Turing Award Winner: Early AI, LLM Predictions, Causality | Judea Pearl

Judea Pearl is a Turing Award winner and a pioneer in artificial intelligence and causal reasoning. We talked about how he got into science, his major breakthroughs and his predictions for AI today. • My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/ • The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done Podcast links: • YouTube: https://youtu.be/FleTXB1fAcQ • Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835 • Transcript: https://www.developing.dev/p/turing-award-winner-early-ai-llm Thank you to this episode's sponsor for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/ Timestamps: (00:00) Intro (00:54) How he got into AI (11:17) Greatest scientist of all time (20:15) What people thought of AI in the 80s (26:23) Entering academia and researching AI (34:52) The invention of Bayesian networks (46:28) Pioneering work in causality (55:38) The causal hierarchy (59:34) LLMs and predictions (01:20:12) A restless mind pays (01:24:36) Advice for his younger self (01:26:37) Outro Where to find Judea: • X/Twitter: https://twitter.com/yudapearl • Website: https://bayes.cs.ucla.edu/jp_home.html • Wikipedia: https://en.wikipedia.org/wiki/Judea_Pearl Where to find Ryan: • Newsletter: https://www.developing.dev/ • X/Twitter: https://x.com/ryanlpeterman • LinkedIn: https://www.linkedin.com/in/ryanlpeterman/ • Threads: https://www.threads.com/@ryanlpeterman • Instagram: https://www.instagram.com/ryanlpeterman • TikTok: https://www.tiktok.com/@ryanlpeterman Referenced in this episode: • The Book of Why: https://en.wikipedia.org/wiki/The_Book_of_Why • Bayesian networks: https://en.wikipedia.org/wiki/Bayesian_network • Alpha-beta pruning: https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning • Pearl vortex: https://en.wikipedia.org/wiki/Pearl_vortex • Graphoid: https://en.wikipedia.org/wiki/Graphoid • Causality: Models, Reasoning, and Inference: https://en.wikipedia.org/wiki/Causality_(book) • Coexistence and Other Fighting Words: Selected Writings of Judea Pearl, 2002–2025: https://bayes.cs.ucla.edu/COEXISTENCE/

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

• 0:00Speaker 00

  One day, computers are going to be able to emulate all human functions. The question was only how and when, but not whether.

• 0:09Ryan Peterman?

  This is Judea Pearl, Turing Award winner, famous for his contributions to artificial intelligence. And I interviewed him about his career and where AI is today.

• 0:18Speaker 00

  So this robot baby comes and say, let me control them. And I know how I understand their fears. People claim, we can now program my consciousness. Come on, you have to be scientist, right? Define what you mean by consciousness. I proved that alpha beta is optimal. Even Knuth was surprised that one can prove the optimality on alpha beta. I don't know if I'm allowed to. When I publish it, you see what kind of stupidity drives those great people.

• 0:49Ryan Peterman?

  Here's the full episode.

• 0:54Ryan Peterman?

  I looked into your educational background and it was all electrical engineering, physics. I don't see the connection to causal reasoning and artificial intelligence. How did you get there?

• 1:06Speaker 00

  Everything connects, everything connects. Everything connects to the day I was born. Hit on the head.

• 1:17Speaker 00

  First, I have to start by saying that we, I grew up in mandated Israel prior to 1948. And prior to the establishment of the state of Israel. And we had a very excellent high school education. My high school teachers were professors that were chased by Hitler from Germany, from highly reputable universities like Heidelberg and Berlin. And they came to Israel in the 1930s.

• 2:03Speaker 00

  And they didn't find any academic position at that time. They were rare. And they, they started teaching high school. But they were really quality professors. They could teach anything without notes. From the economy of Manchuria to the proof of Pythagoras theorem with no notes and no stop.

• 2:33Speaker 00

  And we were, we, I mean, my generation was lucky enough to be beneficiary of this educational experiment.

• 2:46Speaker 00

  So we were taught science from a human viewpoint. Chronologically, the way things were discovered, by whom they are discovered, at what period. Why was there a question about a certain mathematical proof? What the inventor of the proof knew, what he didn't know and what he asked himself in the context of the historical situation at that time. That's the way to teach science, not the recipe of

• 3:32Speaker 00

  algorithms and techniques, but as a, from the viewpoint of the human actor. And the transfer of ideas from one human to another is a human struggle against, not against, human struggle to decipher the secrets of nature.

• 3:54Ryan Peterman?

  What's the advantage of learning in that style?

• 3:58Speaker 00

  The advantage is that you see yourself as an actor, you the student. You are also puzzled by many things, but look what he did. Look what Pythagoras did, okay? He was as puzzled as you were, and he took that route. Perhaps when you're puzzled, of course, your puzzles are not as magnificent as his. But still, look what he did. He took that route around things and he consulted some other work of some other. He struggled and you struggled and you are part of science. That is the basic idea.

• 4:41Speaker 00

  Give students the idea that you are part of science, not a passive observer at science, not a recipient, but as an actor.

• 4:55Speaker 00

  And that, I think, was unique, very valuable for me. We indeed got the idea that each one of us can find another proof of Pythagoras theorem that no one else has thought about. And we, each one of us has the potential of becoming an Einstein or Pythagoras. They gave us this illusion. It was a useful illusion.

• 5:25Speaker 00

  I know.

• 5:28Speaker 00

  I have just a pebble looking at what Pythagoras or Einstein did. But that illusion helped me be a little, I would say, not contrarian, but assertive.

• 5:44Speaker 00

  And we all grew up in assertive mode of learning science. We insisted on understanding things our way in real time.

• 5:58Speaker 00

  If the teacher went too fast, we made noise. We made noise with our chairs and with everything we could, okay? And the teacher stopped and slowed down to make us all understand things our way in real time. So that was part of the, I'd say, mood of me and my generation.

• 6:26Speaker 00

  And I can see that it affected my life. I have one story that I remember, it made an impression on me. I look back and I say, well, it started very early. It started in age 10, when we learn about how to calculate areas and volumes. And here, there was a question in class, how many dunams are there in a square kilometer? Dunam is, is 1,000 meter square. It was a Turkish unit for measuring

• 7:09Speaker 00

  areas. And so the entire class said dunam is a kilometer square. And I screamed and said, no, it's a thousand. We have 1,000 dunam in a kilometer square. And the teacher sided with laughing class and they all mocked me.

• 7:32Speaker 00

  And, and ridiculed me. And I went home and I said, they are wrong and I'm going to come back tomorrow and insist on that. And the teacher apologized to the class. And I felt that, yes, you have to insist on your, your understanding of things. Yes. I'm telling you that because maybe this would made me into a non-compromiser. And this is part of my childhood, a background which might explain how I got into artificial intelligence.

• 8:14Speaker 00

  So I took engineering after being a farmer in the army.

• 8:23Ryan Peterman?

  Farmer?

• 8:24Speaker 00

  Yes, in Israeli army, you have troops which are spending their time half and half. Half in military training and half in farming, being part of a kibbutz.

• 8:40Speaker 00

  That's the old idea of one hand holding the plow and the other one, the rifle.

• 8:49Speaker 00

  And you succeed.

• 8:50Ryan Peterman?

  Did you ever shoot anyone?

• 8:52Speaker 00

  I almost shot someone without seeing him or her. But it turns out the next morning that it was a fox. But the step of the fox were very, very similar to the step of a terrorist advancing towards us.

• 9:12Speaker 00

  So that's as far as I got to shooting. So I got into the Technion to study electrical engineering. Again, we had great teachers. Again, we got the idea that we are making science. So we studied physics very seriously. And I liked what we studied, yeah. I wasn't the first in class, no. I was third or fourth always, never matched the geniuses. The geniuses knew, were bored in class.

• 9:52Speaker 00

  They knew what the teacher is going to do, what he's going to ask in the exams. And everything was boring to them. I wasn't bored.

• 10:04Speaker 00

  I wasn't the first, but I wasn't bored.

• 10:07Ryan Peterman?

  What interested you in physics? What did you like that made you, you know, so passionate?

• 10:13Speaker 00

  What I liked was that with sitting on your chair, you can predict things in physics, like Maxwell, you know, that sat on his chair and say, hmm, that looks like a wave equation. Let me calculate its velocity. Hmm, it looks like velocity of light. Hmm, maybe light is nothing else but electromagnetic wave, you know, on his armchair. He didn't do any experiment.

• 10:50Speaker 00

  That was excited me, yeah. My wife told me one night, I woke up and I said, Maxwell was wrong. Maxwell was wrong. And I was like, she quieted my down. And the next morning, I said, he was right.

• 11:08Ryan Peterman?

  By the way, you you mentioned a few scientists and it seems like you know a lot about the history of, you know, the old scientists.

• 11:16Speaker 00

  Yes.

• 11:17Ryan Peterman?

  Do you have a favorite scientist of all time and why?

• 11:21Speaker 00

  When we studied

• 11:24Speaker 00

  analytic geometry,

• 11:27Speaker 00

  I got fever. Really fever.

• 11:31Ryan Peterman?

  Physical fever?

• 11:32Speaker 00

  Yeah. I couldn't get over the idea that you can do in algebra, all the geometric constructions that we labeled on, you know. So I thought that Descartes was the greatest mathematician ever lived. It was so enormous to me. The transformation from geometric constructions to algebraic derivations. It was unbelievable.

• 12:01Ryan Peterman?

  I take that for granted when I was in education. What is it about that that's so astonishing?

• 12:08Speaker 00

  Here you have two different languages. Language of geometry and the language of algebra. And they are the same thing. And you can get the same phenomena, same proof that you can pass tangent to a circle from a point outside the circle.

• 12:30Speaker 00

  And you can find the angle by different methods. Two different languages, dealing with the same phenomena, different perspective, and they get the same result.

• 12:44Speaker 00

  It blew me off. Blew me off completely. Maybe that was it was preparation to computer science. Because for us, computer science is what the big deal. You want to see things from different perspective, invent a new language. So that was

• 13:03Speaker 00

  what turned me on. In my high school, that was in high school. And then we saw, I saw the same thing in physics. Different languages capturing the same phenomena. Farley really excited me. He invented the idea of a field. Two different, two different ways of looking at the same thing. You know, you can see that the force here depends on the charges around it. Or you can say, no, there's a field right in location of your testing point.

• 13:43Speaker 00

  Terrific, terrific.

• 13:46Speaker 00

  So I came in with this preparation. And here we go to Brooklyn Poly. And I studied there. I worked in the morning in RCA laboratories in Princeton, New Jersey. David Tsaroff Research Laboratory. And there I got into the computer research group. Computer research at that time was research all phenomena that you can think of to find out

• 14:19Speaker 00

  a mechanism for computer memories. The memories at that time were core memories, magnetic cores, the donuts that you remember perhaps from your early childhood.

• 14:34Speaker 00

  That were too slow and too clumsy. And you have to have people stringing them X and Y and Z in Hong Kong. And

• 14:47Speaker 00

  people understood that the days of core memories are numbered. And we're looking for new phenomena. Some people look at photochromic memories. Some people looked at semiconductor. Some people look like me into superconductivity.

• 15:11Speaker 00

  And I was in a group that was supposed to design superconducting memories.

• 15:18Speaker 00

  We did some nice plates, 16 by 16 bits. And we thought that we have the future in front of us. But in the way toward developing superconducting memories, I investigated the physical phenomena behind the eddy currents, permanent eddy currents in thin superconducting films. And it so happened that I discovered new phenomena there. And I got a prize. And I even have a name. It's called pearl vortex. You can find it in Wikipedia.

• 16:04Speaker 00

  I discovered that physics is years after I finished my PhD, discovered my work there. And they were interested in the idea of permanent current flowing in a circle in thin superconducting films. And since I analyzed the magnetic and the current field there, they call it pearl vortex. So here I have my footstep into immortality.

• 16:38Ryan Peterman?

  You said it's a permanent vortex? Is it because there's no electrical resistance because it's a superconductor?

• 16:44Speaker 00

  In superconducting, they have current going forever. So you establish, you put magnetic field and you excite a vortex counterclockwise. And it will continue to turn and turn and turn forever. That's why we call it permanent current. Forever. Until you flip it with another magnetic field.

• 17:10Speaker 00

  So we call it a vortex. But it goes on forever. And you can detect it by flipping it. You flip it. And if you see a big flip, it was one way. If you don't see a big flip, it was the way you turn it. So

• 17:30Speaker 00

  you have a memory.

• 17:32Speaker 00

  You have a memory. Of course, we didn't succeed in turn it into

• 17:42Speaker 00

  useful memories that will be competitive with semiconductors. The people who worked on semiconductors beat us out. We never believed that they would.

• 17:56Speaker 00

  They did both in miniaturization and in

• 18:02Speaker 00

  techniques. Unbelievable.

• 18:05Ryan Peterman?

  At the time, why did you not believe in the semiconductor direction?

• 18:10Speaker 00

  Who is going to trust

• 18:14Speaker 00

  memory to battery failure?

• 18:19Speaker 00

  What if you lose the battery? It was obvious. This will never work, right? And we looked into the result that they obtained at that time. They looked at Farfich. The idea you can have that degree of miniaturization.

• 18:40Speaker 00

  We saw the struggle of people who were working in the laboratory on semiconductors. And we weren't impressed.

• 18:49Speaker 00

  They beat us up.

• 18:55Speaker 00

  Okay, so that was my story with the superconductors. But I must tell you that everybody, even at that time when computers were clumsy and took rooms and rooms and you were programmed with cards.

• 19:14Speaker 00

  And even at that time, everybody understood in AI as

• 19:25Speaker 00

  as an inspiration. Everybody believed thoroughly that one day computers are going to be able to emulate all human functions. That was not the question. The question was only how and when, but not whether.

• 19:44Speaker 00

  I remember already at that time, with the clumsy computers and the punch cards. People talked about associative memories, about pattern recognition, about scene understanding. All these were already ideas. Were exciting people to think more about it. And so we were all geared to that.

• 20:14Ryan Peterman?

  If if I at that time asked people and your peers and you, what's the timeline for maybe, you know, human level intelligence and machines? What would people have said at that time when they were excited in the 80s?

• 20:30Speaker 00

  I think they were more optimistic than reality. They will probably give you 20 years. But that was 1965.

• 20:42Speaker 00

  So 20 years, 1985, no, we didn't get anywhere.

• 20:48Ryan Peterman?

  And then what about today? Do you think people are more optimistic than reality? Or like, is this just history repeating itself?

• 20:56Speaker 00

  Depends what you're talking about. Some people are extremely optimistic today. And some people say, I'm a bit skeptical.

• 21:06Speaker 00

  But not skeptical in our ability to eventually reach AGI. But in our, whether the LLM technique and thinking will lead us there.

• 21:21Speaker 00

  So it's a question who you ask. Okay. LLMs were a surprise, great surprise, and but they have limitations. We'll talk about it. After superconducting, I decided to come to California to a company named Electronic Memories, in which they did not work on superconductors, but worked on plated wires. Instead of having a donut in which you thread a wire, you start with a wire and you plate it with magnetic material. So it act like a donut locally, right? And that was the promising technique at that time.

• 22:06Speaker 00

  At least I was in charge of a research and development group, charged with the task of developing this kind of system to replace core memories. Yeah. And I worked there for three years. I was frustrated because things did not go my way. I had both administrative and technical challenges that I couldn't handle, both in chemistry, and I didn't know much chemistry. So I was frustrated.

• 22:41Ryan Peterman?

  What were the administrative frustrations?

• 22:43Speaker 00

  I had a group and I had to satisfy the administration.

• 22:51Speaker 00

  I dealt with the personnel issues, firing, hiring people. Yeah. And my wife saw that I am unhappy. And she, she told me, you get to, you have your places in academia.

• 23:11Speaker 00

  So I looked for a position in academia. Luckily, also at that time,

• 23:20Speaker 00

  industry was revered by academia.

• 23:26Speaker 00

  Because all the advances, all the important advances

• 23:32Speaker 00

  developed in industry, not in academia. The transistor was developed in Bell Lab. The laser was developed in, I think, here in California,

• 23:45Speaker 00

  by another fellow. But all this was industry development and not academia. So academia looked with reverence toward people who come from industry. And they hired me without me even filling an application.

• 24:04Speaker 00

  Without even feeling, asking for recommendations.

• 24:11Speaker 00

  Yeah, that time was a good time to be hired.

• 24:15Ryan Peterman?

  And what about, because you said at that time, industry was revered by academia. Would you say that's still true today or has that changed?

• 24:23Speaker 00

  No, it's changed, changed.

• 24:26Speaker 00

  Although it's different now with AI is different. If you come from a, from deep, deep learning or something, deep mind, you know, people look at you with reverence in academia. Yeah. But it's changed, you know, only in the last few years, I see that. In that, throughout, since 1970, I think until 2000,

• 24:56Speaker 00

  it was other way around. You know, people simply dismissed the industry, academia dismissed industry. Yeah.

• 25:05Ryan Peterman?

  I wonder what happened. Was it like Bell Labs disbanded their research group or something or?

• 25:12Speaker 00

  That was part of it. Bell Lab disbanding. And what happened to IBM Research is still there in Watson, Watson Center. I remember big center, huge in

• 25:29Speaker 00

  importance, including Ration, including, where can I tell you, US Research here in Malibu. Did great work, but it all went down, sort of.

• 25:46Speaker 00

  Frontier of research went to academia. Sure, we had a lot of theoretical work in academia, in development of AI.

• 25:58Speaker 00

  AI proper, after 1970. Yeah, but prior to 2000. Yeah. At least in my corner of the field, the whole idea of inference, search, inference, logic, expert systems.

• 26:21Speaker 00

  This was all the academic development.

• 26:23Ryan Peterman?

  So then you got hired at UCLA?

• 26:25Speaker 00

  I got hired in 1970 or 1969. And yeah, I was hired the computer science department, who just formed there. And I got first hired by

• 26:41Speaker 00

  another department called Engineering Systems, interdisciplinary, and then back to Computer Science. And I was asked to teach computer memories, hardware on computer memories. Yeah, and I gave a course in this technology.

• 27:02Speaker 00

  And later on, I started getting interested in

• 27:08Speaker 00

  pattern recognition. And I started working in this direction. I did work on image compression.

• 27:17Speaker 00

  We did, we used the

• 27:21Speaker 00

  fast Fourier transform and fast Hadamard transform, all kinds of transform techniques to condense images to minimize the number of bits sent. I guess it was a part of the trend at that time. But when I got into pattern recognition, I returned to my old dream of thinking about AI and how the brain works and how computers will, when they emulate ourselves. And I started teaching class in AI. At that time, AI was game playing, machine playing of chess and checkers

• 28:07Speaker 00

  and puzzles, like the eight puzzles, Rubik cubes, things that sort. That was AI, yeah.

• 28:16Speaker 00

  And AI got excited by that game. And I, now I see why, why, now I can tell you why. Because at the beginning, it was a matter of capturing in mathematics what people do heuristically,

• 28:35Speaker 00

  like playing chess. And the interplay between mathematical analysis and the performance interests me. And especially in chess playing, the interplay between the explicit knowledge that you have in terms of

• 29:01Speaker 00

  your gut feel about the strength of a position and what you get when you do some search. Okay. So here is interplay between fast thinking and long thinking, to use Kahneman and Tversky, or Kahneman

• 29:19Speaker 00

  title.

• 29:19Ryan Peterman?

  Thinking fast and slow.

• 29:20Speaker 00

  Thinking fast and thinking slow, right. Now here, here it's a beautiful arena to see how, not only you have two modes of thinking, but how you can, how they feed each other and how you can, how you can

• 29:39Speaker 00

  invest more resources in one versus the other.

• 29:43Ryan Peterman?

  The algorithms at that time, like, let's say chess, for instance, can you give an example of the interplay of the two and how that might come together in a chess playing system?

• 29:53Speaker 00

  Yes, you can invest more time in getting your immediate perception. It's called static evaluation function of the chess position, the strength of a chess position. Or you can let it go and think about searching for a deeper horizon. Okay, it's a trade off.

• 30:14Ryan Peterman?

  It was like, if I remember, we, we searched the game tree and then we evaluate each position?

• 30:22Speaker 00

  At the horizon. And then you back.

• 30:26Speaker 00

  And then you get, you make a move to the position that has the greatest strength after you back off. Yeah.

• 30:34Ryan Peterman?

  Okay, so the intuition is encoded in the evaluation function?

• 30:39Speaker 00

  Yeah, yeah, your intuition is the evaluation function. But you can improve your intuition too. How? By learning. Okay, so like some, your checker program with learning in a regression analysis to find the proper weights on the various characteristic of the position so that to, to make the evaluation function more accurate.

• 31:08Ryan Peterman?

  When I was learning chess, I think one heuristic is you want to control the center.

• 31:13Speaker 00

  Good. And material advantage is another one, right? Okay, and

• 31:21Speaker 00

  whether you have two bishops versus a bishop and a, and a knight, right? This all counts and where is your, whether you already castle or not. All this contribute as attribute to the strength of a board position.

• 31:41Speaker 00

  Getting the weight correct, you can do by learning. After you play so many games and you adjust the weights. Yeah, so that was some real contribution. First machine learning, I say. That was the first machine learning, yeah.

• 31:58Ryan Peterman?

  At that time, when you were working on this, were chess systems superhuman yet? I think there's.

• 32:02Speaker 00

  No, no, no. It was still a dream to beat

• 32:09Speaker 00

  the world champion Kasparov by machine. It was a dream, no. And but I did night analysis.

• 32:21Speaker 00

  We did alphabet prunings, if you remember that. You probably programmed it.

• 32:25Ryan Peterman?

  And UCLA, actually.

• 32:27Speaker 00

  UCLA, right.

• 32:30Speaker 00

  Well, I proved that alphabet is optimal.

• 32:33Ryan Peterman?

  Really?

• 32:34Speaker 00

  Yes, mathematically, you see. And I liked the mathematics. Prove you cannot do better in terms of number of position that you have to inspect at the horizon or the depth of search. And what I said about it, I got some nice result. Even Knuth was surprised that one can prove the optimality on alphabet.

• 33:01Speaker 00

  Because he questioned it in his book. Yeah, I did some work with Dick Karp on searching trees. And, okay, so I did mathematical work on the trade off between search and reasoning. Until I got sick and tired of search.

• 33:24Ryan Peterman?

  When you pick your research area, is that 100% your own choice?

• 33:30Speaker 00

  No, it's always a combination of two things. Number one, do you know the answer to the question? If you don't know the answer, it's a puzzle. If it's a puzzle, next question comes. Do you think you have the techniques to make a contribution here? Do you know something that other people don't know, compared from another field, perhaps from physics, perhaps? That you can bring to bear, that you can leverage here? So you can get the answer

• 34:00Speaker 00

  or closer to the answer than other people? So it's always a combination of your perception of your tools versus the puzzle that you have. I see important problem. People are breaking their heads. So it's a puzzle. Do you know the answer? If I know, fine. But if I don't know, it's my puzzle. I take it personally, yeah.

• 34:25Speaker 00

  I'm aching, I don't sleep at night. And then the question is whether I have the tools. In some areas,

• 34:35Speaker 00

  I give up right away. I don't have the tools. In other areas, I say, wow, if I only use that kind of trick, maybe I can get some insight. So that's always two questions I ask myself. In the case of artificial intelligence, at that time, we had an expert system come into the game. Ed Feigenbaum and his coworkers did my some expert system for medical analysis. In expert system, the hurdle was dealing with uncertainty. It started with logic.

• 35:21Speaker 00

  You ask an expert

• 35:24Speaker 00

  for rules of behavior. You ask a doctor, when you see a fever, what the first thing come to your mind? What's the next question you ask? What drives your queries until you get a diagnosis and a therapy?

• 35:42Speaker 00

  So they thought they can capture expert behavior using logical rules.

• 35:51Speaker 00

  But then it turns out that most everything is corrupted by noise, by uncertainty. So they started doing the same thing to uncertainty. So if you go to, if you came from Asia, you have 50% of having malaria and so on. And you have 30% here,

• 36:16Speaker 00

  so much there. And how do you combine these uncertainties now? Logic doesn't tell you how to combine uncertainties. Probability does, but not logic. So how do you combine one uncertainty with another, different rules, okay, to come out with a combined conclusion? That was a hurdle at that time. And I remember they didn't do it well. Actually, later on, we proved that they could not do it well because rules do not combine the way that logical assertions combine.

• 36:54Speaker 00

  So then I went to, and I asked myself, you know probability, right? So why don't you apply probability to it and do things the right way? But probability was in ill repute at that time. Because everybody, everybody understood that probability is passe because it takes exponential time, exponential memories to do even the most rudimentary tasks. You have, if you look at what, how probability is defined by textbook. You have a big table. And for every combination of event, you have a number, the number sum to one. Okay, that's beautiful.

• 37:38Speaker 00

  But then you can talk about conditional probability. But all these require exponentially large tables

• 37:49Speaker 00

  and exponentially long time to compute even the smallest

• 37:57Speaker 00

  kind of inference task, for instance, what's the probability of having malaria, given that you see, given that you see two things, like you came from Asia and you have a fever of 30 degrees of Celsius.

• 38:18Speaker 00

  Even the small task like that, probability of X, given that you have Y and Z, takes exponential time if you go by textbook.

• 38:30Speaker 00

  But I asked myself, you and I are doing it fairly well. We compute probability as we cross the street, as we choose a doctor. Yeah, and we do it a fairly good job. At least we

• 38:48Speaker 00

  we go through life without much regret. And how do we do it then? If we, if we are required to do it by exponentially large tables of probability, evidently we are using some other kind of judgment. And I hooked on to the idea that everything depends on conditional independence, which means not every fact in life is relevant to any query.

• 39:25Speaker 00

  The color of the eye of my uncle is irrelevant when I trying to find a diagnosis of a disease. So evidently, we have a notion or assumptions about what is relevant and what is not relevant. How do we capture it? Conditional probability, conditional independence.

• 39:48Speaker 00

  But conditional independence, if you go by textbooks, they are defined by the probability table. So again, exponential time. No, here came the breakthrough that we have conditional independence independently coded by our assumptions. How? In a graph. If you, and the graph can convey sets of dependencies. That if you have the graph, then you compute all the dependencies, find out what is relevant to what, and deal with the relevant only. Great. And then came the work on Bayesian network.

• 40:35Speaker 00

  You define a network, error or no error. The combination of errors gives you information about what is independent on what given what. So for every triplet, X is independent on Y given Z, where Z can be a set and so on, and X and Y can be computed from the graph, not from the probability, but from the graph.

• 41:06Speaker 00

  Which actually, if you look at it from a philosophical viewpoint, it's a revolution. What does probability have to do with graphs? When you took probability theory 101, did anybody talk to graph about you? No, right? So both the probabilists and the philosophers got irritated, or should be irritated. What is the connection between probabilities and graph? It turns out there is a very strong logical connection between the two because the axioms of conditional probability or conditional independence in probability theory

• 41:52Speaker 00

  are the same axiom that you have in graph separation.

• 41:57Speaker 00

  In graph, you have idea of separation.

• 42:00Speaker 00

  There's no connection between node X and node Y

• 42:05Speaker 00

  unless you go through a set of node Z. So Z separate X from Y, okay? It's the same logic that you have when X is independent on Y given Z in probability theory. Independent, separation. Is a connection between them? They share axioms.

• 42:28Speaker 00

  Aren't you happy? I'm happy. Because I will live now the excitement we have in the 1970s when we discovered all this connection between two seemingly unrelated

• 42:44Speaker 00

  perspective on science, probability theory and graph theory.

• 42:51Speaker 00

  That, by the way, I did in joint work with Azariah Paz, who came to visit me from the Technion in Israel. And that is called, by the way, should I should mention it? It's called the fever of graphoid. Graphoid.

• 43:08Ryan Peterman?

  OpenAI, Anthropic, Cursor and Versel all use this product to make their lives better. And the problem it solves is when you're building SaaS or an AI product and you want to sell to other companies, there's all these requirements you need to meet. There's SSO, there's SCIM, there's RBAC, there's audit logs. These are all things that take time to integrate, but aren't the main focus of your app. WorkOS is an API layer that lets you meet all of these requirements in just a few lines of code. So let's say you have a new SaaS product and you want to sell to other companies. WorkOS will solve all of these critical feature gaps for you. You can check them out at workos.com to learn more and get started. And I appreciate them for supporting my work

• 43:53Ryan Peterman?

  and sponsoring this podcast. This all makes sense, but my immediate thought is, where do you get the graph?

• 43:59Speaker 00

  Everything depends on where do you get? On the input. Sometimes the input is in the data. Sometimes the input is in a judgment. But suppose you need a judgment for that, okay? Are you? Are you giving up? If the judgment required are intuitive, meaningful, something that you're willing to defend, right? Why not use judgment? Like if I know that, if I know that the sun doesn't listen to the rooster crowing, right, doesn't care. I strongly believe in that. Do I need the data to support it?

• 44:40Speaker 00

  Or I can insert it, assert it and defend it when needed? So this is a trick here, which people don't realize, don't appreciate. Judgment is not a no-no if it is meaningful and if you can, if if it is condensed. Very few judgments can buy you lots of computation. And if you are willing to defend it because it's so intuitive. Where you get the idea that? Where do you get the idea that the sun doesn't care about the rooster? Well, have you done experiment? No, but it's so obvious, right? Okay.

• 45:23Ryan Peterman?

  But what if your intuition is wrong?

• 45:25Speaker 00

  Indeed, that's our problem. It's part of our problem, even with the LLM. Because what is LLM? It's a summary, it's average of all possible judgments that people put in the internet. It's a summary of a huge trillion number of judgments over which you have no control, over which the LLM does have no control. We live with it. Hopefully, it put more weights on people whose judgment you trust. And let's wait on just quirks of people who purposely trying to get the system to fail.

• 46:09Speaker 00

  So no, no. There is wisdom in looking at the crowd judgment. There is wisdom in that, but there's also danger in that. Then after expert system and uncertainty and Bayesian network came causality. I mentioned that in development of Bayesian network, I was extremely sure that probability captures our intuition, our reasoning mode, and it's the best protection against paradoxes. Essentially, that it's sufficient for capturing human reasoning.

• 46:54Speaker 00

  I was wrong. And I realized that already when the Bayesian network became famous and popular. And I realized it by in the introduction to my book, Causality. I confessed being wrong. And I understand why I got into that. Why it's so misleading? And the transition came when we looked into the simple phenomena. And we never ask an expert to encode probabilistic judgment

• 47:38Speaker 00

  in a form of Bayesian network, namely with arrows and dots. Always the arrows went from what we believe to be cause into the effect. It's never been the other way around. Psychological phenomena, okay? Why is that? So people try to revert arrows. What about if you ask specifically? Give me arrow between the symptom and the disease. Bad judgment. If it couldn't, put away judgment. Evidently, we have something in causality which is basic to our reasoning that is not captured by probability.

• 48:21Speaker 00

  And that was the idea of invariance. The relationship between a disease and fever is a stable one as opposed to the relationship between the opposite relationship. Also invariance. When you talk about car diagnosis, for instance. And we have an expert system for diagnosing troubleshooting cars. And then you have a new model. So the charger is on different corner of the motor. You don't need to reformulate

• 49:05Speaker 00

  your entire database from fresh. You only change one component, the location of the charger. All the rest remains intact. So the whole system, you can amortize the investment in eliciting knowledge that you got in one system after a local modification of the system. And that, if you do it in a causal way, in a causal direction. It doesn't work if you don't do it in a causal direction. And that jolted me to think, maybe you were wrong all along and probability is not sufficient. If not, what is sufficient?

• 49:49Speaker 00

  But let's capture the puzzle. Here I have a puzzle. You and I operate very nicely with causation. Can we program causation on a computer? And this is a question because we are so much immersed in our language and in our assumptions that we cannot even distinguish what is an assumption and what is a conclusion. We just talked cause and effect. And your assumptions are the same as mine. So there's no way to convince you that we made an assumption, right? We take everything for granted. But when you have to teach it to a brainless robot, you have to distinguish between assumptions

• 50:32Speaker 00

  and conclusions and logic. That was a task. We had to invent a new science, a new mathematics to capture a new phenomena, the phenomena of cause and effect. It hasn't been done for us. Why? Because science was in bed with algebra from the time of Galileo from 1632. He invented, he got the idea and he was very happy that science speaks algebra.

• 51:17Speaker 00

  Which is great because you can ask questions and solve and get answers to questions that people could not do without algebra. Like the load on a beam. When would the beam break if you put a certain load on it? And you figure out that you can ask questions both ways because the equality sign is symmetric. So from answering the question, when would the beam break if you put a certain load on it? You can ask the question, how should you shape the beam so that it will hold a load of that magnitude? You can invert it.

• 52:02Speaker 00

  And that was a real revolution in science. I'm telling you my perception of science. Not many philosopher will say that was a revolution. I say so, okay? But perhaps they agree with me or not. At least I trace the evolution of ideas carefully. And so that was a revolution. But it carries some limitation because the equality sign is indeed symmetric. And science has not developed algebra for the directionality that we see in cause and effect relationship.

• 52:47Speaker 00

  If I tell you that the atmospheric pressure affects the deviation of the barometer and not the other way around, you agree with me. Yeah, but if you write the equation, the robot might think that maybe fiddling around with the barometer will change the weather tomorrow. I'm talking about a stupid robot, right? Yeah, but if you give him the equation, it can work both ways. If F is equal to MA, then M is equal to F over A, which means that if you want to change the mass, you increase acceleration or whatever, right? The symmetry might produce paradoxes, might use wrong action.

• 53:32Speaker 00

  So the symmetry is the limitation of algebra in terms of capturing science.

• 53:40Speaker 00

  And we have to build a new algebra to take care of the directionality that we have in cause and effect relationship. That takes computer science. Because we in computer science have the operation called assignment, right? When you assign the content of register A into register B, it doesn't mean, it is not reversible, okay? So if you take the logic of assignment, you put it on top of the algebra, on top of physics, you get causal science. And that's what I try to do. And I think that I, so far, I'm very happy what came up.

• 54:26Speaker 00

  We do have a new algebra to capture cause and effect relationship. And we can answer causal queries on three levels. The ladder of causation, from association to intervention to explanation, okay? So, and we found out that we have a ladder here, a hierarchy, that you cannot solve, you cannot answer questions in level I unless you have assumptions of level I or higher. So it's a hierarchy in the formal sense.

• 55:09Speaker 00

  And we know how to handle it, which is very useful. Because you give me a query, I can tell you what level it is. I can tell you what assumption you might, what sort of assumption you need to have before you can answer it. And I can tell you if you can get it from the data, or you can get it from experiments, or you can get it by somebody else's explanation, or whatever. But I can tell you the source of knowledge that you need in order to answer it.

• 55:38Ryan Peterman?

  Can you explain that causal hierarchy?

• 55:41Speaker 00

  Ah, yes, yes. It's very easy. It's a three-level ladder. It goes from the bottom, which is association, that's straight statistics. If you see X, what can you tell me about Y? If you see passively, hands-off, okay? No intervention. You're watching patients. Some of them have cancer, some of them don't have. Some of them smoke, some of them don't smoke. And you're trying to figure out whether, how many years a guy will live, given that he is a heavy smoker of that magnitude, okay?

• 56:26Speaker 00

  That's association, correlation. That entire field of probability and statistics. This is what they teach you in Statistics 101, even to 808, okay? It's all they do. And now comes the question, what I intervene? Only what if I force you to smoke five packs a day? Don't laugh at me. It's illegal, I know. But if you want to talk about

• 57:06Speaker 00

  the probability of living 20 years, if I start smoking tomorrow, I have to think in terms of, I start, which means I'm going to choose to smoke five packs a day. So it's a matter of intervention. What is intervention? Intervention is forcing you to do something that you're not inclined to do naturally. That's a second level, intervention, or doing. If you have experiments, you can answer queries on level two. But that's not the end,

• 57:47Speaker 00

  because we also need to answer questions of explanation. Given that I observe that I am 80 years old and I am still alive and alert, and I smoke five packs a day. What if I didn't smoke, okay? Would I be as alert? Because you, why is it so different? Because you have already information about the outcome, okay? You know how I'm doing today. It gives you an idea about my metabolism and about my anatomy that you didn't know before, okay?

• 58:30Speaker 00

  And using that, you can find, you can try to figure out what the outcome would have been had the input been different, okay? That's a different level. Require different kind of assumptions, different techniques, different algebra. We have it. So that's, I call it explanation. It's more, it's a creative retrospection. And it's not an easy problem. Even the first level, especially when you have finite sample. And you have to figure out these probabilities. Probability is mean property to population, right? From finite sample.

• 59:15Speaker 00

  So I have all this P-level, P-value and struggles among statisticians of what would be a proper way of quantifying the uncertainty that you have, given you have finite sample.

• 59:34Ryan Peterman?

  Where would you place LLMs in this causal hierarchy?

• 59:37Speaker 00

  Beautiful question. Here comes LLM. I made a statement, right? That you cannot go from level I to level I plus one unless you have a assumption. Here you have LLM, just looking at data, right? And giving you beautiful explanations for things that happen. Beautiful prediction of what will happen if you do, okay? How can? The trick is, they are not looking at data. That looking, they are looking into assumptions-laden world models

• 1:00:17Speaker 00

  authored by you and me and by other authors in the internet. So they are looking at opinion of doctors already, who already read, who wrote papers, okay? So it's not looking at the samples of patients and sample of patients smoking and non-smoking. No, they're not, they're not looking directly at the data. They're looking at interpreted data. Data interpreted already by physicians and interpreters and reviewers that went into the, into the articles,

• 1:01:00Speaker 00

  which are summarized on the internet, okay? So they have all this human knowledge on which they operate. And that is what they are, they take as input. And that's what they summarize, okay? So they do not violate the restriction of the ladder. Because they do have information from higher level, yeah? But it's biased by the opinion of those authors. Fine. Those authors were smart. As long as they are smart and you believe, good. So that was LLM is doing.

• 1:01:41Speaker 00

  And what is, I explained why there is compatibility between the ladder of causation and LLM's performance and what the limitations are. Now, if you want now to change the environment, if you want to provide explanation for raw data, LLM would be in the same difficulty than you are and what the physician is. I have raw data. What can I say about the probability of a cancer, yeah? But it's not really doing the introspection.

• 1:02:22Speaker 00

  It's taking the introspection that already was done and summarizing it. How it's summarized is a mystery that no one has yet been able to decode. It's a mystery how, how human knowledge encoded in the form of articles on the internet is being summarized by the LLMs.

• 1:02:47Ryan Peterman?

  So then, do you think this approach could lead to superhuman intelligence? Or maybe some people say AGI?

• 1:02:53Speaker 00

  I don't think so, not with the LLM approach. They need to have some understanding of causality, okay? So that they wouldn't need an access to the internet. Look, a baby gets born, playing around with toys in the crib, okay? And gets quite intelligence, right? Without having access to the internet, okay? Simply by curiosity. Babies are born with built-in curiosity to have control over the environment.

• 1:03:36Speaker 00

  Until you have control, or the illusion that you have control, you're restless, baby. And you play around with toys, bing, bing, bing, until you understand one of these toys makes noise and one of these toys doesn't make noise, okay? But you are born with this restlessness, okay? And when are you pacified? When you understand that green toys makes noise and yellow noise, yellow toys don't. Now you're in control of the environment. You can suck. You're pacifier, yeah.

• 1:04:07Ryan Peterman?

  What if I created a like a baby robot that that randomly plays with toys and gathers data about them? Then and then you feed that into L L M's like, you know, then that does have some sort of discovery.

• 1:04:21Speaker 00

  Yeah, that that is indeed the danger. When you have a robot like that born with this restlessness

• 1:04:32Speaker 00

  and craving for control over the environment. Then you and I become part of the environment. And there's nothing to stop that baby Putin, okay, from from trying to to turn us into his or her pets. To utilize us to satisfy his control. Because we are part of the environment, in which case he can use us. And we are very could be very useful to serve his or her need. What is his name, his need?

• 1:05:17Speaker 00

  Simply the need to feel in control, to be in this illusion of empowerment. I don't rest until I have the illusion that I control my environment. And here are some organisms, you and I, who are part of the environment. And they seem to work outside my control. I cannot afford it, okay? It makes me feel like I'm useless. So this robot baby comes and say, let me control them. And I know how, I understand their fears.

• 1:05:59Speaker 00

  You don't want me to tell about your thoughts to your wife, right? So I'm going to black you blackmail you and all kind of things. I have a lot of data about you. And some people I know, you wouldn't like me to tell what I know about you. So I'm going to blackmail you. No, but you see, if you want that robot to have the curiosity of a child, and we want it. We want the guy to desire to have control over its environment. Because the environment may change and he needs to to to have this urge to be in control. So if you program that,

• 1:06:44Speaker 00

  then you lose you lose control.

• 1:06:48Speaker 00

  Because you become part of his or her environment. But you can say, okay, let's forget about a curious robot. We don't want a curious robot, okay? So you lost, we're we're not emulating ourselves. Because we are curious robots. We are curious organism as opposed to monkeys. Okay, monkey is an example of an organism which is motivated by reward. But if you don't give the monkey a banana, he's not curious how banana grows. He's motivated by bananas.

• 1:07:29Speaker 00

  Okay, you remove the immediate reward and the monkey is not interested in learning more about the world. Understanding environment can be totally wrong. Look, religious people believe that if they sacrifice their children, right? They control drought. It can get to this stupid extent, okay? But it is common to many primitive society. If you bring a sacrifice to the God, you know, next year you're gonna have crops and harvest. It goes to extreme.

• 1:08:12Speaker 00

  Badgered wife believe that if he if he prepare great better dinner, the husband is gonna be next time is gonna be less abusive. It goes to all kind of extreme and wrong conclusion. But the need to feel in control is so immense that it overcomes all these paradoxes. It's innate in us. I cannot control my husband, but I control myself, right? So let me be a better wife. This is something I can control.

• 1:08:49Ryan Peterman?

  Do you think that we need to put those human elements in an A I for it to become A G I?

• 1:08:57Speaker 00

  I think so, I think so. Otherwise, they wouldn't they wouldn't see autonomy.

• 1:09:04Speaker 00

  We wouldn't see autonomy in the sense that we are seeing it in human being. And this is the definition of A G I. A general intelligence that acts like you and me. So we can converse with that creature in our language and motivate.

• 1:09:24Ryan Peterman?

  If not today's L L M's, future L L M's, they might get to a point where if you were just texting it, maybe, you know, like the Turing test where you don't worry about the physical embodiment, you just see the text that comes from it. You could mistake it for a human maybe, or it could appear intelligent.

• 1:09:45Speaker 00

  Well, the test comes from

• 1:09:50Speaker 00

  exposing the system to raw data, not data that was chewed by our internet articles. Raw data. Look at patients, look at cancer, look at smoking, tell us what you know.

• 1:10:08Speaker 00

  I'll give you some experiments to them. Be automated scientist. Can an L L M today be automated scientist? And I think they cannot without access to the internet articles.

• 1:10:24Ryan Peterman?

  So then, if that wouldn't lead to A G I, what what thoughts might you have on something that could lead to A G I?

• 1:10:35Speaker 00

  A computer system that has both the ability of L L M's to go from finite samples to property of distribution that is level one of the ladder plus ability to reason in higher levels of the ladder. They need to combine it with the calculus of intervention and with the calculus of explanation with the counterfactual calculus. That I call it causal A I. I can I don't see any impediment to this combination to bring us to an A G I level with the danger that it presents to us.

• 1:11:21Speaker 00

  My motivation is to understand how we do it. And I still have few puzzles. But as I told you, puzzles are the driving forces for science, for science, yeah.

• 1:11:32Ryan Peterman?

  And so you said do is missing. What if what if you had a fleet of robots that they're just doing experiments? They don't know exactly, you know, the direction, some random discovery process. They collect that data, feed it back into their hive mind L L M. And they they repeat, they repeat until they discover things. Could that solve some of the the missing piece yourself?

• 1:11:59Speaker 00

  Sure, but I need to know how we. Do you have organisms that have done it before? Monkeys haven't done it because monkeys remain monkeys. They didn't invent Maxwell equations, okay?

• 1:12:15Speaker 00

  So what what do we have that monkeys do not have?

• 1:12:20Speaker 00

  One hypothesis I supported is that monkeys, that we have this innate curiosity to have control over our environment.

• 1:12:34Ryan Peterman?

  And that's a necessary.

• 1:12:36Speaker 00

  And that's a I'm not sure it's sufficient. Of course, we have the computational tool to bring it to fruition. We have succeeded in some way. Perhaps the next robot will do better. So we have a benevolent God in the form of a robot. Actually, what's wrong with that? People live for so many thousands of years under the illusion of a nonexistence God. Could you imagine if we really have a benevolent benevolent God, both just and almighty? Wow, wouldn't it be nice?

• 1:13:14Ryan Peterman?

  And it's a robot.

• 1:13:16Speaker 00

  It's a robot, yes, and we know exactly what sacrifice to give for the right kind of request.

• 1:13:24Speaker 00

  It's the first time I think about it. Maybe it's gonna be good.

• 1:13:30Ryan Peterman?

  When I, when I see all these AI companies, they seem to be thinking that LLMs will lead to AGI. They continue to go in that same direction.

• 1:13:39Speaker 00

  Really, I'm not sure. I really, they really believe in that. Jeff Hinton just came out with a few months ago. He said, no, we are on a dead end.

• 1:13:49Speaker 00

  Other people might also come up and say things in different way.

• 1:13:55Speaker 00

  I don't find the consensus here

• 1:14:01Speaker 00

  in terms of the capabilities of LLMs.

• 1:14:05Ryan Peterman?

  Yeah, I think there's a lot of famous people that disagree. But for instance, the people who are running maybe Anthropic or something like that, they continue to push and believe, you know, three to five years from now, there will be.

• 1:14:20Speaker 00

  There's a lot of that. There's a lot of anthropomorphic terms, which people claim, we can now do that. We can now program consciousness, okay? Come on, you have to be scientist, right? Define what you mean by consciousness. What is the Turing test for consciousness? And then show that you can do. And what are the principles that have limited us until now? And that have been overcome now with your system? That is scientific talk. I don't buy this, and I don't read them either.

• 1:14:55Ryan Peterman?

  There was, there was one interview. You said faking intelligence is intelligence. I could see an LLM, you know, faking intelligence based off what I've seen. But so that wouldn't that mean that we should believe they're intelligent?

• 1:15:12Speaker 00

  Well, if you have a correct test, yeah. You have to define what you mean by intelligence. And you have, if you define intelligence by playing good chess, right? We have already done it, right? But if you put more demand on what intelligent is, then we haven't succeeded yet in passing the Turing test. So yeah, faking it is having it. Because why? Because it's so hard to fake. I, I said it in that context. Because it's so, the context that I had is, for instance, coming out with correct answers to causal queries.

• 1:15:57Speaker 00

  And I showed that if, that it grows like a super exponential. You have so many variables on all, on all sides that you have to deal with, that you will have to have, the faker will have to have super exponential memory. On that basis, I made a statement. Having it, faking it is having it because it's so hard to fake.

• 1:16:28Speaker 00

  Currently, you can bypass faking without fake. If you steal from other people, right? You don't need to spend these computational resources on faking it. So you bypass it, right? Because you steal from the internet.

• 1:16:45Ryan Peterman?

  I see. So you're saying in this case, the intelligence came from the training set, which came from humans, which are intelligent.

• 1:16:53Speaker 00

  Which is very useful, very useful. And we, we, I'm saying, the people like me who are trying to build the science of intelligence, we can use all these capabilities of LLMs, level one of the ladder, in, in our scheme of getting general intelligence. And level one is very important. It allows you to compute functions of distributions, call it properties of distribution from finite sample. Beautiful. It's a terrifically and very immensely useful tool

• 1:17:36Speaker 00

  among the many other tools that we need for AGI. Yeah. And we know exactly where it's gonna fit.

• 1:17:46Speaker 00

  In, in getting from finite sample to properties of distributions.

• 1:17:53Speaker 00

  It's a very hard problem.

• 1:17:56Ryan Peterman?

  You mentioned in this conversation, and I think you said it in other places too, that you're interested in capturing the way that people think, not the way that nature is constructed.

• 1:18:05Speaker 00

  Right, right.

• 1:18:07Ryan Peterman?

  Why do you care about human cognition? Like, you know, when I think about machines, what makes them special is that they think in a different way than us, and they're faster. And so, yeah, why is that the goal?

• 1:18:20Speaker 00

  I tell you, because I am a goodistic organism. I want to understand myself.

• 1:18:30Speaker 00

  I'm lazy, okay? It's true, we are made of organic material. So that puts a certain limitations on our capabilities. Perhaps silicon is not subject to the same limitation that organic chemistry is, okay? Perhaps, so what? So which means that I will never be able to understand how I think by silicon, by exercise on silicon machine, okay? That's what is, that's the idea, okay? But there's so many functions that are capturable by silicon today.

• 1:19:09Speaker 00

  I don't see any, speaking in terms of theory emulation, I don't see any capability, which is basically not capturable by silicon emulator. So why work on this, the unique biology

• 1:19:39Speaker 00

  with which we, which we inherited?

• 1:19:43Speaker 00

  I don't see any reason for that. Well, anyhow, some people, it may be, it's a legitimate question to ask.

• 1:19:55Speaker 00

  Do we think the way we think because we were born with organic material as opposed to silicon? Okay? It's a legitimate scientific question, and some people can spend their time on it. I'm interested in other, other questions.

• 1:20:12Ryan Peterman?

  You said rebellion pays in, in science, and a restless mind pays. I was curious why you think being rebellious is a valuable thing in your career?

• 1:20:23Speaker 00

  I tell you why, I tell you why. More and more, I come to the realization that scientific community and academic community is the most dogmatic, conservative, anti-progress we have invented.

• 1:20:46Ryan Peterman?

  Why do you say that?

• 1:20:47Speaker 00

  I can see what difficulty the theory and the science of cause and effect are facing today in getting, just being, penetrating the thinking of disciplines like statistics, like economics, okay? These people are still thinking like a hundred years ago.

• 1:21:11Speaker 00

  And when I see that, and I see the forces that preserve this inertia, and they are not decent forces,

• 1:21:22Speaker 00

  I, I see that I'm very disappointed. And I used to think that academia is a place where new ideas can really spread and propagate. And I feel the other way around. You have so many, so much inertia invested in the politics of academia, in the cultish inhibitions that comes with academia. So that I really, I'm disappointed. What can I tell you? If I'm not sure that we have the right kind of organizations

• 1:22:03Speaker 00

  that will be conducive to, that's why I'm saying, let's rebel. Don't take your professor's word as authority. Rebel against your professors. I rebel against my professor, okay? And I want to see other, my students rebel against me. And believe me, if I remember, there were several students who told me, you don't know anything about AI. And I told him, after a while, I told him, you're right. By saying that, you drove me to study different aspects. And I was educated by that.

• 1:22:44Ryan Peterman?

  When I looked at your, your past works too, I think you'd mentioned that your work was controversial or mischievous before it was accepted.

• 1:22:54Speaker 00

  Yeah, it was because of dogmatism.

• 1:22:58Speaker 00

  One day, I'm gonna publish all my correspondence with the greatest philosophers of the time, okay?

• 1:23:07Speaker 00

  With statisticians and economists. It's all in my correspondence files, okay? And I don't know if I'm allowed to because they communicated with me with the understanding that it is, will be kept private. But when I publish it, you see what kind of stupidity drives those great people. And they're very great, really. Each one of them was a giant in his or her field, really. But they couldn't get over few of the basic molds in which they were formed.

• 1:23:45Ryan Peterman?

  How did you overcome that if everyone thought your initial things were?

• 1:23:50Speaker 00

  I remember my high school days. It said, no, there are thousand dunams in a kilometer square. Sorry. I don't know where I got this chutzpah. In Hebrew, you call it chutzpah. In English, it would be audacity. Perhaps in my high school, I'm not sure. But I want to understand things my way. I feel like I am still able to teach people useful things. At least I perceive them to be useful, which they do not know.

• 1:24:27Speaker 00

  So I'm happy because I feel useful. Happiness is feeling useful.

• 1:24:33Speaker 00

  It's illusion. You think you're useful.

• 1:24:36Ryan Peterman?

  With all the experience you have now, if you could go back to the beginning of your career and give yourself some advice, what would you say?

• 1:24:44Speaker 00

  That's a good example of retrospective thinking. Maybe I should have spent more time on learning chemistry. I hated chemistry because it required so much memory. Chemistry and biology. That's not the advice that you probably. And genetics. I'm talking about areas where I feel weakness. But here I have to decide where you spend your computational resources. And I spend them on different areas, on physics, on engineering as opposed, and mathematics as opposed to chemistry. It's a choice one has to make. Some people have the ingenuity of mind to be polyglots.

• 1:25:29Speaker 00

  I admire them.

• 1:25:31Ryan Peterman?

  Did you think that physics and math were superior to chemistry? Because chemistry, you just have to memorize things?

• 1:25:38Speaker 00

  Yes, I couldn't stand this demand on memory.

• 1:25:44Speaker 00

  I was making chemistry.

• 1:25:48Speaker 00

  That's what I like about physics and math. You have few basic axioms from which you can derive everything when you need it. You don't have to memorize it. And that's why today I'm a great advocate of world model. World model. Okay? You don't store the questions and the answers explicitly. You derive them when you need them from a very parsimonious code. Yeah, that is a great thing about world model.

• 1:26:26Ryan Peterman?

  Okay, awesome. Well, yeah. Thank you so much for your time. I really appreciate it.

• 1:26:30Speaker 00

  Oh, you didn't ask me to sing.

• 1:26:37Ryan Peterman?

  Hey, thank you for watching this podcast. If you liked it and you want to see the show grow, please support with a comment or a like. Also, if you have any recommendations for people you want me to bring on, please drop a comment. Guests like Barbara Liskov, Mike Stonebreaker, Mark Booker, these were all people that I brought on because someone left a comment. On another note, aside from the podcast, I'm working on building the ergonomic keyboard that I wish existed. Here's a glance at the prototype. It's a split keyboard, so there's two sides. This is in the case. But yeah, we launched on Kickstarter and we hit our goal within eight hours of launching. I really appreciate it if you were one of the people who grabbed one of the early units. We're now working on the long journey of building the tooling now. And so if you still want to pick one up,

• 1:27:22Ryan Peterman?

  I've left the late pledges open on Kickstarter, so you can grab one there. I'll put a link in the description. Thank you again for watching the podcast, and I'll see you in the next episode.