Everyone Is Betting on Bigger LLMs. She's Betting They're Fundamentally Wrong. (Eve Bodnia, Founder & CEO of Logical Intelligence)

[00:00] There's just a lot of healthcare areas where robotics can be crucial, like robotics for surgeries. Imagine if you let a lamp drive it, somebody says, oh, you know, the 20% of the time during brain surgery, it can go to like the wrong area. I'm so sorry. You can't have that. You need really fast inference. You need to speak on a millisecond, microsecond scale. Do it. HDI should be just like natural intelligence, something which plans, something which is able to predict, [00:30] us as humans. I see that AGI to me is that ecosystem, but for the AI models to serve us as humans. Kona is really cheap. It's very efficient. It doesn't need any special hardware. If your data changes, if your environment changes, you need to be able to recognize it like this. So I want people always be in charge of what AI can be doing. My biggest nightmare is if AI is doing something it was not meant to be doing. So you need to work with different architecture, which can self-align, which can adapt its behavior, which can be precise when it needs to be [01:00] be precise. [01:06] Every major AI company is building a better language model. [01:11] But Yves Bodnia thinks they're all solving the wrong problem. [01:15] Eve is the founder and CEO of Logical Intelligence. [01:19] a new AI lab built on an entirely different architecture. [01:24] The company's energy-based reasoning model thinks in abstract mathematical space, [01:28] solves problems with formal guarantees of correctness,

[01:32] and runs fast enough [01:33] to control a surgical robot. [01:35] or design a chip [01:36] in real time. [01:37] In our conversation, Eva and I discuss why she left academia to start a company, [01:42] What LLMs fundamentally can't do [01:45] The true nature of creativity [01:48] And the wisdom. [01:49] of legendary mathematician Grigori Perelman. [01:52] I'm Mario. [01:53] and this is The Generalist. [01:54] I'm really excited about today's sponsor, Granola. Simply put, Granola is the AI notepad for people in back-to-back meetings. I've been using Granola for over a year now, and honestly, it's a tool that has transformed the way I work. [02:08] Granola takes meeting notes for you without any intrusive bots joining your calls. You can jot down rough notes like you always do, and in the background, Granola transcribes and turns those notes into clear, useful notes when the meeting ends. [02:22] You can also chat with your notes, which is one of my favorite features. [02:26] If someone says something on the call that you didn't quite catch or want to learn more about, [02:31] granola can help you out it's an amazing way to be better informed during a conversation without having to interrupt everyone else's flow [02:38] You can also have Granola review all your recent conversations [02:42] to pull out to-dos, write a weekly recap, or surface interesting ideas you might have forgotten. Another thing I love. To get started with Granola, head to granola.ai slash mario. [02:53] And for new users, you can get three months free with the code Mario. So go to granola.ai slash Mario and use code Mario for three months free.

[03:04] Eve, I'd love to start with an anecdote of yours that I saw on your Twitter account, which super intrigued me given the work that you do, namely that as a teenager, [03:15] You apparently met... [03:17] Grigori Perelman, one of the greatest living mathematicians, [03:22] and had a fascinating encounter with him. How did that happen? [03:26] Yeah, apparently I was very lucky because it was not easy to meet him. So I had like... [03:32] Some are... [03:34] internship in St. Petersburg. [03:37] I was working on just some websites just to make some extra money. [03:42] And he was very noticeable in the community. He always, like... [03:47] So by the office, we had like a little park. [03:50] and he was like walking the same route the same time every single day [03:55] And he looked very different according to the standards of Swabic culture. [04:02] So people obviously paid attention to him and he was like a little celebrity in the community and people didn't know he was a mathematician. I don't know why I'm like naturally curious person and I'm attracted to different kinds of bizarre people. I love people. [04:17] all kinds of people but bizarre peoples are my favorite and hence i went to academia for my career but that's a hard story and he just said that he was working on a problem in topology and [04:30] Yeah, so I obviously I wanted to know more, but he was not really talkative.

[04:37] And when he went on news, he was all over the place and I can't say more because [04:44] There's nothing else to say more about it. [04:47] So I was just lucky to [04:49] to ask like a few questions and I also ask him like why you did not get [04:53] a field smell doll. [04:55] And he said, [04:57] There are so many people who are behind me. [04:59] and I took sort of the work and put the pieces together [05:04] And like, [05:06] It's an ego thing to receive it. So that was my understanding. While like those people did not receive it. [05:13] See what I mean? Yes. Yeah. For folks who are maybe less familiar, he's, as you mentioned, sort of famous for declining the Fields Medal. Yeah. Yeah. [05:23] It's actually funny, he was trying to... [05:26] reduce attention to himself, but it created the opposite effect. Yes. It made everyone curious as to how he could turn down this, you know, the sort of Nobel version for mathematics. I'm curious, when you say, uh, [05:40] bizarre people are your favorite people. That resonates with me. I think there's a part of me that feels the same. Well, what does that feel like for you? Like, what do you find most interesting about these strange birds? [05:55] I just love this unconventional... [05:57] Reasoning. [05:58] and thinking in general and [06:01] the point of view of those people to the world and that's what [06:05] like the major component of

[06:07] being creative right you just need to be able to think outside of your box [06:12] But if you're thinking outside of your box, you're obviously like most likely going to look different, which is not true sometimes, but sometimes some people it correlates and. [06:21] I claim myself being normal, although most people laugh when I say it, but I... [06:27] So I feel home when I'm surrounded by bizarre people. [06:31] Looking at your background, I don't mean this offensively at all. I mean it complementarily. You definitely don't seem normal. I don't know that many people who are studying particle physics when they're 13. So, you know, I'm super excited to get into all of this. But the Perelman story for me is so interesting because... [06:48] as we'll get into, you're really building a form of mathematical genius yourself that hopefully extrapolates in lots of interesting ways, too. [06:57] Maybe to take a step back, how did you grow up? Why were you so fascinated by by mathematics and science from from such a young age? [07:07] The answer is I don't know because I've always been like this. [07:11] And... [07:12] Since like I was a kid, I was reading books, I was trying to understand some fancy mathematics because to me, [07:19] math and science in general, it's that representation of thinking outside of the box. I see like our human experience, we sort of bounded by our evolutional memory. [07:32] And it's. [07:34] very hard [07:35] Because of that, to create something outside of this evolutional things. So this is why when we invent something, we're trying to tie it to things we already seen.

[07:45] And I was looking for ways [07:47] to create something [07:49] which has nothing to do with this intuition of what we meet in daily life. [07:56] So for example, you know, when people invent black holes, [08:01] it's not something you see in daily life, right? It's very counterintuitive. So quantum physics in general [08:07] In mathematics is that [08:09] tool which can take your imagination [08:12] beyond [08:13] your traditional daily limits and [08:16] It has certain rules and you just need to follow the rules. [08:20] and, [08:20] you know, the stepping stone it brings you is sort of correct. [08:24] Like you could trust it. [08:26] But it also gives you something you might not understand and it takes like us time to understand what it gave us. [08:33] The notion of quantum mechanics took a very long time to build intuition around [08:38] notion of black holes in general, quantum information is very counterintuitive area. So interesting. Yeah. I was like attracted to this kind of idea. Can I. [08:47] Can I be exposed to the tools which will take my personal imagination outside of my limit? [08:53] And the answer, like, was in theoretical physics and mathematics. [08:58] And yeah, I was just like so obsessed with it from day one. [09:03] It's amazing. I feel like the way that you describe [09:06] that passion is how maybe I might have described writing for me. There's some amount of unification in so many of these things where, you know, whatever the right methodology is to allow your imagination to go into these interesting places. But that's such a beautiful description. You bring a very interesting point.

[09:25] Uh, I think... [09:27] I met somebody who said similar things to me and he was working in literature, like he was a writer. And he said that words have so many different shades and so many different languages. They have their own sort of colors and shades. [09:43] And, [09:44] It's like such a not easy decoding problem to decode what's in your brain in the form of language in the most accurate way. [09:52] Like language is something which we solve naturally. Like in mathematics, it's called manifold hypothesis or manifold problem. So it's interesting. I like that you bring it up. Can you actually tease out the manifold hypothesis a bit more so I understand that analogy better? Like when you say there's some comparison there with language, what's the sort of connection? [10:13] Yeah, so the idea is your brain thinks in some sort of abstract way. [10:19] Our thoughts is sometimes not in language, it's like in something else, right? It's a mixed [10:24] images it's a mix of words it has something else and for different people it's a different thing [10:30] And when you try to communicate, you need to find [10:33] representation of what's in your brain, [10:35] In language space, [10:37] in the most accurate way and [10:40] Sometimes it's like your life depended on it, like in the court. So words can take you very far. Um, [10:49] But also like in literature, in poetry, it's... [10:52] Like language is becoming an important part of channeling that creative.

[10:58] whatever is in your brain. See what I'm saying? Yeah, 100%. No, I think it's sort of a lossy distillation in some way. Like language is the best we have in many instances of trying to explain to people what we have in our heads and [11:12] It can be, you know, obviously so beautiful and descriptive, but there's still something that you lose in that translation. [11:19] Yeah, so what you're describing is the heart of this manifold hypothesis is when you try to, [11:25] take something very complex and you map it to something very simple and that simple [11:31] needs to describe as accurate as possible what you originally meant to be. There is a huge information loss in like translation. And yeah, it's it's a very popular question in AI in [11:47] biology, [11:48] just like anything related to [11:51] to brain and languages and [11:53] communication. [11:55] Yeah, it feels like we're riffing in this really interesting way, but it actually feels like it also deeply connects to what you're building. [12:01] On that subject, actually, I wondered if you'd ever come across [12:07] This. [12:07] piece written by the novelist Cormac McCarthy on the Kekulé problem. [12:13] where he talks about, I think, the mathematician... [12:15] Kekule or scientist or something like that, where he came up with the structure of, I think, [12:20] Benzene? [12:21] uh, [12:22] in this space beyond language, you know, he arose sort of with it from a dream with this snake like form of, you know, I'm doing a bad job explaining the science here. I clearly I I encoded it as a story, not as a science, you know, as a work of science. But it was really sort of even this legendary writer talking about how.

[12:42] Clearly, there is this part of our mind that works. [12:46] way beyond language in this unconscious symbolic way. [12:49] - Yeah, and it's amazing how like, [12:52] ability to extrapolate of our brain from one space to another this is also like a major component for being creative for creativity in general i was also curious about the questions like is there anything like consciousness how can we define consciousness [13:09] Because then it brings you up to what you're talking about. There is a level of subconsciousness which takes over the processing. [13:17] which is not directly related to your daily life, [13:20] But this is something which can bring you something new. So some form of latent space of your subconsciousness, which always scans the information and just digest it and [13:31] gives you something back. And there's so many scientists and just creative people in general [13:38] uh get the insights from dreams or sometimes [13:41] work on one area or another area. And like when I was doing this EBM, I'm always thinking about the EBM, forgive me, like every topic people ask me, it all goes back to the EBM. No, I understand. [13:54] Yeah, I was working in like entirely different [13:57] field, which was nowhere close related to the EBS. Yes. [14:02] The brain has amazing ability to extrapolate things from one place to another. And for listeners, EBM is energy-based model. It's the type of sort of AI that you're working on with logical intelligence. Yeah, yeah.

[14:16] not based on language. [14:17] Not based on language. Yes. Which is so interesting. Were your parents mathematicians or scientists like that sort of inform the way that you grew up such that these were the conversations you were having at home? [14:30] No, actually, most people in my family, either in medical school or they're artists, we did not have such conversations at home. [14:40] And actually I was driving people not by having this conversations. [14:46] And did you grow up in St. Petersburg? You mentioned that that's where you met Perelman. [14:51] Yeah, so part of it, I was all over the place. I was born in Kazakhstan. I left with my parents to Russia. [15:00] And... [15:01] like when it was eight or something. [15:04] And we just... [15:05] we traveled a lot like we lived in new towns every three years on average because of uh [15:12] like my family job situations. [15:15] And also I have very large family, so there was always a relative in another town we could visit and maybe like stay longer. [15:22] And when I was 18, I moved to the U.S. In sort of researching you, it seemed like you had also spent... [15:29] sometime around 13 or 14 working at CERN studying particle physics. How does that end up? [15:36] Not that young. Not that young. Okay. I was like, how on earth does that happen? I'm not like a genius child prodigy or whatever, you know, like 13, you go to school.

[15:47] I just was lucky to participate in a different Olympiad and I was lucky to win one and was invited to choose any school in Russia. So I chose something related to theoretical physics and math in general. [16:02] and [16:03] I was finishing up the high school, [16:06] So, [16:07] I was like high school age and they gave me a project and they're like, well, we, we working, like there's a place called CERN and we're trying to study new particles and, uh, make like new particles. And back then Higgs mechanism and everything about Higgs was a big deal. I got my first project, which was related to, uh, PP collisions and sort of trying to understand the origins of this quark gluant plasma. So before the universe was formed, that was quark gluant [16:37] of interesting plasma effects and [16:39] just interesting numerical problem. So they just gave it to me and I sold it. We published the paper. [16:45] And then I was like, well, maybe I should just go and work at CERN. And we obviously had a lot of connections there. And it's a great community. I love spending time in Switzerland. It's one of my favorite countries. [16:58] Yeah, and then I moved to the US. [17:01] So yeah, you know, nothing, nothing crazy just as a high schooler. [17:05] a little now people like it's actually go even like there's ego even younger [17:12] So what you see is B, and there's a lot of students we had,

[17:18] who were like at the end of the middle school for research summer program and they could already do like coding and [17:25] understand things I did not understand at that age. I think right now kids are very, very smart. And back then, high school was like not also a big deal. There's a lot of high schoolers in Russia. [17:40] very smart and so you went to berkeley when you were 18 and and then ended up staying in california through your phd what were you you studying and perhaps as you sort of have the benefit of hindsight now uh what were the pieces of that journey that maybe informed how you've thought about uh what you're building with logical intelligence well the team was always the same just trying to understand uh the fundamentals laws of nature [18:06] I was specifically captured by the notion of symmetry. It's something which doesn't change while everything else change. [18:14] And. [18:15] There are beautiful mathematics how to describe and how to define and how to derive what is not changing. [18:23] So I'm like, everything in nature has this phenomena. So maybe that's going to be my theme. And I'm just going to... [18:30] study this sort of invariance that's what it's called things which are not changing as everything else is changing maybe i'm going to study this in brain and particle physics and condensed matter because mathematics was the same but ability to extrapolate this knowledge from one domain to another [18:47] makes you to bring something new to these fields. So I was focused on mathematical foundations to understand the symmetry groups and symmetry groups describe particle physics. And if you understand particle physics, you can work with condensed matter because there are some similar phenomena, just different equations, but the idea is the same.

[19:08] And if you deal with condensed matter, you obviously start dealing with quantum physics, quantum information. [19:14] and naturally somebody from neuroscience department comes over and say hey you know we're using some [19:21] mathematics which [19:23] It's like traditional, doesn't give us anything new. [19:26] So maybe your methodology is going to help us describe something new. [19:30] And brain is also [19:32] can be described [19:34] the same equations as condensed matter because there are some things that doesn't change in your brain while everything else changes. [19:41] So I'm like, okay, maybe interested to see what kind of invariance we can find with certain brain activities. And I was studying that. So the, the team was always the same, just the areas were different. And naturally when you start talking about brain, people like. [19:57] maybe you could apply this to AI and this is where like, [20:01] I felt the gap. [20:03] I'm like, oh, no, I cannot use the same tools on LLM. [20:08] And I started digging into LLMs and I'm like, [20:11] Well, I'm not claiming I have understanding how brain works, but I spent some years trying to understand. And for me, it was clear it was not like... [20:20] searching for patterns in any language. It was, as a writer, you feel it yourself. You're not just like, what do you create? You're not just searching for patterns. It's like something else. And you have a freedom how to decode it. And that realization to close this gap kind of put me on this path. So interesting. And there are so many pieces of that that I'm excited to think through together. To take a step back to maybe where you sort of started a piece of this journey, symmetry and sort of the idea of invariance.

[20:50] in nature. Maybe maybe it's not possible to, you know, explain it to a word cell like me. But like, what is the what would be an example of that where you see like this sort of concept of invariant symmetry in nature? Everywhere, literally everywhere. [21:06] So the example of symmetry, I have like a water cup and [21:10] Like, if you are irritated 360 degree, it doesn't change. [21:14] That brings me back to the point. So I kind of like enforced that symmetry into the object. [21:20] Then just think of magnets, like if you take smaller pieces of metal and you put them on the table, [21:27] sort of like a metallic dust and then you put the magnet and they start arranging itself in the pattern so we say the tree is broken it didn't have a order [21:38] I mean, it had disorder and then [21:40] it acquired some order and that's called order rather. [21:44] So pretty much any ordering in nature can be described. [21:48] Bye. [21:49] symmetries of some form. [21:51] And your brain is another example. [21:54] So like, [21:56] Your brain naturally [21:59] in like the background mode of our brain is very disorganized and dream. [22:04] as well. [22:06] But sometimes you have like very clean, clear thought or thought process. [22:11] So it's the same idea, right? You had something disorganized and then suddenly it became organized. You need to keep that order of chaos around it for things to be creative so you can come up with new things at the same time. So you need to have this notion of entropy.

[22:27] And the same phenomenon occurring in condensed matter, this is the subject about materials and material studies. [22:35] how it's used in very hot temperature, very cold temperature, [22:38] And they all have different properties, like high temperature semiconductor behave different from the low temperature ones. The same mathematics can be applied to a brain. And it turned out to be the same ideas can be applied to AI. Wow, that is so interesting. And those are... [22:56] very, very helpful analogies. [22:58] When I was looking into some of the papers you published and some of your prior work, you know, I certainly can't pretend to understand all of it, but there were some interesting things. [23:10] indications of your thinking, even in places like the acknowledgements, like one of your acknowledgements, I think you thank both a fields medalist and also your Hindu gods. [23:22] And it made me wonder how, how you think about spirituality, how you think about, I imagine there's some depth to, uh, [23:30] what mathematics and [23:32] and science maybe means to you in that domain? I really like some books from Rick Rubin. [23:39] he uh he the way he thinks about creativity in general like it's not [23:44] Well, obviously, it's different theories about it, and I don't want to go deeper about it, but I love this idea that [23:51] You just need to sort of like, [23:53] relax and channel and it just comes to you like this obviously this ties us to discussion of subconscious mind and how often you think about the problem how often you're exposed to like different areas and sometimes your subconscious mind is very creative or even conscious mind so i historically grew up

[24:13] Even though I was growing up in Russia, my mom was very spiritual and she was practicing Hinduism. [24:20] So I was exposed to meditation since [24:23] relatively early age. [24:24] and I was always thinking that [24:27] part of our creativity does not belong to us it just belongs to the entire world and we just hear us [24:33] sort of a channel like yes connected on the level of consciousness subconsciousness [24:40] we all live in beings and [24:41] you know, sometimes [24:43] It just comes to you and you're just grateful and you like... [24:47] it does not belong to you. You just hear it. [24:49] to receive it and to give it to the world. I... [24:53] Really love that. [24:55] uh framework for creativity in part because i remember [25:00] reading a book, the book Impro by Keith Johnstone. I'm not sure if you've ever read it, but it's [25:07] in theory about improvisational theater. But one of the things he talks about is how different cultures have such a different connection to creativity where they do connect it to the divine. [25:18] and how that really frees you as an artist much more. Because if you connect creativity solely to your ego, then you feel really self-conscious about what you show other people. Whereas if you see yourself as a channel, [25:32] then it's sort of, you know, you sort of can separate yourself from it in some way. [25:36] Exactly. You still feel responsible, but you just... [25:40] create unconditionally. [25:43] And I think about ego a lot. And honestly, running a business is the biggest test for any human ego. Because you're facing competition. You're facing different kinds of...

[25:54] fears but you're also facing other side of things which like positive and feed your ego [26:00] So you always like have to force yourself to stay grounded. [26:05] But also that brings a question of ownership, right? What we create doesn't really belong to us. It's definitely belong to us, but does belong to you. [26:14] Not really. So this brings us back to the point of Grigory Perelman. [26:19] he was working on something people worked for a very long time [26:23] and [26:24] You can't just isolate one person and say, "Oh, obviously he's brilliant. He is genius." [26:29] he took all these pieces of a puzzle and put them in the picture. [26:33] But in reality, [26:35] Each of these pieces of the puzzle was a very hard work for multiple generations of mathematicians. [26:42] And this comes in every science field. [26:45] So honestly, I [26:46] I don't like this idea of Nobel Prize in general, it's just given to one person. [26:51] or one group, but it should be given to the entire community, because [26:55] all of us matters and all of us connected and collectively we contribute to its highest will. Maybe in your PhD or somewhere else, you mentioned there was a phase where maybe you were doing more [27:07] experimental physics and that that was more challenging and that you never sort of wanted to go back to a [27:13] touching lab equipment again. What was it about that sort of [27:17] modality or, you know, it's expression for you that didn't work? Well, I'm actually a very sarcastic person. So it was like it was a little message to my experimental team who just saved my ass during this era of my PhD. And I love them. That was one of the best parts of my PhD. But they were like, you just need to like do something else because it's too much pain in the ass.

[27:46] I honestly also don't see that there is a need to separate experimental science and theoretical science. I know there are some people who just think that theory is the most important, and some people who think experiments are the most important. But for real world, you need both. [28:04] And you need to have decent understanding of both. So for example, there was like some supersymmetry theories. People were like, oh, now we understand how the particle physics work. We're going to build a large hydrogen collider. And this is what we discover. But. [28:18] When they actually build it, the discoveries were different from what they expected. So experimental side of things telling you what's real and what's not for the physical world. [28:28] And theory is telling you about different kinds of options, giving you assumptions. [28:33] So if you just isolate yourself only on experiments, you're going to feel limited. You still need theories to test. [28:40] But. [28:41] you're only like focusing on limitations of the physical world. [28:44] for your science, for your constraints. And if you force yourself only on the theoretical side, [28:51] You just might end up working on something which might never be useful. And which is fine. Like I was, I had a bunch of useless topics during my PhD, like multidimensional donuts. And none of this would ever be useful. So no judgment. [29:04] Maybe let's jump ahead to... [29:07] this sort of period in your [29:10] your career when you start to [29:12] maybe recognize the applicability of the things you were thinking about with regard to AI. You sort of mentioned this a little bit earlier, but how did that start to come together and what was the process of that really formalizing into...

[29:25] hey, maybe I actually should start a company around this. [29:28] So the first thought was like, I published a paper with that solution and received my tenureship and just be a professor my whole life. The second thought was like, there's a lot of academic papers out there. [29:41] And right now, I think we're facing that issue, even crisis, when people using AI to publish papers and some very deep papers and very deep subject. If you're not really an expert, you cannot tell the difference. Like you don't know what's real, what's not real anymore. [29:57] My PhD on archive, there was like 500 papers a day. So it's a lot of noise. It's signal versus crisis. And we don't have tools to like actually identify what's real and what's relevant and how to find it. And I was like, if I publish my paper, it's just going to be one of those 500 papers a day. [30:16] And, [30:17] Like it might get lost and it might just live in this academic realm. [30:22] And in industry, I have a chance to actually bring it to life. [30:26] and [30:27] like, actually work with real people who's gonna use it, get real feedback. [30:31] and close that marquee gap. [30:33] and market gap is huge because right now only i is just language-based models and all around us is not necessarily language-based model so i'm like why don't i focus on [30:45] closing this gap if I have this opportunity. And opportunity came, universe provided. At the end of my PhD, I met an investor and he just like, okay, we [30:54] You have your team, so you have your thing. Let's just keep it going. I'm like, yeah, sure. Taking a step maybe back before you meet this investor, which I would love to hear about, but it sounded like you spent a bunch of time sort of analyzing LLMs and what they could and couldn't do. What were the frustrations or the limitations that you saw that sort of fit around your work so clearly?

[31:15] Obviously, I was studying the brains and I'm like, okay, [31:19] Let's look at LLM. It's a bunch of neural networks and there's some layers and we're optimizing for having like [31:25] correct ways for certain things. [31:28] and [31:28] like can it extrapolate knowledge like real intelligence the answer is no [31:33] Alums are really good at operating facts in existing space. [31:37] And sometimes people say, oh, it created something new, but you don't really, it's new for you, but you don't really know how new it is. It was there out in the internet you've never seen. Yes. It could be that case. Or it can be just like, there's some... [31:51] facts in some papers which were combined which is yeah it's new it's great people can do it that's how like literally science works for some part combining facts this one side of things and i was worried about like lms cannot create really fundamentally new things i also was worried about scalability of it and how [32:12] easier is to to [32:15] keep up the same performance level as environment change. So back to Dimitris. So LLMs to me really mimicking intelligence and [32:25] there's a whole ecosystem of different companies who are trying to advertise tools how to mimic intelligence even better. [32:32] But reality is... [32:34] you just need to work with different architecture. [32:37] to reasoning and this architecture needs to be inspired from your brain how how we evolved like we have latent space which basically keeps um

[32:46] sort of idea of your tasks on the back of your mind. You work with finite size of neurons, you don't need the gigafactory and, you know, GPUs and everything. So your brain operates on like 20 watts and you have... [33:00] You have ways to select the information, like signal versus noise and decode it in the different forms. Like it can be language, it can be dance, it can be singing, it can be drawing. Like language is just a small part of this world. [33:15] Although it's very [33:16] part. [33:17] And I'm like, okay, I... [33:19] I cannot just take an LLM and [33:21] make it better to create something new. It's just incremental improvement, but it's still tied to the language. I need to create something which doesn't care about language, but it can speak language if it wants to. [33:33] So it has to think in an abstract way, just like your brain. It thinks in an abstract way, and then they have freedom. What language I'm going to speak to? What am I going to do? If I drive a car, I don't need language at all, right? And the architecture we built is exactly that. [33:49] um it thinks in an abstract space it's a vector space and [33:53] you could do so it have all the reasoning happens in that space and then you can have a layer of LLMs if you want to [34:00] people speak to your model, or you want to speak back to people, LLM just an interface. It's just a user interface out there. [34:08] But for a body, you don't need even that, right? Sometimes you need to control the circuits, like you put... [34:15] AI as a brain in some robot and robot is doing some acting.

[34:19] You don't need any language to communicate to the circuits. EBM reasoning model can speak directly to the circuits. You need to speak on a millisecond/microsecond scale. [34:28] do it, which we do. But yeah, in this case, you don't need another land. [34:33] as a user interface. And [34:36] yeah so i was thinking about this building architecture having this on the back of my mind half of my life and [34:43] He just came and [34:45] That's it. [34:46] Wow, amazing. This episode is brought to you by Persona, the B2B identity platform helping businesses verify users, fight fraud, and build trust. Fraudsters are already using AI to spoof faces, voices, and documents, so your defenses need to adapt just as fast. [35:03] Persona helps secure some of the Internet's largest and most trusted platforms with identity verification. [35:09] If you're building a product where trust matters, identity should be a priority. [35:13] you've probably already experienced Persona without realizing it. [35:17] verifying your LinkedIn profile, signing up for Etsy, [35:20] or renting a scooter with lime. [35:22] trusted by leading companies like Square, Brex, and Twilio. [35:25] Persona gives you the building blocks to create identity flows that adapt to your customers, risk tolerance, and locales you operate in. [35:33] Whether you're verifying age, onboarding businesses, [35:36] or automating KYC. [35:38] It's fully configurable, so you can launch in days, not quarters. [35:42] Want to see for yourself? Generalist listeners get a free year of the starter plan. Head to withpersona.com slash generalist and check it out.

[35:51] It is really fascinating to see [35:54] you know, on your website, you have just like, you know, an example of how, [36:00] the logical intelligence models work differently and can solve different problems compared to traditional llms and it really [36:08] kind of blew my mind for folks that maybe want to go check it out, I would recommend it. But you sort of generate a super hard Sudoku. [36:16] and then simultaneously test how long it takes to solve that Sudoku for logical intelligence. And then, you know, [36:24] Gemini, ChatGPT, Opus, whatever you want to pick. [36:29] And it is just fascinating to see how instant it is with your approach and how basically all of the other models work. [36:36] either don't really get there ever or they get there incorrectly. And so that's sort of maybe the smallest dividend of how this sort of manifests differently. You know, obviously, this is [36:47] broadly applicable maybe at maturity, but they're probably sort of initial [36:52] applications of this that make most sense. So I would be curious how you think about, about that piece of it. [36:57] Yeah, so where it makes sense are the areas which are [37:03] mission-curricular areas, so somewhere you don't want to have mistakes, and also, which is not necessarily tied to language. [37:09] So robotics, hardware is the future. In robotics, you cannot use LLMs, unfortunately, although many people try. [37:17] just because it's very expensive, it's still playing the guessing game. [37:21] uh you need really fast inference like

[37:23] it's very hard to bring LLM to like microsecond scale or [37:28] like the circuit level scale, I mean, the use cases we're targeting as [37:32] Pretty much robotics, tube design. [37:34] creating formally verified chip design code generation, formally verified code generation. [37:41] So when I'm saying formally verified, it means it guarantees mathematical correctness of things. For example, there's, everyone loves wipe coding. [37:50] And a vibe coding. [37:52] is like how it pushes the industry right now it's optimizing the pipelines of people and so on but it's still on people to debug [38:00] So we want to help people not to debug and be focused on being creative. [38:05] like, [38:06] create things instead of being focused on [38:09] Like what kind of languages I'm going to use. [38:12] you know, what kind of bugs I'm going to face, what kind of tests I'm going to do. And you want to deliver it at scale, meaning you need to stop Vibcoding. You need to move to Vibcode specifications. So [38:24] That allows you to generate code, which is always correct. [38:28] But still, you're going to be guiding the whole process. You're going to say, hey, generate me an autopilot. This is the hardware I have. [38:35] And. [38:35] the requirements for this car, like I wanted to follow direction, always listen to people. So you're in charge of the constraint as people. [38:43] as a human and for me it's a very big deal because we want to protect people in the ai driven world i want people always be in charge of what ai can be doing so my biggest nightmare is if ai is doing something it was not meant to be doing so the way to do this is

[39:01] is to have these constraints provided by people. It can come in natural language, but then AI is doing [39:08] Thanks. [39:09] And it fulfills these constraints all the time. So it's not allowed to deviate from the tasks. So LLM can never do this because LLM hallucinate. [39:18] So you need to work with different architecture, which can self-align, which can adopt its behavior, which can be precise when it needs to be precise. [39:26] So for people, [39:27] precision doesn't come naturally, right? Like, [39:30] be [39:32] We like, yes, if we want to build a bridge, we go to engineering school. [39:37] Yes. [39:38] Mathematics is not the natural part of our brain. We evolved on jumping trees and eating bananas, right? [39:45] That's why if you feed your LLM with mathematics, it doesn't help really. [39:50] become your greater mathematician or something and natural intelligence [39:55] and my dream [39:56] AI is not precise by default, but it has option to be precise. It's as if in your brain there's a calculator, you know, when you need it, you turn it on. [40:05] So mission critical industry makes sense. Any kinds of robotics, any kinds of manufacture, any kinds of chip design. Chip design is the area when it's needed the most. [40:17] People can use AI to put designs of the circuits on the wafers. [40:22] but they cannot guarantee correctness of this design and it's a very difficult problem to solve. [40:28] Also, [40:29] it can cost you millions of dollars if you make a mistake at this stage because it goes in production and then you learn it doesn't work so all of this time and effort it's going to slow you down slow down your production you're going to

[40:42] feel frustrated. [40:43] So we're solving all of these problems. [40:46] That's one thing. [40:47] The second thing is there's just a lot of healthcare areas. [40:52] where robotics can be crucial, like, [40:55] robotics for surgeries [40:58] Imagine if you let a lamb drive it, somebody says, oh, you know, the 20% of the time during brain surgery, it can go to like the wrong area. I'm so sorry. The next word was the wrong word. [41:08] You can't have that. So smart energy grids, literally the systems which power your entire town, does not involve any language. It involves a giant data analysis in real time, searching for patterns in real time, making estimate, forecast predictions, what's going to come next, depending on many variables. So this is a very great use case for energy-based reasoning model. And finally, [41:38] And, you know, same problem forecasting. [41:41] analysis of different kinds of data in real time. [41:44] also for mythology sorry i just you see the use cases are like it [41:49] So much of it. But pharmacology, it involves analysis sometimes in real data for certain patients. You need to analyze your blood samples, having genetic background, having language part of it, because patients are going to speak to you and, you know, come up with real-time solutions for it. [42:08] for certain diseases. As a sort of [42:10] point of context or clarification, why have you called it an energy-based model? Like, why is that the right sort of

[42:17] uh, way to think about what it is you're doing. [42:20] It's actually not. It's. [42:22] This should be called energy-based reasoning model with latent variables. [42:28] Okay. But it's so long and it's such a new area. I don't know anyone except us and Jan's company who is working on this at the moment, but I'm sure there's going to be a lot more as we speak more and, you know, people learn about it. But energy, let me break down like some words and maybe it can give you the big picture. [42:50] Perfect. [42:51] Yeah, so energy-based [42:53] principle is not new it's everywhere in physics examples would be you go [42:59] I don't know, from your office to your home, you want to pick the shortest path. You're minimizing your energy. As you're speaking to me on this podcast, you're not jumping because you want to minimize energy and focus. Energy minimization principle. The light travels the shortest path, right? Straight to your eye. So everything in nature wants to minimize energy. That's what we call the energy-based principle. [43:22] There are LLMs which utilize an energy-based principle for a certain navigation of certain [43:28] you know, networks, like layers and so on. So it's a huge term, very broad term. It's not making anything unique about us having this term. [43:38] So where things come in unique is [43:41] the latent variable. [43:43] So what is latent variable? Again, inspiration came from our brain, which has this part of latent spaces called part of your brain, which keeps.

[43:53] sort of mental image of the world. [43:56] Example, the mental image of the world, like, you know, we know some rules about the data. [44:01] Like, my favorite... [44:03] Example, like I'm a coffee addict. I could drink coffee all the time. If somebody knows this fact, this rule, and they see like, oh, there's a coffee cup and there's Eve, the rule must be Eve's going to drink the coffee. Somebody else bring me coffee, I'll take it. I never can say no to coffee. So it doesn't matter how many data points you provide, the rules are still the same. [44:24] So those rules about the world collected by latent space of your brain and also by our model. So our model not just taking the data around itself. And data can be any form. It can be visual. It can be audio. It can be video. [44:39] It can be language, it can be anything. [44:42] It takes those data, [44:44] and it learns the rules about the data. [44:47] And those go to its latent space. And the way we navigate it is we're using this energy-based principle. [44:54] Yes. [44:55] completes a picture. I can go very far with this. I'll stop. Okay. Okay. Perfect. If you, if there's something that you think, Hey, you know, we, you should definitely explain this, but I thought that was really, that was really interesting. You know, as you were explaining the different, [45:09] Applications. [45:11] of what you're building. [45:13] What sort of came to my mind is what is the trade-off if there is one that this type of model has to make versus an LLM? Because it sounds like...

[45:23] It has the ability to be [45:25] Much higher precision, which, you know, obviously is better and seems like it's more energy efficient and I imagine more cost efficient or maybe that's the trade off. Very cost efficient. [45:36] So, [45:39] To be smart, you don't need to be big as an AI model. There are some tasks we thousands time faster than big tech LLMs. [45:47] for tasks LLMs can solve. [45:50] And we run it on one single OH100 GPU. [45:53] So it's the cheapest kind of GPU and it's only like one of them. So, and we have the range of parameters of the models. We have like 20 million. The highest we have at the moment is 200 million parameter. [46:06] contrast the bigger land models are like billions and billions parameters we're building small models which allows you to put in circuits in your chip and your energy grid [46:16] This is what [46:17] big tech alarms will never be able to do they build one brain for all and all [46:21] And this one brain is forced to be all possible roles, a doctor and engineer and so on. So, [46:30] A lot of issues with that because of LLM extrapolation issues. Like it does not extrapolate the knowledge, at least the current form of architectures. [46:39] So you're building more specialized models. Is that... [46:44] It's specialized, but it doesn't have to be. It's actually a very generalizing model. So because it doesn't play in a guessing game, it's very cheap. [46:51] Because of its architecture, it sees all possible scenarios at the same time. So that demo is on our website, took us $4. And there was like 20,000 people using it.

[47:03] and [47:04] for the llms it took like 15 000 [47:07] dollars in contrast to solve that two percent it's like the cost deficiency is [47:14] It's crazy. [47:16] And that's how it should be, right? It's like if you make something which thinks, you're no longer playing a guessing game. You should know the answer straight ahead. [47:24] The president of the Santa Fe Institute, David Krakauer, had like a really... [47:29] apt way of describing [47:31] intelligence, which is doing more with less. And most of these LLMs do [47:38] more with more and sometimes not that much more with a lot more. It sounds like you've sort of [47:43] found a way to [47:44] yeah, constrain, like not use nearly as orders of magnitude, less parameters and cost and energy, but find more out of it in these applications that, [47:56] that as you're describing it, maybe have like, [47:58] Yeah, the ability to extrapolate and generalize much more. I think you've said [48:03] that one of your models sort of shows [48:06] credible signs of AGI, [48:08] What was it that made you think that? [48:11] That's not me, that's journalists. Oh really? Okay. [48:15] A journalist said that. [48:17] I'm joking. So, um, [48:19] Let's talk about AGI. Oh, this is the first part of the conversation where I saw you put your hand to your head in frustration. You know, to me, what is AGI is like the same what is consciousness. There are different philosophies, like, oh, this is what we call AGI. And this is a different situation. Everyone has...

[48:37] separate, like you have your own version of AGI. [48:41] Nobody defines this form of AGI and it changes over time. So what was called AGI by different people 10 years ago is probably not what we're going to call it right now. [48:52] So before I speak AGI, I'll tell you what AGI is for me. So AGI... [48:57] should be [48:58] Just like natural intelligence, something which plans, something which is able to predict, produce new knowledge, be cheap. [49:06] and efficient. [49:08] be adaptive to the environment. [49:10] Um, [49:11] It should reason. It should not mimic any kind of reasoning, but also it should be compatible with... [49:18] all the tools we have possibly like we have LLMs for the language so [49:24] You need to have your model compatible with the language component because language is a big part of our world. So. [49:31] I'm sure there's going to be a lot of different forms of AI. And to me, AGI is having that ecosystem. [49:38] which sort of serves us as humanity. [49:41] in the safest possible way and the most productive way. [49:45] So, [49:46] we as people, [49:47] like there's some collective notion of consciousness and there's some collective effort we build cities we have different professions each of us is good at something very specific and that's how we bring this talent to the world and we monetize it so we get something back but also we give something to the world so we created a system to serve us as humans and i

[50:10] I see that AGI to me is that ecosystem, but for the AI models to serve us as humans. [50:16] So, [50:17] I see that Kona is really cheap. [50:20] It's very efficient, it doesn't need any special hardware. [50:24] So we don't need to create special chip to run this architecture. [50:30] It's adaptive, it's self-aligning. [50:33] Self-alignment is a big part. Like if your data changes, if your environment changes, you need to be able to recognize it like this and change your behavior. [50:42] And their behavior always need to be the subject of the constraints given by people. [50:48] So, [50:49] We do see that. [50:50] I already mentioned this part that self-alignment is a crucial part. Yes. So LLMs do not have self-alignment feature. [50:58] um lms are good at what you train it for so if you train it on one specific task you can't ask it [51:05] to do something else. [51:06] And there is a big hope and belief that if LLM is going to be bigger, it's suddenly going to change its behavior and suddenly becomes adaptive and planning and predicting the future, forecasting and so on. [51:20] But I haven't seen it yet. What is the most impressive thing Kona has done that maybe has blown your mind the most? Knowledge extrapolation. [51:29] did not expect that expected [51:32] expected in a theoretical level, did not expect [51:36] to see it as quickly as we see it now, [51:39] in experimental level.

[51:41] So the beauty of this energy-based reasoning model, because they're not attached to any language, you can build space for. [51:49] rules about your world. [51:51] like the rules like if you if you're self-driving car for example there's going to be rules like [51:57] How am I behaving around people? [52:00] How am I behaving around like town environment, like the city, you know, sometimes the road is closed and this road I cannot take and they change my behavior. Also the weather, which can change. So you can have different sorts of rules. [52:15] sitting in different latent spaces. [52:17] and [52:18] There's a beautiful mathematical way to like force it. [52:21] to talk to each other of these latent spaces. [52:25] but in a very, very fast way. When this idea came, we were like, [52:31] I don't know. So it sounds beautiful. Just like supersymmetry. Let's build a Hadron Collider and test what's real and what's not. And it was real. And the model was so tiny. And I was expecting to see [52:48] some sort of [52:49] Change in complexity. [52:51] What do I mean by that is sometimes different [52:55] disordered system, when they're small enough, they behave one way. And when they're big enough, they start behaving different way. [53:02] So, [53:03] I was thinking this behavior may come if the system is bigger enough. Like, for example, if we grow the model to 8 billion parameter, maybe we'll see it.

[53:11] We saw it right away. Like 16 million is the moment we saw it. 16 million per hour. It's so tiny. [53:18] and [53:19] It blew my mind. [53:21] You have such an interesting team. You have founded Logical Intelligence with your husband. You also have a field medalist on the team. And then Jan LeCun is an advisor. How did that group come together? Jan is an advisor. Jan is a founding chair of our technical board. A founding chair. [53:42] OK, well, maybe Jan is the right place to start then. How does that work, given that I think you were mentioning, you know, maybe it's just you and. [53:52] Jan's own company that are sort of playing in this, in this space with this approach. [53:57] Well, when you invent something relatively... [54:01] new, [54:02] People usually go to internet and read papers about it. When we created this, [54:07] We could not just go like read about it and see like, oh, what we can predict, what's going to happen next, how it's going to scale. [54:15] So we did not have this luxury. [54:17] and this is this used to be like a real science scientific approach when you have to come up with a series of experiments to evaluate all the boundaries all the constraints [54:27] of your invention. For us, Jan was the only person to talk to about it because he's been in this field for a very long time. [54:35] He knew this area in and out, and he's been both in industry and academia side. [54:41] So the moment that worked,

[54:43] We showed him and [54:46] We're like, okay, let's scale it. [54:48] So Jan is helping our team to scale the model. [54:54] and evaluate any [54:56] critical constraints we might face as we scale in it because again nobody's done this before but we have very deep understanding what's happening and what's going to happen next and [55:07] every moment we know what we're doing. [55:10] right now but back then we didn't know and we had some guesses and predictions but now we like understand it and we know like now we solved it so [55:22] Jan is amazing. His knowledge is like so deep. [55:27] And because he's also a professor and he's also teaching. So there's a community of people who also exposed to like different kinds of energy-based architectures. [55:38] So it's always great to engage with those communities and [55:41] I'm happy that Jan created [55:43] this ecosystem, you're literally the father of this ecosystem. [55:47] So we honored that we worked together. [55:50] His company is focused on Jepas, but they are also doing a lot more things and I'll let them speak for themselves. [55:58] But the beautiful part about our collaboration is our model compatible with Jan's model. So, [56:06] His jabba is like the moment when we could see [56:10] It's. [56:11] Photobotics, it's selecting signal versus noise,

[56:14] and they output [56:15] can become our input and we, so they're doing like a lower, lower reasoning planning and we're doing the higher reasoning planning. [56:22] So our architecture is very compatible with each other. [56:25] So I'm excited to see what we're going to create together next. [56:30] So that's what about Jan. [56:32] My husband, so I've been married for 14 years, literally half of my life. We sort of grew up together with my husband and we always talked about math and [56:41] When it was legal, I got married. So I got married at 18. [56:44] We have two kids. [56:46] um so mathematics and [56:49] AIs and ABMs was always a part of our family. Yeah, so... [56:54] I'm glad we're doing this together. [56:56] you know. [56:57] My husband... [56:58] is the ICPC champion. He won this championship in 2009. [57:03] And this is how we moved to the US because Neta, back then Facebook, just invited him. [57:08] and um [57:09] Yeah, I was hoping a little bit recruiting people, like recruiting different talents from ICPC world back then, because I spoke multiple languages and that was useful. And Facebook was much smaller because of this ICPC exposure. [57:21] the the [57:22] train, [57:23] through this competition, brilliant people who [57:26] uh forced to solve very hard problem in like five hour scale and also not just solve it but also write the code and write algorithm [57:34] and make sure it works. So that's kind of what you see in the startup environment. And we obviously grew up, like my husband and I, we grew up in this environment. We did it ourselves. [57:44] And we had a lot of childhood friends who just became the core of this company.

[57:48] So we have like eight ACPC medals in the company at the moment. And I also brought Mike Friedman. [57:54] because [57:56] the same subject was interested in like how AI works, what's [58:03] is there any similarities between brain and AI? And I met him during my PhD, when he was a Google quantum AI. You mentioned that [58:11] part of the [58:12] the story of this company and, and, you know, maybe the, one of the, [58:16] key steps in going from [58:18] hey, maybe I... [58:19] write a paper about this discovery versus turning it into a company was finding an investor and that sort of making it more official. Who was that first investor and how did it sort of change the trajectory potentially? [58:31] There were three people. Very good, very well connected. [58:35] people in Silicon Valley and did not find them. They found me. One of them found me. Wow, that's awesome. [58:42] we met at a conference and started talking and [58:45] yeah, he was [58:48] pretty known in Silicon Valley. There was something that I saw that you... [58:54] cited on Twitter, which was the collection of Feynman letters. I think it's called Reasonable Deviations from the Norm or something like that, which is a really beautiful collection. I read a [59:07] in preparation for this. And one of them was interesting to me because he literally... [59:13] is talking about [59:15] sort of versions of AI. And I think he sort of,

[59:18] cites the idea that [59:21] uh, [59:21] you know, the sort of unbreakable challenge is to make a, [59:25] you know, a computer smarter [59:28] without it becoming much, much slower with its memory. [59:31] I'm curious if that's been something [59:34] that you've [59:35] thought about as you've been building this or you've returned to that collection often? [59:40] Definitely. Well, don't return often, but I read this book like so many times at some point of my life. [59:46] also not just for that reason but also multiple reasons outside of that but [59:52] Yeah. [59:53] Natural intelligence is able to produce new data and use this new data to navigate the world. That doesn't mean that you need so much data in your life. But it's not fair to compare it to people, though, because we evolved like there's years of evolution. [1:00:08] millions of years for us but for intelligent the evolution is going to be different [1:00:14] But I'm a strong believer that you [1:00:16] need to start with some data sets. [1:00:19] was. [1:00:21] very rich data, like not just the data, but also the rules about the data which AI is able to work with and [1:00:27] That data set alone should be the foundation and then it should be able to create new knowledge based on this data. So for example, [1:00:34] People's analogy would be like you learning how to play piano and you'll start taking Bach pieces, but then you can move to Mozart. Like you're not trained on any Mozart pieces ahead of time, right? You just use the same rules, you know? [1:00:47] how the keyword works,

[1:00:49] you know what to expect and you're kind of good to go. So you were able to create a new set of skill to play in Mozart and then people start picking up guitar. Well, guitar is very far from piano from the technical standpoint, but the fundamentals of music are similar and you can extrapolate. [1:01:06] So that notion of ability to extrapolate in the most cheapest possible way, [1:01:11] While being a small model, [1:01:13] was the key things for me. [1:01:16] I think you mentioned [1:01:17] somewhere that piano is your your biggest love outside of mathematics but you don't get the chance to practice as much anymore. [1:01:26] Are you really studying my Twitter? [1:01:29] It's a funny, it's, you know, it's like people's diaries in some way. You really get an insight beyond [1:01:34] papers no yeah it's it's a diary and i should be making it less of a diary because pr team has a lot of comments from my twitter i was just curious like what you know really what the role of music has been in your life and in the way that you come up with ideas because i imagine [1:01:52] you know, you've obviously sort of, [1:01:54] relied on a great deal of creativity to come up with with this version of things but i imagine [1:01:59] You will need to keep fostering that sense of invention throughout the company's life. And yeah, I wonder how you find it today if it's not through piano, for instance. It's just the general principle. I believe for myself, a creativity is you need to be able to detach. [1:02:16] so you can come back to the problem and have sort of fresh eyes looking at the problem. I also need to detach your mind.

[1:02:24] and your mind is your biggest enemy. It's gonna tell you all of these monkey talks and [1:02:29] create doubts and concerns so [1:02:31] There are so many ways to shut it down. [1:02:33] I mean in a positive way. You could meditate, you can play piano. I have two children so I spend time with them. [1:02:41] and spend time with friends. [1:02:44] And... [1:02:45] yeah playing piano if i can um trying to [1:02:49] learn some [1:02:51] some piano sheet music, like something complicated I've never done before. I always love just learning. Also reading a bunch of books if I can, but now obviously I have less and less time and [1:03:02] But I also enjoy doing research myself. So I just enjoy going sometimes and reading new papers on different architectures. And what brings me a lot of joy is just to see like lots of emails from [1:03:15] all over the world like more speak about this publicly it resonates with a lot of people and there's a lot of people who like hey i got inspired by this i want to create my own architecture so this is like things which make my day i love to see just people [1:03:30] Wanted to be creative. [1:03:32] because they like something and they share it with you so i'm very grateful and sometimes i [1:03:37] I engaged with those people and one of them became our research intern. And certainly... I always like to end... [1:03:44] the conversation with a couple of [1:03:47] light thought experiments. One is if you had, um, [1:03:52] Unlimited resources and no operational constraints. What is an experiment you would like to run?

[1:03:59] I would do the same thing. [1:04:01] As you're doing now? Yeah. That's always a good sign. I'm not doing this like to make money or something. And I was in academia, which is definitely not the place where you make money. [1:04:12] I just, I love this with my whole heart. [1:04:15] and [1:04:16] I love [1:04:17] to create environments for people to reach their highest potential as researchers, as [1:04:23] engineers and so on and it's like academia is a place when you combine both of this worlds together but also industry is you can do the same but at a much larger scale so [1:04:37] 100% I would do the same. Okay, amazing. And then a final question. If you had the power to assign a book to everyone on Earth to read and know that they could understand it well, what would you want to assign to people? [1:04:50] Well, I'm also a strong believer that most of the issues around us, we create ourselves using our mind. So I'm a big fan of [1:05:00] any some sort of [1:05:02] Um, [1:05:03] East philosophy books [1:05:05] and [1:05:07] techniques how can you navigate your mind when it becomes overwhelming [1:05:12] So, [1:05:14] I don't have like a specific book in mind because there are so many of that, but this is a direction I would recommend. Because if you ground yourself, [1:05:21] If you feel good inside, [1:05:24] everything outside is going to be a manifestation of how good you feel inside. [1:05:29] So if you'll,

[1:05:30] naturally happy, you're going to share your happiness. You share your creativity with others around yourself. [1:05:35] So to take care of the world, you start to take care of yourself first. And it goes the other way, right? If you're angry at something, you're gonna be angry at everyone else around you. So it's, [1:05:47] Contagious. [1:05:48] Um, so I would recommend [1:05:50] that we work on our own self first and it's a basis for creativity it's a basis for [1:05:58] being a valuable creative member of community no matter where you are like AI or [1:06:04] medical school or industry, academia, it doesn't matter where you are. People are people. [1:06:10] and [1:06:10] Yeah, so. [1:06:12] Letting It Go from Hopkins is my favorite. And that's a sort of book inspired by Eastern philosophy? [1:06:18] Yeah. It's a [1:06:19] Western view on Eastern philosophy and that Eastern philosophy come from Mahayana Buddhism. [1:06:26] So that French of Buddhism, which is like roughly 2000 years old, like the moment when they start having a ways to write things down instead of, [1:06:35] memorize it. So it was like a new branch of Buddhism. And there's also the same traditions shared in Hinduism. And I love [1:06:45] I love how it sort of overlapped [1:06:48] It has like psychology and it has a notion of spirituality and [1:06:52] It explained in a language for people who don't really expose to either of those branches during like a fall.

[1:07:01] amazing uh well i i certainly will check out that book and uh i have really enjoyed this conversation so thank you so much [1:07:10] Thank you. Appreciate it. [1:07:28] I'd be grateful if you could take a moment to leave one. [1:07:30] For all past episodes and more, [1:07:32] Visit us at thegeneralist.substack.com. [1:07:35] dot com. [1:07:36] See you next time as we continue to explore. [1:07:39] the future. [1:07:41] *music*