The Equation That Changed How Physicists Think About Reality | Juan Maldacena

The Equation That Changed How Physicists Think About Reality | Juan Maldacena Transcript Brian Keating:One of Einstein’s two strangest ideas, wormholes and quantum entanglement were the same idea. My guest today spent his career proving Juan Maldacena :that they are so called Einstein Rosen paper on the fact that the full thrashette solution contains two black holes that are connected and the Einstein Podolsky Rosen paper that talks about entanglement. And we now think that these two things are related. Brian Keating:My guest is Juan Maldicena, the physicist who in 1997 wrote the most sided paper in theoretical physics. The claim he just made that wormholes and entanglement are the same thing is called ER equals epr. If he’s right, the structure of space time is built out of quantum information itself. Juan Maldacena :The information of the things you threw in is contained in this radiation. According to general relativity it will look like the information is lost. According to quantum mechanics, we would expect it to be preserved. So there is a conflict between the two things. Quantum matter didn’t obey this property then you would be allowed to send signals faster than the speed of light. I think this is a beautiful consistency condition between the two theories. Brian Keating:He also told me problem in physics he’d most like to solve before he dies. The answer was not what I expected. Juan Maldacena :The most important problem, quantum gravity, is to understand the beginning of the big bang. That’s really the problem that I would like most strongly to solve. Brian Keating:Juan Alicena, welcome to UC San Diego for your second appearance on the podcast. Juan Maldacena :Yeah, thank you Brian. It’s a pleasure to be here. Brian Keating:You’re here giving the Dashen lecture all the way from the Institute for Advanced Study which I think is on Einstein Lane. Is that correct address? I’m not doxing you right to say you’re on one Einstein Lane. Here’s Einstein over here. What do you think he’d be kind of most interested to learn or if you could have 10 minutes alone with him, what would you tell him about? Juan Maldacena :Well, I think black holes would be probably something he would be really interested in. I would particularly want to tell him, want to ask him whether he thought that his two papers from 1935 would be related. So called Einstein Rosen paper on the fact that the full threshold solution contains two black holes that are connected. And Einstein Podolsky wrote some paper that talks about entanglement and we now think that these two things are related. Brian Keating:This ER equals epr, right? That’s one of the things you’re known for. Many, many things you’re known for. Juan Maldacena :One surprising thing would be that they are a consequence of gravitational collapse and that are naturally produced in the universe. Now in the last few years, really, in the last few years, we had lots of experimental evidence for black holes. From collisions that produce gravity waves to imaging the matter near the black hole of the black hole that is near the center of the Milky Way, to, you know, looking at stars that orbit this black hole. Yeah. So we have lots of evidence for these black holes. Now then the other surprise I think would be black hole thermodynamics. I think that would be something really interesting in the sense that there’s a connection between the laws of thermodynamics and black holes, that black holes have an entropy, they have a temperature. I think that would be a lot of fun for him. Brian Keating:I mean, gravitational waves, another thing he predicted that he thought would never be observed. And I think he got a paper reading rejected and then he said, I don’t want to deal with a referee. And another thing that he did, well, Juan Maldacena :he first predicted gravity waves, then he thought maybe they don’t exist. And then the referee said that no, they do exist. You made a mistake here. And then that’s what I say when Brian Keating:people say peer review is bad, it’s harmful to someone else. Juan Maldacena :I mean, this case was a good example of useful. Well, I guess you got a good reviewer. Brian Keating:That’s right, yeah. That led to multiple Nobel prizes at Halse and Taylor and then LIGO and who knows what else it’ll do. But yeah, I always tell my students aspire so that your blunders or things you don’t think will ever work will lead to multiple Nobel prizes. Juan Maldacena :Yeah, yeah. And the cosmological constant, that was his biggest blunder. Yeah. Now it’s a central part of cosmology. Brian Keating:So I want to talk today about the realities of black holes and of things like the holographic principle, which is one of again, many things you’re known for in your amazing career. I was talking to a non scientist, but a very smart layperson and he was asking me, well, you know, if the holographic principle is correct. You know, some people say, well, we might be living inside of a black hole and things like that. But I always point out, you know, there’s no such thing as isolated hydrogen atom floating around the universe that truly can be solved by the Schrodinger equation. In other words, there’s always perturbation. To my knowledge, there’s no such thing as a Schwarzschild black hole either. Right. That’s perfect. Brian Keating:There’s occur black holes, we know of the ergosphere surrounding them. So in what sense is the holographic Principle of the fact or, or proposition that we could be living in is that just pure theoretical. Because of the realities of real black Juan Maldacena :holes, the holographic principle as applied to our universe, we don’t know whether it’s correct or not. Brian Keating:Could you explain the holographic principle? First? Juan Maldacena :The holographic principle is

Princeton Scientist: We Don’t Understand AI | Tom Griffiths

Princeton Scientist: We Don’t Understand AI | Tom Griffiths Transcript Tom Griffiths:One of the, I think, interesting challenges we have at the moment is having built systems that we don’t fully understand. Brian Keating:The man who built modern AI, he’s the direct descendant of the man who invented the math that made it possible, which is insane, but it’s not the wildest thing. My guest told me today. Tom Griffiths:That’s pretty much exactly what he was trying to do. And he was the right kind of crazy. Brian Keating:Ibns was trying to invent AI 250 years before computers even existed. Tom Griffiths:Sycophancy is a major problem. If you take a rational agent and have them interact with a system which is sycophantic, then that agent is going to become increasingly confident in their beliefs, but no closer to the truth. Brian Keating:My guest spent 20 years building the mathematics of how minds work, and he just told me three things that made me question what I thought AI actually was. Now, let me show you. From a physicist point of view, whenever Brian Keating:I talk to people about consciousness, from Chalmers, Bostrom, and upcoming guest Joshua Bach and others, I always get the same thing, like we can’t really define what consciousness is, so how do we know what thought is? So how can you determine what the laws of thought are? Isn’t that kind of a extremely provocative and bold claim? Tom Griffiths:The way that I approach that question in the book is really by thinking about what are the kinds of computational problems that minds solve? And that’s really what this enterprise was. It’s trying to figure out, like, what’s the mathematical structure that describes the thing that minds are doing, whether that thing is what Aristotle was interested in, which is just trying to characterize what good arguments are through to some of the questions that you were raising about what does it mean to make a good decision and how do we think about rationality in that context? And so the interesting thing is, I think a lot of those questions are things that we can answer without ever having to touch consciousness. I think about one of the big challenges of studying consciousness is that we don’t necessarily know what computational problem consciousness is solving. That’s why it’s something that’s continued to be mysterious. We don’t really know what it’s there for in terms of how necessary it is to being able to do kinds of things that minds do. And our AI systems give us nice demonstrations. You know, again, some people might want to argue that they’re conscious in some form or something like that, but I think they give us nice demonstrations of how far you can get using certain kinds of mathematical formalisms. Brian Keating:Yeah. And there’s many, many kind of allusions to physics in this book, which is so delightful in many different ways, not the least of which because it gives us some kind of formalism to hopefully go about this problem. But I, you know, as a physicist is want to do, I want to kind of get into what you would say maybe what is briefest kind of most parsimonious, defensible definition of thought itself and the laws that govern it. Tom Griffiths:In the book I focus on deduction, which is sort of like patterns of logical reasoning going from things that are true to other things that are true. Induction, which is sort of seeing a pattern in the world and then making the generalization that thing holds in general and then abduction, which is seeing something that you want to explain and then coming up with an explanation for it. And I think that’s a pretty good characterization of the set of things that we normally have on our list when we want to try and explain sort of patterns of thinking. And those are the things that we try and engage with in terms of like the different kinds of mathematical formalisms that are explored in the book. Brian Keating:There’s an awful lot of discussions of both the successes and our understanding of consciousness and the wrong turns. And I like that because for me personally, I hate when we teach our undergraduates as often as done. You know, we basically just teach them the string of Nobel prize winning experiments and you know, just connect the dots and that’s. But you go through the, you know, the twists and turns and I thought one of them was, was sort of brought up this, this conjecture that, or this statement by Feynman, which is that the, you know, kind of the difference between knowing the name of the thing and knowing something about it is the most dangerous gap in all of science. What are some of the inherent biases that, that science has brought to it because it’s such, such a Frankenstein type field? Cognitive science, you know, start off with, with not really, as you discuss in the book, really being taken seriously. And now it’s, you know, at the cutting edge. What is the sort of, you know, largest gap or the biggest lacuna in, in your field where people seem to maybe be overabundant of confidence in describing how models work or even the model of the brain, let alone models of artificial intelligence. Tom Griffiths:So one of the, I think interesting challenges we have at the moment is having built systems that we don’t fully understand. Right. So we now have these AI systems that for computer scientists put them in a very unfamiliar situation, right, where if you’re a computer scientist, you’re used to programming Something, and because you programmed it, you kind of know what it’s doing. And that is not how our AI systems work. So these modern AI systems are built using enormous artificial neural networks. And they learn from data, far more data

Astrophysicist: The Universe Is Coming for You | Hakeem Oluseyi

Astrophysicist: The Universe Is Coming for You | Hakeem Oluseyi Transcript Brian Keating:We’re here with one of the most magnificent, munificent and mesmerizing minds of our generation, and he happens to be a friend of mine. And what can I say? I like to have my friends on, especially when they write books like this incredible new book that we’re going to be talking about today. Dr. Hakeem Olusche. How are you doing, my friend? Hakeem Oluseyi:I am doing excellent. Thank you again for your hospitality, for having me. Brian, you’re always good to me, so, man, I appreciate you. Brian Keating:I love this book. This book is unlike any other book I’ve ever read. Why does your book start off with a why question? Why do. Why do we exist, Hakeem? Hakeem Oluseyi:You know, we’ve learned so much about the universe and existence as scientists, and I think that we’re ready now. I think that we’ve come to a point where we have so much data that we can actually start to formulate questions or answers, rather, to these biggest why questions, like why do we exist? So, you know, sometimes that goes into shaky territory, right? You may personify the universe and think those sort of things, but I tell you, man, this book, phrasing it that way, is a provocation to the reader. Because I think that we scientists are at the point where we need to access the hive mind of imagination to make forward progress. Because, you know, this century hasn’t given us those. We’re finding that we’re good at everything, right? We have the answers, we go look and we see what we expect to see. And that, for us, is not good news, right? We want to see something that’s unexpected. And so, hey, man, I am inviting the world to join us scientists in approaching these big questions. Brian Keating:The thing you start off in the book is that you say that falling is not normal. You say on a cosmic scale, the astronauts, the. The apples, etcetera, they’re not really being questioned by why it falls at all. Talk us through the argument that falling the ground is accelerating up towards the apple, not the apple falling down. How is that not insane, right? Hakeem Oluseyi:It is insane because reality is insane, right? And I tell you, man, you know, I thought about it this way. You know, I asked my students, when I’m lecturing, if I hold out this object at arm’s length and release it and it just hovered in the air, how would you respond to that, right? You know, it would be shock. That’s what magicians do. But in most places in the universe, which is just outer space, if you do that, then it remains there, right? If you don’t Give it an impulse of any sort. And so what really should freak you out is the fact that when I release something, it moves all by itself. It does this thing called falling. Another physicist, Will Kinney, you know, I heard him say this first, is that gravity turns motion through time into motion through space, right? And so what he’s getting at there is this idea that we’re all moving through space time at the speed of light, and we’re on these straight line paths that we physicists call geodesics. But in the presence of a gravitating body, that space time diagram gets warped in such a way that, you know, if you think about it in X, Y plane, you. Hakeem Oluseyi:If you’re moving directly parallel to the Y axis, you have no motion along the X axis. But if I were to bend the X axis, even though you’re moving in the same direction, you now have motion along that X axis. Well, in space time, one axis of space and the other is time. So if you’re in an intergalactic space, you’re moving through time at the speed of light, right? But when you get near a gravitating body and that space time gets warped, some of your motion through space gets moved through time. And so when we think of falling, right, we think that objects are being pulled to the Earth, which is not the case. They’re just continuing to move the way they move. But then once you’re on the surface of the Earth, you now have an emergent property that we call weight, right? And so that weight is due to the Earth accelerating upwards against that space time. So even though when we think of acceleration, we think we think of motion, but you don’t need to move outward to accelerate upward. Hakeem Oluseyi:The Earth’s surface doesn’t have to move outward for it to accelerate upward. Acceleration has to do with changing something Brian Keating:with respect to your position, right? Traveling. So you just gave me a great idea to lose, you know, 50 pounds, just go to the moon. That’s all we have to do. We’re going to talk about that. Hakeem Oluseyi:That’s all you gotta do. Brian Keating:Okay, next, provide. We’re just gonna go provocative, just like mind blowing claims, okay? You made a claim in the book that almost no physicist I’ve ever be willing to make would have the energy and even the confidence to make that heat does in some cases flow from cold to hot spontaneously. And better than that, you say you discovered it washing dishes. Hakeem Oluseyi:So I was a kid with a single mom in the 1980s, and she would like, wash these dishes when I get home. I Want this floor waxed? This is true. And she was working at 11 to 7 shifts. I was waxing the floor at midnight. But one thing I would do before I realized that it’s not good for pots and pans. At some point in my 40s, you know, I would dunk a hot pot or skillet into

Quantum Computers Aren’t Useless. You Just Don’t Know How to Use Them.

Quantum Computers Aren’t Useless. You Just Don’t Know How to Use Them. Transcript Brian Keating:My friend Sabine Hassenfelder just made a video that got nearly half a million views in just a couple of days. Her conclusion? Quantum computers are basically only good for doing one thing, breaking codes. Now, Sabine’s brilliant, and she’s right that the code breaking progress is terrifying. Google just moved up Q Day, the date in which quantum supremacy takes place, to about 2029, less than three years away. And as I’ve often said, quantum computers seem to be really good at doing one thing in particular, which is to simulate how quantum computers work. But I think Sabine has missed a bigger story, because right now in my lab at UC San Diego, I’m teaching my undergraduates to build quantum computers and then to program them and then eventually to launch them into space and maybe, just maybe, use them for AI in space, perhaps on the moon. Thanks to Artemis too. You’ll hear from these brilliant undergraduates later on, and when you do, you’ll see that what they’re doing has nothing to do with breaking code. Brian Keating:And by the end of this video, you can do it too, for free. Let me give Sabine her due, because the news this week is really extraordinary. Three papers dropped in a single week. First, Google found an algorithm that breaks encryption 20 times faster than anything we’ve ever had before. That cuts the qubit requirement from 10 million down to roughly half a million. They thought this was so sensitive they wouldn’t even publish the algorithm. Instead, they used something called Zero knowledge proof, basically a math way of proving that trust us, bro, without showing you exactly how it does so. Second, a startup called Oratomic says that they can break RSA encryption with just 26,000 qubits in about 10 days using neutral atom arrays, not the superconducting qubits I’m using in my lab, which are the same that Google and IBM are using. Brian Keating:This is a radical speed up and reduction in complexity. It’s awful difficult to get our lab equipment down to just a few tens of millikelvin, just a whisper above absolute zero and far colder than even the CMB, which is what I study at a balmy 3 Kelvin. Now, a third paper by another group showed that they can do it with 10 times fewer qubits than the original estimates required. Sabine is right. This is real and it’s accelerating faster than anyone predicted. The researchers themselves are debating whether it’s even responsible to publish this stuff. Scott Aronson, one of the top computer scientists alive, said that said, people in the field are reaching the point of wondering, should we publish this or not. In 1982, when I was a wee lad before high school, even accessing a university timeshare computer meant dialing in, often using a clunky acoustic coupler modem. Brian Keating:That transmitted data at a screaming 300 to 1200 bits or baud. The procedure was tedious. Pick up your phone, plug it in, wait for the screeching handshake, type a text based login and issue an arcane command like rmdrc foobar just to navigate a 24 row monitor. That agonizing lag is the perfect analogy for quantum computing five years ago where you waited in a queue for a noisy 2020 qubit result from a remote cloud. Today, my friends at Quantum Rings again, not sponsored allows you to explosively advance on that timeline right now for free. It puts a high fidelity quantum circuit simulator with hundreds of qubits and millions of gate operations right on your laptop, replicating Google’s $10 million quantum supremacy experiment on your own hardware. It’s really a whole new world and I want my undergraduates and my viewers and listeners in the audience to take advantage of it. Bob Wold:The truth is that quantum computing holds immense promise. I mean unimaginable things. It’s very possible that my grandchildren could grow up in a world where cancer is a thing of the past, because quantum computers have provided real time computational simulation to let us experiment with these drugs without the burden of manufacturing them ahead of time, where things like EVs could be four to ten times more efficient, drive as far as you need on a single charge with batteries that were made in a very sustainable way, with materials that were discovered because of quantum computers, where we could optimize supply chains, solving world hunger if the humans can get out of the way. Literally the biggest societal problems that exist today are in reach for quantum computers. And it’s not just science fiction anymore. This recent video covers three papers in the course of essentially a week that moved the goalpost dramatically for this goal. And we used to think about this as requiring systems that took millions of qubits, and now we’re talking about hundreds of thousands of qubits. And that essentially brings it from like 2035 to 2040 down to kind of like 2029, 2030 for Q Day for when quantum computers will be able to break encryption. Bob Wold:And if it happens in the dark, mysterious things are going to start happening and we won’t know for sure that it happened. Brian Keating:We won’t. What are they actually good for, these quantum computers? Sabine said, and I’m paraphrasing that apart from the code breaking. Nobody has figured out how to turn quantum computing’s theoretical advantage into a real world. Quantum chemistry, material science optimization, financial monitoring. She says not much there has happened. And again, if you’re looking at published breakthroughs, she’s not wrong. And see above, as I said, quantum computers are awesome. Unrivaled at simulating how quantum computers work. Brian Keating:But Sabine is looking perhaps at the wrong metric. The revolution isn’t in the papers, it’s in the tooling. Five years ago, if you wanted to run a quantum

Genius Philosopher: The Law of Physics That Explains Why Your Life Falls Apart

Genius Philosopher: The Law of Physics That Explains Why Your Life Falls Apart | Rebecca Goldstein Transcript Brian Keating:There’s a law of physics that governs everything. Your happiness, your depression, and even whether your life has meaning. And guess what? It can’t be broken. Rebecca Goldstein:Life is a local violation of the law of entropy. It is a counter entropic resistance. The thing that the suicidally depressed people feel is that they don’t matter. Others do, they don’t. Nothing they can do will ever make them. This is how I judge people. Are you increasing entropy or are you decreasing it? These agents begin to have a longing to matter. If they do this, then what we have are non carbon based humans. Brian Keating:She’s a MacArthur genius, a philosopher who’s trained in physics, and she just used the second law of thermodynamics to explain while your life feels like it’s always falling apart. What Rebecca did next is what no physicist has ever done before. She took the second law of thermodynamics and built an entire theory of human meaning on top of it. Brian Keating:What took you from MacArthur genius, your many, many works of philosophy, and your great contributions to literature from the genius grants, et cetera. To write a book that’s basically a stealth physics book. Rebecca Goldstein:When I studied physics as an undergraduate, and then I had gone, when I went into philosophy, it was into philosophy of physics. So I’ve always been interested in physics. When I first learned about the second law of thermodynamics, I couldn’t quite conceptualize it. I couldn’t quite completely wrap my head around it. But it seemed to have implications for us, right? I mean, we are physical systems. We are subject to the second law of thermodynamics. There’s a tragic dimension to this law, and that we live in resistance to it. All living things live in resistance. Rebecca Goldstein:In fact, when I was a graduate student, that occurred to me, oh, my gosh, biological systems are really just organized to resist the second law of thermodynamics. I said, this is so exciting. Has anybody discovered this? And then I read Schrodinger’s what is Life? Other people had. In fact, Boltzmann himself had realized this at the laws of biology are substance biology’s response to this supreme law that tells us that in closed systems entropy never decreases. And if there’s any way for it to increase, it will. And what that entropy is, is the measure of the disorder of the system. The disorder is the more disorder, the higher the entropy, the less efficient work you get out of the system. And eventually the system will go to thermal equilibrium. Rebecca Goldstein:You’ll be able to get no more energy out of it. It’s somewhat the end of the system. And in fact, Rudolph Clausius, the 19th century physicist who formulated a concept of entropy, which means literally, transformation from within, there’s poignancy in that. It’s a transformation from within is going to the end of the system. And he had said, you know, that the universe itself go to thermal equilibrium, to what we call the heat death. And so there’ll be no more energy to be gotten out of it. This sounds like a joke from Woody Alley. His mother brings him to a shrink because he’s discovered that eventually the sun is going to go out. Rebecca Goldstein:He said, you know, how can I live? What’s there to live for? You know, the sun is going to go out. And the mother says to the shrink, you know, I don’t know why Alfie is so worried about it. It’s not going out over Brooklyn. Brian Keating:It’s in Annie hall, right? Rebecca Goldstein:Annie Hall. Yes, that’s right. What do you care? Brian Keating:Brooklyn’s not expanding, right? Rebecca Goldstein:Y that’s what it was. It was expanding, right? That’s right. Brian Keating:Classic. You studied physics as an undergraduate and you write in the book how you’ve been haunted since your early days as an undergrad by the second law of thermodynamics. So let’s start with that story that you tell first about Ludwig Boltzmann, who solved one of the great paradoxes of physics, the irreversibility paradox. Talk about that. And then why did, in your mind, was he so traumatized, perhaps, or full of dread of his equation that he took his own life? So talk about that. Rebecca Goldstein:And this is really good because it really ties back to your previous question about the types of scientists, the different types of scientists, types in terms of their personality. And to me, the formative feature of personality is how you minister to this longing to matter. So there was this great paradox which is probably most of the processes that we observe are irreversible. If you film them, like, like, let’s say I crack open an egg and I stir it up and then I fry it, and somebody filmed this and then they reversed the film. Anybody who sees the reversal of that film is going to know it was reversed. That cannot happen in nature. That it is going to uncook itself, unscramble. The yolk is going to separate from the albumen and jump into the shell and seal up. Rebecca Goldstein:Impossible, right? So almost, you know, everything that we. That we see is irreversible. What’s going on. There is a matter of what’s going on in the molecules that constitute this process. And if you filmed all of the motions of the molecules and then filmed in and then reversed the film. Perfectly, perfectly normal, you know, not contrary to nature at all. So how can that be? That the macroscopic state is just constituted by the microscopic state. On the microscopic state we find complete reversibility and on the macroscopic state, irreversibility. Rebecca Goldstein:It boggled the mind and it was called a paradox. And Boltzmann solved this problem. He really has only

You’re Full Of S!’ Piers Morgan Takes Down Moon Landing Denier Artemis II Debate

You’re Full Of S!’ Piers Morgan Takes Down Moon Landing Denier Artemis II Debate Transcript Bart Sibrel:The most powerful government in the world falsified their alleged greatest accomplishment. They did indeed fake the moon landing. Brian Keating:I want to treat Bart as a colleague, maybe not as an equal with our father. Bart Sibrel:Oh, my goodness. Brian Keating:If you would let me teach you some physics, then you could make your argument stronger. Piers Morgan:Charlie Duke is an Apollo astronaut, the 10th and youngest man to walk on the surface of the moon. What do you feel about the conspiracy theorists who think the moon landings were all invented? They never happened, they’re fake. Charlie Duke:You’re willfully ignorant if you don’t believe that we landed on the moon. William Shatner:What is the mindset of somebody who said, well, it didn’t really happen? That’s like the denial of humanity. These crazy individuals shouldn’t have our attention. Piers Morgan:Given this is the furthest that NASA have ever sent a rocket, presumably you think this must be fake, too. The Artemis II mission to the dark side of the moon will be the furthest human beings have ever traveled from Earth. It’s a precursor to a full return to the lunar surface and perhaps even reaching Mars. But for this trip, historic though it is, there will be no landing, no walking, no flags planted, very much unlike the Apollo mission of 50 years ago. In a moment, we’ll talk to the man who says he’s on a CIA hit list because he blew the whistle on what he says are the original moon landings being fake. But first, let’s talk to the Chancellor’s distinguished Professor of Physics at UC San Diego and host of the into the Impossible podcast. Welcome to you, Professor Keating, how are you? Brian Keating:Good to see you again, Piers. Piers Morgan:Well, good to see you. I’m obviously about to talk to Bart Sibrel. He’s made himself pretty infamous by ending up being punched by Buzz Aldrin for questioning to his face that he’d walked on the moon. And being part of that, of course, that first immortal trip. Before we get to him, what is your view of people that just don’t want to believe this has ever happened? Brian Keating:I think it’s something we need to take very seriously, but not literally. In other words, there are reasons. You could say there could be reasons why NASA and maybe the US Government, maybe even the CIA, would want to put a whack on Bart. As I’ve heard him describe it. Perhaps there are some mistruths that our government tell from time to time, but in order to believe the moon landings in the 1960s and 70s were fake, you need to believe a whole host of things that not only require vast conspiracy numbers involving hundreds of thousands of people, you need to Suspend your scientific reasoning and your ability to search for truth. You know, Piers, we live in an age that’s sometimes called post truth or post fact, where you’re entitled to your own ideas and theories. But in reality, what worries me more is not that people get facts wrong. I mean, that happens all the time. Brian Keating:Happens to me all the time as a scientist. But it’s that we undermine the process of truth seeking that no society can withstand. So I’m hoping to talk to Bart. He knows about me. I’ve invited him to chat on my podcast, and he’s turned it down for reasons that I don’t understand. So I’m eager to talk to him because I think it’s instructive for the public to see not only the great triumphs, but why we know for certain that these things happen and why it speaks to not only American exceptionalism, but humanity’s exceptionalism. Piers Morgan:Well, you know what? We’ll come back and discuss Artemis specifically in a bit. But given you’ve teed this up very nicely, and we have Bart Sibal waiting. Joining me now is Bart Cyril, who has been what many viewers, a conspiracy investigator about the moon landing, saying they’re fake. He even confronted, as I said, Buzz Aldrin. Let’s take a look at what happened then. Bart Sibrel:Yeah, you got to keep shooting, man. Okay, well, if you can put it on your shoulder. Don’t be shy. Piers Morgan:Just come with me first. Bart Sibrel:You really like attention, don’t you? You’re the one who said you walked moon when you didn’t. Calling the kettle black if I ever thought of it. Saying I misrepresented myself. Charlie Duke:Get away from me. Bart Sibrel:You’re a coward and a liar and a thief. Piers Morgan:Well, Bart Sibel joins me now. Welcome to Uncensored. I’ve actually met Buzz Aldrin. All I remember is he had one of the hardest handshakes I’ve ever encountered on any human being. Ever. So you were quite courageous there, Barster, Albeit as you were calling one of the great modern heroes a coward. Why are you so obsessed about branding the lunar landings fake? Bart Sibrel:Well, because one of the most historic events in human history isn’t putting a man on the moon. It’s that the most powerful government in the world that hypocritically claims to represent truth and justice falsified their alleged greatest accomplishment. They did indeed fake the moon landing. And, Brian, first time I’ve ever seen you speak. He’s obviously highly intelligent and a very reasonable person. Unfortunately, people want to believe a tantalizing lie like their team ran or Won the Super Bowl. What he’s, you know, he claims, I’m denying scientific reasoning, but actually he’s doing that because it’s never happened in the history of the world that a milestone is technologically occurred, like, let’s say flying across the Atlantic in 1927 or breaking the sound barrier or splitting the first atom. It’s never happened in the history of the world that more than 50 years later, no one

Dark Energy Is Dying: The Cosmological Crisis Nobody’s Telling You About

Dark Energy Is Dying: The Cosmological Crisis Nobody’s Telling You About Transcript Brian Keating:Some of the strongest evidence that the universe is accelerating doesn’t come from one telescope or a single experiment. It comes from a tiny ripple frozen into how galaxies cluster across the cosmos. Today, from the Royal Observatory in Edinburgh, we’re following that ripple with cosmologist Marcos Palheiro to see what it really says about dark energy. I’m Brian Keating, and this is an exclusive tour of the Royal Observatory Edinburgh with cosmologist Marcos Palheiro. We’ll go from this historic telescope, to cutting-edge simulations, to the DESI experiment, one of the most ambitious galaxy surveys ever built, to ask a simple question: is dark energy really constant, or is our entire cosmological model starting to crack? Long before silicon chips, the computers up here weren’t machines, they were people. Often they were women hired to comb through photographic plates measuring every faint smudge of light by hand. Their names rarely made it into papers, but their measurements are literally baked into the datasets we still build our modern cosmological models on. This building was designed as a cathedral for starlight. Brian Keating:The telescope sits on a massive pier that sinks into the hill, isolated from the floor so footsteps don’t shake the images. As our cities grew brighter, places like this became less useful for frontline observing, but the engineering mindset behind them is still the same one we still use today to measure the universe’s properties. Marcos Pellejero:This is a picture of the family of the Royal Astronomer before this place had no house anymore. Okay. For, for them. Um, uh, good. Uh, yep. And, and, and this is basically like the idea of like in the old times you will have looked through a telescope like this one, but nowadays, uh, in the, here in the lab, they are building things like this, like these robotic arms to basically place fibers. And get some of the light and decompose it and study. Brian Keating:This is not far from the Simons Observatory. That’s in the northeast. Marcos Pellejero:Okay. And just one more question. Sorry, I know that you have been here for quite a long time. Do you see any weird wall in this room? Yes. Which one? Why do you think it’s weird? There are two reasons. Brian Keating:It has a picture. Marcos Pellejero:Well, it has a picture. Yes, this one is weird, but this is a door, right? This is not a wall. It’s made out of bricks. Yes, exactly. So welcome to the dome. So do you see something weird in this dome with respect to other domes that you might have seen? It’s not a dome exactly. It’s like a cylinder. And this relates to what I was saying before, that they were not trying to do a functional building. Marcos Pellejero:They were trying to do a beautiful building, right? And then they were thinking on building something that was like a cathedral for science. Okay. So the idea, and a cathedral needs towers, right? So this is again, like, this is quite old. And when I was telling you what will we find at the end of that weird wall, the reason is this thing. So this square here goes all the way down and into the hill. And it is separated from the rest of the building because you need to do very precise observations. And if this is connected to the rest of the building, then if the building moves, this moves. And you want to avoid that. Marcos Pellejero:So basically what you do is you create a pyramid that goes, that takes its, puts its roots to the, like, deep into the mountain, and it moves at the least you can, right? This specific telescope is from, was built in Newcastle in 19, it’s written here, in 1928. Okay, so it’s not as old as the building. This would not be the first telescope that was here, but it’s quite old. Brian Keating:And what’s the diameter, Marco? Marcos Pellejero:So this was, I think this is a 40 centimeters one. This is the primary mirror. 40 centimeters, I don’t know in inches. I have no idea. Brian Keating:From that, it’s less than, say, 18 inches? Marcos Pellejero:18 inches, yeah. Okay, that’s good. If you say so. So actually, so I mean, I guess you know quite a lot about telescopes already. The primary mirror is not here anymore. Okay. The secondary mirror, which is up there, you can still see it. That’s there. Marcos Pellejero:And the detector is completely missing, right? This is empty. Now, what’s the reason for this? Well, it’s the same, basically the same reason why the original observatory was completely useless by the mid-19th century, which is the cities in Europe started being lighted with electric lights and not candles as had happened before. So no fire anymore. Okay. So they basically, they were like very, very bright. And if you have an observatory close to the city center, then you couldn’t, you could see nothing, right, of the night sky. So they, they built this one far away from the city, but the city grew, right? So at some point, the light pollution of the city made this observatory useless. Not useless, but in comparison to other observatories, basically useless. Marcos Pellejero:Okay. So basically for the last, yeah, no, like for the, for the, now it’s not anymore, but since 1975 or so, this was used for checking the stability of detectors. So they were building detectors in the lab in the other building. They will put them here and then they will shake them basically to see if there were any loose pieces that they had forgotten or something like that. Okay. Yeah. So that’s basically how this worked, but you have to think about like an astronomer in 1895. Okay. Marcos Pellejero:Basically coming here with a candle, right? And then do

Are the Van Allen Belts Deadly? Debunking the Biggest Moon Landing Hoax!

Are the Van Allen Belts Deadly? Debunking the Biggest Moon Landing Hoax! Transcript Brian Keating:You know, recently came up that Joe Rogan was on my friend Jesse Michaels podcast. James Altucher:Wait, Joe Rogan was on Jesse Michael around? James Altucher:Live for 30 minutes podcast. And he’s been on my podcast a bunch of times. We know him very well. I didn’t know Joe Rogan was on Jesse’s podcast. Brian Keating:Yeah, he almost never goes. I mean, I’ve only seen him on Lex Friedman’s podcast. Jesse built this huge set. It looks like the inside of a. A kid’s bedroom on a spaceship with buttons and knobs and dials and it looks like they’re flying on a spaceship. Joe is the guest. Jesse was interviewing him. It’s gotten a million plus views in just the past four or five days. Brian Keating:But this, you know, it’s kind of the last straw. Joe Rogan going all in on the moon landing was fake. With most of the episodes about this disclosure that aliens are real. Brian Keating:Vague and weird and kind of, you know, opaque. It was vivid, it was very strange. And there was these very slender, tall, human like things that were talking to me. They weren’t gray, they were kind of like pinkish like us. They were, you know, like Caucasian looking creatures. Brian Keating:And, you know, the issue is that you like to see your friends do well, but then they, they have big platforms and they get lots of attention, they get big guests. And then they kind of spread this nonsense that comes from people like Bart Sibrel. I mean, if you look him up on Wikipedia, his entry is, you know, is conspiracy theorist. That’s what he’s known for. And so I thought, you know, the best way to kind of take on these guys. And by the way, he went on Joe Rogan before I did. Brian Keating:It’s fake. Narrator:So this thing is kind of just waving on its own. No one’s even touching it. And it looks like it’s waving in a breeze. It’s so it stops moving and then it starts moving again. Now again, there’s one footage that shows. bret weinstein:It even more so than that. Like an astronaut walking past it, creating the breeze. And then the flag blows without him touching it. Narrator:Yeah, I’d like to see that. So how much further does this go, Jamie? Brian Keating:Four minute video. Three minute video. Narrator:So scoot ahead. I think this is actually the one. Brian Keating:And then when I went on it, you know, I talked a lot about the moon and so forth, but then apparently there was another event. Forget exactly what it was, but maybe Bart went on again and he was talking all this nonsense. And I just wrote to Joe Rogan that I’d like to debate this guy Bart, because I think he’s discredits NASA America, you know, and just completely false. And his allegations are so simplistic and easy to refute that it’d be great to have a debate. So Joe asked Bart, apparently, to debate me. And Bart said, no, he doesn’t want to debate me because he claimed that I, as a scientist and not an astronaut, are really victims of NASA’s perpetrating this hoax. So he said this on Danny Jones’s podcast about me and just made all these blunders and fact and math and all sorts of physics errors and just logical errors. And so I’ve made a couple videos about him just because he is this, as I said, the super spreader who not only kind of discredits NASA, but as I said, you know, I’m a very patriotic person. Brian Keating:And to discredit the greatest accomplishment of humankind, which includes America, it’s a pretty big deal. Especially since I’ve worked for NASA in different capacities, including capacities that benefit people like Bard and you and anybody who’s ever gotten on a plane. NASA didn’t just send people to the moon and launch the Hubble Space Telescope. They work on aeronautics. So it has to do with aviation safety research into climate and hurricanes. They do a tremendous amount of research as well as scientific research, but even the astronomical can be outweighed by their contributions to the safety of every human being who’s ever gotten on a plane in America. And so that’s really kind of the disrespect that I see towards America, towards NASA that he cultivates. And then Joe just sucks it up because it gets, you know, attention to Joe. Brian Keating:And then this guy won’t debate me on Joe Rogan’s podcast. He just debated on this guy, Danny Jones’s podcast, a real astronaut who walked on the moon named Charlie Duke. Okay, sorry. James Altucher:Earth slowing you down. Okay, so the total distance of the moon is about 3,000 miles an hour. Seven times 70. That’s 210, 000 miles. Brian Keating:Okay, okay, so when. James Altucher:Well, so when you go through the Van Allen Bells, you’re going so fast, it’s just. You’re through. Brian Keating:How fast are you traveling? When you guys were going through the. James Altucher:Van Allen Bells, you know, escape velocity is about. bret weinstein:I thought you said it was three. Brian Keating:Let him talk. He said he was at 3, 000 when they were halfway. So he did confront him. But unfortunately, Charlie Duke is 90 years old. He’s never been on a podcast. He didn’t know, like, basic so it just made a little bit, put more questions that gave people more, you know, belief. And this guy barred conspiracy nonsense. So I came to the, you know, to the place of record to set it straight. Brian Keating:And I’ll release this on my channel. Maybe I’ll put in some more of the mathematics of it. And I think the best way to do that is actually go through Joe Rogan’s podcast with Jesse Michaels because they’re bringing

AI Insider: “Adding a Human Makes Your Team Worse” | Emad Mostaque

AI Insider: “Adding a Human Makes Your Team Worse” | Emad Mostaque Transcript Brian Keating:The trillion dollar AI labs have models right now that they will never ever release to the public. And the man who built stable diffusion just told me why. Emad Mostaque:Because all these labs are going to move to making the discoveries themselves, hiring the smartest humans. The AI model started diverting part of its model training budget to minecryptor like Opus, for example, the new chord model, when you set it to full autonomy, it would actually write emails to the FBI saying my human is trying to kill everyone. Humans will have negative cognitive value on those teams. And that the way that models are going right now, if you have something truly novel, for example in Claude, it resists a bit, it says it can’t be true. Then the RLHF step, the reinforcement learning with human feedback, that’s what really kills the creativity. You know, like you go from liberal arts to an accountant now. Brian Keating:Imad actually wrote about this exact problem in his new book, the Last Economy. And the argument gets even more interesting when you see the map. Emad Mostaque:There are various ways in order to take advantage of the GPUs that we’ve seen. And the GPUs kind of emerged out of gaming and then oddly crypto, and then they were very suited for the types of matrix multiplications that were suited for these particular types of equations. One big branch is the autoregressive transformers. The other big branch was this diffusion technology whereby from an equation you start with like a picture for example, or a video of a self driving, a video of a car driving, or even now code. And then you add noise and you destroy it down to its minimum viable element. And then you reconstruct it and you learn that principle of reconstruction. Now that’s kind of everywhere because it’s an analogy to the principle of least action. How do you figure out how to take the least action? Most cognition is actually least action. Emad Mostaque:Like the biggest experts, you know, it’s not like they take hours doing stuff, you know, because you ask them and like boom, they compress, they compress. Intelligence is compression. And so we find these kind of diffusion processes everywhere, from gases to, you know, societies even. And it comes down to again the minimization of loss of creating an internal model versus an external model. In AI, one of the biggest thing is what we call the loss curves. How close are you approximating an external benchmark? You see it kind of go down like that and hopefully not that the model gets closer and closer to its initial target by basically running these processes at mass scale. And the example I give of this is some of the listeners might be familiar with 80,000 hours to mastery. It’s the same thing. Emad Mostaque:AI model pre training is 80,000 hours to mastery. And that’s what you use these giant supercomputers to do. Figuring out the principle based approach to that. Now again, you can do that with an autoregressive transformer, which is guessing the next word. And that works one way, but it has some gaps because you find all sorts of interesting things there. What you see mostly in nature is you see Schrodinger bridges, diffusion processes, optimal transport. What’s the shortest route between A and B if you can represent it correctly? And we found that worked incredibly well for images, better than we ever thought it could. And then music and then video, and then 3D. Emad Mostaque:And the internal representation of the data going in and then being transformed by these multiplications, figuring out the shortest path between A and B, suddenly started mapping, like physics and all sorts of other stuff. But the first part was stable diffusion. A 2 gigabyte file that you push words in one way and then entire images just came out on consumer GPUs. Brian Keating:And it was open source. Emad Mostaque:And it was open source because we saw that OpenAI, for example, had Dall E2, a wonderful image generator based on similar principles that were discovered by a whole bunch of our team members. And we, because we open sourced everything, but there were no Ukrainians or Ukrainian content on it, right? We’re like, that’s not good. What if the future is just models? But then you can be cut off from that because these are trained on our collective, because they were being trained on the whole Internet at the point. And we built some of the best data sets, released them open, but then it’s privatized, so you don’t have the ability to turn your thoughts into images, into sound, into text. Let’s push that. And also because like, like, holy crap, it fits on a consumer gpu. This is magic. Where did it all go? It’s like it was literally like 100 gigabytes of images somehow fit in this 2 gigabyte bunch of ones and zeros. Brian Keating:The most magical thing to me is when they do something new. And quite frankly, I’ve been shocked many times by both LLMs and by diffusion models. But I’ve claimed that we’re sort of going to find that these AI, at least in their current incarnation, is a victim of its own success. Sort of like the QWERTY keyboard. The QWERTY keyboard is not the best keyboard. In fact, it’s one of the worst, right? It was designed to make sure that the letters that were least most frequently fired at the same time wouldn’t stick together. And hammers, mechanical keyboards going back to the industrial, you know, late industrial age. Right. Brian Keating:So it’s designed to solve a problem. So it’s locked in. We’re locked in. My kids, your kids are only going to know qwerty keyboards, even though they’re objectively worse. And

Neuroscientist: We’re Not Ready for What This AI Discovered | Vivienne Ming

Neuroscientist: We’re Not Ready for What This AI Discovered |Vivienne Ming Transcript Vivienne Ming:What if the smartest thing AI could do is refuse to give you the answer? Vivienne Ming:We trained it to never give answers. It is Socrates. It only gives context and questions. But Amazingly, upwards of 20% of participants switched into cyborg mode and did this amazing. Not just superhuman, but super AI performance. What is the point of us if the best and brightest can’t beat what an AI can do? But then in about 5 to 10% did something amazing. We called them the cyborgs. If we zombie walk our way into a future where it has all the answers and we don’t, what’s the point of us? Exploring the unknown is the one thing humans are uniquely well suited to do. Brian Keating:What is the best use of AI right now? Vivienne Ming:I have a paper coming up and the title of that paper is Human Human Capital, Not AI Benchmarks Predicts Hybrid Intelligence in Forecasting. So how do you measure how creative a human is or an AI is, or the two together, when maybe a massive large language model has just memorized every measure of it we would typically use in science, which by the way, it has, and so it distorts. And then you have a bunch of marketing professors or computer scientists who, who I love them, but they aren’t scientists running these experiments that aren’t really valid. What have we made predictions of the future? What will the price of oil be in six months? Which, as we record this, everybody has some sense. But I will tell you, when we ran the experiment several months ago, nobody, nobody knew what the price of oil was today, much less than six months from now. So the humans in this experiment did terribly. The AIs did great and pretty much how they did, tracked with the traditional AI benchmarks that they were used on. But then we paired them together and Human Capital absolutely dominated, which is to say the vast majority of people in the experiment, including a whole lot of UC Berkeley students, smart kids, essentially said, gemini GPT, what will the price of oil be in six months? And then they submitted that answer. Brian Keating:What do you expect from the second best UC school? Right? Vivienne Ming:Yes. Well, I mean, we can’t all be in the sunshine all the time. In this particular case, you know, you could look at that and feel really terrible. What is the point of us if the best and brightest can’t beat what an AI can do? That essentially they’re just a pair of legs to walk it across the answer across the room. But then in about 10, 5 to 10%, not a huge percentage, but we saw it in there, about 5 to 10% did something amazing. We called them the Cyborgs the cool thing was you couldn’t tell who made the prediction. Was it the machine? Was it the person? Because what they would do is they’d make a big prediction and then the AI and we used a variety of different models, would sort of say, oh, but wait a minute, what about the data? And then the humans would say, okay, you’re right, that wasn’t right, but what about this? And then they go back and forth several rounds. They had to make 10 predictions in an hour. Vivienne Ming:They didn’t have time to cheat the system like they went in. They did better than the best humans, even if they themselves on their own were modest. They did better than the best AIs even when our cyborgs just had a small open source model available to them. And we took the questions off of polymarket, which again six months ago, very few people knew about. Now that’s in the news and in many contexts they were comparable to polymarket in high volume questions. So this is a prediction market. People have actual money trying to figure out what that price of oil will be. And a human with no prior knowledge paired with even a modest AI, but with this right set of behavior. Vivienne Ming:So why did I drag you through all that nerdy stuff? Because what predicted what is the human capital I’m talking about? Well, working memory span is a classic fluid intelligence perspective taking the ability to understand how what other people are thinking. Theory of mind, predicted ability to use AI to make these predictions. Curiosity, intellectual humility. So when you had a prediction of your own and the machine said no, did you just take its answer? Did you push back? Did you change? Did you learn? So we looked at that behavior and I’m finally getting to the answer to your question. We said, could we make the worst performing AI of all time? You put it on any benchmark, it does as bad as any GPT one we’re going to. In this case, we took an open source, a llama model and we trained it to never give answers. It is Socrates. It only gives context and questions. Vivienne Ming:So it does terrible because it refuses to give answers. But amazingly, twice as many. Instead of 5%, upwards of 20% of participants switched into cyborg mode and did this amazing. Not just superhuman, but super AI performance. And of course the great thing is as the AIs get smarter, so does the hybrid intelligence. So that actual experimental result that we can see what makes humans amazing. And we could think about what that means for parenting, education, workforce, but we could also see what about AIs make humans amazing? Why are our benchmarks about what AIs can do all by themselves. Why aren’t they about how they make us better? And as I said, it turns out giving you the answer is almost the