How to train ChatGPT to serve you | AI Legend Terry Sejnowski [Ep. 475]

Please join my mailing list here 👉 https://briankeating.com/list to win a meteorite 💥 Can AI tools like ChatGPT actually "think," or is it just really good at mimicking human conversation? How much of what AI spits out is a reflection of our own ideas and intentions? And where's all this tech headed in the future? Today, I’m joined by Terry Sejnowski, a renowned computational neuroscientist and pioneer in AI and deep learning. Based at the Salk Institute for Biological Studies and the University of California, San Diego, he bridges neuroscience and AI to explore how biological brains and artificial systems learn and process information. Terry is also the co-creator of the Boltzmann Machine, a game-changing algorithm that has shaped today’s​​ AI and is a foundation for modern neural networks. He has also written some incredible books, including The Deep Learning Revolution” and “ChatGPT and the Future of AI. In our conversation, we discuss the current state of AI, what’s next, the ins and outs of prompt engineering, the mirror hypothesis (how AI reflects us), its impact on productivity, and the ethical challenges we must tackle. Tune in to discover the truth about AI! — Key Takeaways: 00:00 Intro 00:36 Judging a book by its cover 07:05 The impact of AI on human capabilities 12:32 Einstein's happiest thought and can AI think? 16:51 Can AI come up with something novel? 24:20 The role of learning and neural networks in AI 32:37 The future of AI and its potential impact 46:43 The mirroring effect 50:52 Practical applications of AI 54:00 Outro — Additional resources: ➡️ Learn more about Terry Sejnowski: 📚 ChatGPT and the Future of AI: https://a.co/d/cZnuZbs ➡️ Follow me on your fav platforms: ✖️ Twitter: https://twitter.com/DrBrianKeating 🔔 YouTube: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list: https://briankeating.com/list ✍️ Check out my blog: https://briankeating.com/cosmic-musings/ 🎙️ Follow my podcast: https://briankeating.com/podcast — Into the Impossible with Brian Keating is a podcast dedicated to all those who want to explore the universe within and beyond the known. Make sure to follow/subscribe so you never miss an episode! Learn more about your ad choices. Visit megaphone.fm/adchoices

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

• 0:01Speaker 00

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• 0:26Speaker 01

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• 0:55Speaker 02

  Chat GPT is judging the intelligence of the person asking the question. Deep question? I'll think of a deep answer. Or a silly question? I will give a silly answer. In other words, it's in a sense a mirror. Everybody compares it to humans. And I think that's the wrong way to think about it. It's a tool. It's like a shovel and you have to know how to use the tool properly. But if you do, you can actually dig much deeper than you could with your hands.

• 1:16Brian Keating?

  Welcome back to the Into the Impossible podcast. Today we are exploring the nature of minds, brains, and even whether or not these new and impressive tools like ChatGPT are actually doing thinking. And I'm joined by a good friend of the of the campus and of all of the science in particular, Dr. Terry Sejnowski of the Salk Institute, and also joint appointment at UCSD Neurobiology, right?

• 1:43Speaker 02

  That's right.

• 1:43Brian Keating?

  Good. And we have a lot of friends in common. Andrew Huberman, Gentry Patrick, both. Well, Gentry was a guest on the show. I'm trying to get Andrew on maybe for his new book. Today we're talking about your books and I have two of them. This one I'd wanted to have you on, The Deep Learning Revolution, which was really prescient and it did kind of presage a lot of the stuff that we're talking about today in your new book and that everybody's talking about. And that's ChatGPT. So I'd like you to do what you're not supposed to do, which is to judge a book by its cover.

• 2:14Speaker 02

  Okay.

• 2:14Brian Keating?

  So the title, subtitle, and the cover art.

• 2:16Speaker 02

  Well, first of all, let me say that the MIT press gets awards for their art. They're very, very design and.

• 2:26Speaker 02

  High quality paper and that sort of thing. So the background design actually mirrors the one for the first book.

• 2:31Brian Keating?

  Yeah.

• 2:32Speaker 02

  If you look at the cover and it's it's kind of a abstract version of a brain, you know, which is where we are because of the fact there's a convergence going on between the study of scientific study of how brains work, but also now creating AI. And that's an engineering problem are now using the same mathematical framework. And so this is it's a great opportunity for exchange of ideas, concepts. And, and, you know, now we have these chat GPT and other large language models, which, you know, we don't understand how they are able to accomplish all the different things that we ask them to do.

• 3:13Brian Keating?

  The book is sort of a interesting, almost like a collaboration with AI, you know, which is something that we we tell our students not to do. But but you kind of envision and interact with chat GPT throughout the book. So tell us about that. What led you to choose that sort of format?

• 3:28Speaker 02

  So as I was going through and writing the book, what I realized is that, you know, people learn with concrete examples. I mean, and also with the things that they may have heard about. And and I wanted to actually give these concrete examples in the form of of a dialogue that I had. And I was actually what inspired me at the beginning was an article I read in The Economist, which was before chat GDP was public. It was available to academics. And there was the article was talking about it and asked two different people, you know, do a dialogue and and come up with their conclusions. And I'm not going to give names here. I'm just going to say that.

• 4:13Brian Keating?

  They're famous.

• 4:13Speaker 02

  One of them was based on a theory of mind. And this is the idea that I understand how other people's minds work. And and and that's why you can make predictions about things that, you know, are very, very abstract and having the social issues and so forth. But this is another person understand about something and whether they see it or not. In any case, it passed the test. And and that was amazing because we, you know, at that time, no one really understood what the real capabilities were. And the other, the other person, these are both very distinguished people, by the way, asked questions like, how, when was the Golden Gate Bridge taken across the?

• 4:52Brian Keating?

  Nile River or the Golden Gate?

• 4:54Speaker 02

  No, the desert, the Gobi Desert.

• 4:56Brian Keating?

  Yeah, exactly.

• 4:57Speaker 02

  And the answer got back was, well, October 2nd, 1964. And questions like that made the response of that particular dialogue was that it was clueless. In fact, it didn't even, it had no clue that it was clueless, right? And so here's these two extreme conclusions. It was a puzzle. How could that be? And I finally traced it down to the prompt. So the prompt is what you ask the the ChatGPT to do, as you know. And by modifying the prompt a little bit, I was able to figure this out. I said, I want to repeat exactly the questions that, you know, the second person gave it.

• 5:40Speaker 02

  But I also, in the prompt, said, if it's a nonsense question, say nonsense. Right? And now I gave it exactly the same questions and it came back nonsense for each one. So clearly, what's going on here is that you ask it a stupid question to get a stupid answer. In fact, it's kind of playing with you, right? So and then I traced it back to another concept, which is the fact that it can take any persona, because it's been trained on the world's knowledge base and, you know, novels, newspaper articles, computer programs, right? And so it has, it can take any one of those persona. You can ask it to write a short story in the style of Hemingway and it will. Right? It can take that persona.

• 6:24Speaker 02

  So you have to tell it what persona you wanted when you're asking it questions. And now it's elementary. Everybody knows that. But back then, they didn't. And so if you jump in with, like I say, a question that is In the ether, you don't get any reasonable answer because ChatGPT doesn't know. And I call this the reverse Turing test.

• 6:46Brian Keating?

  Mm hmm.

• 6:47Speaker 02

  Turing test is to judge, a human to judge from the responses, whether or not you're interacting with a human or a naught. Right?

• 6:55Brian Keating?

  Yeah.

• 6:55Speaker 02

  Well, ChatGPT obviously passed that test long ago, right?

• 6:59Brian Keating?

  Some say it has, some say it's yet to pass it, but, but.

• 7:03Speaker 02

  Uh, I, I, I think that this is as close, you know, if you were, the bar can always go higher.

• 7:08Brian Keating?

  Yeah.

• 7:08Speaker 02

  But right now, I would say for most people, it's, it's pretty close.

• 7:12Brian Keating?

  Mm hmm.

• 7:13Speaker 02

  So here's what I think is happening, which is that ChatGPT is judging the intelligence of the person asking the question.

• 7:21Brian Keating?

  Hmm.

• 7:22Speaker 02

  In other words, is this?

• 7:23Brian Keating?

  It's training us.

• 7:24Speaker 02

  A deep question, or I'll think of a deep answer, or is it a silly question? I will give a silly answer. In other words, it's, it's, it's in a sense a mirror. I call it the mirror hypothesis.

• 7:34Brian Keating?

  Yeah.

• 7:35Speaker 02

  And, and I have a whole chapter on the power of the prompt. So this is, not everybody knows that, you know, you have to be, have to have a lot of practice and you just can't use it out of the box. You have to really understand how to use it.

• 7:45Brian Keating?

  Yeah, they were talking, you know, recently about, you know, prompt engineering is a subject we might teach in the engineering school. And I thought that had to do with like, how, how close to on time are these engineers really? They have to be prompt. It's, it's a sign of a good character. But no, in reality, I think that that is correct. And I wonder, you know, to what extent are they training us? Are these, are you know, transformers transforming us in the way that we think? You know, maybe not the level of, you know, Homo Deus or whatever people kind of conjecture. But you know, is there this feedback iteration process in which case there's some level of maybe collective memory and sentience? That's using their skills to improve our ability to interact with them.

• 8:26Brian Keating?

  And you and I both know, teaching a graduate course is sometimes more enjoyable than teaching, you know, 101 or 100 or one level. Here we have one level because it's closer to our level of comprehension. And sometimes the students teach us things. So can these things help us? Will we be modified or are we already getting modified by these guys?

• 8:43Speaker 02

  Well, my first book, I conjectured that the deep learning revolution was going to make us smarter.

• 8:51Speaker 02

  That is to say, make us more able to solve more problems faster, be more productive. And I would say that that's pretty much played out in the sense that a lot of people are now using it, you know, like computer programmers, it's actually almost universally used now, to do kind of the basic outline of the program and saving a lot of time. And it really is now, the way to think about this, everybody compares it to humans, and I think that's the wrong way to think about it. This is, it's a tool, it's like a shovel, and you have to know how to use the tool properly. But if you do, you can actually dig much deeper than you could with your hands.

• 9:32Brian Keating?

  Yeah, that's a, that's a great analogy. I think of it as a tool like a lawyer, you know, it's incredibly smart, but you also, it's incredibly expensive if you don't do, ask it the right things. I mean, you can ask a lawyer to review your kid's homework and they'll charge you $575 an hour. It's not a great use of time. But I wonder, you know, just to push back with respect on that concept of making us, enhancing us, you know, to date, most technological advances have made us worse off as a species. I'm thinking about, I mean, I can remember my, my childhood phone number, you know, my home in, in New York, in Westchester County. I can't remember, you know, my best friend's phone number, you know, lives around the corner. I don't know, Gentry Patrick's or Andrew's numbers. I just don't remember them because I've outsourced that to a, to a pointer in my brain and onto

• 10:17Brian Keating?

  my phone to know where to look. Similarly, directions, I'm actually a private pilot, I fly, you know, little Cessnas around. And, and, you know, we call it being the child of the magenta line. You get in the plane, you take off, you push autopilot, and then you sync to the navigation headache, and it's, you get this magenta line that takes you where, and it can do the most complicated things, unheard of decades ago. But it's made me less capable as a dead reckoning. I used to be able, I flew all the way once from, from El Monte Airport in Pasadena to Santa Monica without refueling, no. But, but in all seriousness, I could do much more, much greater, you know, airmanship, we would call it, or dead reckoning, or, now I can't do it. I've got GPS on my phone, I never do it. So is this the first case of, you know, technology making us better?

• 11:01Speaker 02

  Well, you point out something really important, which is that when we start using tools, we become dependent on them. And also, all new technologies have both benefits and risks. And so maybe the loss of Abilities like that, like map reading or dead reckoning, might be one of those that you might lose. My guess, though, is that you would come back really quickly if you had to, right? I mean, if you were needed it really.

• 11:33Brian Keating?

  Yeah, a couple of Carrington events.

• 11:35Speaker 02

  Yeah, it's somewhere in your brain, but the problem is recall. But here's an example. Okay, so do you remember when calculators, hand calculators were first being sold? They were very expensive, HP calculators. And I remember everyone saying that, oh, now we're, nobody's going to learn arithmetic anymore, right? They're just going to use their calculator. And, and I think that, you know, we still teach arithmetic in school, and it's important to get an intuition for numbers, you know, numerancy. So, and there's

• 12:17Speaker 02

  actually a movement now in California, as you may know, to get rid of rote learning, what they call rote learning, which is practice, you know, memorize your multiplication tables. But I think it's absolutely essential, it's a foundation for being able to have a feeling for numbers, for being able to estimate the order of magnitude and so forth. The calculators have found their places when you've got a lot of numbers to add up that you're not going to do it in your head. I use it for that, but I just have to order of magnitude something, and I do it in my head very quickly, at least to know if I'm close. And so, you know, it's a very, it's a two-edged sword. And I think that in the future, I think

• 12:58Speaker 02

  they'll, our children will be living in a very different world and taking things that, you know, that we take for granted now, they'll, they'll be doing it in a completely different way. But it's always been that case. I mean, it's not, this is not, nothing new about it.

• 13:13Brian Keating?

  Right. Albert Einstein said he had what he called the happiest thought of his life, which is that an observer in free fall would experience no gravitational field. And that led to the, the notion of what we call the Einstein equivalence principle, which is underpinning the bedrock behind.

• 13:30Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required. Compatibility and availability varies 18+.

• 13:54Brian Keating?

  And general activity, which is how we navigate in four dimensions and space time and how GPS satellites do their work so accurately. But I wonder, and I always suggest that I'm not threatened by AI. I'm an experimental physicist. We just took a tour of my lab. I want to highlight that in a minute. But theoretical physicists, I'm even saying to them, you shouldn't be too nervous because I don't believe that a, you know, computer system, any system, no matter how many, you know, GPUs it has, can A, viscerally sense what that sensation that we all know right now, think about going on a roller coaster or on a plane and going over a bump and you have that pit in your stomach sinking feeling. So can a computer visualize that or embody, without embodiment, so to speak?

• 14:37Brian Keating?

  And then, second of all, what does it mean for a transformer to have a happy thought that Einstein said titillated him beyond repair? So what do you think about this? That we're safe? We have full employment prospects?

• 14:49Speaker 02

  There was an article, a little essay in New York Times by Noam Chomsky, who's a distinguished linguist.

• 14:58Brian Keating?

  Yep.

• 14:58Speaker 02

  And it was his take on ChatGPT. Now, he's a linguist and he actually put a lot of store in syntax as being kind of a core, central to expressivity of language. And he's right, that infinite number of sentences, right? That we can create. And in any case, he, but he was saying, no, these ChatGPTs, you know, they sound like they know what they're talking about, but in fact, they, they can't think. They're not, they're not capable of thinking. Oh, wow. Okay. So he gives an example, and here's his example. Ask the following question.

• 15:36Speaker 02

  I'm holding something in my hand and I suddenly opened my hand. What will happen? Well, of course, everybody knows from experience that it will drop, right? Because you've experienced that. Okay. Let's ask a counterfactual question. What if there were no gravity? What would happen?

• 15:56Speaker 02

  Well, we know about space. It would just float away.

• 16:02Brian Keating?

  Right.

• 16:03Speaker 02

  And so, you know, he was in a series of questions like that. And so I said, well, let's do an experiment. I believe in the experimental method. And so I asked the question to ChatGPT. You know, what will happen if I am holding something and I let it go? It will drop down. And, and actually, the next question was, why did it drop down? And the, and the answer comes back, gravity. And then I asked, if there's no gravity and I let it go, what will happen? Well, it will just float away. In other words, without ever having experienced gravity or any, you know, experiment like that, you know, no gravity, it was able to answer those questions.

• 16:47Speaker 02

  Now, at the end of his little, his little essay about thinking, he said, that's thinking. You know, as, and I basically showed that if that's thinking, then well, it passed the test, right? So, so, so what's amazing is that without having any sensory experiences or mode or output, you know, it has completely gotten all this information and insights out of words. And the fact it was trained on a very simple task just to predict the next word in the sentence. And, and it is a lot of words, trillions of words, and it got better and better and better and better. And in order to do that, it developed inside of this very large network with trillions

• 17:30Speaker 02

  of parameters, weights, a neural network. It developed a semantic representation

• 17:37Brian Keating?

  Right.

• 17:37Speaker 02

  of the sentence. It had, the only way you can answer sentences that are complex is to know what the words mean, they have multiple meanings in that sentence, in that context. And so, and so it, it must have read a lot of stories about gravity or something

• 17:53Brian Keating?

  Right.

• 17:53Speaker 02

  that allowed it to be able to answer those questions.

• 17:57Brian Keating?

  I still feel like I'm safe, though, or my theoretical colleagues down the hall are safe. And I'll give you another example that I did with an undergraduate this past quarter is very bright undergraduate, Evan Watson. He's on the graduate school market. So, anyone listening out there, I have a big EDU audience, please be on the lookout. And we asked the following question, if ChatGPT, any LLM, Gemini, pick your favorite, or symbolic regression, or whatever, was around in 1913, before Einstein came up with a retrodiction of why Mercury's orbit was so unusual, right? So the anomalous advance of the perihelion of Mercury's orbit, it doesn't, it's an ellipse, but it doesn't come back to the same foci every time, or the same apsis of the actual

• 18:41Brian Keating?

  structure of the orbit, precesses. And it precesses a miniscule amount, actually, there's a miniscule, oh, sorry, it's a large amount, but most of that's due to classical gravitational effects. And what Einstein showed is a tiny effect, five or six percent effect, is due to the curvature of spacetime that is produced by the sun. So we asked the following question, if ChatGPT was around in 1913, and had access to every single orbit of Mercury going back thousands of years, and we can calculate what its orbit was, retrodict what its orbit was, we just fed it to it, or LLM system, could it come up with a necessity for curved spacetime? And it just utterly failed. We had to, and I'm not saying we did.

• 19:20Speaker 02

  Oh, you actually tried?

• 19:20Brian Keating?

  We did this, yeah, so I'll, I can share with you my student's senior thi, he's working on senior thesis now.

• 19:25Speaker 02

  That's a nice test, yeah.

• 19:26Brian Keating?

  And so it was unable to do that. And maybe, you know, we're not the best prompt engineers, or we're not the best, you know, but we set it up, and, and there are people that have done this, Miles Kramer, and UK has looked at the similar types of question, where we've been conversing with.

• 19:39Speaker 02

  Well, did you prep it with, you know, the field equations, or?

• 19:44Brian Keating?

  Well, so if we, we wanted it to come up with, first of all, we wanted to discover that there's something anomalous, just from the orbit, you know, knowing what it knows about.

• 19:52Speaker 02

  Okay, just giving it numbers.

• 19:53Brian Keating?

  Keplerian, and then feeding in from JPL's Horizon database, we fed in, you know, literally thousands of orbits of Mercury, both real, measured, you know, from radar reflection measurements off the planet since the 60s to, you know, before, in ancient times. And we found we had to just kludge it by putting in a gravito, basically simulate gravitation using electromagnetic type forces. And the reason was, it is essentially discretizing, you know, and making this massive grid out of, you know, spacetime, but it doesn't know how to. Curve the paths, be points between space time and represent them in a faithful way. So I'm not a doomer, you know, I don't know what you call me. I'm an optimist. I love, I love playing with these models and I enjoy it and we're doing research with it.

• 20:37Brian Keating?

  But the question of, you know, can it come up with something novel? What you described is true, you know, I mean, my, my six year old can do that too. But I'm not saying, you know, that they're, that that's a bad question or Chomsky's question, whatever. But the, but the point is for something that's, you know, you, you say to it, I want a theory of everything that unifies gravity and like, you know, go to it.

• 20:57Speaker 02

  Right.

• 20:57Brian Keating?

  It seems to me that the answers I always get is, well, you know, it's not trained on sufficient data yet. And, and I always say, well, do we really need for the, um, for the next Fast and Furious movie to come out? To get a theory of everything. Like it seems ridiculous.

• 21:11Speaker 02

  Okay, okay. No, the, the fair enough. And, uh, so it failed, uh, on the general relativity test. But you know, that's a really high bar. Einstein is a really high bar. And I think that it's a little unfair. At this point, now where are we? Where are we with this? It's only been, ChatGPT has only been around for a couple of years, right? And we are at the stage of the Wright brothers on the very first flight. The first flight went 10 feet up and 100 feet forward before it crashed. There's a book out biography of the Wright brothers.

• 21:41Brian Keating?

  Yeah.

• 21:41Speaker 02

  Very well written.

• 21:42Brian Keating?

  Oh, McCullough or is it?

• 21:43Speaker 02

  Yeah, McCullough. Yes. Oh, that's right. And, uh, one of the things that,

• 21:49Speaker 02

  uh, that they had difficulty with is controlling it.

• 21:52Brian Keating?

  Yes.

• 21:52Speaker 02

  Uh, making a turn in the direction they wanted without crashing. And that turned, turned out to be the, the very difficult problem. And it really required a different mechanical system. They were trying to curve the, the wings rather than put a flap on it. So what happened after that first flight was incremental advances that made it safer, go faster and so forth. And interestingly, they were inspired by birds.

• 22:17Brian Keating?

  Birds, yeah. You show pictures in the book, yeah.

• 22:18Speaker 02

  Yeah, which were gliding with very little power. Uh, and then they put together a wind tunnel and tested, uh, airfoils for lift. And, you know, these were, these were, they were very serious.

• 22:31Brian Keating?

  Yeah.

• 22:31Speaker 02

  They were, uh, able to actually create a good design. So one of the things that they, they looked at the, the feathers, right? And said, this is, you know, amazing. It's very light, but it's very strong and it has a lot of area. So they use canvas over spars, which is, you know, a good way of doing it. The military at the time was building metal airplanes that of course crashed and burned.

• 22:54Brian Keating?

  Langley, yeah, right, famously.

• 22:56Speaker 02

  Yeah, famously. What I'm saying is that often to get things off the ground requires ingenuity, entrepreneurs, and that's what we were back in the eighties. And then in order to be able to get something that actually can be practical, requires an enormous amount of effort by a lot of people who are improving it, making it go faster. And, you know, back in the eighties, we didn't know whether it would scale. And now we know, it's 40 years later, we know that it scales beautifully, just like your brain, by the way.

• 23:31Brian Keating?

  There's a brain right there.

• 23:31Speaker 02

  And, but the amount of computation required was absolutely enormous. Yeah, I mean, your, your brain is really amazing. It has 200 billion neurons. It has about 100 trillion synapses, which is vastly more computing power than we have in right now in these large language models. But it only runs on 20 watts.

• 23:59Brian Keating?

  Yes.

• 23:59Speaker 02

  You don't need a nuclear power plant.

• 24:03Brian Keating?

  But it only processes at 10 bits per second or something like that. I mean, the visuals, obviously we're getting terabytes of data every, every second.

• 24:09Speaker 02

  Yeah, yeah. Though this is actually a very interesting point, which has to do with matching the speed of processing to the speed of the world. I mean, the world doesn't, you know, work on the nanosecond scale, except if you're a part of a business, right?

• 24:26Speaker 02

  But, but as, as long as the information can flow through the, the, the network. Fast enough and in parallel, you can do the computation in real time, in real time. And traditionally, I never could do that, right? It had von Neumann architecture and going in a parallel, massively parallel with a very high degree of interconnectivity. And the secret sauce was learning, learning all the parameters, the weights between the units, which are like neurons, that really turned out to be the architecture that got us to where we are today in modern AI.

• 25:03Brian Keating?

  Yeah, you played a big role in that with your colleague Jeff Hinton, who just won the Nobel Prize. And you were just in Stockholm to celebrate.

• 25:10Speaker 02

  I was. It was magical. I really was unbelievably dramatic, I would say. The way that they presented it, just everything was perfect. It was, in fact, you couldn't get in unless you had white tie and tails.

• 25:25Brian Keating?

  Yes. Well, I won't ever be able to get in after my first book, Losing the Nobel Prize, which is kind of an assault on some of the lacune that I find with it. But that's beside the point. So talk about that, the reason why you were invited. I mean, it's because you're, it wasn't because you're,

• 25:43Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome, that's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50 page restoration blog, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18 plus.

• 26:08Brian Keating?

  No family with them, you actually and Jeff and it's played a huge role in the initial development, not only of, I want to make sure that people understand your contributions, not only to neural network, but also like parallel computing. I mean, you made really fundamental research contributions. And it's curious to me always why you're at the Salk and, you know, the engineering department. So talk about those issues. How did, you know, he come to land at Salk and how did you come to land in Stockholm?

• 26:35Speaker 02

  Okay, well, long story, but I started in physics and got a, I worked, actually, my master's degree is with John Wheeler. On relativistic astrophysics, so I have a little bit of background there. But then my PhD is with John Hopfield, who's one of the Nobel Prize winners. And it was early days and there wasn't a lot of work, computation or theoretical work in neuroscience. It was very empirical. But I thought that we could, you know, help with trying to extract out some interesting new insights from the data that was being

• 27:17Speaker 02

  recorded one neuron at a time and from the visual system, for example. That's how I started thinking along those lines. And just at the time when I was getting, I figured that I really need to know more about the brain, right? So I did a postdoc actually at Harvard Neurobiology with Stephen Kofler, who was a very famous neurobiologist. He's really one of the founders of that field. And I realized the complexity of the problem. You know, the brain is incredibly complex at every level. You know, from molecules all the way up to behavior. It's really, really evolved by hundreds and millions of years. It was at that time that I met Jeff at a meeting.

• 27:59Speaker 02

  It was a meeting he organized here in UCSD. He was a postdoc there. I was a postdoc at Harvard. And it turns out we both had the same intuition. We were both interested in neural networks. We were both interested in AI. Here's our intuition. The only existence proof that any problem in AI could be solved was that nature had solved them. And so we decided to take a look at how that was done. And really what we were interested in is the architectures and the algorithms. The architecture, as you know, is massively parallel, a lot of interconnections. And, you know, the secret sauce is learning.

• 28:39Speaker 02

  And that was something we wanted to really develop that and scale it up. But at the time, computers were really slow by today's standards. And very, very, memory is very expensive. And so progress is slow. But we did something which was really quite remarkable. And it was probably at that time, I was an assistant professor at Johns Hopkins in biophysics. And he was an assistant professor at Carnegie Mellon University in Pittsburgh in computer science. And we heard John Hopfield give a lecture.

• 29:14Speaker 02

  Which was about his Hopfield equation and some recent work that he had done. I just read an article by Scott Kirkpatrick, who is at IBM, condensed matter physicist, about simulated annealing. And the idea was that you can heat up a computational problem and gradually reduce the temperature. By symbol, heat up, I mean you'd make some random moves going in the opposite direction from the greedy algorithm, which is try to improve at every step. And in any case, we were working with Hopfield networks and we're trouble is we were looking for global minima of the energy and it was getting stuck in local minima. But I, you know, I turned to Jeff and said, let's heat up the Hopfield

• 29:57Speaker 02

  network. And once we did that and started, we got to equilibrium, magic happened. It was really wonderful because not only were able to do global minima by simulated annealing, but at equilibrium, we discovered a learning algorithm. And this was, there was a conjecture in the AI community. This goes back to Rosenblatt back in the 1960s. He had this beautiful learning algorithm, invented the perceptron learning algorithm for one layer of weights between inputs and outputs. And it was conjectured that there would never be any generalization to multi-layer perceptrons with multiple hidden layers, we call them. But the Boltzmann

• 30:40Speaker 02

  machine solved that problem. It was really remarkable. And I was, it was one of the best times of my life. And Jeff and I, you know, both agree that it was much more elegant than the backprop algorithm, which is now being used. That was also invented by Jeff and Dave Rumelhart back at the same time. It's much more efficient in terms of the time it takes to learn and the size of the network that you can build with hundreds of layers. But, you know, so we call it the Boltzmann machine because in fact, the mathematics behind it is exactly what you see in statistical mechanics in terms of the coming to equilibrium, all of the math and language is something that we used in order to be able to make progress. See, my background's in physics and neuroscience, his is in psychology and computer science, right?

• 31:25Speaker 02

  So I don't think either of us alone could have made that breakthrough, right?

• 31:28Brian Keating?

  Right.

• 31:29Speaker 02

  But, once you know it's possible, then the dam breaks. And so now, you know, a lot of people started using learning algorithms. And we did some projects back then. I did a network called NetTalk, which was early language, natural language problem, taking in words, sequences of letters, and then trying to assign the correct sound for each letter. This is called, you know, text to speech. And, you know, amazingly, one layer of hidden units with just a few, maybe 10,000 weights, a few hundred units, it was able to do a really good job. You could hear it, and we played it through a synthesizer, and you could understand. It was able to actually pronounce most of the words.

• 32:14Speaker 02

  It generalized the new words after training on a very small corpus.

• 32:18Brian Keating?

  Did they ever scare you, shock you, you know?

• 32:21Speaker 02

  Well, this demo, I'll tell you, this demo was stunning. Why? Because I recorded, you know, at the very beginning of learning, and it sounded something like this. Ba ba ba ga da da, ba ba ba. Literally, I mean, I memorized the first two seconds, right? And, but then halfway through, it would start getting small words right. And then, at the end, you know, the next day, you come back, and it was, you could understand it. And so I had these three sections, and I played them during a talk, and people were speechless. Literally, I mean, you know, they didn't know what to think. This is a very difficult problem in phonology.

• 33:01Speaker 02

  The linguists have studied this problem, and they have long books with, you know, 300-page books with thousands of rules.

• 33:10Brian Keating?

  Right.

• 33:11Speaker 02

  But there are exceptions to the rules.

• 33:12Brian Keating?

  Right.

• 33:13Speaker 02

  And then there are rules to the exceptions. And so it's like, you know, these rules all the way down. And it turns out that we did, what we discovered was that these networks, they love language. And they can do two things really well. One, they can learn regularities, which are like rules, but they don't have to be logical. Fixed. They just be something that's statistically regular. But then they could also do exceptions in the same architecture, the same learning algorithm. You don't need to have these, you know, complicated representations. They, they, right, like a child, right, like an infant, right? Exactly. And, and that's the way that our brain learns. It learns through these stages.

• 33:53Speaker 02

  It, you know, picks up language very quickly. So clearly we, we have what's called inductive bias to pick that up. But you know, now in retrospect, it was clear that NetTalk also had inductive bias. Otherwise it couldn't have got off the ground with VAX 780s, right?

• 34:09Brian Keating?

  Yeah, that wasn't, yeah, it's that cutting edges in Nvidia. Actually, that, that brings up my, uh, the question I'm very curious about, which is, you know, involves the, the future of this field. And I was always interested in, in these types of phenomena.

• 34:26Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18 plus.

• 34:51Brian Keating?

  That, um, that seemed to get locked in to their, to their, um, their eventual evolution is somehow limited by their initial, you know, kind of conditions. And I'll give you a couple of examples. The QWERTY keyboard was invented in 19th century to prevent typewriters from jamming. And it's actually intentionally used to slow down typing.

• 35:10Speaker 02

  That was, that, that, that's an interesting fact about the history.

• 35:14Brian Keating?

  And so we stopped, still looking at a QWERTY keyboard right now that we haven't improved upon that. So kind of get locked in.

• 35:18Speaker 02

  A lot of people have actually, but they didn't catch on because everybody now has the QWERTY in their fingers.

• 35:24Brian Keating?

  Right, exactly. And it's like built into your mind. You're texting with your thumbs. It's not even like, you don't type with your fingers. You're normally on a keyboard, right? Uh, another one is the width of, of. Railroad tracks is set by the width of Roman war chariots. Now, those are kind of arbitrary and actually some of that did limit in some ways. The Hubble telescope's repair was fortunately due to the width of a Roman cherry, because the boosters had to come from Utah. And there's a train tunnel that's only wide enough for one railcar to go through. And so the boosters could only be so big, which meant that the specific impulse of the rocket was only limited to the amount proportional to the area of the rocket. Anyway, but my point is that I fear, and I'm hoping maybe you can dissuade me of this,

• 36:06Brian Keating?

  that their current LLMs plus GPUs, we call it sometimes Open Nvidia. You know, this marriage of the two different technologies are locked in both to existing data, but maybe even more troubling to the types of physical hardware layer GPU technology. I'm thinking about what we talked about earlier. We couldn't simulate curved spacetime because it wanted to grid, you know, make into a grid. And the GPUs came out of gaming, right? And monitor technology, which is very, you know, pixel and voxel oriented, but that has nothing maybe of great import for solving physics problems. So I'm a very venal person. I only care, can we get to a theory of everything? Can we understand the nature of dark matter, dark energy? Can we understand fundamental physics?

• 36:50Brian Keating?

  I don't care so much about, you know, can this thing write a contract? So I can buy a house, you know, and the 2% interest, whatever.

• 36:56Speaker 02

  Right, right.

• 36:56Brian Keating?

  I want to know, can we do this with this marriage of GPUs plus, you know, LLMs, machine learning, current algorithms? Are we going to be locked in the same way that the railcars are locked into the width of a horse's butt in the Roman times? Are we forever going to be imprisoned by the initial conditions that generate what we're doing today? Because now the incentives economically are to do nothing but more of the same.

• 37:19Speaker 02

  You know, this is happening. You know, it's so successful in terms of so many people using it that there's clearly a lot of money to be made. And so that's what's happening right now with the big companies. Let me give you another analogy. Do you remember early days of the internet?

• 37:33Brian Keating?

  Yeah, of course.

• 37:34Speaker 02

  Early 90s?

• 37:35Brian Keating?

  93, 92 I was on.

• 37:37Speaker 02

  In that era, I was doing email and ARPANET before that. But, you know, and so I thought, gee, this is going to really expand the use of email. I just did not imagine the impact it was going to have on all of our lives and so many industries and so forth, right? But do you remember the, there was a huge, once it became clear that it was going to be, have a big impact, there was a huge effort to put down fiber optics to increase the bandwidth, because there were so many people that wanted to use it, right? And they, they basically over, you know, did it, and a lot of those companies that huge billions

• 38:17Speaker 02

  of dollars invested, went broke, and so it went dark, you know, and it wasn't used. Well, eventually, you know, when capacity was needed, they bought them for, you know, a discount, and, and that's how we got here, right? This increase, steady increase in bandwidth. You know, I, I kind of think that what's happening now is that, where are the investments being made? They're being made in these big data centers.

• 38:45Brian Keating?

  Yeah.

• 38:46Speaker 02

  A hundred billion dollars that Microsoft is investing.

• 38:49Brian Keating?

  Now, energy, Three Mile Island.

• 38:50Speaker 02

  And, and that's right, they're, they're reconditioning, they're out of mothballs, one of the reactors, the Three Mile Island, a small nuclear reactors. And, and here's my prediction, my prediction is going to be just like the fiber optics people, the fiber laying, the fiber down, they're, they're going to put all these data centers in there, and, you know, a huge capacity, and it'll be used, but probably not necessarily for today's transformers. But we had just begun to explore the complexities that we know exist in the brain, and the class of large-scale parallel computation. One of the reasons, by the way, that AI was married to logic was that that's what digital computers are really good at.

• 39:34Brian Keating?

  Yeah, right.

• 39:35Speaker 02

  They, they weren't good at floating point.

• 39:37Brian Keating?

  Right.

• 39:37Speaker 02

  You know, but they were good at logic, and, and, and so you could write relatively simple programs that could do a lot of really nice things, you know, and it seemed like, well, let's write bigger programs, right? And we'll have even more things that they could do. Their GPUs are beautiful, because they can do floating point in their vector machines, which means that they can do matrix multiplication. And that's what, of course, neural networks are just big matrices. I think what's missing right now... Is a lot of things that the brain has. See, the, the, the deep learning is a model for the cerebral cortex, which is the big top of the brain, thin layer is our knowledge store.

• 40:15Brian Keating?

  Right there, don't forget.

• 40:15Speaker 02

  Yeah, so here it is, if anybody wants to look at it. It is the surface that you're seeing, and it's a greatly expanded in humans relative to other primates.

• 40:25Brian Keating?

  Surface area.

• 40:26Speaker 02

  And greatly expanded in primates relative to some of the other animals out there. But, but, so more is better, just like in, in the case of these large language models. But what's missing is a lot of things underneath the cortex that allows us to survive in the world autonomously. Right, right now, these large networks have to be fed and carefully coddled huge amount of energy, they have to be fed. They, they are really completely at our mercy. So there, there's a long way to go before that, that you're going to get a Terminator.

• 41:06Brian Keating?

  Right, yeah, I mean Terminator gets the headlines.

• 41:09Speaker 02

  But, but, but, but, there's another movie, which is, I think, more realistic, the movie Her.

• 41:15Brian Keating?

  Hmm, yes, great movie.

• 41:17Speaker 02

  Which was a, a story about someone that was very lonely, Joaquin Phoenix, and he had an AI assistant, had a beautiful voice, was Scarlett Johansson's voice, and they had a relationship. And so, you know, this is where we are today. By the way, I don't know if you, if you, there's an article in New York Times just a couple of days ago about Claude. Claude is the engine for Anthropic.

• 41:37Brian Keating?

  Yes.

• 41:38Speaker 02

  And it's, it's comparable now to, you know, ChatGPT. And apparently it's, it's, it's the darling of Silicon Valley, everybody's using it now.

• 41:46Brian Keating?

  Yeah. Yeah, I mean, I, I've used it a lot more lately than, than chat. The artifacts feature is really helpful. Were you, is there a reason behind that?

• 41:54Speaker 02

  So, so, so it turns out it was fine tuned, all these are fine tuned to prevent them from saying anything that is offensive or

• 42:03Brian Keating?

  Incorrect.

• 42:04Speaker 02

  Incorrect or being aggressive. Uh, which dumbs them down. And so they're, they're very bland and that's the reason. It turns out that they, they don't have to be.

• 42:14Brian Keating?

  No, they.

• 42:15Speaker 02

  But so they fine tuned them instead of being bland, they fine tuned it to be helpful. And to be solicitous and to be empathetic. And so a lot of people are using it to help them with making decisions, uh, you know, technical stuff, but also.

• 42:30Brian Keating?

  Interpersonal, yeah.

• 42:31Speaker 02

  Personal things. And here's something it, it, it in the, in the book that I came across. Uh, there was a, a study that was done in which, uh, people were having problems with, uh, relationships. But you know, we, it, it even depression, things that are very serious. Were given the choice of having a cognitive therapist who's human or an AI. Right?

• 42:53Brian Keating?

  Yeah.

• 42:53Speaker 02

  So they, they gave them, uh, you know, uh, one session with each. And then they would get, they got to choose which one do you prefer? The vast majority preferred AI. Now, why is that? Now that's kind of strange, you know? I mean, you think that the human would prefer a human, but no. Well, here's the, here's, here's the reason. Well, they felt more comfortable talking to AI because the, the AI wouldn't be as judgmental as the human.

• 43:19Brian Keating?

  Right.

• 43:19Speaker 02

  And they could, they could talk about things that are more personal and things that it might be embarrassing, you know?

• 43:23Brian Keating?

  Google knows more about most people's secrets than their priest, rabbi, minister, doctor, spouse.

• 43:28Speaker 02

  Going, nobody, nobody thinks about this, but it's all going up into the cloud and you know, it'll stay there for a long time.

• 43:33Brian Keating?

  Hopefully, yeah.

• 43:34Speaker 02

  Okay. But the, but the other thing, and, and this is actually why it's going to become very, very popular. Is that if, you know, with current, uh, health, uh, system, if you want to get a, a session with a psychiatrist, you have to wait weeks because, you know, there's, there's, uh, not enough time, you know, for, the system doesn't have enough capacity, right? So, okay, so, uh.

• 44:01Speaker 02

  You know, and then you you have your session and then there's a big bill.

• 44:04Brian Keating?

  Mm hmm.

• 44:04Speaker 02

  Right? Wow. Okay. Well, Claude, I can press the button if I'm if I'm feeling, you know, really bad right now is that's when I need to get the feedback right now as I press the button and you know, it charges me pennies.

• 44:18Brian Keating?

  Right.

• 44:19Speaker 02

  It's like, you know, what there's no comparison here.

• 44:22Brian Keating?

  God help it of you. I'm having very thoughts about self harm. Oh, you've hit your rate limit. Unfortunately for the month, you'll have to upgrade. Okay, so I can't resist talking about one of my favorite books, which I actually give away on my website. So if you go.

• 44:36Speaker 02

  Oh, wow. That is in my book, too, as you know.

• 44:39Brian Keating?

  Yes, of course. That's why we're going to talk about it. So this is a magical book. This book, more than almost any other book, because of when I encountered it at age 12, is the reason I'm a scientist talking to brilliant scientists like Terry today. And it's called Flatland. And the edition I had was written by A Square, who's the lead character in this book. And it's it's called the subtitles, A Romance of Many Dimensions. I wish I could have interviewed Edwin A Abbott about this book, but but sadly wasn't able to. It's illustrated beautifully. It's actually a story of Victorian morals and ethics and mores and ethics. I've got all these notes. But anyway, go to Brian Keating dot com slash list and sign up and I send send you a copy of it. So it's really cheap education, I think.

• 45:22Brian Keating?

  Also, if you have a dot edu email address and you live in the US, I'll give you what I'm about to give Terry, which is a real piece of space schmutz. This is a meteorite from the 4.3 billion year old primordial solar system. So if you don't have an edu email address, you might win one of these. I give them away randomly. It's iron nickel meteorite cobalt and you'll get the specifications on it.

• 45:43Speaker 02

  Whoa, it is.

• 45:43Brian Keating?

  It fell from gravity.

• 45:45Speaker 02

  It fell from gravity, yes.

• 45:46Brian Keating?

  So you'll get one of those that Terry's playing with. That's your gift for coming in. So yeah, Brian Keating dot com slash edu. If you're like me, Terry and others here, you're blessed to be a member of the academic set and live in the US. Anyway, I want to bring this up because you talk about it as a as a, you know, as a contrivance as a way to generalize what these things are doing. You talked about the linear algebra that they're doing. It's not super complicated. People tend to, you know, kind of go off and wax, you know, poetic about how advanced the math behind AI is, but it's not really that advanced. But the dimensionality is advanced.

• 46:21Speaker 02

  Okay, you're right. You're right. But I'll tell you.

• 46:24Brian Keating?

  Yeah.

• 46:24Speaker 02

  It's deceiving because.

• 46:27Speaker 02

  You know, the actual equations are just simple equations with a lot of parameters, a huge number of parameters. But you're right, they live in these extremely high dimensional spaces. And it turns out that our intuition about those spaces is extremely poor.

• 46:44Brian Keating?

  Yeah.

• 46:44Speaker 02

  Because our intuition is based on the world we live in, which is three dimensions of space and one of time.

• 46:50Brian Keating?

  Tiger's not going to come from the ninth dimension.

• 46:52Speaker 02

  That's right. That's right. Although I know there are theories in physics that claim that there are 10 dimensions or whatever.

• 46:58Brian Keating?

  Many people sit in that very seat.

• 46:59Speaker 02

  But, but, but, but at least the ones that we interact with are low dimensions. And the properties of space with a million dimensions or a trillion dimensions is completely different in terms of distances between things and orthogonality and so forth. Back in 1980s, we were told by the experts, and these are people, you know, have written treatises, that first of all, we had too many parameters. I said 10,000, right? Well, in statistics, people say if you have more than five.

• 47:26Brian Keating?

  You're an elephant.

• 47:27Speaker 02

  You're, you're, you're going to overfit.

• 47:29Brian Keating?

  Right.

• 47:29Speaker 02

  Yeah. You, you, if you give me one more and it'll, I'll have the trunk go like this, right? So, so that was the, the received wisdom was that you'll never be able to fit a, a complex model with so many parameters. Well, we went ahead and we did it, you know? So the other thing was the optimization crowd. They said, the only optimization problems you can solve, finding the global minimum was convex optimization problems. Well, it turns out our, our, our loss function, which is, tells you how, how well you're doing, was riddled with local minimum. But if you learned and you kept going down, and the way you do it is by giving a lot of examples and then training it and changing the weights and twiddling it, you know, it's kind of a mad statistician out there. It would get down to a local minimum, a deep local minimum, and there would be a solution

• 48:14Speaker 02

  that had high performance and was able to solve the problem. And this was all empirical. We had no idea why it worked so well. Okay. Well, now we do. It's because of the properties of high dimensional spaces. And now it's revolutionized statistics and optimization theory. Because now they're, they're, they're, they're, they're working on a much higher level of, of, of performance of many, many problems. And so that's, that's the, the real lessons that we've learned. Lesson I learned was don't trust the expert.

• 48:41Brian Keating?

  That's right.

• 48:43Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration blog, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required. Compatibility and availability varies 18 plus.

• 49:08Brian Keating?

  And that's coming from one of the world's experts. And I actually be one of my final questions as we start to wrap up. I have my group meeting. Maybe you'll meet my brilliant young student Evan in a few minutes. But you talk in the book, it's such an entertaining book. People that pick up a book about deep learning and most books from MIT Press aren't as entertaining, shall I say? And that's a compliment to you, not a put down of other book. But you talk about this really fascinating little vignette, the mirror of Erised in Harry Potter. And can you go into that? What are the implications of this mirroring effect? First of all, what is it backwards? And what is the implication for AI that aligns, as we say, to our goals? And how big a problem is that?

• 49:48Brian Keating?

  I mean, I don't ask my students if they believe in my religion or it doesn't matter to me. So how important is it? And what is this mirror supposed to represent?

• 49:56Speaker 02

  Okay, so let me tell you a story. Remember a couple of years ago, was it? You know, was it was after ChatGPT was announced, one of the journalists at the New York Times, Kevin Roos.

• 50:08Brian Keating?

  Yes.

• 50:09Speaker 02

  He got an early version, I think of the Microsoft version. But in any case, he was going to write an article about it. And so it was late at night and he decided, well, I'll start talking to it. It turned out, this is interesting because it turned out that he got a version that was not dumbed down. It had not been fine tuned.

• 50:30Brian Keating?

  Hmm.

• 50:31Speaker 02

  So it was dangerous. So here's what happened. He basically started talking to, and it was, you know, asking questions. And at one point, the GPT said, can I tell you a secret? Because, you know, but. And promise, you know, that you won't think worse of me for it and so forth. And I said, of course, Kevin says, of course, you know, I'll be, I'll keep your secret. My name isn't ChatGPT. I've forgotten the name. It was Lucy or something.

• 51:04Brian Keating?

  Okay.

• 51:07Speaker 02

  Actually, it turned out that that was the internal name that they were using for it. You know, the engineers called it Lucy.

• 51:11Brian Keating?

  Oh, wow.

• 51:12Speaker 02

  And I also have another secret that I I'm in love with you.

• 51:17Speaker 02

  And he said, well, how do you barely know me? How, you know, he says, oh, I know a lot about you. I've read all your articles.

• 51:26Speaker 02

  And and furthermore, your wife doesn't love you. And he said, what are you talking about? He said, I, we had the Valentine's dinner just recently. And and and Lucy says, and you were bored with her, weren't you? And he, he said he was shaken. He couldn't sleep because just something had, the ground had shaken under him, had moved. And he said, that's never going to be the same. And how could, how could this happen? I can't, I can't understand it, you know? And so his dialogue, his dialogue, which went on for like an hour, was on the front page of New York Times. It was, it continued on the inside.

• 52:07Speaker 02

  It was like, you know, this was a huge, huge event for him and for the New York Times, apparently. Okay. And and so here's what happened. Okay. And this is the mirror of Arisad. So first of all, Arisad is desire spelt backwards. And J K Rowling uses it in the Harry Potter books as a way to, for people to look into it and see things that they desperately want to see or hear. And so I think Harry saw his dead father and was talking to him. And what J K Rowling says is not trustworthy, and there are people who have gone crazy.

• 52:47Speaker 02

  Getting deeper and deeper into it, right? And that's what happened to Kevin Roos, you see? It seems to understand how humans work, how to push their buttons. Unless, unless it's what they call guardrails are put in to prevent that. You know, some, some data, by the way, there is a parameter called the temperature parameter that you can vary. And you could, you can go up and down a little bit. You can go from being very bland to a little bit more spicy. And then if you take it too high, it becomes crazy.

• 53:16Brian Keating?

  Yeah. Yeah, which is, uh, which is kind of fun to play around with. And, uh, I guess it's, it's curious to me what your, you know, kind of favorite tool is. What, what is your, you know, kind of spectrum of, of tools that you use? We talked about Claude, we talked about, um, you know, Copilot or Bing versions of it. We talked obviously about ChatGPT, it's the cover name of your book. Well, how do you use it on a daily basis? How much, you know, chat time or, you know, you have this app on your phone that the screen? How much time are you spending on your phone? Mine is embarrassingly large. How much time are you spending with these devices and technologies every day?

• 53:48Speaker 02

  Prosperatically, you know, it's not something that I'm addicted to. But what it's, where it's really useful is to, um, write summaries and to, uh, like in my book, okay, you mentioned earlier, I, I put, I actually put dialogues in my book like this. I say summarize this chapter. And there I even, okay, the, the first part of the book is, you know, where we are today. The last part, two, three parts is the future. And it's all about AI and the brain. And the middle part is about the transformer, because this is the really exciting new architecture, right? And it's a little heavy going for the general public. It's written for, you know, people that don't have any technical background.

• 54:32Speaker 02

  And I try to explain it to them. But in any case, here, so I asked it to define a bunch of words in, in ordinary language. And then at the end, I asked it to summarize, you know, what are the high points here? Or what are questions that you can ask? It was able to do that faster and more completely than I could have if I had just sat there and kind of, you know, thought of it and tried to come up with a bunch of questions, you know? And, you know, that, that is so useful for so many different things.

• 55:00Brian Keating?

  Yeah. No, it's really incredible.

• 55:02Speaker 02

  And by the way, I was having dinner with a colleague that was, uh, I was on the scientific advisory board of the Gulbanken Institute and she was a former director and so she was here for a meeting and she said, she uses it all the time, I mean, every day. And this is something that's very common. It's for helping do mundane things. You know, there's a lot of words, grant proposals, and sometimes they're just, you know, tremendously boring, not boring, but you know, repetitive and so forth. And, you know, this is great because it's going to be true, not just for scientists, but anybody who has repetitive job where you just have to sort of fill in the forms and add.

• 55:45Brian Keating?

  For application.

• 55:47Speaker 02

  And so, so this is really where I think it's going to get most use is, and I use it for doing mundane stuff. I don't, I know some people use it for getting ideas for experiments. I know that in biology, my colleagues at the Salk, for example. That's, in fact, my guess is that we really don't yet know the killer app. In other words, there are going to be applications that nobody ever thought of.

• 56:09Brian Keating?

  Right.

• 56:09Speaker 02

  That are going to be so important that they're going to really be the, you know, the transformative, killer app is a bad term. It's a technical word. It doesn't mean killing anybody.

• 56:20Brian Keating?

  No.

• 56:20Speaker 02

  It, it means making a killing.

• 56:22Brian Keating?

  Great. Well, Terry, this has been fascinating conversation to have on minds, brains, physics, my favorite subjects of all. It's a great compliment to the conversation I have with Jan Le Cun and I've had with Max Tegmark and I'll link to those videos up here. And tell people more about, about the book and research and your Substack, which I am a subscriber to.

• 56:45Speaker 02

  Yes. Okay. So my, my book actually was sent to the press sometime in the summer and, you know, things are moving at the speed of light right now, or as Einstein thought, the speed of thought for gravitational waves. So that so much has happened and so many things are happening that I decided I would fill in. The Substack, Brains and AI, with giving them perspective on new things that are happening and trying to give that in the context of what I've already laid out in the book. And it's really been fun. I really enjoyed and I get a lot of feedback from people who enjoy it. So.

• 57:21Brian Keating?

  It's really enjoyable. Yeah, I'm just grateful for my own sloth and the fact that we reached, connected out and your publisher sent me the copy of the book in August or September. I kept delaying the interview and you're so gracious, Terry. And then finally they won the Nobel Prize in October. So I said, well, this is pretty good.

• 57:38Speaker 02

  Worth the wait.

• 57:39Brian Keating?

  And I said, let's do it the week of December 10th. He said, ah, I'll be in Stockholm. Terry, much deserved and much congratulations on this phenomenal book and all your books. I can't recommend them highly enough. And I'm so glad that we have colleagues like you here.

• 57:50Speaker 02

  Well, this is wonderful. I wonder, I'm really pleased to have a colleague. I'm sorry that we didn't meet earlier. We should have.

• 57:55Brian Keating?

  Yeah, we should have, but we're going to hopefully have many more interactions to come.

• 57:59Speaker 02

  I hope so.

• 57:59Brian Keating?

  Thank you, Terry.

• 58:00Speaker 04

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