeah, it's all prediction.

• 29:46Speaker 01

  It's all prediction.

• 29:46Lex Fridman?

  It's like this black box, you can ask basically any question, low level or high level.

• 29:50Speaker 01

  So we started off with that observation. It's all, it's like this nonstop prediction. And I write about this in the book about, and then we asked, how do neurons actually make predictions? And physically, like, what does the neuron do when it makes a prediction? And or the neural tissue does when it makes prediction. And then we asked, what are the mechanisms by how we build a model that allows you to make prediction? So we started with prediction as sort of the fundamental research agenda, if in some sense, like, and say, well, we understand how the brain makes predictions. We will understand how it builds these models and how it learns, and that's the core of intelligence. So it was like, it was the key that got us in the door to say, that is our research agenda. Understand predictions.

• 30:31Lex Fridman?

  So in this whole process, where does intelligence originate, would you say? So if we look at things that are much less intelligent to humans, and you start to build up a human through the process of evolution, where's this magic thing that has a prediction model or a model that's able to predict? That starts to look a lot more like intelligence. Is there a place where, Richard Dawkins wrote an introduction to your, to your book, an excellent introduction. I mean, it puts a lot of things into context. It's funny, just looking at parallels for your book and Darwin's Origin of Species. So Darwin wrote about the origin of species. So.

• 31:18Lex Fridman?

  What is the origin of intelligence?

• 31:20Speaker 01

  Yeah, well, we have a theory about it, and it's just that it's a theory. Theory goes as follows. As soon as living things started to move, they're not just floating in sea, they're not just a plant, you know, grounded someplace. Soon as they started to move, there was an advantage to moving intelligently, to moving in certain ways. And there's some very simple things you could do, you know, bacteria or single cell organisms can move towards a source of gradient of food or something like that. But an animal that might know where it is and know where it's been and how to get back to that place. Or an animal that might say, oh, there was a source of food someplace. How do I get to it? Or there was a danger. How do I get to it? There was a mate. How do I get to them? There was a big evolutionary advantage to that.

• 32:05Speaker 01

  So early on, there was a pressure to start understanding your environment, like where am I? And where have I been? And what happened in those different places? So we still have this neural mechanism in our brains. It's in the mammals, it's in the hippocampus and entorhino cortex. These are older parts of the brain. And these are very well studied. We build a map of our environment. So these neurons in these parts of the brain know where I am in this room and where the door was and things like that.

• 32:39Lex Fridman?

  So a lot of other mammals have this.

• 32:41Speaker 01

  All mammals have this, right? And almost any, any animal that knows where it is and get around must have some mapping system, must have some way of saying, I've learned a map of my environment. I have hummingbirds in my backyard and they, and they go to the same places all the time. They have to, they must know where they are. They just know where they are when they're, they're not just randomly flying around. They know, they know particular flowers they come back to. So we all have this. And it turns out it's very tricky to get neurons to do this, to build a map of an environment. It's just, and so we now know, there's this, these famous studies that's still very active about place cells and grid cells and these other types of cells in the older parts of the brain and how they build these maps of the world. It's really clever.

• 33:24Speaker 01

  It's obviously been under a lot of evolutionary pressure over a long period of time to get good at this. So animals now know where they are. What we think has happened, and there's a lot of evidence to digest this, is that that mechanism, we learn to map like a space. Is was repackaged, the same type of neurons was repackaged into a more compact form. And that became the cortical column. And it was it was in some sense genericized, if that's a word. It was turned into a very specific thing about learning maps of environments to learning maps of anything, learning a model of anything, not just your space, but coffee cups and so on.

• 34:06Speaker 01

  And it got sort of repackaged into a more compact version, a more universal version, and then replicated. So the reason we're so flexible is we have a very generic version of this mapping algorithm, and we have 150,000 copies of it.

• 34:22Lex Fridman?

  Sounds a lot like the progress of deep learning.

• 34:25Speaker 01

  How so?

• 34:26Lex Fridman?

  So take neural networks that seem to work well for a specific task, compress them, and multiply it by a lot, and then you just stack them on top. It's like the story of transformers in.

• 34:42Speaker 01

  Yeah. But in fact, deep learning networks, they end up, you're replicating an element, but you still need the entire network to do anything.

• 34:51Lex Fridman?

  Right.

• 34:52Speaker 01

  Here, what's going on, each individual element is a complete learning system. This is why I can take a human brain, cut it in half, and it still works. It's it's pretty amazing.

• 35:03Lex Fridman?

  It's fundamentally distributed.

• 35:04Speaker 01

  It's fundamentally distributed, complete modeling systems. So but that's that's our story we like to tell. I I would guess it's it's likely largely right. But you know, it's there's a lot of evidence supporting that story, this evolutionary story. The thing which brought me to this idea is that the human brain got big very quickly. So that that led to the proposal a long time ago that, well, there's this common element just instead of creating new things, it just replicated something. We also are extremely flexible. We can learn things that we had no history about. Right? And so that tells you that the learning algorithm is very generic, is very kind of universal,

• 35:48Speaker 01

  because it's it doesn't assume any prior knowledge about what it's learning. And so you combine those things together and you say, okay, well, how did that come about? Where did that universal algorithm come from? It had to come from something that wasn't universal. It came from something that was more specific. And so anyway, this led to our hypothesis that you would find grid cells and place cell equivalents in the neural cortex. And when we first published our first papers on this theory, we didn't know of evidence for that. It turns out there was some, but we didn't know about it. And since then, so then we became aware of evidence for grid cells in parts of the neural cortex. And then now there's been new evidence coming out. There's some interesting papers that came out just January of this year. So our, one of our predictions was if this evolutionary hypothesis is correct, we would

• 36:33Speaker 01

  see grid cell, place cell equivalent, cells that work like them to every column in the neural cortex. And that's starting to be seen.

• 36:39Lex Fridman?

  What does it mean that, why is it important that they're present?

• 36:43Speaker 01

  Because it tells us, well, we're asking about the evolutionary origin of intelligence, right? So our theory is that these columns in the cortex are working on the same principles. They're modeling systems. And it's hard to imagine how neurons do this. And so we said, hey, it's really hard to imagine how neurons could learn these models of things. We could talk about the details of that if you want. But let's, but there's this other part of the brain. We know that learns models of environments. So could that mechanism that learns to model this room be used to learn a model of the water bottle? Is it the same mechanism? So we said, it's much more likely the brain's using the same mechanism, which case it would have these equivalent cell types.

• 37:26Speaker 01

  So it's basically, the whole theory is built on the idea that these columns have reference frames and they're learning these models and these, these grid cell create these reference frames. So it's, it's basically the major, in some sense, the major predictive part of this theory is that we will find these equivalent mechanisms in each column in the neural cortex, which tells us that's, that, that, that's what they're doing. They're learning these sensory motor models of the world. So just to, we're pretty confident that would happen, but now we're seeing the evidence.

• 37:55Lex Fridman?

  So the evolutionary process nature does a lot of copy and paste and see what happens?

• 38:00Speaker 01

  Yeah. Yeah, there's no direction to it, but, but it just found out like, hey, if I took this, these elements and, and made more of them, what happens? And let's hook them up to the eyes and let's hook them up to ears and. And

• 38:12Lex Fridman?

  And that seems to work pretty well.

• 38:13Speaker 01

  Yeah.

• 38:14Lex Fridman?

  Like for us. Again, just to take a quick step back to our conversation of collective intelligence. Do you sometimes see that as just another copy and paste aspect is copying and pasting these brains and humans and making a lot of them and then creating social structures that then almost operate as a single brain?

• 38:37Speaker 01

  Uh, I wouldn't have said it, but you said it and it sounded pretty good.

• 38:41Lex Fridman?

  So to you, the brain is fundamental is a is like a is its own thing.

• 38:47Speaker 01

  Yeah, I mean, our our goal is to understand how the neural cortex works. We can argue how essential that is to understanding the human brain because it's not the entire human brain. You can argue how essential that is to understanding human intelligence. You can argue how essential this to to, you know, sort of communal intelligence. I I'm not, I didn't, our goal was to understand the neural cortex.

• 39:10Lex Fridman?

  Yeah. So what is the neural cortex and where does it fit in the various aspect of what the brain does? Like, how important is it to you?

• 39:18Speaker 01

  Well, obviously, again, we I mentioned again in the beginning, it's it's about seventy to seventy five percent of the volume of a human brain. So it's, you know, it dominates our brain in terms of size. Not in terms of number of neurons, but in terms of size.

• 39:32Lex Fridman?

  Size isn't everything, Jeff.

• 39:34Speaker 01

  I know, but it's it's nothing, it's nothing to be, it's not that. We know that all high level vision, hearing and touch happens in the neural cortex. We know that all language occurs and is understood in the neural cortex, whether that's spoken language, written language, sign language, with the language of mathematics, language of physics, music, math, you know, we know that all high level planning and thinking occurs in the neural cortex. If I were to say, you know, what part of your brain designed a computer and understands programming and and creates music? It's all the neural cortex. So then that's just an undeniable fact. If, but then there's other parts of our brain are important too, right? Our emotional states, our body, regulating our body.

• 40:16Speaker 01

  So the way I like to look at it is, you know, could you, can you? Understand the neocortex without the rest of the brain. And some people say you can't, and I think absolutely you can. It's not that they're not interacting, but you can understand. Can you understand the neocortex without understanding the emotions of fear? Yes, you can. You can understand how this system works. It's just a modeling system. I make the analogy in the book that it's like a map of the world. And how that map is used depends on who's using it. So how our map of our world in our neocortex, how we manifest as a human, depends on the rest of our brain. What are our motivations? You know, what are my desires? Am I a nice guy or not a nice guy? Am I a cheater or not a cheater?

• 40:59Speaker 01

  You know, how important different things are in my life. So, but the neocortex can be understood on its own. And I say that as a neuroscientist, I know there's all these interactions. And I want to say I don't know them and we don't think about them. But from a layperson's point of view, you can say it's a modeling system. I don't generally think too much about the communal aspect of intelligence, which you've brought up a number of times already. So that's not really been my concern.

• 41:28Lex Fridman?

  I just wonder if there's a continuum. From the origin of the universe, like this pockets of complexities that form living organisms. I wonder if we're just, if you look at humans, we feel like we're at the top. But I wonder if there's like just where everybody probably, every living type pocket of complexity is probably thinks they're the, pardon the French, they're the shit. They're at the top of the pyramid.

• 41:58Speaker 01

  Well, if they're thinking.

• 42:00Lex Fridman?

  Well, then what is thinking? What the, in a sense, the whole point is in their sense of the world, they, their sense is that they're at the top of it.

• 42:12Speaker 01

  I think

• 42:12Lex Fridman?

  What is a turtle?

• 42:13Speaker 01

  But you're, you're, you're bringing up, you know, the, the, the problems of complexity and complexity theory are, you know, it's a huge, interesting problem in science. And, you know, I think we've made surprisingly little progress in understanding complex systems.

• 42:28Lex Fridman?

  Right.

• 42:28Speaker 01

  In general. And so, you know, the Santa Fe Institute was founded to, to study this and, and even the scientists there will say it's really hard. We haven't really been able to figure out exactly, you know, that science hasn't really congealed yet. We're, we're still trying to figure out the basic elements of that science. What, you know, where does complexity come from and what is it and how you define it? Whether it's DNA creating bodies or phenotypes or it's individuals creating societies or ants and, you know, markets and so on. It's, it's a very complex thing. I'm not a complexity theorist person, right? And I, I think you need to ask, well, the brain itself is a complex system. So can we understand that? I think we've made a lot of progress understanding how the brain works.

• 43:12Speaker 01

  So, uh, but I haven't broadened out to like, oh, well, where are we are in the complexity spectrum? It's like, um, it's a great question.

• 43:21Lex Fridman?

  I prefer for that answer to be we're, we're not special. It seems like if we're honest, most likely we're not special. So if there is a spectrum, we're probably not in some kind of significant place.

• 43:34Speaker 01

  I think there's one thing we could say that we are special. And, and again, only here on earth. I'm not saying I'm bad. Is that if we think about knowledge, what we know. We clearly, human brains have, um, the only brains that have a certain types of knowledge. We're the only brains on, on this earth to understand, uh, what the earth is, how old it is. The, the universe is a picture as a whole. We're the only organisms to understand DNA and the origins of, you know, of species. No other species on, on this planet has that knowledge. So if we think about, I, I like to think about, you know, one of the endeavors of humanity is to understand the universe as much as we can.

• 44:19Speaker 01

  I think our species is further along in that, undeniably. Whether our theories are right or wrong, we can debate, but at least we have theories. You know, we know that what the sun is and how its fusion is and how what black holes are and, you know, we know general theory of relativity and no other animal has any of this knowledge. So, from that sense that we're special, are we special in terms of the hierarchy of complexity in the universe? Probably not.

• 44:48Lex Fridman?

  Can we look at a neuron? Yeah, you say that prediction happens in the neuron. What does that mean? So neuron traditionally is seen as the basic element of the the brain.

• 44:59Speaker 01

  So we, I mentioned this earlier, that prediction was our research agenda.

• 45:03Lex Fridman?

  Yeah.

• 45:03Speaker 01

  We said, okay, how does the brain make a prediction? Like, I'm about to grab this water bottle and my brain is predicting what I'm going to feel on all my parts of my fingers. If I felt something really odd on any part here, I'd notice it. So my brain is predicting what it's going to feel as I grab this thing. So what is that? How does that manifest itself in neural tissue? Right? We got brains made of neurons and there's chemicals and there's neurons and there's spikes and they're connected, you know, where is the prediction going on? And one argument could be that, well, when I'm predicting something, a neuron must be firing in advance. It's like, okay, this neuron represents what you're going to feel and it's firing, it's sending a spike. And certainly that happens to some extent.

• 45:47Speaker 01

  But our predictions are so ubiquitous that we're making so many of them, which we're totally unaware of, just the vast majority of them, you have no idea that you're doing this, that it, there wasn't really, we were trying to figure, how could this be? Where are these, where are these happening? Right? And I won't walk you through the whole story unless you insist upon it, but we came to the realization that most of your predictions are occurring inside individual neurons, especially these, the most common neuron, the pericerebral cells. And there are, there's a property of neurons. We, everyone knows, or most people know that a neuron is a cell and it has this spike called an action potential and it sends information. But we now know that there's these spikes internal to the neuron. They're called dendritic spikes.

• 46:32Speaker 01

  They travel along the branches of the neuron and they don't leave the neuron. They're just internal only. There's far more dendritic spikes than there are action potentials, far more. They're happening all the time. And what we came to understand that those dendritic spikes, the ones that are occurring, are actually a form of prediction. They're telling the neuron, the neuron is saying, I expect that I might become active shortly. And that internal, so the internal spike is a way of saying, you're gonna, you might be generating external spikes soon. I predicted you're going to become active. And, and we've, we've, we wrote a paper in 2016, which explained how this manifests itself in neural tissue and how it is that this all works together.

• 47:16Speaker 01

  But the fact, we think it's, there's a lot of evidence supporting it. So we, that's where we think that most of these predictions are internal. That's why you can't be, they're internal neuron, you can't perceive them.

• 47:27Lex Fridman?

  Well, from understanding the, the prediction mechanism of a single neuron, do you think there's deep insights to be gained about the prediction capabilities of the mini brains within the bigger brain and the brain?

• 47:39Speaker 01

  Oh, yeah, yeah, yeah. So having a prediction side of their individual neuron is not that useful. You know, what, so what? The way it manifests itself in neural tissue is that when a neuron, a neuron emits these spikes, so a very singular type of event. If a neuron is predicting that it's going to be active, it emits its spike very, a little bit sooner, just a few milliseconds sooner than it would have otherwise. It's like, I give the analogy in the book, it's like a sprinter on a, on a starting blocks in a, in a race. And if someone says, get ready, set, you get up and you're ready to go. And then when your race starts, you get a little bit earlier start. So that, it's that, that ready, set is like the prediction, then the neuron's like ready to go quicker. And what happens is when you have a whole bunch of neurons together and they're all

• 48:24Speaker 01

  getting these inputs, the ones that are in the predictive state, the ones that are anticipating to become active, if they do become active, they, they happen sooner, they disable everything else, and it leads to different representations in the brain. So you have to. It's not isolated just to the neuron. The prediction occurs within the neuron, but the network behavior changes. So what happens under different predictions, different inputs have different representations. So how I, what I predict is going to be different under different contexts. You know, what my input will be is different under different contexts. So this is, this is a key to the whole theory, how this works.

• 48:59Lex Fridman?

  So the theory of the thousand brains, if you were to count the number of brains, how would you do it?

• 49:06Speaker 01

  The thousand brain theory says that basically every cortical column in your neural cortex is a complete modeling system. And that when I ask, where do I have a model of something like a coffee cup? It's not in one of those models. It's in thousands of those models. There's thousands of models of coffee cups. That's what the thousand brains.

• 49:23Lex Fridman?

  Then there's a voting mechanism.

• 49:25Speaker 01

  Then there's a voting mechanism, which leads, which is the thing you're, which you're conscious of, which leads to your singular perception. That's why you perceive something. So that's the thousand brain theory. The details of how we got to that theory are complicated. It wasn't, we just thought of it one day. And one of those details, we had to ask, how does a model make predictions? And we've talked about just these predictive neurons. That's part of this theory. It's like saying, oh, it's a detail, but it was like a crack in the door. It's like, how are we going to figure out how these neurons build, do this? You know, what is going on here? So we just looked at prediction as like, well, we know that's ubiquitous. We know that every part of the cortex is making predictions. Therefore, whatever the predictive system is, it's going to be everywhere.

• 50:08Speaker 01

  We know there's a gazillion predictions happening at once. So this is where we can start teasing apart, you know, ask questions about, you know, how could neurons be making these predictions? And that sort of built up to now what we have, the thousand brains theory, which is complex, you know, which is, I can state it simply, but we just didn't think of it. We had to get there step by step, very, it took years to get there.

• 50:31Lex Fridman?

  And where does reference frames fit in? So, yeah.

• 50:36Speaker 01

  Okay. So again, a reference frame, I mentioned earlier about the, you know, a model of a house. And I said, if you're going to build a model of a house in a computer, they have a reference frame. And you can think of reference frame like Cartesian coordinates, like X, Y, and Z axes. So I could say, oh, I'm going to design a house. I can say, well, the, the front door is at this location, X, Y, Z. And the roof is at this location, X, Y, Z, and so on. That's the type of reference frame. So it turns out for you to make a prediction, and I walk you through the thought experiment in the book where I was predicting what my finger was going to feel when I touch the coffee cup. It was a ceramic coffee cup, but this one will do. And what I realized is that to make a prediction what my finger is going to feel, like it's going to feel different than this, which it feel different if I touch the hole or

• 51:21Speaker 01

  this thing on the bottom. Make that prediction, the cortex needs to know where the finger is, the tip of the finger, relative to the coffee cup. And exactly relative to the coffee cup. And to do that, I have to have a reference frame for the coffee cup. It has to have a way of representing the location of my finger to the coffee cup. And then we realized, of course, every part of your skin has to have a reference frame relative to things it touch. And then we did the same thing with vision. But so the idea that a reference frame is necessary to make a prediction when you're touching something or when you're seeing something and you're moving your eyes or you're moving your fingers, it's just a requirement to know what to predict. If I have a, if I have a structure, I'm going to make a prediction, I have to, I have to know where it is, I'm looking or touching it. So then we said, well, how do neurons make reference frames?

• 52:05Speaker 01

  It's not obvious. You know, X, Y, Z coordinates don't exist in the brain. It's just not the way it works. So that's when we looked at the older part of the brain, the hippocampus and the anterolarynx, where we knew that in that part of the brain, there's a reference frame for a room or a reference frame for environment. Remember I talked earlier about how you could know, make a map of this room? So we said, oh, that they are implementing reference frames there. So we knew that reference frames needed to exist in every cortical column. And so that was a deductive thing. We just deduced it has to exist.

• 52:39Lex Fridman?

  So you take the old mammalian ability to know where you are in a particular space and you start applying that to higher and higher levels.

• 52:50Speaker 01

  Yeah, first you apply it to like where your finger is. So here's the way I think about it. The old part of the brain says, where's my body in this room?

• 52:56Lex Fridman?

  Yeah.

• 52:57Speaker 01

  The new part of the brain says, where's my finger relative to this, this object?

• 53:02Lex Fridman?

  Yeah.

• 53:02Speaker 01

  Where is a section of my retina relative to this object? Like where, where is, I'm looking at one little corner, where is that relative to this patch of my retina?

• 53:11Lex Fridman?

  Yeah.

• 53:12Speaker 01

  And then we take the same thing and apply it to concepts, mathematics, physics, you know, humanity, whatever you want to think about.

• 53:20Lex Fridman?

  And eventually you're pondering your own mortality.

• 53:22Speaker 01

  Well, whatever. But the point is, When we think about the world, when we have knowledge about the world, how is that knowledge organized, Lex? Where is it in your head? The answer is it's in reference frames. So the way I learned the structure of this water bottle, where the features are relative to each other. When I think about history or democracy or mathematics, the same basic underlying structure is happening. There's reference frames for where the knowledge that you're assigning things to. So in the book, I go through examples like mathematics and language and politics. But the evidence is very clear in the neuroscience. The same mechanism that we use to model this coffee cup, we're going to use to model high level thoughts. Your demise of humanity, whatever you want to think about.

• 54:05Lex Fridman?

  It's interesting to think about how different are the representations of those higher dimensional concepts,

• 54:13Lex Fridman?

  higher level concepts? How different the representation there is in terms of reference frames versus spatial?

• 54:19Speaker 01

  But interesting thing, it's a different application, but it's the exact same mechanism.

• 54:25Lex Fridman?

  But isn't there some aspect to higher level concepts? They seem to be hierarchical. Like they just seem to integrate a lot of information into them.

• 54:35Speaker 01

  So is our physical objects. So take this water bottle. I'm not particular to this brand, but this is a Fiji water bottle. And it has a logo on it. I use this example in my book. Our company's coffee cup has a logo on it. But this object is hierarchical. It's got like a cylinder and a cap, but then it has this logo on it. The logo has a word. The word has letters. The letters have different features. And so I don't have to remember, I don't have to think about this. So I say, oh, there's a Fiji logo on this water bottle. I don't have to go through and say, oh, what is the Fiji logo? It's the F and I and a J and I, and there's a hibiscus flower. And, oh, it has the stamen on it. I don't have to do that. I just incorporate all of that in some sort of hierarchical representation.

• 55:20Speaker 01

  I say, you know, put this logo on this water bottle.

• 55:24Lex Fridman?

  Yeah.

• 55:24Speaker 01

  And then the logo has a word and the word has letters. All hierarchical.

• 55:29Lex Fridman?

  All that stuff is big. It's amazing that the brain instantly just does all that.

• 55:32Speaker 01

  Yeah.

• 55:33Lex Fridman?

  The idea that there's water, it's liquid, and the idea that you can drink it when you're thirsty. The idea that there's brands.

• 55:41Speaker 01

  Yeah.

• 55:41Lex Fridman?

  And then there's like, all of that information is instantly like, built into the whole thing once you proceed.

• 55:48Speaker 01

  So I wanted to get back to your point about hierarchical representation. The world itself is hierarchical. Right? And I can take this microphone in front of me. I know inside, there's gonna be some electronics. I know there's gonna be some wires and I know there's gonna be a little diaphragm that moves back and forth. I don't see that, but I know it. So, everything in the world is hierarchical. You just go into a room, it's composed of other components. The kitchen has a refrigerator, you know, the refrigerator has a door, the door has a hinge, the hinge has screws and pins. So, anyway, the modeling system that exists in every cortical column learns the hierarchical structure of objects. So, it's a very sophisticated modeling system in this grain of rice. It's hard to imagine, but this grain of rice can do really sophisticated things. It's got 100,000 neurons in it. It's very sophisticated.

• 56:33Speaker 01

  So, the same mechanism that can model a water bottle or a coffee cup can model conceptual objects as well. It's, that's the beauty of this discovery that this guy Vernon Mountcastle made many, many years ago, which is that there's, there's a single cortical algorithm underlying everything we're doing.

• 56:52Lex Fridman?

  So, common sense concepts and higher level concepts are all represented in the same way?

• 56:58Speaker 01

  They're set in the same mechanisms, yeah. It's a little bit like computers, right? All computers are universal Turing machines. Even the little teeny one that's in my toaster and the big one that's running some cloud server someplace. They're all running on the same principle. They can apply different things. So, the brain is all built on the same principle. It's all about learning these models, structured models, using movement and reference frames. And it can be applied to something as simple as a water bottle and a coffee cup, and it can be applied to thinking like, what's the future of humanity? And, you know, why do you have a hedgehog on your desk? I don't know.

• 57:34Lex Fridman?

  Nobody knows.

• 57:37Speaker 01

  I think it's a hedgehog.

• 57:38Lex Fridman?

  That's right. It's a hedgehog in the fog. It's a Russian reference. Does it give you any? Inclination or hope about how difficult it is to engineer common sense reasoning? So how complicated is this whole process? So looking at the brain.

• 57:56Lex Fridman?

  Is this a marvel of engineering? Or is it pretty dumb stuff stacked on top of each other over?

• 58:03Speaker 01

  It can be both. Can it be both? Can it be both, right?

• 58:07Lex Fridman?

  I don't know if it can be both, because if it's an incredible engineering job, that means it's so evolution did a lot of work. It.

• 58:18Speaker 01

  Yeah, but then, but then it just copied that. Right, so as I said earlier, the figuring out how to model something like a space is really hard. And evolution had to go through a lot of tricks. And these, these, these cells I was talking about, these grid cells and place cells, they're really complicated. This is not simple stuff. This neural tissue works on these really unexpected weird mechanisms.

• 58:40Speaker 01

  But it did it. It figured it out. But, but now you could just make lots of copies of it.

• 58:45Lex Fridman?

  But then finding, yeah, so it's a, it's a very interesting idea that's a lot of copies of a basic mini brain. But the question is, how difficult it is to find that mini brain that you can copy and paste effectively?

• 59:00Speaker 01

  Well, today, we know enough to build this. I'm sitting here with, you know, I, I know the steps we have to go. There's still some engineering problems to solve, but we know enough. And it's not like, oh, this is an interesting idea. We have to go think about it for another few decades. No, we actually understand it pretty well details. So not all the details, but most of them. So it's complicated, but it is an engineering problem. So in my company, we are working on that. We are basically laid out roadmap, how we do this. It's not going to take decades. It's been a few years.

• 59:37Speaker 01

  Optimistically, but I think that's possible. It's, you know, complex things. If you understand them, you can build them.

• 59:44Lex Fridman?

  So in which domain do you think it's best to build them? Are we talking about robotics, like entities that operate in the physical world that are able to interact with that world? Are we talking about entities that operate in the digital world? Are we talking about something more like, more specific, like is done in the machine learning community? Where you look at natural language or computer vision? Where do you think is easiest to?

• 1:00:12Speaker 01

  It's the first, it's the first two more than the third one, I would say.

• 1:00:18Speaker 01

  Again, let's just use computers as an analogy.

• 1:00:22Speaker 01

  The pioneers of computing, people like John von Neumann, Turing, they created this thing, you know, we now call the universal Turing machine, which is a computer, right? Did they know how it was going to be applied, where it was going to be used, you know, could they envision any of the future? No, they just said, this is like a really interesting computational idea about algorithms and how you can implement them in a machine. And we're doing something similar to that today. Like we are, we are building this sort of universal learning principle that can be applied to many, many different things.

• 1:00:56Lex Fridman?

  But the robotics piece of that, the interactive.

• 1:00:59Speaker 01

  Okay, all right, let's be specific. You can think of this cortical column as what we call a sensory motor learning system. It has the idea that there's a sensor and that it's moving. That sensor can be physical. It could be like my finger and it's moving in the world. It could be like my eye and it's physically moving. It can also be virtual. So it could be, an example would be, I could have a system that lives in the internet that, that actually samples information on the internet and moves by following links. That's, that's a sensory motor system.

• 1:01:29Lex Fridman?

  So something that echoes the, the process of a finger moving along a coffee cup.

• 1:01:35Speaker 01

  In a very, very loose sense. It's, it's like, again, learning is inherently about discovering the structure in the world and discover the structure in the world. You have to move through the world, even if it's a virtual world, even if it's a conceptual world. You have to move through it. You don't, it doesn't exist in one, it has some structure to it. So here's, here's a couple of predictions that getting what you're talking about. In humans, the same algorithm does robotics, right? It moves my arms, my eyes, my body, right? And so in my, in the future, to me, robotics and AI will merge. They're not going to be separate fields because they're gonna, the, the, the, the algorithms to really controlling robots are going to be the same algorithms we have in

• 1:02:17Speaker 01

  our brain, that, the brain, that these sensory motor algorithms, I, today, we're not there, but I think that's gonna happen. And, um, and then, so, but not all AI systems will have, be robotics. Um, you can have systems that have very different types of embodiments. Some will have physical movements, some will not have physical movements. It's a very generic learning system. Again, it's like computers, the Turing machine is, is like, doesn't say how it's supposed to be implemented, doesn't tell you how big it is, doesn't tell you what you're gonna apply it to, but it's an, it's a computational principle. Cortical column equivalent is a computational principle about learning. It's about how you learn, and it can be applied to a gazillion things. This is why I think this is, I think this impact of AI is gonna be as large, if not larger, than computing has been in the last century, by far.

• 1:03:02Speaker 01

  Because it's, it's getting at a fundamental thing. It's not a vision system or a learning system. It's a, it's not a vision system or a hearing system. It is a learning system. It's a fundamental principle, how you learn, the structure in the world, how you gain knowledge and be intelligent. And that's what the thousand brain says was going on. And we have a particular implementation in our head, but it doesn't have to be like that at all.

• 1:03:22Lex Fridman?

  Do you think there's going to be some kind of impact? Okay, let me ask it another way. What do increasingly intelligent AI systems do with us humans in the following way? Like, how hard is the human in the loop problem? How hard is it to, to interact? The finger on the coffee cup equivalent of having a conversation with a human being? So how hard is it to fit into our little human world?

• 1:03:51Speaker 01

  Uh, I don't, I think it's a lot of engineering problems. I don't think it's a fundamental problem. I could ask you the same question. How hard is it for computers to fit into a human world?

• 1:04:01Lex Fridman?

  Right, that, I mean, that's essentially what I'm asking. Like, how, um, much are we elitist, are we as humans? Like, we try to keep out, uh, systems?

• 1:04:13Speaker 01

  I don't know. I, I, I don't know, so I think. I'm not sure that's the right question. Let's look at computers as an analogy. Computers are a million times faster than us. They do things we can't understand. Most people have no idea what's going on when they use computers. How we integrate them in our society? Well, we don't think of them as their own entity. They're not living things. We don't afford them rights. We rely on them. Our survival as a seven billion people or something like that is rely on computers now.

• 1:04:47Lex Fridman?

  Don't you think that's a fundamental problem? That we see them as something we can't, we don't give rights to?

• 1:04:54Speaker 01

  Computers?

• 1:04:55Lex Fridman?

  Yeah, computers. So robots, computers, intelligent systems, it feels like for them to operate successfully, they would need to have a lot of the elements that we would start having to think about. Like, should this entity have rights?

• 1:05:11Speaker 01

  I don't think so. I think it's tempting to think that way. First of all, I don't think anyone, hardly anyone thinks that for computers today. No one says, oh, this thing needs a right. I shouldn't be able to turn it off. Or, you know, if I throw it in the trash can and hit it with a sledgehammer, I might form a criminal act. No, no one thinks that. And now we think about intelligent machines, which is where you're going. And all of a sudden, like, well, no, we can't do that. I think the basic problem we have here is that people think intelligent machines will be like us. They're going to have the same emotions as we do, the same feelings as we do. What if I can build an intelligent machine that absolutely could care less about whether it was on or off or destroyed or not? It just doesn't care. It's just like a map.

• 1:05:55Speaker 01

  It's just a modeling system. It has no desires to live, nothing.

• 1:06:00Lex Fridman?

  Is it possible to create a system that can model the world deeply and not care about whether it lives or dies?

• 1:06:08Speaker 01

  Absolutely. No question about it.

• 1:06:10Lex Fridman?

  To me, that's not 100% obvious.

• 1:06:12Speaker 01

  It's obvious to me. So we can debate it if you want. Where does your, where does your desire to live come from? It's an old evolutionary design. I mean, we could argue, does it really matter if we live or not? Objectively, no. Right? We're all going to die eventually.

• 1:06:32Speaker 01

  But evolution makes us wanna live. Evolution makes us wanna fight to live. Evolution just wanna care and love one another. And to care for our children and our relatives and our family and so on. And those are all good things. But they come about not because we're smart, because we're animals that grew up. You know, the hummingbird in my backyard cares about its offspring. You know, they, every living thing in some sense cares about, you know, surviving. But when we talk about creating intelligent machines, we're not creating life. We're not creating evolving creatures. We're not creating living things. We're just creating a machine that can learn really sophisticated stuff. And that machine, it may even be able to talk to us.

• 1:07:14Speaker 01

  But it doesn't, it's not gonna have a desire to live unless somehow we put it into that system.

• 1:07:20Lex Fridman?

  Well, there's learning, right? The thing is.

• 1:07:23Speaker 01

  But you don't learn to like wanna live. It's built into you.

• 1:07:26Lex Fridman?

  Well, so people like Ernest Becker argue. So, okay, there's the fact, the finiteness of life. The way we think about it is something we learned. Perhaps, so, okay.

• 1:07:40Speaker 01

  Yeah, and some people decide they don't wanna live. And some people decide, you know, you couldn't, but the desire to live is built in DNA, right?

• 1:07:47Lex Fridman?

  But I think what I'm trying to get to is in order to accomplish goals, it's useful to have the urgency of mortality. It's what the Stoics talked about, is meditating in your mortality.

• 1:07:57Speaker 01

  Yeah.

• 1:07:58Lex Fridman?

  It might be a very useful thing to do, to die and have the urgency of death. And to realize that, to conceive yourself as an entity that operates in this world that eventually will no longer be a part of this world. And actually conceive of yourself as a conscious entity might be very useful for you to be a system that makes sense of the world. Otherwise, you might get lazy.

• 1:08:23Speaker 01

  Well, okay, we're gonna build these machines, right? So are we talking about building AIs?

• 1:08:29Lex Fridman?

  What, but we're, we're building the.

• 1:08:33Lex Fridman?

  The equivalent of the cortical columns, the

• 1:08:36Speaker 01

  The neocortex.

• 1:08:37Lex Fridman?

  The neocortex. And the question is, where do they arrive at? Because we're not hard coding everything in where

• 1:08:46Speaker 01

  Well, in terms of, if you build the neocortex equivalent, it will not have any of these desires or emotional states. Now, you can argue that that neocortex won't be useful unless I give it some agency, unless I give it some desire, unless I give it some motivation. Otherwise, it'll be just lazy and do nothing. Right? You could argue that. But on its own, it's not going to do those things. It's just not, it's just not going to sit there and say, I understand the world, therefore I care to live. No, it's not going to do that. It's just going to say, I understand the world.

• 1:09:14Lex Fridman?

  Why is that obvious to you? Why, why, why don't, do you think it's, okay, let me ask it this way. Do you think it's possible it will at least assign to itself agency and perceive itself in this world as being a conscious entity as a useful way to operate in the world and to make sense of the world?

• 1:09:39Speaker 01

  I think intelligent machine can be conscious, but that does not, again, imply any of these, these desires and goals and that you're worried about. We can, I have a, we can talk about what it means for a machine to be conscious.

• 1:09:52Lex Fridman?

  And by the way, not worry about, but get excited about. It's not necessarily that we should worry about it.

• 1:09:56Speaker 01

  So I think there's a legitimate problem or not problem, a question asked. If you build this modeling system, what's it going to model?

• 1:10:04Lex Fridman?

  Yes.

• 1:10:04Speaker 01

  Right? What's, what's its desire? What is, what's its goal? What are we applying it to? Right? So that's an interesting question. One thing, if, and it depends on the application. It's not something that inherent to the modeling system. It's something we apply to the modeling system in a particular way. So if I wanted to make a really smart car. It would have to know about driving in cars and what's important in driving in cars. It's not going to figure that out on its own. It's not going to sit there and say, you know, I've understood the world and I've decided, you know, no, no, no, no. We're gonna have to tell it. We're gonna have to say like, so I imagine I make this car really smart. It learns about your driving habits, it learns about the world.

• 1:10:43Speaker 01

  And it's just, you know, is it one day going to wake up and say, you know what, I'm tired of driving and doing what you want. I think I have better ideas about how to spend my time.

• 1:10:53Lex Fridman?

  Well, okay.

• 1:10:54Speaker 01

  No, it's not gonna do that.

• 1:10:55Lex Fridman?

  Well, part of me is playing a little bit of devil's advocate, but part of me is also trying to think through this because I've studied cars quite a bit. And I've studied pedestrians and cyclists quite a bit. And there's part of me that thinks that there needs to be

• 1:11:13Lex Fridman?

  more intelligence than we realize in order to drive successfully. That game theory of human interaction seems to require some deep understanding of of human nature that.

• 1:11:30Lex Fridman?

  Okay, when a pedestrian crosses the street, there's some sense, they, they look at a car usually. And then they look away. There's some sense in which they say, I believe that you're not going to murder me. You don't have the guts to murder me. This is the little dance of pedestrian car interaction is saying, I'm going to look away and I'm going to put my life in your hands. Because I think you're human, you're not going to kill me. And then the car, in order to successfully operate in like Manhattan streets, has to say, no, no, no, I am going to kill you. Like a little bit, there's a little bit of this weird inkling of mutual murder. And that's a dance and we somehow successfully operate through that.

• 1:12:14Speaker 01

  Do you think you were born of that? Did you learn that social interaction?

• 1:12:19Lex Fridman?

  I think it, it might have a lot of the same elements that you're talking about, which is we're leveraging things we were born with and applying them in the context that.

• 1:12:29Speaker 01

  All right, I would, I would have answered it. I would have said that that kind of interaction is learned. Cause you know, people in different cultures have different interactions like that. If you cross the street in different cities and different around the world, they have different ways of interacting. I would say that's learned and I would say an intelligent system can learn that too. But that does not lead, and the intelligent system can understand. Humans, it could understand that, you know, just like I can study an animal and learn something about that animal, you know, I could study apes and learn something about their culture and so on. I don't have to be an ape to know that. I may not be completely, but I can understand something. So an intelligent machine can model that. That's just part of the world. It's just part of the interactions. The question we're trying to get at, will

• 1:13:12Speaker 01

  the intelligent machine have its own personal agency that's beyond, you know, what we assign to it, or its own personal, you know, goals, or will it evolve and create these things? My confidence comes from understanding the mechanisms I'm talking about creating. This is not hand-wavy stuff. It's down in the details. I'm gonna build it. And I know what it's gonna look like, and I know what's it gonna behave. I know what the kind of things it could do and the kind of things it can't do. Just like when I build a computer, I know it's not gonna on its own decide to put another register inside of it. It can't do that. No way. No matter what your software does, it can't add a register to the computer. So in this way, when we build AI systems, we have to make choices about the, how we

• 1:13:57Speaker 01

  embed them. So I talk about this in the book. I said, you know, it's, an intelligent system is not just a neocortex equivalent. You have to have that, but it has to have some kind of embodiment, physical or virtual. It has to have some sort of goals. It has to have some sort of ideas about dangers, about things it shouldn't do, like, you know, like we build in safeguards into systems. We have them in our bodies. We have put them in the cars, right? My car follows my directions until the day it sees I'm about to hit something and it ignores my directions and puts the brakes on. So we can build those things in. So that's a very interesting problem, how to build those in. I think my differing opinion about the risks of AI for most people is that people assume that somehow those things

• 1:14:42Speaker 01

  will disappear automatically or evolve. And intelligence itself Begets that stuff or requires it. But it's not. Intelligence of the neurocortex equipment doesn't require this. The neurocortex equipment just says, I'm a learning system. Tell me what you want me to learn and I'll tell you. Ask me questions and I'll tell you the answers. But in that, again, it's again, like a map. It doesn't, a map has no intent about things, but you can use it to solve problems.

• 1:15:09Lex Fridman?

  Okay, so the building, engineering, the neurocortex in itself is just creating an intelligent prediction system.

• 1:15:17Speaker 01

  Modeling system.

• 1:15:18Lex Fridman?

  Modeling, sorry, modeling system.

• 1:15:19Speaker 01

  Yeah.

• 1:15:20Lex Fridman?

  You can use it to then make predictions and then, but you can also put it inside a thing that's actually acting in this world.

• 1:15:28Speaker 01

  You have to put it inside something. It's, again, think of the map analogy, right? A map on its own doesn't do anything.

• 1:15:34Lex Fridman?

  Right.

• 1:15:35Speaker 01

  It's this inert, it's just, it could learn, but it's this inert. So we have to embed it somehow in something to do something.

• 1:15:41Lex Fridman?

  So, so what's your intuition here? You had, you had a conversation with Sam Harris recently that was sort of, you've had a bit of a disagreement and you're sticking on this point. You know, Elon Musk, Stuart Russell, kind of have a worry existential threats of AI. What's your intuition? Why, if we engineer increasingly intelligent neurocortex type of system in the computer, why that shouldn't be a thing that we?

• 1:16:12Speaker 01

  It was interesting, you used the word intuition and Sam Harris used the word intuition too. And, and when he used that intuition, that word, I immediately stopped and said, oh, that's the cause of the problem. He's using intuition. I'm not speaking about my intuition.

• 1:16:25Lex Fridman?

  Yes.

• 1:16:25Speaker 01

  I'm speaking about something I understand, something I'm going to build, something I am building, something I understand completely, or at least well enough to know what, it's on, I'm guessing, I know what this thing's going to do. And I think most people who are worried, they have trouble separating out, they don't have, they don't have the un-knowledge or the understanding about like, what is intelligence? How's it manifest in the brain? How's it separate from these other functions in the brain? And so they imagine it's going to be human-like or animal-like. It's going to have, it's going to have the same sort of. Drives and emotions we have, but there's no reason for that. That's just because there's there's unknown. If you if the unknown is like, oh, my God, you know, I don't know what this is going to do. We have to be careful. It could be like us, but really smarter. I'm saying, no, it won't be like us.

• 1:17:08Speaker 01

  It'll be really smarter, but it won't be like us at all. And and but I I'm coming from that, not because I just guessing. I'm not intuit using intuition. I'm basically like, OK, I understand this thing works. This is what it does. I'm explaining it to you.

• 1:17:21Lex Fridman?

  OK, but to push back. So I also disagree with the the intuitions that Sam has. But but so disagree with what you just said, which, you know, what's a good analogy? So if you look at the Twitter algorithm in in the early days, just recommender systems, you can understand how recommender systems work. What you can't understand in the early days is when you apply that recommender system at scale to thousands and millions of people, how that can change societies.

• 1:17:53Speaker 01

  Yeah.

• 1:17:54Lex Fridman?

  So the the question is, yes, you're just saying this is how an engineer neural cortex works. But the like, when you have a very useful TikTok type of service that goes viral, when your neural cortex goes viral.

• 1:18:10Speaker 01

  Yeah.

• 1:18:10Lex Fridman?

  And then millions of people start using it, can that destroy the world?

• 1:18:13Speaker 01

  No. Well, first of all, this is back. One thing I want to say is that AI is a dangerous technology. I don't I'm not denying that.

• 1:18:20Lex Fridman?

  All technology is dangerous.

• 1:18:21Speaker 01

  Well, and AI maybe particularly so.

• 1:18:23Lex Fridman?

  Yeah.

• 1:18:23Speaker 01

  OK, so am I worried about it? Yeah, I'm totally worried about it. The the thing where the the narrow component we're talking about now is the existential risk of AI. Right? So I want to make that distinction because I think AI can be applied poorly. It can be applied in ways that, you know, people are going to understand the the consequences of it. These are all potentially very bad things. But they're not the AI system creating this existential risk on its own. And that's the only place I disagree with other people.

• 1:18:55Lex Fridman?

  Right, so I I think the existential risk thing is humans are really damn good at surviving. So to kill off the human race would be very, very difficult.

• 1:19:06Speaker 01

  Yes, but you can even, I'll go further. I don't think AI systems are ever gonna try to. I don't think AI systems are ever gonna like, say, I'm gonna ignore you. I'm gonna do what I think is best. I don't think that's gonna happen, at least not in the way I'm talking about it. So the Twitter recommendation algorithm is an interesting example. Let's let's use computers as an analogy again, right? I build a computer. It's a universal computing machine. I can't predict what people are gonna use it for. They can build all kinds of things. They can they can even create computer viruses. It's, you know, all kinds of stuff. So there's some unknown about its utility and about where it's gonna go. But on the other hand, I pointed out that once I build a computer,

• 1:19:49Speaker 01

  it's not gonna fundamentally change how it computes. It's like I use the example of a register, which is a part, internal part of a computer. You know, I say it can't just say, because computers don't evolve. They don't replicate, they don't evolve, they don't, you know, the physical manifestation of the computer itself is not gonna, there's certain things it can't do. Right, so we can break into things like, things that are possible to happen, we can't predict, and things that are just impossible to happen. Unless we go out of our way to make them happen, they're not gonna happen unless somebody makes them happen.

• 1:20:18Lex Fridman?

  Yeah, so there's there's a bunch of things to say. One is the physical aspect, which you're absolutely right. We have to build a thing for it to operate in the physical world, and you can just stop building them, you know, the moment they're not doing the thing you want them to do.

• 1:20:34Speaker 01

  Or just change the design.

• 1:20:36Lex Fridman?

  Or change the design. The question is, I mean, there's, it's possible in the physical world, this is probably longer term, is you automate the building. It makes it makes a lot of sense to automate the building. There's a lot of factories that are doing more and more and more automation to go from raw resources to the final product. It's possible to imagine that it's obviously much more efficient to keep, to create a factory that's creating robots that do something, you know, that do something extremely useful for society. It could be a personal assistant, it could be, it could be your toaster, but a toaster that's much, has deeper knowledge of your culinary preferences. And that could

• 1:21:15Speaker 01

  Well, I think now you've hit on the right thing. The real thing we need to be worried about Lex is self replication.

• 1:21:21Lex Fridman?

  Right.

• 1:21:21Speaker 01

  That is the thing that we're

• 1:21:22Lex Fridman?

  In the physical world.

• 1:21:23Speaker 01

  Yeah, or even the virtual world. Self replication, because self replication is dangerous. It's probably more likely to be killed by a virus. You know, or a human engineered virus. Anybody can create, you know, this, the technology is getting so, almost anybody, well, not anybody, but a lot of people could create a human engineered virus that could wipe out humanity. That is really dangerous. No intelligence required. Just self replication. So, um, so we need to be careful about that. So when I think about, you know, AI, I'm not thinking about robots building robots. Don't do that. Don't build a, you know, just

• 1:22:00Lex Fridman?

  Well, that's because you're interested in creating intelligence. It seems like self replication is a good way to make a lot of money.

• 1:22:08Speaker 01

  Well, fine, but so is, you know, maybe editing viruses is a good way too. I don't know. The point is, if, as a society, when we want to look at existential risks, the existential risks we face that we can control, almost all evolve around self replication.

• 1:22:25Lex Fridman?

  Yes. The question is, I don't see a good way to make a lot of money by engineering viruses and deploying them on the world. There could be, there could be applications that are useful.

• 1:22:36Speaker 01

  But let's separate out, let's separate out, I mean, you don't need to, you only need some, you know, terrorists who wants to do it because it doesn't take a lot of money to make viruses. Let's just separate out what's risky and what's not risky. I'm arguing that the intelligence side of this equation is not risky.

• 1:22:51Lex Fridman?

  It's not risky.

• 1:22:51Speaker 01

  It's not risky at all. It's the self replication side of the equation that's risky. And I'm not dismissing that. I'm scared as hell.

• 1:22:59Lex Fridman?

  It's like the paperclip maximizer thing.

• 1:23:01Speaker 01

  Yeah.

• 1:23:03Lex Fridman?

  Those are often like talked about in the same conversation. I think you're right. Like creating ultra intelligent, super intelligent systems is not necessarily coupled with a self replicating, arbitrarily self replicating systems.

• 1:23:19Speaker 01

  Yeah. And you don't get evolution unless you're self replicating.

• 1:23:21Lex Fridman?

  Yeah.

• 1:23:22Speaker 01

  And so I think that's the gist of this argument, that people have trouble separating those two out. They just think, oh, yeah, intelligence looks like us. And look how, look at the damage we've done to this planet. Like how we've, you know, destroyed all these other species. Yeah, well, we replicate, which eight billion of us or seven billion of us now, so.

• 1:23:40Lex Fridman?

  I think the idea is that the more intelligent we're able to build systems, the more tempting it becomes from a capitalist perspective of creating products. The more tempting it becomes to create self reproducing systems.

• 1:23:54Speaker 01

  All right, so let's say that's true. So does that mean we don't build intelligent systems? No, that means we regulate, we understand the risks, we regulate them.

• 1:24:03Lex Fridman?

  Yeah.

• 1:24:04Speaker 01

  You know, look, there's a lot of things we could do as a society, which have some sort of financial benefit to someone, which could do a lot of harm. And we have to learn how to regulate those things. We have to learn how to deal with those things. I will argue this, I would say the opposite. Like, I would say having intelligent machines at our disposal will actually help us in the end more, because it'll help us understand these risks better, it'll help us mitigate these risks better. There might be ways of saying, oh, well, how do we solve climate change problems? You know, how do we do this, or how do we do that? That, just like computers are dangerous in the hands of the wrong people, but they've been so great for so many other things, we live with those dangers. And I think we have to do the same with intelligent machines. We just, but we have to be constantly vigilant about this idea of,

• 1:24:48Speaker 01

  A, bad actors doing bad things with them, and B, don't ever, ever create a self-replicating system.

• 1:24:56Speaker 01

  And by the way, I don't even know if you could create a self-replicating system that uses a factory, that's really dangerous. You know, nature's way of self-replicating is so amazing. You know, it doesn't require anything, it just, you know, the thing and resources and it goes, right?

• 1:25:11Lex Fridman?

  Yeah.

• 1:25:12Speaker 01

  If I said to you, you know what, we have to build, our goal is to build a factory that can make, that builds new factories. And it has to end-to-end supply chain. It has to mine the resources, get the energy. I mean, that's really hard. It's, you know, no one's doing that in the next, you know, 100 years.

• 1:25:32Lex Fridman?

  I've been extremely impressed by the efforts of Elon Musk and Tesla to try to do exactly that. Not Not from raw resource. Well, he actually, I think, states the goal is to go from raw resource to the final car in one factory.

• 1:25:48Speaker 01

  Yeah.

• 1:25:49Lex Fridman?

  And that's, that's the main goal. Of course, it's not currently possible, but they're taking huge leaps.

• 1:25:54Speaker 01

  Well, he's not the only one to do that. This, this has been a goal for many industries for a long, long time.

• 1:25:59Lex Fridman?

  It's difficult to do. Well, a lot of people, what they do is instead they have like a million suppliers and then they, like there's, everybody's manufacturing.

• 1:26:08Speaker 01

  They all co-locate them and they, and they tie the systems together.

• 1:26:11Lex Fridman?

  It's a fundamentally distributed system.

• 1:26:13Speaker 01

  I, I think that's, that also is not getting at the issue I was just talking about, which is self-replication. It's, I mean, self-replication means there's no entity involved other than the entity that's replicating. Right? And so if there are humans in this, in the loop, that's not really self-replicating, right? It's, unless somehow we're duped into doing.

• 1:26:37Lex Fridman?

  Well, but it's also, I don't necessarily

• 1:26:43Lex Fridman?

  agree with you because you, you've kind of mentioned that AI will not say no to us. I, I just think.

• 1:26:49Speaker 01

  They will. Yeah, yeah.

• 1:26:50Lex Fridman?

  So like, I think it's a useful feature to build in. I'm just trying to like, put myself in the mind of engineers to sometimes say no, you know, if you.

• 1:27:02Speaker 01

  Yeah, well, I mean, I gave, I gave, I gave an example earlier, right? I gave an example of my car.

• 1:27:07Lex Fridman?

  Yeah.

• 1:27:07Speaker 01

  Right? My car turns the wheel and, and applies the accelerator and the brake, as I say, until it decides there's something dangerous.

• 1:27:15Lex Fridman?

  Yes.

• 1:27:15Speaker 01

  And then it doesn't do that.

• 1:27:17Lex Fridman?

  Yeah.

• 1:27:17Speaker 01

  Now, that was something it didn't decide to do. It's something we programmed into the car. And so, good, it's a good idea, right? The question again isn't like, if we create an intelligent system, will it ever ignore our commands? Of course it will sometimes. Is it going to do it because it came up, came up with its own goals? That serve its purposes and it doesn't care about our purposes? No, I don't think that's going to happen.

• 1:27:44Lex Fridman?

  Okay. So let me ask you about these super intelligent cortical systems that we engineer and us humans.

• 1:27:51Lex Fridman?

  Do you think with these entities operating out there in the world, what does the future, most promising future, look like? Is it us merging with them? Or is it us? Like, how do we keep us humans around when you have increasingly intelligent beings? Is it one of the dreams is to upload our minds in the digital space? So can we just give our minds to these systems? So they can operate on them? Is there some kind of more interesting merger? Or is there more discommunication?

• 1:28:23Speaker 01

  In the third part of my book, I talked about all these scenarios. And let me just walk through them.

• 1:28:27Lex Fridman?

  Sure.

• 1:28:30Speaker 01

  The uploading the mind one.

• 1:28:31Lex Fridman?

  Yes.

• 1:28:32Speaker 01

  Extremely really difficult to do. Like, we have no idea how to do this, even remotely right now. So it would be a very long way away. But I make the argument, you wouldn't like the result. And you wouldn't be pleased with the result. It's really not what you think it's going to be. Imagine I could upload your brain into a computer right now. And now the computer's sitting there going, hey, I'm over here. Great, get rid of that old bio person. I don't need him. You're still sitting here.

• 1:28:59Lex Fridman?

  Yeah.

• 1:28:59Speaker 01

  What are you gonna do? No, no, that's not me. I'm here, right? Are you gonna feel satisfied then? Then, but people imagine, oh, I'm on my deathbed and I'm about to expire. And I push the button and now I'm uploaded. But think about it a little differently. And so I don't think it's going to be a thing because people, by the time we're able to do this, if ever, because you have to replicate the entire body, not just the brain. It's really, I walk through the issues. It's really substantial.

• 1:29:27Lex Fridman?

  Do you have a sense of what makes us us? Is there, is there a shortcut to it can only save a certain part? That makes us truly ours?

• 1:29:36Speaker 01

  No, but I think that machine would feel like it's you too.

• 1:29:39Lex Fridman?

  Right.

• 1:29:39Speaker 01

  Right? You have two people, just like I have a child, right? I have two daughters. They're independent people. I created them, well, partly.

• 1:29:46Lex Fridman?

  Yeah.

• 1:29:47Speaker 01

  And.

• 1:29:50Speaker 01

  I don't, just because they're somewhat like me, I don't feel on them and they don't feel like on me. So if you split apart, you have two people. So we can come back to what, what makes, what consciousness is if you want, we can talk about that. But we don't have a remote consciousness. I'm not sitting there going, oh, I'm conscious of that. You know, I mean, that system over there. So let's stay on our topic.

• 1:30:09Lex Fridman?

  Okay, sure.

• 1:30:10Speaker 01

  One was uploading your brain.

• 1:30:11Lex Fridman?

  Yep.

• 1:30:12Speaker 01

  Ain't gonna happen in a hundred years, maybe a thousand, but I don't think people are gonna wanna do it. The merging your mind with, uh, you know, the neural link thing, right? Like, again, really, really difficult. It's, it's one thing to make progress to control a prosthetic arm. It's another to have like a billion or several billions, you know, things and understanding what those signals mean. Like, it's the one thing that like, okay, I can learn to think some patterns to make something happen. It's quite another thing to have a system, a computer, which actually knows exactly which cells it's talking to and how it's talking to them and interacting in a way like that. Very, very difficult. We're not getting anywhere closer to that.

• 1:30:51Lex Fridman?

  Interesting. Can I, can I, uh, can I ask a question here? What, so for me, what makes that merger very difficult practically in the next 10, 20, 50 years is like literally the biology side of it, which is like, it's just hard to do that kind of surgery in a safe way. But your intuition is even the machine learning part of it, where the machine has to learn what the heck it's talking to. That's even hard.

• 1:31:17Speaker 01

  I think it's even harder. And it's not, it's, it's easy to do when you're talking about hundreds of signals. It's, it's a totally different thing to say, talking about billions of signals.

• 1:31:27Lex Fridman?

  So you don't think it's the raw, it's a machine learning problem? You don't think it could be learned?

• 1:31:31Speaker 01

  Well, I'm just saying, no, I think you'd have to have detailed knowledge. You'd have to know exactly what the types of neurons you're connecting to. I mean, in the brain, there's these, they're neurons that do all different types of things. It's not like a neural network. It's a very complex organism, system up here. We talked about the grid cells earlier, the place cells, you know, you have to know what kind of cells you're talking to and what they're doing and how their timing works and all, all this stuff, which you can't, today is no way of doing that, right? But I think it's, I think it's a, I think the problem, you're right that the biological aspect of it, like who wants to have a surgery and have this stuff inserted in your brain? That's a problem, but this is when we solve that problem. I think the, the information coding aspect is much worse. I think that's much worse. It's not like what they're doing today. Today, it's simple machine learning stuff. Because you're doing simple things.

• 1:32:15Speaker 01

  But if you want to merge your brain, like I'm thinking on the internet, I'm merge my brain with the machine and we're both doing. That's a totally different issue.

• 1:32:23Lex Fridman?

  That's interesting. I I tend to think if the okay, if you have a super clean signal from a bunch of neurons, at the start, you don't know what those neurons are. I think that's much easier than the getting of the clean signal.

• 1:32:39Speaker 01

  I think if you think about today's machine learning, that's what you would conclude. I'm thinking about what's going on in the brain and I don't reach that conclusion. So we'll have to see.

• 1:32:49Lex Fridman?

  Sure.

• 1:32:49Speaker 01

  But I don't think even even then, I think there's kind of a sad future. Like, you know, do I do I have to like plug my brain into a computer? I'm still a biological organism. I assume I'm still gonna die. So what what have I achieved? Right? You know, what have I achieved today?

• 1:33:05Lex Fridman?

  Oh, I I disagree. I that we don't know what those are, but it seems like there could be a lot of different applications. It's like virtual reality is to expand your brain's capability to to to like to read Wikipedia.

• 1:33:18Speaker 01

  Yeah, but but fine, but but you're still a biological organism.

• 1:33:21Lex Fridman?

  Yes, yes. You know, you're still you're still mortal. You still.

• 1:33:24Speaker 01

  All right. So so what are you accomplishing? You're making your life in this short period of time better, right?

• 1:33:29Lex Fridman?

  Just like we uh having the internet made our life better. Yeah.

• 1:33:33Speaker 01

  Yeah, okay. So I I think that's of of if I think about all the possible gains we can have here, that's a marginal one. It's an individual. Hey, I'm better. You know, I'm smarter. But, you know, fine, I'm not against it. I just don't think it's earth changing.

• 1:33:48Lex Fridman?

  I but it so this is the true of the internet. When each of us individuals are smarter, we get a chance to then share our smartness. We get smarter and smarter together as like as a collective. This is kind of like the sand colony of.

• 1:34:01Speaker 01

  But that's why don't I just create an intelligent machine that doesn't have any of this biological nonsense? This is all the same. It is. It's everything except don't burden it with my brain.

• 1:34:10Lex Fridman?

  Yeah.

• 1:34:11Speaker 01

  Right. It has a brain. It is smart. It's like my child, but it's much, much smarter than me. So I have a choice between doing some implant, doing some hybrid weird, you know, biological thing that's bleeding and all these problems and limited by my brain, or creating a system which is super smart that I can talk to, um, that helps me understand the world, that can read the internet, you know, read Wikipedia and talk to me.

• 1:34:32Lex Fridman?

  I, I guess my, uh, the open questions there are, what does the manifestation of superintelligence look like? So like, what are we going to, you, you talked about, why do I want to merge with AI? Like, what, what's the actual marginal benefit here? If I, if we have a super intelligent system.

• 1:34:52Speaker 01

  Yeah.

• 1:34:53Lex Fridman?

  How will it make our life better?

• 1:34:56Speaker 01

  So let's, let's, that's a great question, but let's break it into little pieces. All right. On the one hand, it can make our life better in lots of simple ways. You mentioned like a care robot or something that helps me do things, cooks. I don't know what it does, right? Little things like that, we got super better, smarter cars. We can have, you know, better agents, aids helping us in our work environment and things like that. To me, that's like the easy stuff, the, the simple stuff in the beginning.

• 1:35:20Lex Fridman?

  Um, um, and so in the same way that computers made our lives better in ways, many, many ways,

• 1:35:26Speaker 01

  I will have those kinds of things. To me, the really exciting thing about AI is sort of its transcendent, transcendent quality in terms of humanity. We're still biological organisms. We're still stuck here on Earth. It's going to be hard for us to live anywhere else. Uh, I don't think you and I are going to want to live on Mars anytime soon. Um, and, um, and we're flawed. You know, we may end up destroying ourselves. It's totally possible. Uh, we, if not completely, we could destroy our civilizations. You know, it's just face the fact, we have issues here. But we can create intelligent machines that can help us in various ways. For example, one example I gave, and that sounds a little sci fi, but I believe this.

• 1:36:09Speaker 01

  If we really wanted to live on Mars, we'd have to have intelligent systems that go there and build the habitat for us, not humans. Humans are never gonna do this. It's just too hard. But could we have a thousand or 10,000, you know, engineer workers up there doing this up, building things, terraforming Mars? Sure, maybe we can move Mars. But then, if we wanna, if we wanna go around the universe, should I send my children around the universe? Or should I send some intelligent machine, which is like a child, that represents me and understands our needs here on Earth, that could travel through space? So it's sort of, in some sense, intelligence allows us to transcend our, the limitations of our biology. And don't think of it as a negative thing.

• 1:36:51Speaker 01

  It's in some sense, my children transcend my, my biology too, because they, they live beyond me.

• 1:36:57Lex Fridman?

  Yeah.

• 1:36:58Speaker 01

  And we impart, they represent me, and they also have their own knowledge, and I can impart knowledge to them. So intelligent machines will be like that too, but not limited like us.

• 1:37:06Lex Fridman?

  But the question is, there's so many ways that transcendence can happen, and the merger with AI and humans is one of those ways. So you said intelligent, basically beings or systems propagating throughout the universe, representing us humans.

• 1:37:25Speaker 01

  They represent us humans in the sense they represent our knowledge and our history, not us individually.

• 1:37:30Lex Fridman?

  Right, right. But, I mean, the question, is it just a database with, with a really damn good model?

• 1:37:40Speaker 01

  No, no, they're conscious, conscious just like us.

• 1:37:43Lex Fridman?

  Okay, but just different?

• 1:37:45Speaker 01

  They're different, just like my children are different. They're like me, but they're different. These are more different. I guess maybe I've already... I kind of, I take a very broad view of our life here on Earth. I say, you know, why are we living here? Are we just living because we live? Is it, are we surviving because we can survive? Are we fighting just because we want to just keep going? What's the point of it?

• 1:38:08Lex Fridman?

  Yeah.

• 1:38:09Speaker 01

  Right? So to me, the point, if I ask myself, what's the point of life is? What transcends that ephemeral sort of biological experience is, to me, this is my answer, is the acquisition of knowledge, to understand more about the universe and to explore. And that's partly to learn more, right? I don't view it as a terrible thing if the ultimate outcome of humanity is we create systems that are intelligent, that are offspring, but that are not like us at all. And we stay, we stay here and live on Earth as long as we can, which won't be forever, but as long as we can. And, but that would be a great thing to do.

• 1:38:54Speaker 01

  It, it, it's not a, it's not like a negative thing.

• 1:38:57Lex Fridman?

  Well, would you be okay then if the human species vanishes, but our knowledge is preserved and keeps being expanded by intelligent systems?

• 1:39:11Speaker 01

  I want our knowledge to be preserved and expanded.

• 1:39:14Lex Fridman?

  Yeah.

• 1:39:15Speaker 01

  Am I okay with humans dying? No, I don't want that to happen. But if, if it does happen, what if we, we were sitting here and this is, we were the last two people on Earth and were saying, Lex, we blew it, it's all over, right?

• 1:39:26Lex Fridman?

  Yeah.

• 1:39:27Speaker 01

  Wouldn't I feel better if I knew that our knowledge was preserved and that we had agents that knew about that, that were, you know, that were, that left Earth? I would want that. It's better than not having that, you know? I make the analogy of like, you know, the dinosaurs, the poor dinosaurs, they lived for, you know, tens of millions of years. They raised their kids, they, you know, they, they fought to survive, they were hungry, they, they, you know, they did everything we do, and then they're all gone.

• 1:39:51Lex Fridman?

  Yeah.

• 1:39:51Speaker 01

  Like, you know, and, and if we didn't discover their bones. Nobody would ever know that they ever existed, right? Do we want to be like that? I don't want to be like that.

• 1:40:00Lex Fridman?

  There's a sad aspect to the, and it's kind of, it's jarring to think about that it's possible that a human-like intelligent civilization has previously existed on Earth. The reason I say this is like, it is jarring to think that we would not, if they weren't extinct, we wouldn't be able to find evidence of them.

• 1:40:20Speaker 01

  After a sufficient amount of time.

• 1:40:22Lex Fridman?

  After a sufficient amount of time. Of course, there's like, like basically humans, like if we destroy ourselves now, human civilization destroy ourselves now, after sufficient amount of time, we would not be, we'd find the evidence of the dinosaurs, we would not find evidence of us humans.

• 1:40:38Speaker 01

  Yeah, that's, that's kind of an odd thing to think about, although I'm not sure if we have enough knowledge about species going back for billions of years, that we could, we could, we might be able to eliminate that possibility, but it's an interesting question. Of course, this is a similar question to, you know, lots of intelligent species throughout, throughout our galaxy that have all disappeared.

• 1:40:58Lex Fridman?

  Yeah, that's super sad that, um, there, exactly, that there, there may have been much more intelligent alien civilizations in our galaxy that are no longer there.

• 1:41:08Speaker 01

  Yeah.

• 1:41:09Lex Fridman?

  Um, you actually talked about this, um, that humans might destroy ourselves.

• 1:41:15Speaker 01

  Yeah.

• 1:41:15Lex Fridman?

  And how we might preserve our knowledge.

• 1:41:19Speaker 01

  Yeah.

• 1:41:21Lex Fridman?

  And advertise that knowledge to other.

• 1:41:25Speaker 01

  Advertise is a funny word to use.

• 1:41:27Lex Fridman?

  PR, from a PR perspective.

• 1:41:28Speaker 01

  There's no financial gain in this.

• 1:41:32Lex Fridman?

  You know, like make it like from a tourism perspective, make it interesting. Can you describe how?

• 1:41:37Speaker 01

  Well, there's a couple of things. I, I broke it down into two parts, actually three parts. One is, um, you know, there's a lot of things we know that, what if, what if we were, what if we ended, what if our civilization collapsed? Yeah, I'm not talking tomorrow, you know, we could be a thousand years from now, like, you know, we don't really know, but, but historically would be likely at some point.

• 1:41:57Lex Fridman?

  Time flies when you're having fun.

• 1:41:59Speaker 01

  Yeah, that's a good way to put it. You know, could we, and then, then intelligent life evolved again on this planet? Wouldn't they want to know a lot about us and what we knew? Wouldn't they wouldn't be able to ask us questions? So one very simple thing I said, how would we archive what we know? That was a very simple idea. I said, you know what? It's not wouldn't be that hard to put a few satellites, you know, going around the sun. I mean, upload Wikipedia every day and that kind of thing. So, you know, if we end up killing ourselves, well, it's up there. And the next intelligent species will find it and learn something. That would be, they would like that. They would appreciate that. So that's one thing. The next thing I said, well, what if, you know, how outside of our solar system? We have the SETI program. We're looking for these intelligent signals from everybody.

• 1:42:42Speaker 01

  And if you do a little bit of math, which I did in the book, and you say, well, what if intelligent species only live for 10,000 years before, you know, technologically intelligent species, like ones are really able to do the stuff we're just starting to be able to do? Well, the chances are we wouldn't be able to see any of them because they would have all been disappeared by now. They would, they've lived for 10,000 years and now they're gone. And so we're not going to find these signals being sent from these people because, but I said, what kind of signal could you create that would last a million years or a billion years? That someone would say, damn it, someone smart lived there. We know that. That would be a life changing event for us to figure that out. Well, what we're looking for today in the SETI program isn't that. We're looking for very coded signals in some sense.

• 1:43:25Speaker 01

  And so I asked myself, what would be a different type of signal one could create? I've always thought about this throughout my life. And in the book I gave one, one possible suggestion, which was we now detect planets going around other, other suns, other stars, excuse me. And we do that by seeing this, the, the slight dimming of the light as the planets move in front of them. That's how we detect planets elsewhere in our galaxy. What if we created something like that, that just rotated around our, around the sun? And it blocked out a little bit of the light in a particular pattern that someone said, hey, that's not a planet. That is a sign that someone was once there. You can think, what if it's beating up pie, you know, three point, whatever. So I did.

• 1:44:10Lex Fridman?

  From a distance, you can.

• 1:44:11Speaker 01

  It's from a distance, broadly broadcast, takes no continual activation on our part. This is the key, right? No one has to be sitting here running a computer and supplying it with power. It just goes on. So we go, it continues. And, and I argued that part of the SETI program should be looking for signals like that. And to look for signals like that, you ought to figure out what the, how would we create a signal? Like, what would we create that would be like that? That would persist for millions of years? That would be broadcast broadly, you could see from a distance. There was unacclivable, came from a, a, by, an intelligent species. And so I gave that one example, um, cause they don't know what I know of, actually. And then, and then finally, right?

• 1:44:51Speaker 01

  If, if our, ultimately our solar system will die at some point in time, you know? How do we go beyond that? And I think it's possible, if it's all possible, we'll have to create intelligent machines that travel throughout the, throughout the, the solar system or throughout the galaxy. And I don't think that's going to be humans. I don't think it's going to be biological organisms. So these are just things to think about, you know, like what's the, you know? I, I don't, I don't want to be like the dinosaur. I don't want to just live and I go, that was it, we're done, you know?

• 1:45:19Lex Fridman?

  Well, there is a kind of presumption that we're going to live forever, which, uh, I, I think it is a bit sad to imagine that the message we send is, as you talk about, is that we were once here? Instead of we are here.

• 1:45:35Speaker 01

  Well, it could be we are still here, uh, but it's more of a, it's more of an insurance policy in case we're not here, you know?

• 1:45:43Lex Fridman?

  Well, I don't know, but there's something, I think about, we, we as humans don't often think about this, but it's like, like whenever I, um, record a video, I've, I've done this a couple of times in my life. I've recorded a video for my future self, just for personal, just for fun. And it's always just fascinating to think about that, preserving yourself for future civilizations. For me, it was preserving myself for a future me, but that's a little, that's a little fun example of archival.

• 1:46:19Speaker 01

  Well, these podcasts are, are preserving you and I.

• 1:46:22Lex Fridman?

  In a way, yeah.

• 1:46:22Speaker 01

  For future, um, hopefully well after we're gone.

• 1:46:26Lex Fridman?

  But you don't often, we're sitting here talking about this.

• 1:46:31Lex Fridman?

  You are not thinking about the fact that you and I are going to die. And there'll be like 10 years after, somebody watching this and we're still alive.

• 1:46:41Speaker 01

  You know, in some sense I do. I'm here because I want to talk about ideas. And these ideas transcend me and they transcend this time and in on our planet. We're talking here about ideas that could be around a thousand years from now or a million years from now. I, when I wrote my book, I had an audience in mind and one of the clearest audiences was.

• 1:47:06Lex Fridman?

  Aliens?

• 1:47:06Speaker 01

  No, were people reading this a hundred years from now?

• 1:47:09Lex Fridman?

  Yes.

• 1:47:10Speaker 01

  I said to myself, how do I make this book relevant to someone reading this a hundred years from now? What would they want to know that we were thinking back then? What would make it like that was an interesting, it's still an interesting book. I'm not sure I can achieve that, but that was how I thought about it because these ideas, especially in the third part of the book, the ones we were just talking about, you know, these crazy, it sounds like crazy ideas about, you know, storing our knowledge and, and, you know, merging our brains with computers and sending, you know, our machines out into space. It's not going to happen in my lifetime. And they may not even happen in the next hundred years. It may not happen for a thousand years, who knows? But we have the unique opportunity right now, we, you, me and other people like this,

• 1:47:52Speaker 01

  to sort of at least propose the agenda that might impact the future like that.

• 1:47:58Lex Fridman?

  That's a fascinating way to think, both like writing or creating. Try to make, try to create ideas, try to create things that hold up in time.

• 1:48:10Speaker 01

  Yeah, you know, understanding how the brain works, we're going to figure that out once. That's it. It's going to be figured out once. And after that, that's the answer and people will, people will study that thousands of years now. We still, we still, you know, venerate Newton and Einstein. And, and, you know, because, because ideas are exciting even well into the future.

• 1:48:33Lex Fridman?

  Well, the interesting thing is like big ideas, even if they're wrong, are still useful. Like.

• 1:48:41Speaker 01

  Yeah, especially if they're not completely wrong.

• 1:48:43Lex Fridman?

  Like, you're right, right, right.

• 1:48:44Speaker 01

  Right, Newton's laws are not wrong, they're just Einstein's are better. So.

• 1:48:50Lex Fridman?

  Well, it's, so, yeah, I mean, but we're talking, when Newton and Einstein were talking about physics, I wonder if we'll ever achieve that kind of clarity about understanding, um, like, complex systems and the, this particular manifestation of complex systems, which is the human brain.

• 1:49:05Speaker 01

  Oh, I'm, I'm totally optimistic we can do that. I mean, we're making progress at it. I don't see any reason why we can't completely, I mean, completely understand in the sense, um, you know, we don't really completely understand what all the molecules in this water bottle are doing. But, you know, we have laws that sort of capture it pretty good. And, uh, so we'll have that kind of understanding. I mean, it's not like you're gonna have to know what every neuron in your brain is doing.

• 1:49:30Lex Fridman?

  But enough to, uh, first of all, to build it.

• 1:49:33Speaker 01

  Yeah.

• 1:49:33Lex Fridman?

  And second of all, to do, you know, do what physics does, which is like have, uh, concrete experiments where we can validate.

• 1:49:40Speaker 01

  I, I, we're, we're, we're, this is happening right now. Like, it's not, this is not some future thing. You know, I'm very optimistic about, I know about our, our work and what we're doing. We'll have to prove it to people. Um, but, um,

• 1:49:56Speaker 01

  I, I, I consider myself a rational person and, um, you know, until fairly recently, I wouldn't have said that. But right now, where I'm sitting right now, I'm saying, you know, we, we could, this is gonna happen. There's, there's no big obstacles to it. Um, we finally have a framework for understanding what's going on in the cortex and, um, and that's liberating. It's, it's like, oh, it's happening. So, I, I can't see why we wouldn't be able to understand it. I just can't.

• 1:50:22Lex Fridman?

  Okay. Oh, so, I mean, on that topic, let me ask you to play devil's advocate. Is it possible for you to imagine, look, look 100 years from now, and looking at your book,

• 1:50:37Lex Fridman?

  in which ways might your ideas be wrong?

• 1:50:40Speaker 01

  Oh, I worry about this all the time. Um.

• 1:50:45Lex Fridman?

  That's still useful.

• 1:50:46Speaker 01

  Yeah. Yeah.

• 1:50:50Speaker 01

  I think there's, you know, um, well, I can, I can best relate it to like things I'm worried about right now. So we talked about this voting idea, right? It's happening. There's, there's no question it's happening, but it could be far more. There's, there's enough things I don't know about it that it might be working in ways differently than I'm thinking about the kind of what's voting, who's voting? You know, where are representations? I talked about, like, you have a thousand models of a coffee cup like that. That could turn out to be wrong, uh, because it may be, maybe there are a thousand models that are sub models, but not really a single model of the coffee cup. I mean, there's things, these are all sort of on the edges, things that I, I present as like, oh, it's so simple and clean. Well, that's not that.

• 1:51:35Speaker 01

  It's always going to be more complex and, um, and there's parts of the theory which I don't understand the complexity well. So I think, I think the idea of this brain is a distributed modeling system is not controversial at all, right? It's not, that's well understood by many people. The question then is, are each cortical column an independent modeling system?

• 1:51:59Speaker 01

  I could be wrong about that. Um, I don't think so, but I worry about it.

• 1:52:04Lex Fridman?

  My intuition, not even thinking why you could be wrong, is the same intuition I have about any sort of physicist, uh, like string theory. That we as humans desire for a clean explanation and a hundred years from now. Intelligent systems might look back at us and laugh at how we try to get rid of the whole mess. By having simple explanation, when the reality is it's, it's way messier. And in fact, it's impossible to understand. You can only build it. It's like this idea of complex systems and cellular automata. You can only launch the thing, you cannot understand it.

• 1:52:43Speaker 01

  Yeah, I think that, you know, the history of science suggests that's not likely to occur. The history of science suggests that, look, as a theorist, and we're theorists, you look for simple explanations, right? Fully knowing that whatever simple explanation you're going to come up with is not going to be completely correct. I mean, it can't be. I mean, it's just, it's just more complexity. But that's the role of theorists play. They, they sort of, they give you a framework on which you now can talk about a problem and figure out, okay, now we can start digging more details. The best frameworks stick around while the details change. You know, again, you know, the classic example is Newton and Einstein, right?

• 1:53:25Speaker 01

  You know, Newton's theories are still used. They're still valuable. They're still practical. They're not like wrong. It's just they've been refined.

• 1:53:34Lex Fridman?

  Yeah, but that's in physics. It's not obvious, by the way. It's not obvious for physics either that the universe should be such that's amenable to these simple.

• 1:53:43Speaker 01

  But it's so far it appears to be, as far as we can tell.

• 1:53:48Lex Fridman?

  Yeah, I mean, but as far as we could tell, and but it's also an open question whether the brain is amenable to such clean theories. That's the, not the brain, but intelligence.

• 1:53:59Speaker 01

  Well, I, I, I don't know. I would take intelligence out of it. Just say, you know, um, well, okay.

• 1:54:07Speaker 01

  The evidence we have suggests that the human brain is, is, is A, at the one time extremely messy and complex, but there's some parts that are very regular and structured. That's why we started the neocortex. It's extremely regular in its structure.

• 1:54:21Lex Fridman?

  Yeah.

• 1:54:21Speaker 01

  And unbelievably so. And then I mentioned earlier, the other thing is it's, it's universal abilities. It is so flexible to learn so many things. We don't, we haven't figured out what it can't learn yet. We don't know, but we haven't figured it out yet. But it learns things that it never was evolved to learn. So those give us hope. That's why I went into this field because I said, you know, this regular structure, it's doing this amazing number of things. There's got to be some underlying principles that are, that are common. And other, other scientists have come up with the same conclusions. And so.

• 1:54:55Lex Fridman?

  It's promising.

• 1:54:56Speaker 01

  It's promising. And, and that's, and whether the theories play out exactly this way or not, that is the role that theorists play. And so far it's worked out well, even though, you know, maybe, you know, we don't understand all the laws of physics, but so far it's been pretty damn useful. The ones we have, our, our, our theories are pretty bit useful.

• 1:55:17Lex Fridman?

  You mentioned that we should not necessarily be, at least to the degree that we are, worried about the existential risks of artificial intelligence relative to human risks from human nature being existential risk. What aspect of human nature worries you the most in terms of the survival of the human species?

• 1:55:39Speaker 01

  I'm disappointed in humanity, in humans. I mean, all of us, I'm one, so I'm disappointed myself, too. It's kind of a sad state. There's two things that disappoint me. One is how it's difficult for us to separate our rational component of ourselves from our evolutionary heritage, which is, you know, not always pretty, you know? Rape is an evolutionary good strategy for reproduction. Murder can be at times, too. You know, making other people miserable at times is a good strategy for reproduction.

• 1:56:20Speaker 01

  It's just, and so now that we know that, and yet we have this sort of, you know, you and I can have this very rational discussion, talking about, you know, intelligence and brains and life and so on. So it seems like it's so hard, it's just a big transition to get humans, all humans, to make the transition from being like, let's pay no attention to all that ugly stuff over here. Let's just focus on the interesting. What's unique about humanity is our knowledge and our intellect.

• 1:56:47Lex Fridman?

  But the fact that we're striving is in itself amazing, right? The fact that we're able to overcome that part, and it seems like we are more and more becoming successful at overcoming that part.

• 1:57:00Speaker 01

  That is the optimistic view, and I agree with you.

• 1:57:02Lex Fridman?

  Yeah.

• 1:57:03Speaker 01

  But I worry about it. I'm not saying I'm worrying about it. I think maybe that was your question. I still worry about it.

• 1:57:07Lex Fridman?

  Yes.

• 1:57:08Speaker 01

  You know, we could end tomorrow because some terrorist could get nuclear bombs and, you know, blow us all up. Who knows, right? The other thing I think I'm disappointed is, and it's just, I understand it, it's, I guess you can't really be disappointed, it's just a fact, is that we're so prone to false beliefs. We, you know, we have a model in our head. The things we can interact with directly, physical objects, people, that model's pretty good, and we can test it all the time, right? I touch something, I look at it, I talk to you, see, my model's correct. But so much of what we know is stuff I can't directly interact with. I can't, I only know because someone told me about it. And so, so we're prone, inherently prone, to having false beliefs because if I'm told something, how am I gonna know it's right or wrong, right?

• 1:57:53Speaker 01

  And so then we have the scientific process, which says, we are inherently flawed. So the only way we can get closer to the truth is by looking for contrary evidence.

• 1:58:06Lex Fridman?

  Yeah. Like this conspiracy theory, this, this theory that scientists keep telling me about, that the earth is round. As far as I can tell, when I look out, it looks pretty flat.

• 1:58:18Speaker 01

  Yeah.

• 1:58:19Lex Fridman?

  So, yeah, there is, there is a tension, but it's also, I tend to believe that we haven't figured out most of this thing, right? Most of nature around us is a mystery, and so it.

• 1:58:34Speaker 01

  But that doesn't worry, does that worry you? I mean, it's like, oh, that's, that's like a pleasure, more to figure out, right?

• 1:58:39Lex Fridman?

  Yeah, that's exciting, but I'm, I'm saying like, there's going to be a lot of quote unquote, wrong ideas. I mean, I've been thinking a lot about engineering systems like social networks and so on, and I've been worried about censorship and thinking through all that kind of stuff, because there's a lot of wrong ideas, there's a lot of dangerous ideas. But then I also read a history, read history and see when you censor ideas that are wrong. Now, this could be a small scale censorship, like a young grad student who comes up, who like raises their hand and says some crazy idea. A form of censorship could be, I shouldn't use the word censorship, but like, de-incentivize them from, no, no, no, no, this is the way it's been.

• 1:59:24Speaker 01

  Yeah, yeah, you're a foolish kid, don't think.

• 1:59:26Lex Fridman?

  Yeah, you're foolish. So in some sense, those wrong ideas, most of the time end up being wrong, but sometimes end up being.

• 1:59:37Speaker 01

  I agree with you. So I don't like the word censorship. At the very end of the book, I, I ended up with a sort of a plea or a recommended force of action. And the best way I could, I know how to deal with this issue that you bring up. Is if everybody understood as part of your upbringing in life, something about how your brain works, that it builds a model of the world, uh, how it work, you know, how basically builds that model of the world. And that the model is not the real world, it's just a model. And it's never going to reflect the entire world and it can be wrong. And it's easy to be wrong. And here's all the ways you can get a wrong model in your head, right?

• 2:00:20Speaker 01

  It's not prescribe what's right or wrong, just understand that process. If we all understood the processes and I got together and you say, I disagree with you, Jeff. And I said, Lex, I disagree with you that. At least we understand that we're both trying to model something. We both have different information, which leads to our different models.

• 2:00:36Lex Fridman?

  Yeah.

• 2:00:36Speaker 01

  And therefore I shouldn't hold it against you and you shouldn't hold it against me. And we can at least agree that, well, what can we look for in, in that's common ground to test our, our beliefs?

• 2:00:45Lex Fridman?

  Hmm.

• 2:00:46Speaker 01

  As opposed to so much, uh, as we raise our kids on dogma, which is this is a fact and this is a fact and these people are bad and, and, you know, where if everyone knew just to, to be skeptical of every belief and why and how their brains do that, I think we might have a better world.

• 2:01:06Lex Fridman?

  Do you think the human mind is able to comprehend reality? So you talk about creating models that are better and better. How close do you think we get to, uh, to reality? There's sort of the wildest ideas is like Donald Hoffman saying, we're very far away from reality. Uh, do you think we're getting close to reality or no?

• 2:01:29Speaker 01

  Because it depends on what you define reality. Uh, we are getting, we have a model of the world that's very useful, right?

• 2:01:36Lex Fridman?

  For, for basic goals of survival.

• 2:01:38Speaker 01

  And our, the things we want, pleasure, whatever, right? Um, so that's useful. Um, I mean, it's really useful. Oh, we can build planes, we can build computers, we can do these things, right? Uh, I don't think, I, I don't know the answer to that question. Um, I think that's part of the question we're trying to figure out, right? Like, you know, obviously, if you end up with a theory of everything, that really is a theory of everything, and all of a sudden, everything comes into play, and there's no room for something else, then you might feel like we have a good model of the world.

• 2:02:08Lex Fridman?

  Yeah, but if we have a theory of everything, and somehow, first of all, you'll never be able to really conclusively say it's a theory of everything, but say somehow, we are very damn sure it's a theory of everything. We understand what happened at the Big Bang and how just the entirety of the physical process. I'm still not sure that gives us an understanding of the next many layers of the hierarchy.

• 2:02:32Speaker 01

  Yeah, maybe not.

• 2:02:33Lex Fridman?

  Abstractions that form.

• 2:02:34Speaker 01

  Well, also, what if string theory turns out to be true? And then you say, well, we have no reality, no modeling, what's going on in those other dimensions that are wrapped into it on each other, right? Or the multiverse, you know?

• 2:02:47Lex Fridman?

  I honestly don't know how, for us, for human interaction, for ideas of intelligence, how it helps us to understand that we're made up of vibrating strings that are like 10 to the whatever times smaller than us. I don't, you know, you could probably build better weapons or better rockets, but you're not going to be able to understand intelligence.

• 2:03:08Speaker 01

  I guess, I guess.

• 2:03:09Lex Fridman?

  Maybe better computers.

• 2:03:10Speaker 01

  No, you won't be able, I think it's this more purely knowledge. It might lead to a better understanding of the beginning of the universe, right? It might lead to a better understanding of, I don't know. I guess, I think the acquisition of knowledge has always been one where you pursue it for its own pleasure, and you don't always know what is going to make a difference or not.

• 2:03:35Lex Fridman?

  Yeah, you're pleasantly surprised by the weird things you find. Do you think, for the neocortex in general, do you think there's a lot of innovation to be done on the machine side? You know, you use the computer as a metaphor quite a bit. Is there different types of computer that would help us build?

• 2:03:54Speaker 01

  I mean, what are the physical manifestations of intelligent machines?

• 2:03:57Lex Fridman?

  Yeah.

• 2:03:57Speaker 01

  Or is it? Oh, no, it's going to be totally crazy. We have no idea how this is going to look out yet. You can already see this. Today, of course, we model these things on traditional computers. And now GPUs are really popular with neural networks and so on. But there are companies coming up with fundamentally new physical substrates. That are just really cool. I don't know if they're gonna work or not.

• 2:04:26Speaker 01

  But I think there'll be decades of innovation here.

• 2:04:29Lex Fridman?

  Yeah.

• 2:04:29Speaker 01

  Totally.

• 2:04:30Lex Fridman?

  Do you think the final thing will be messy like our biology is messy? Or do you think it's the it's the old bird versus airplane question? Or do you think we could just

• 2:04:43Lex Fridman?

  build airplanes?

• 2:04:44Speaker 01

  Yeah.

• 2:04:46Lex Fridman?

  That that fly way better than birds in the same way we can build

• 2:04:52Lex Fridman?

  electrical neural cortex.

• 2:04:54Speaker 01

  Yeah. You know, can I can I can I riff on the bird thing a bit? Because I think it's interesting. People really misunderstand this. The Wright Brothers.

• 2:05:03Speaker 01

  The problem they were trying to solve was controlled flight. How to turn an airplane? Not how to propel an airplane. They weren't worried about that.

• 2:05:10Lex Fridman?

  Interesting. Yeah.

• 2:05:11Speaker 01

  They already had, at that time, there was already wing shapes, which they had from studying birds. There's already gliders that carry people. The problem is if you put a rudder on the back of a glider and you turn it, the plane falls out of the sky. So the problem was, how do you control flight? And they studied birds and they actually had birds in captivity. They watched birds in wind tunnels and they observed them in the wild. And they discovered the secret was the birds twist their wings when they turn. And so that's what they did on the Wright Brothers Flyer. They had these sticks that you would twist the wing. And that was that was their innovation, not their propeller. And today, airplanes still twist their wings. We don't twist the entire wing, we just twist the tail end of it. The the the flaps, which is the same thing. So today's airplanes fly on the same principles as birds would observe by.

• 2:05:56Speaker 01

  So everyone get that analogy wrong. But let's step back from that, right? Once you understand the principles of flight, you can choose how to implement them.

• 2:06:05Lex Fridman?

  Yeah.

• 2:06:05Speaker 01

  No one's gonna use bones and feathers and muscles. But they do have wings and we don't flap them, we have propellers. So when we have the principles of of computation that goes on to modeling the world in a brain. We understand those principles right clearly. We have choices on how to implement them. And some of them be biological like and some won't. And But I do think there's gonna be a huge amount of innovation here. Just think about the innovation when in the computer, they had to invent the transistor, they invented the silicon chip, they had to invent, you know, then the software. I mean, there's millions of things they had to do, memory systems. We're gonna do, it's gonna be similar.

• 2:06:46Lex Fridman?

  Well, it's interesting that the deep learning, the effectiveness of deep learning for specific tasks is driving a lot of innovation in the hardware, which may have effects for actually allowing us to discover intelligent systems that operate very differently, or at least much bigger than deep learning.

• 2:07:04Speaker 01

  Yeah, interesting.

• 2:07:05Lex Fridman?

  So ultimately, it's good to have an application that's making our life better now because the capitalist process, if you can make money.

• 2:07:14Speaker 01

  Yeah, yeah.

• 2:07:15Lex Fridman?

  That works. I mean, the other way, Neil deGrasse Tyson writes about this, is the other way we fund science, of course, is through military, so like conquests, so.

• 2:07:25Speaker 01

  Here, here's an interesting thing we're doing on this regard. So we've decided, we used to have a series of these biological principles, and we can see how to build these intelligent machines, but we've decided to apply some of these principles to today's machine learning techniques. So one of the, we didn't talk about this principle, one is sparsity in the brain. Most of the neurons are inactive at any point in time, sparse, and the connectivity is sparse, and that's different than deep learning networks.

• 2:07:50Speaker 01

  So we've already shown that we can speed up existing deep learning networks anywhere from ten to a factor of a hundred, I mean, literally a hundred, and make them more robust at the same time. So this is commercially very, very valuable. And so, you know, if we can prove this actually in the largest systems that are commercially applied today, there's a big commercial desire to do this. Well, sparsity is something that doesn't run really well on existing hardware. It doesn't really run really well on GPUs and on CPUs. And so that would be a way of sort of bringing more, more brain principles into the existing system on a commercially valuable basis.

• 2:08:35Speaker 01

  Another thing we can think we can do is we're gonna use these dendrites, models of, we, I talked earlier about the prediction occurring inside a neuron, that basic property can be applied to existing neural networks and allow them to learn continuously, which is something they don't do today. And so the

• 2:08:52Lex Fridman?

  The dendritic spikes that you were talking about.

• 2:08:55Speaker 01

  Yeah, well, we wouldn't model them as spikes, but the idea that you have that today's neural networks have this company called a point neurons, which is a very simple model of a neuron. And by adding dendrites to them, it's just one more level of complexity. That's in biological systems, you can solve problems in continuous learning and rapid learning. So we're trying to take, we're trying to bring the existing field. And we'll see if we can do it. We're trying to bring the existing field of machine learning commercially along with us. You brought up this idea of keeping, you know, paying for it commercially along with us as we move towards the ultimate goal of a true AI system.

• 2:09:32Lex Fridman?

  Even small innovations on neural networks are really, really exciting.

• 2:09:35Speaker 01

  Yeah.

• 2:09:36Lex Fridman?

  Is it seems like such a trivial model of the brain and applying different insights. That just even like you said, continuous learning or making it more asynchronous. Or maybe make it more dynamic or like incentivizing.

• 2:09:56Speaker 01

  Or more robust, even just more robust.

• 2:09:58Lex Fridman?

  Uh, and making it somehow much better incentivizing sparsity somehow.

• 2:10:05Speaker 01

  Yeah. Well, if you can make things a hundred times faster, then there's plenty of incentive.

• 2:10:10Lex Fridman?

  That's true.

• 2:10:11Speaker 01

  People, people are spending millions of dollars, you know, just training some of these networks now, these, uh, these transformer networks.

• 2:10:18Lex Fridman?

  Let me ask you the big question, how for young people listening to this today in high school and college, what advice would you give them? In terms of which career path to take and maybe just about life in general.

• 2:10:34Speaker 01

  Well, in my case, um, I didn't start life with any kind of goals. I was, when I was going to college, it's like, oh, what do I study? Well, maybe I'll do this electrical engineering stuff, you know? It wasn't like, you know, today you see some of these young kids are so motivated, like I'll change the world. I was like, you know, but they have it. And, um, but then I did fall in love with something besides my wife, but I fell in love with this like, oh my God, it would be so cool to understand how the brain works. And then I, I said to myself, that's the most important thing I could work on. I can't imagine anything more important because if you understand how the brain's work, you build intelligent machines and they could figure out all the other big questions of the world, right? So, and then I said, but I want to understand how I work. So I fell in love with this idea and I became passionate about it.

• 2:11:19Speaker 01

  And this is, you know, a trope, people say this, but it was, it's true. Because I was passionate about it, I was able to put up almost so much crap.

• 2:11:31Speaker 01

  You know, I was, I was in that, you know, I was that person said, you can't do this. I was, I was a graduate student at Berkeley when they said, you can't study this problem. You know, no one's can solve this, or you can't get funded for it. You know, then I went to do, you know, mobile computing and it was like, people say, you can't do that. You can't build a cellphone, you know? So, but all along I kept being motivated because I wanted to work on this problem. I said, I want to understand the brain works. And I got myself, you know, I got one lifetime, I'm going to figure it out, do the best I can. So by having that, because you know, these, it's really, as you pointed out, Lex, it's really hard to do these things. People, it just, there's so many downers along the way. So many obstacles that get in your way. Yeah, I'm sitting here happy all the time, but trust me, it's not always like that.

• 2:12:13Lex Fridman?

  That's, I guess, the happiness, that the, the passion is a prerequisite for surviving.

• 2:12:19Speaker 01

  Yeah, I think so. I think that's right. And so I, I don't want to sit to someone and say, you know, you need to find a passion and do it. No, maybe you don't. But if you do find something you're passionate about, then, then you can follow it as far as your passion will let you put up with it.

• 2:12:35Lex Fridman?

  Do you remember how you found it, how the spark happened?

• 2:12:40Speaker 01

  And why specifically for me?

• 2:12:42Lex Fridman?

  Yeah, like, because you said, it's such an interesting, so like, almost like later in life, by later, I mean, like, not when you were five.

• 2:12:49Speaker 01

  Yeah.

• 2:12:50Lex Fridman?

  You, you didn't really know, and then all of a sudden you fell in love with it.

• 2:12:53Speaker 01

  Yeah, yeah, I, there was, there was, there was two separate events that compounded one another. One, when I was probably a teenager, it might have been seventeen or eighteen. I made a list of the most interesting problems I could think of. First was, why does the universe exist? Seems like not existing is more likely.

• 2:13:10Lex Fridman?

  Yeah.

• 2:13:10Speaker 01

  The second one was, well, given it exists, why does it behave the way it does? You know, it's laws of physics, why is it equal mc squared, not mc cubed? You know, that's an interesting question, I don't know. Third one was, like, what's the origin of life? And the fourth one was, what's intelligence? And I stopped there. I said, well, that's probably the most interesting one. And I put that aside as a teenager. But then, when I was twenty-two. And I was reading the, um, no, it was, oh, excuse me, I was seventy, it was nineteen seventy nine, excuse me, nineteen seventy nine. I was reading, uh, so I was, at that time I was twenty two. Uh, I was reading, uh, the September issue of Scientific American, which is all about the brain. And then the final essay was by Francis Crick, who, of DNA fame.

• 2:13:53Speaker 01

  And he had taken his interest to studying the brain now. And he said, you know, there's something wrong here. He says, we got all this data. All this fact, this is nineteen seventy nine, all these facts about the brain, tons and tons of facts about the brain. Do we need more facts? Or do we just need to think about a way of rearranging the facts we have? Maybe we're just not thinking about the problem correctly.

• 2:14:15Lex Fridman?

  Yeah.

• 2:14:16Speaker 01

  You know, cause he says, this shouldn't be, this shouldn't be like this, you know? So I read that and I said, wow. I said, I don't have to become like an experimental neuroscientist. I could just look at all those facts and try to, and become a theoretician and try to figure it out. And I said, that I felt like it was something I would be good at. I said, I wouldn't be a good experimentalist, I don't have the patience for it. But I'm, I'm a good thinker and I love puzzles. And this is like the biggest puzzle in the world, it's the biggest puzzle of all time. Like all the puzzle pieces in front of me. Damn, that was exciting.

• 2:14:53Lex Fridman?

  And there's something, obviously you can't convert it to words, that it just kinda sparked this passion. And I have that a few times in my life, just something, um,

• 2:15:03Speaker 01

  Yeah.

• 2:15:04Lex Fridman?

  Just, just like you, uh, it grabs you.

• 2:15:07Speaker 01

  Yeah, I thought that was something that was both important that I could make a contribution to.

• 2:15:11Lex Fridman?

  Yeah.

• 2:15:12Speaker 01

  And so all of a sudden it felt like, oh, it gave me purpose in life.

• 2:15:14Lex Fridman?

  Yeah.

• 2:15:15Speaker 01

  You know?

• 2:15:15Lex Fridman?

  I honestly don't think it has to be as big as one of those four questions.

• 2:15:19Speaker 01

  No, no.

• 2:15:19Lex Fridman?

  I think you could, you can find those things in, in the smallest.

• 2:15:23Speaker 01

  Oh, absolutely.

• 2:15:23Lex Fridman?

  I'm with, uh, David Foster Wallace said like the key to life is to be unborable. Um, I think, I think it's very possible to find that intensity of joy in the smallest of things.

• 2:15:34Speaker 01

  Absolutely, I'm just, you asked me my story.

• 2:15:36Lex Fridman?

  Yeah, yeah, no, but I'm actually speaking to the audience. It doesn't have to be those four. You happen to get excited by one of the bigger questions of.

• 2:15:45Lex Fridman?

  In the universe, but uh, but that even the smallest things and watching the Olympics now.

• 2:15:50Speaker 01

  Oh, yeah.

• 2:15:50Lex Fridman?

  Uh, just giving yourself life, uh, giving your life over to the study and the mastery of a particular sport is fascinating and, and uh, if, if it sparks joy and passion, you're able to, in the case of the Olympics, basically suffer for like a couple of decades to achieve.

• 2:16:08Speaker 01

  I mean, you can find joy and passion just being a parent. I mean, it's.

• 2:16:11Lex Fridman?

  Yeah. Yeah. The, the parenting one is funny. So I, I was, uh, not always, but for a long time wanted kids and get married and stuff. And especially that has to do with the fact that I've seen a lot of people that I respect get a whole nother level of joy from kids. And, uh, you know, at first is like, you're thinking is, well, like, I don't have enough time in the day, right? If I have this passion to solve.

• 2:16:39Speaker 01

  Which is true.

• 2:16:39Lex Fridman?

  Yeah. Yes. But like, if I want to solve intelligence, how's this kid's situation going to help me? But then you, you realize that, uh,

• 2:16:50Lex Fridman?

  you know, like you said, the things that sparks joy and it's very possible that kids can provide even a greater or deeper, more meaningful joy than those bigger questions.

• 2:17:01Speaker 01

  Yeah.

• 2:17:01Lex Fridman?

  When they, they enrich each other. And that, that seemed like, um, obviously when I was younger, it's probably a counterintuitive notion because there's only so many hours in the day. But then life is finite and you have to pick the things that give, give you joy.

• 2:17:15Speaker 01

  Yeah. But you know, also you understand you, you can be patient too. I mean, it's finite, but we do have, you know, whatever 50 years or something.

• 2:17:24Lex Fridman?

  It's also long. Yeah.

• 2:17:25Speaker 01

  So in my case, you know, in my case, I had to give up on my dream of the neuroscience because I, I was a graduate student at Berkeley and they told me I couldn't do this and I couldn't get funded. And, you know, and, and so I went back in, in, went back in the computing industry for a number of years. I thought it would be four, but it turned out to be more. But I said, but I said, I'll come back. You know, I definitely, I'm definitely going to come back. I know I'm going to do this computer stuff for a while, but I'm definitely coming back. Everyone knows that. And it's same with like raising kids. Well, yeah, you still, you have to spend a lot of time with your kids. It's fun, enjoyable. Um, but that doesn't mean you have to give up on other dreams. It just means that you have, you have to wait a week or two to work on that next idea.

• 2:18:04Lex Fridman?

  Well, you, you talked about the.

• 2:18:08Lex Fridman?

  The darker side of me, disappointing sides of human nature that we're hoping to overcome so that we don't destroy ourselves. I tend to put a lot of value in the broad general concept of love, of the human capacity to, of compassion towards each other, of just kindness, whatever that longing of like just the human, human to human connection. It connects back to our initial discussion. I tend to see a lot of value in this collective intelligence aspect. I think some of the magic of human civilization happens when there's, a party is not as fun when you're alone.

• 2:18:46Speaker 01

  Yeah, I totally agree with you on these issues.

• 2:18:49Lex Fridman?

  Do you think, from a neurocortex perspective, what role does love play in the human condition?

• 2:18:56Speaker 01

  Well, those are two separate things from a neurocortex point of view. I don't think it, it doesn't impact our thinking about human, about the neurocortex. From a human condition point of view, I think it's core. I mean, we get so much pleasure out of loving people and helping people. So, you know, I, I'll rack it up to old brain stuff and maybe we can throw it under the bus of evolution if you want. That's fine. It doesn't impact how I think about how we model the world, but from a humanity point of view, I think it's essential.

• 2:19:32Lex Fridman?

  Well, I tend to give it to the new brain and also I tend to think that some aspects of that need to be engineered into AI systems, both in their ability to have compassion for other humans and their ability to maximize love in the world between humans. So I'm more thinking about the social networks. So like whenever there's a deep integration between AI systems and humans, so specific applications where it's AI and humans, I think that's something that's often not talked about in terms of.

• 2:20:10Lex Fridman?

  Metrics over which you try to maximize,

• 2:20:14Speaker 01

  Yeah.

• 2:20:15Lex Fridman?

  Uh, like which metric to maximize in a system. It seems like one of the most powerful things in societies is the capacity to love.

• 2:20:26Speaker 01

  It's fascinating. I think it's a great way of thinking about it. You know, I have, I have been thinking more of these fundamental mechanisms in the brain as opposed to the social interaction between, the interaction between humans and AI systems in the future, which is, and I think if you think about that, you're absolutely right. But that's, that's a complex system. I can have intelligent systems that don't have that component, but they're not interacting with people, you know, they're just running something or building, building someplace or something, I don't know. But if you think about interacting with humans, yeah, it's, it's gonna, but it has to be engineered in there. I don't think it's gonna appear on its own.

• 2:21:02Lex Fridman?

  That's a good question.

• 2:21:04Speaker 01

  Yeah, well, we could.

• 2:21:05Lex Fridman?

  But in terms of, uh, uh, from a reinforcement learning perspective, whether the darker sides of human nature or the better angels of our nature win out.

• 2:21:19Speaker 01

  Yeah.

• 2:21:20Lex Fridman?

  Statistically speaking, I don't know. I tend to be optimistic and hope that love wins out in the end. You've done a lot of incredible stuff and your book is driving towards this fourth question that you started with on the nature of intelligence. What do you hope your legacy for people reading a hundred years from now? How do you hope they remember your work? How do you hope they remember this book?

• 2:21:49Speaker 01

  Well, I think as an entrepreneur or scientist or any human who's trying to accomplish some things, I have a view that really all you can do is accelerate the inevitable.

• 2:22:03Speaker 01

  It's like, you know, if we didn't figure out, if we didn't study the brain, someone else would study the brain. If, you know, if Elon Musk didn't make electric cars, someone else would do it eventually. And if, you know, if Thomas Edison didn't invent a light bulb, we wouldn't be using candles today. So what, what you can do as an individual is you can accelerate something that's beneficial and make it happen sooner than would have. That's, that's really it. That's all you can do. You can't create a new reality that it wasn't gonna happen. So from that perspective, I would hope that our work, not just me, but our work in general, people would look back and said, hey, they really helped make this better

• 2:22:42Speaker 01

  future happen sooner. They, you know, they helped us understand the nature of false beliefs sooner than we might have. They made it. Now we're so happy that we have these intelligent machines doing these things, helping us that that maybe that solved the climate change problem and they made it happen sooner. So I think that's the best I would hope for. Some would say, those guys just moved the needle forward a little bit in time.

• 2:23:06Lex Fridman?

  Well, I do, it feels like the progress of human civilization is not, is, there's a lot of trajectories. And if you have individuals that accelerate towards one direction, that helps steer human civilization. So I think in a long stretch of time, all, all trajectories will be traveled. But I think it's nice for this particular civilization on earth to travel down one that's.

• 2:23:34Speaker 01

  Yeah, well, I think you're right. Look, we have the, take the whole period of, you know, World War II Nazism or something like that. Well, that was a bad sidestep, right? We've been over there for a while. But, you know, there is the optimistic view about life that, that ultimately it does converge in a positive way. It progresses ultimately, even if we have years of darkness. So, yeah, so I think you could perhaps, that's accelerating the positive could also mean eliminating some bad missteps along the way, too. But, but I'm an optimistic in that way. I, you know, despite we're talking about the end of civilization, you know, I, I think we're going to live for a long time. I hope we are. I think our society in the future is going to be better. We're going to have less discord. We're going to have less people killing each other, you know, we'll solve,

• 2:24:19Speaker 01

  we'll make them live in some sort of way that's compatible with the carrying capacity of the earth. I'm optimistic these things will happen. And all we can do is try to get there sooner.

• 2:24:30Lex Fridman?

  And at the very least, if we do destroy ourselves, we'll have a few satellites orbiting

• 2:24:36Lex Fridman?

  that will tell alien civilizations that we were once

• 2:24:40Speaker 01

  Or maybe our future, you know, future inhabitants of Earth, you know, imagine we, you know, the planet of the ape scenario, you know, we kill ourselves, you know, a million years from now, a billion years from now, there's another species on the planet.

• 2:24:51Lex Fridman?

  Curious creatures were once here.

• 2:24:53Speaker 01

  Yeah.

• 2:24:54Lex Fridman?

  Jeff, thank you so much for your work and thank you so much for talking to me once again.

• 2:24:59Speaker 01

  Well, it's great. I love what you do. I love your podcast. You have the most interesting people, me aside.

• 2:25:05Speaker 01

  So it's a real service, I think you do for, in a very broader sense, for humanity, I think.

• 2:25:12Lex Fridman?

  Thanks, Jeff.

• 2:25:12Speaker 01

  All right. It's a pleasure.

• 2:25:14Lex Fridman?

  Thanks for listening to this conversation with Jeff Hawkins, and thank you to Code Academy, BioOptimizers, ExpressVPN, Aesleep, and Blinkist. Check them out in the description to support this podcast. And now let me leave you with some words from Albert Camus. "An intellectual is someone whose mind watches itself." I like this because I'm happy to be both halves, the watcher and the watched. Can they be brought together? This is a practical question we must try to answer. Thank you for listening and hope to see you next time.