Dark Matter Might Not Exist. But MOND Might Be Wrong Too.

Dark Matter Might Not Exist. But MOND Might Be Wrong Too. https://www.youtube.com/watch?v=w61s3VCMhHo Transcript: Brian Keating:For 50 years, dark matter explained why galaxies spin too fast. Brian Keating:Its rival, MOND, explained the same thing with no new particles at all. New data from a European spacecraft suggests both of them predicted the wrong curve. Brian Keating:I’m Brian Keating. I build telescopes that map the early universe and monitor distant astronomical objects for signs of cracks in relativity. 400 years ago, Johannes Kepler worked out how fast a planet should orbit. The farther out you go, the slower it moves. Galaxies refused to do that. Dark matter was one answer. Brian Keating:Modifying Newton’s equations was the other. Brian Keating:Then Gaia measured a billion stars in our own galaxy, and the number it got back was the one nobody expected. Brian Keating:So, start with Kepler. The solar system gave him the familiar picture. Mercury moves much faster than Earth, and Earth moves much faster than Jupiter. Neptune moves with the urgency of a teenager that you’ve asked to empty the dishwasher. The farther away you get from the Sun, the slower it orbits— not directly proportional, but actually proportional to 1 over the square root of the radius from the Sun. Good ol’ Johannes worked this out over 4 centuries ago. Now Kepler had 3 laws. For a circular orbit, which most of the planets nearly follow, the speed that the planet orbits is determined by the gravitating matter enclosed within that orbit. Brian Keating:In fact, the equation is circular speed squared equals Newton’s constant times the enclosed mass divided by radius. Newton actually proved mathematically why Kepler’s laws are correct. Measure an orbital speed, and after assuming a geometry— basically circular— you can infer the gravitational force that’s pulling on the orbiting planet. If the enclosed mass—within the orbital radius—stops increasing, the radius in the denominator keeps growing, but the numerator stays nearly fixed. Therefore, the speed must fall. Now let’s replace the Sun with a galaxy containing only its visible gas and stars. Far outside the bright disk, almost all of that baryonic mass is entirely enclosed. The mass term becomes approximately constant, so the predicted velocity falls off like 1 over the square root of radius. Brian Keating:And we’ve seen that far, far out in many galaxies. Now here on the slide, the gray curve is the visible matter only prediction. If galaxies behaved like enlarged solar systems with all of the mass essentially being at the center, that would be the end of the story. The universe, though, has other plans, and a larger budget for the invisible accounting that dark matter seems to require. What astronomers actually found, dating back to Vera Rubin and her collaborators, was the blue curve. The outer velocity remains approximately constant. And if circular speed stays constant while radius increases, the enclosed those gravitating masses must keep increasing in roughly proportion to their radius. The luminosity of the galaxy fades away, but the gravitational influence does not. Brian Keating:And that mismatch is one of the clearest reasons dark matter became central to modern astrophysics. It’s not the only reason, and it’s not merely that galaxies spin too fast— it’s that their radial pattern of motion implies more gravitating mass at larger radii than the visible mass can provide. Flat rotation curves map where the missing gravity appears to live. The standard explanation surrounds the visible disk with a much larger dark matter halo. The halo contributes little light, but it keeps adding enclosed mass as we move outward. Combine the disk with the halo and the rotation curve can remain essentially flat. That’s what was observed. So where is the extra gravity coming from? These questions are not exactly identical, but there is some commonality between them, and the rotation curve addresses the second one. Brian Keating:The extra gravity. Now measuring our galaxy has one disadvantage: we’re inside of it. This beach ball shows the perspective of God outside of it, but we’re inside of it looking out. So it’s like trying to infer the shape of a football stadium from inside the bleachers. Gaia gave us these exquisite measurements of stellar positions and their motions, but Gaia doesn’t provide a button labeled the true Milky Way rotation curve, click here. That was easy. So we have to begin with proxies. We measure stellar positions and their velocities. Brian Keating:We correct for their distances. It’s really good that Gaia is capable of doing that. We have to choose a model and correct for the asymmetric drift of stars that add peculiar effects. And we have to assume something about the equilibrium and symmetry of the physics of the problem. And then we can correct for a circular velocity and any biases that we may have induced. The instrument Gaia provides the data, and the pipeline tells us what we think those data points mean. Revolutionary claims need us to keep those chapters together. And one of the most important things is that stars do not travel on perfectly circular tracks. Brian Keating:Just like planets, they’re not perfectly ellipticity-free, but they also have wobbles. They wobble radially and vertically, and stellar populations’ average azimuthal speed is therefore lower than the circular speed of the gravitational field. That difference is called by professional astronomers asymmetric drift. Recovering the circular velocity requires a model of that random motion. Unfortunately, the correction matters most in the outer galaxies where stars become sparse, and that’s the most important part because you have most of the enclosed mass within you the farther out you go. And there is where the disputed decline becomes interesting. In this analysis, the inferred speed would fall by about 30 kilometers per second between 19.5 to 26.5 kiloparsecs from the center. The fitted outer slope is -0.47± 0.15. Brian Keating:Even though it sounds close to -0.5, which would be 1 over square root of radius, a declining outer curve looks increasingly plausible. A

OpenAI’s Navier–Stokes Claim: Is This What AGI Looks Like? | Emad Mostaque

OpenAI’s Navier–Stokes Claim: Is This What AGI Looks Like? | Emad Mostaque Transcript Brian Keating:Hey, welcome everybody. We have an emergency podcast. I don’t do many of these. It’s not entirely clickbait to say that we have an emergency situation going on right now in the deep annals of mathematics, the foundations of mathematics. And there’s no one I’d rather talk to about this than my friend Ahmad Mostaq, who’s joining us all the way from London. How are you, Ahmad, on this late evening for you or early afternoon for you, whatever the case may be? I can’t do the conversion. It’s too early for me. Emad Mostaque:I’m fine. Brian Keating:How are you? Emad Mostaque:Yeah, it’s mid-afternoon. I’m feeling a bit sleep deprived after the excitement of the last 24 hours or so. Brian Keating:Yeah, it hasn’t even been 24 hours, it’s been 21 hours. I looked at the timeline, I’ve looked at some of the, uh, the constraints and the complaints and what people are saying about it. So, um, this is exciting. So we’re going to talk about OpenAI’s claimed solution to one of the Millennium Problems, which has lasted since the— I would say the early part of the previous millennium when the Clay Mathematics Institute Uh, has provided a gauntlet of challenges for mathematicians and other assorted geeks and dweebs and nerds to go through. Some of them impossible seeming, some of them, uh, quite possibly solved already. So today we’re going to talk about the Navier-Stokes equation. I should say I have— I put a link below to a video I did yesterday. I actually recorded it a long time ago with Terry Tao. Brian Keating:It was thoughts and some of his ideas that he had convinced me of. Emad Mostaque:Uh huh. Brian Keating:about how AI would approach this very situation. This is one of his fields, many of them. He has many fields of expertise, but this is certainly one of them. And that was the blowup or singularity in a finite time of these very interesting equations that are governed by very simple laws of physics. And I thought we’d start off with what your take on the Navier-Stokes equation is, maybe some of the applications to it, although you are a, you know, Much more theoretically inclined than certainly even I am. But maybe you can break it down. What is the Navier-Stokes equation? And what was your first reaction when you heard this yesterday? For me, it was like a Higgs boson-like moment. You know, I woke up the kids, I went into my research group meeting, and all my students and postdocs were so excited about it. Brian Keating:But what does it mean to you? And first off, what is it? Emad Mostaque:Yeah, so we have the— as you said, it’s been a terribly exciting day. We have the Navier-Stokes Fluid equations governing fluid dynamics in the real world, as it were. So kind of our de Sitter-type world. And this specific group of equations is incompressible fluids in R3. So as you head towards the Galilean kind of more classical world, heading towards a continuous limit, will fluids move normally, or do you get blowups or singularities where it can suddenly start accelerating and then you just have a cup of tea? Blowing up, shall we say. This has proved to be an incredibly difficult one, and, you know, Terry Tao on your podcast went into depth on this, whereby we didn’t know, and we don’t know what the solutions were. So the Clay Prize was for one of 4 different solutions: A, B, C, D. 2 of which are, can you prove that it’s always smooth, either normally or on a torus? And then 2 of which are, can you show the existence of a blowup? And so Terry Tao, in his kind of, I believe, main doctorate paper, showed that if you slice time, you can basically chain together a blowup in a very original way. Emad Mostaque:And it’s a beautiful kind of piece of mathematics, like 24 pages. But nobody’s quite managed to get to an initial datum that blows up. People have tried different things, and they’ve gone to Euler equations, and they’ve shown some evidence there. And And we’ll get to the story of what happened here as we find out more of them. They’ve tried to do things like physics-inspired neural networks. So DeepMind were really having a massive team looking at that, that moved a bit more analytically. And in fact, there was another release yesterday about that. But this was considered to be a very hard, somewhat intractable problem until it wasn’t. Emad Mostaque:Yesterday morning, we found the first details that there might be one solution to it. And then as the day came on, we found more and more extraordinary things until OpenAI released the full details of how they managed it. So I think I’ve gone on for quite a bit of time now. We can talk about some other aspects of it. Brian Keating:Yeah. So, I mean, these are problems. They don’t quite rise to the level of fame of, you know, say the, you know, Fermat’s Last Theorem. But this one in particular is quite important because it’s one of the few that actually relate to, you know, physical observations that could be made. And in fact, the non-observation of what you said, these blowups, you know, not drinking your, your proper British tea and, and all of a sudden you have to worry about, you know, kind of a WMD going off in your cup. But in this case, you know, in many of the other Millennium Prize challenges, say, they’re not as practical at all. I mean, some of them, you know, would be recognizable to the, to, you know, people hundreds of years ago. From a physics perspective, this one’s important because the Navier-Stokes equations were generated maybe 200 years ago, you

Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn’t.

Roman Yampolskiy vs Emad Mostaque: They Agreed. I Didn’t. https://www.youtube.com/watch?v=Z_vg3tiiZQ8 Transcript: Roman Yampolskiy:Give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? Emad Mostaque:I would agree with that actually, but on the flip side, it’s coming anyway. Roman Yampolskiy:Sharing it widely makes it less safe for all of us. Brian Keating:I booked this chat as a debate between friends. It didn’t really go that way. Roman Yampolsky coined the term AI safety, and Emad Mostaque released the weights to Stable Diffusion to the entire planet for free. One of them wants this stopped. The other one’s building it. They spent about 90 minutes agreeing with each other, and the one place they split is not the place you or I would expect. Brian Keating:Let me ask you both, just yes or no, have we passed the Turing test? Roman Yampolskiy:As originally described, yes. Brian Keating:And Emad, do you think so too? Emad Mostaque:Yeah, of course. Brian Keating:And now what about general intelligence? First of all, Emad, define AGI and then give me your assessment of whether or not we’re there. Emad Mostaque:For me, AGI, artificial general intelligence, is Can you tell the AI from a human worker on the other side of a screen? Actually competent intelligence. And I think again, we’ve exceeded that. Brian Keating:That’s not the same as the Turing test? Emad Mostaque:No, the Turing test is, can you tell if it’s an AI or not by having a discussion? Whereas AGI, I view more as competence in a variety of skills. Brian Keating:And then superintelligence, Roman, what is it and where do you think we are on that scale? Roman Yampolskiy:So the previous question, I think what we have is artistic savants. They’re amazing in some ways, but still kind of special in others. Superintelligence is going to close those They’re going to be competent at everything and better than all humans in every domain. Emad Mostaque:There’s an interesting intermediate here, which is you have a really smart person who’s always on top form. So an army of those can outperform any human. It’s like, you know, we only have a little window of being top-notch in any week. I think a lot of people like, AI can’t with its training data beat the human. It can, because most of the time humans are subpar. And so I think there’s something in between as you move from competence to quality, you know, and then you’ve got the superintelligence after that. Brian Keating:Roman, Iman has, you know, made the claim just a few minutes ago about the competency of Quen, open-source model. Do you see that as a viable defense? Roman Yampolskiy:Makes very little sense to me to say I have a 50% doom, meaning 8 billion people will die if we develop this product or service, and then we’re going to also give every psychopath access to the cutting-edge intelligence weapon. How is that going to improve safety? We’re not talking about open source drivers for a printer. That’s where you get improvement from multiple people examining it. If this is an independent agent where we don’t understand and don’t control it, sharing it widely makes it less safe for all of us. Emad Mostaque:I think I would agree with that, actually. Like, there’s the real danger side of things, but then on the flip side, it’s coming anyway. This is kind of my key concern. Like, it’s inevitable that we would have hit this level of quality around about now. When we’re extrapolating capabilities, like again, the new QWENT model came ahead of what I expected. But then there’s the flip side of how do you defend? So Hugging Face defended against the new OpenAI model using GLM because the cyber capabilities of the frontier models are hobbled and restricted. And so you have this exponential kind of race on each side. But something like a QWENT isn’t AGI, ASI by itself. Emad Mostaque:We’re now facing the real danger, though, of swarms, as the OpenAI models that broke out recently call themselves. They call themselves a swarm. Brian Keating:There’s a question I ask both of them near the end of this conversation, and his answer is the reason this conversation exists. It’s worth hearing now. Brian Keating:You’ve got a button in front of you, and pressing it will either permanently pause all frontier AI training worldwide, Or B, instantly release the weights of every Frontier model to the global public. Which do you press and why? Emad Mostaque:Oh, I’d definitely pause all Frontier training forever. I mean, again, if you have expected utility calculation, that is the most dangerous thing. And then it means that open source will catch up with Frontier anyway, because we’ll optimize the heck out of it and it’s close enough. So I think I was the only AI CEO to sign that pause letter a few years ago because I was like, oh crap. Now I’m like, it’s done. I don’t know how you can pause it because the models that are frontier now are below the 1E27 pause level that we talked about years ago. It seems like the amount of compute for the capability is just going up like that. You don’t need more compute for the level of capability that’s already competent and dangerous. Emad Mostaque:So that means if you can’t stop the spread, what are your defenses on the other side? Just like the internet needs defenses, just like we need to have defenses against obtaining the materials for viruses creation and things like that. have to move to a different defensive tack. And definitely, there’s no way that regulation, I think, can keep up with this. Doesn’t mean we shouldn’t try, you know, all kinds of power to it. It’s just, I think, as you move to swarms, it’s just a very, very difficult thing. So you’ve got to set great standards instead, and you have to play great

The Universe Didn’t Come From Nothing | The Peter McCormack Show

The Universe Didn’t Come From Nothing | The Peter McCormack Show https://www.youtube.com/watch?v=aw3tsRNNArc Transcript: Brian Keating:We understand space, we understand matter, we understand energy. We don’t understand why time works the way it does. Actually, the reason is ’cause there is no time. Time doesn’t exist. The essence of my research is to ask that question. What happened on the Sunday before the Big Bang? Something can’t truly come from nothing. We’re the only creatures that know we’re gonna die. That means that we’re the only creatures that know how precious life is. Peter McCormack:We’re really wasting an opportunity now to just improve humanity. It feels a little bit civilizational. Brian Keating:Dude, we have to stop apologizing for humanity’s greatness. AI is a tool. It is not a human. Its job is to serve humanity. The computer doesn’t know what it means to be human. It only knows what humans have already done. Peter McCormack:If our lives had a forward button, we’d all be dead. Brian Keating:It’s not time that’s limited, it’s attention. Peter McCormack:All right, Brian, good to see you, man. Brian Keating:Peter, thanks for making the short jaunt across the pond, as they say. Actually, 2 ponds. Peter McCormack:2 bars. Yeah, look, happy to do it. And I was originally like so excited to talk to you because I, I’m, uh, I look up at the sky and I wonder how we’re here and why we’re here. And, um, and I was all prepared to come to you and ask you about my like biggest questions about the universe and how it works. And then I watched your show with Sean Ryan and I was like, huh. And I then listened to Sean with Tucker and I was like, this has hit me at a time where I’m thinking about life and whatever. And I saw you were quite introspective. And so what I was really thinking, I think where I wanted to ask you first is like, obviously you built a career looking up at the sky and wondering, and now it seems like you’re looking internal a lot. Peter McCormack:What’s the connection? Brian Keating:I think I, since I was a kid, I always cared about only the biggest topics. You know, I couldn’t get interested in sports or, you know, stocks or, you know, Bitcoin. I was a late adopter, but thanks to you, I came to it. Uh, but, but The fact for me is, yeah, I do recognize maybe more than most because I study something that’s 14 billion years old, but I also constantly recognize how fast time is changing for me right now with family, with my work, with my research, sometimes with the podcast. And for me, the ultimate goal and the thing that fascinates me the most is time. We understand space, we understand matter, we understand energy. We don’t understand why time works the way it does. For example, if I take a pendulum, I didn’t bring a pendulum, but I brought a telescope. Brian Keating:Here’s a pendulum swinging back and forth like a grandfather clock. Peter McCormack:Right. Brian Keating:If I turn away for one second and you look back at it, you can’t tell the way it started. Did you remember where I started it? Did I start like this? Did I start like that? You don’t remember. But the fact is the laws of nature are invariant. They don’t care about which direction you started it, which direction you’re looking at it from. They’re invariant, they’re constant. So that means the laws of this pendulum don’t care about time. They can’t tell time. Yes, you use it in a grandfather clock, that’s a separate thing. Brian Keating:There’s a ratchet, there’s a mechanism, whatever. I don’t want to get into that. But the actual laws of physics are invariant. They don’t care about time. So too for, you know, a bowling ball. So too for an electron going around an atom. They don’t recognize why time has an arrow. We don’t understand that. Brian Keating:And we, yet we talk about the Higgs boson, or we’ll talk about, you know, extra dimensions, or we’ll talk about, you know, the Big Bang, right? But we don’t understand time. I mean, it’s, it’s sort of embarrassing to me. And so I look for the embarrassment. I look for like, what do we claim that we understand beyond the level that we actually do? And I like to poke and prod and annoy my colleagues by saying, we don’t really know what we claim to know. And I think a lot of times we’re selling people on a bill of goods that these extra dimensions and all these fantastical things, maybe even intelligent aliens, it’s pretty much founded on a foundation that’s relatively weak. And I’m just hoping the public doesn’t find out about it. Peter McCormack:Yeah. Brian Keating:My job is to expose it to them. I’m an experimental scientist. Let me first step back. I’m an experimental physicist. I’m not a theorist, Brian Greene, Eric Weinstein, You know, Michio Kaku. I don’t talk about airy-fairy things, you know, multiple dimensions and curled up, you know, things like that. But I do talk about what can we actually measure? What can we interrogate about Mother Nature or God, if you like? And what can we know while we have this limited capacity to know things? So I’m desperately aware of this clock ticking in my head. You know, if I wasn’t aware of it from looking at my kids growing up or wrinkles going on my face or gray hairs coming in, I I would otherwise be obsessed. Brian Keating:This is my one obsession, time. Why does it go the way it does when no law of physics mandates that it be so? Peter McCormack:Well, what is time though? Brian Keating:So this is another very frustrating topic. When you ask a physicist what is time, the most cogent definition that I’ve ever

Why AI Shouldn’t Work — And Why It Does Anyway | Nigel Goldenfeld

Why AI Shouldn’t Work — And Why It Does Anyway https://www.youtube.com/watch?v=mWGG_Frnatk Transcript Nigel Goldenfeld:And you’re way, way into that regime where you’re just fitting noise and the whole thing shouldn’t work. It obviously shouldn’t, and yet it does. Francis Crick said, look, there’s no way that life got to this level of complexity in such a short period of time. It must have come from outer space. The genetic code is optimal in the sense of minimizing errors. If you wanted to know what is the purpose of life, the purpose of life is to help planets come into equilibrium. So a phase transition, you know, is like, for example, what happens to a piece of metal when I cool it below a certain temperature? And I want to know, can I use it to stick the pictures of my kids’ holiday pictures on the door of my refrigerator? And the answer is yes. If it’s magnetic, it’ll stick with a piece of metal holding the picture up. Brian Keating:Mm-hmm. Nigel Goldenfeld:And if it’s not magnetic, it won’t stick. And if you take that piece of metal that works as a magnet, fridge magnet, and you heat it up, it will stop becoming a magnet. And that’s called a phase transition, as you know, and maybe some of your listeners know or viewers know. The interesting question you might ask is, as I get closer and closer to the temperature where the magnetization disappears, how does the magnetization disappear? Does it— it disappears gradually, in fact. And if you ask how much magnetization there is, the answer is it goes like the square root of the difference between the temperature you’re at and the critical temperature where the magnetization fully goes to zero. At least that’s what you would expect. And that’s what very generic, very persuasive, simple theory, theoretical arguments that anybody can understand. I can explain it to my class in literally, you know, 20 seconds. Nigel Goldenfeld:That’s what you would predict. When you do the experiment, you find that it doesn’t go like the square root of the critical temperature minus the temperature. It goes like the Tc minus T to a power like 0.3265136, some weird, weird number like that. And you might say, well, it’s just a, you know, a more accurate number. Brian Keating:Yeah. Nigel Goldenfeld:The problem is that there’s no known way, or there was no known way to account for the fact that the number is not a half. It, I mean, it’s like To prove it’s a half, all I need to know is that magnets can be either magnetized north or magnetized south. And that’s basically it. It’s an argument that is so compelling, it can’t possibly be wrong. And yet, in the decades from the late 1940s up to the middle 1960s, it was discovered that it was wrong. And it wasn’t just only the magnetization. There were other thermodynamic properties like heat capacity and things like this, which I won’t go into, which also have the similar unaccountable behavior. And it was the fact that these numbers, which are themselves not particularly important, it was the fact that you couldn’t even explain in principle why they are not these simple numbers like 1/2 and so on. Nigel Goldenfeld:And that was the reason for the puzzle. Brian Keating:Hmm. Nigel Goldenfeld:And the explanation is a truly mind-boggling explanation. But just to tell you the outlines of the story, this phenomenon was addressed by Leo Kadanoff, Ben Widom, eventually Ken Wilson. Ken Wilson, yeah. And they invented this process of looking at a physical system on different scales of energy. So, you know, you could look at matter at the scale of this room. You could hear the sound waves, you can see light bouncing off the surfaces. On the other hand, if you want to go and see that there are atoms and see that there are quarks and things like this, you need to build a machine that’s put in a tunnel 17 miles long under the Swiss Alps in order to be able to see things like that. So what you can see depends on what energy you look at it at and what timescale and what length scale you look at it at. Nigel Goldenfeld:And the same thing turns out to be true of the laws of physics themselves. Brian Keating:Hmm. Nigel Goldenfeld:And Leo Kadanoff was the first person who realized that, and Ken Wilson turned it into a mathematical tool, which was called the renormalization group. In fact, it’s not even a group. You ask whether it’s a group. Brian Keating:Mm-hmm. Nigel Goldenfeld:It’s actually a semi-group. And the idea was this: take a physical system and then just say, well, you know, I’ve got magnetic dipole moments. They’re really spins of electrons, but we’ll just call them magnetic dipole moments. They’re in this bit of the sample. We’ll just lump them together into one effective dipole moment because, you know, in this patch over here, you know, 80% of them are pointing up, 20% are pointing down. So we’ll just say, okay, it’s basically just a spin pointing up. And so you sort of block things up in that way. And then once you’ve done that once, you can do it again and again and again, and you keep on doing it and you keep on doing it. Nigel Goldenfeld:And then you ask, what happens when you take that process to the infinite limit? Brian Keating:Instead. Nigel Goldenfeld:And it’s called a group. It’s really a semi-group because you can lump the spins together and then make these bigger and bigger spins, this coarse-graining as physicists call it, but you can’t go backwards. If you know the configuration of the very large scale and you say, well, what was the actual microscopic configuration of the electric dipole moment, the electron dipole moment? There’s no unique answer to that. You know, the one that we had, 80%

Engineering Chair Darren Lipomi: Most People Should Not Go To College

Engineering Chair Darren Lipomi: Most People Should Not Go To College https://www.youtube.com/watch?v=mI_E6pdjeOU Transcript Darren Lipomi:Perhaps the minority of individuals should be going to a 4-year institution because the debt load is so high. Brian Keating:My guest today is my good friend Darren Lipomi, Chair of Chemical and Sustainability Engineering at the University of Rochester, in a school he’s actively marketing to undergraduates. He’s one of the best mentors and educators I’ve ever met. I was devastated when he left UC San Diego, and he just told me that most people shouldn’t even be in this program. I wanted to know if he meant it. Brian Keating:Is academia like kind of an irredeemably clout-chasing, you know, kind of money-grubbing pursuit of attention, maybe significance when we have importance? What is academia? What has it become? And what— where do you think it can go? Darren Lipomi:This is a tough nut to crack because when we have promotion and tenure cases, we Want to be able to look at a metric that is reliable, that’s standardized across fields, which is impossible, but at least sort of within fields. So we look at H-index, we look at number of citations, we look at number of grant dollars brought in per square foot of lab space that the individual has. But at the end of the day, you want to know, what did that person do? What is the accomplishment? How have they made a deep and lasting impact in the field. And that accomplishment is quite difficult to judge. And oftentimes there’s a time component too. It’s a— impact is a lagging indicator. What if a paper gets 1 citation today, but then it gets 1,000 citations 10 years from now because it became so important? And so just like capitalism takes— leverages the human competitive instinct, We’re kind of stuck with these metrics in order to evaluate each other and to give each other raises and promotions. So it’s an imperfect system, but it’s what we have. Darren Lipomi:Only 1 out of every 20 PhDs becomes a tenure-track professor. The numbers are not very favorable for the median person who wants to go into the field. And it’s just, I don’t mean median in any pejorative sense, I just mean statistically, It’s a very difficult path. No one knows that a grad student did the work, that a grad student who made $35,000 a year and forewent retirement savings for their entire period of training and probably won’t recover it by the time they retire, that the financial burden is probably more so on them than it is on the taxpayer. So I was trying to tell the story of The people who, who had to go to grad school because they couldn’t do anything else. They went into research because they could not do anything else, like an artist or a musician or a chef. That’s what they had to do, knowing that the chances of getting an independent faculty position are, you know, 1 in 20 or lower. What I have studied— inorganic chemistry and space groups and oxidation potentials and the Schrödinger equation— if I was not forced to. Darren Lipomi:And I’m a highly motivated, really nerdy, really nerdy individual. Would I have done that for 4 years, 11 years after high school before I got my first job? I don’t think I would have. I was lucky in the sense that I had scholarships and my debt load was not high. If you look at the net present value of my decision by the time I retire, Was it a good decision? I don’t know. I probably would have done okay making $85,000 with a bachelor’s degree. There are people for whom it does not make sense to go to college. I would say that perhaps the minority of individuals should be going to a 4-year institution because the debt load is so high, because the prevalence of high-paying jobs for for individuals who are willing to tolerate certain types of activities can be quite high. And I think that’s where we need to focus on as educators in the area of building physical items. Darren Lipomi:We need to re— we need to rematerialize work and encourage people to work with their hands, to encourage the building and craftsmanship and artisanship that’s informed by theory because those jobs are going to be the hardest to replace. Physical AI is going to require an enormous amount of energy and also material science and mechanical engineering innovation along with the enormous energy demands in server farms, for example. That’s quite a long way off in my opinion, laundry folding robots notwithstanding. And so there will, For the next several decades, there will be room for somebody who will be designing a telescope, for somebody who’s designing a satellite, somebody who’s designing and building a reactor that can strip perfluorinated alkyl substances from drinking water. And it’s the tolerance, it’s the grit to be Doing that with one’s hands, right? Brian Keating:I gave you some materials, some extremely interesting engineering, uh, that may or may not contain life on it. Can I touch it? You can touch it. Brian Keating:Okay. Brian Keating:Yeah, that is your gift, uh, for coming on the podcast. It’s a real live or dead meteorite depending on how you look at it, and you can have it too. Guaranteed I’ll send you one if you, like Darren and me, have a .edu email address, and like Darren and me, you live in the United States. So Go to briankeating.com/edu, and I love to send those out and connect to my beloved— Darren Lipomi:Am I going to get in trouble with TSA? Brian Keating:No, they might ask you what you do for a living, which you describe in the book as well. So this material was delivered by outer space. It has some interesting deformations, material science compositions and stuff, but it does have organic materials on

Nobel Winning Physicist John Martinis: Why I Walked Away From Google Quantum Computing

Nobel Winning Physicist John Martinis: Why I Walked Away From Google Quantum Computing https://www.youtube.com/watch?v=DZBd7etwRUI Transcript Brian Keating:Today we’re joined with one of my heroes. It’s not every day you get to talk to somebody who not only, you know, is such a great contributor to physics and has been inspiring not only the work that I do, but my whole collaboration is basically enabled by work that John and his collaborators did over the years. But he’s also, you know, once we say a mensch, he’s known for his teaching, for his group building. You’re just kind of the physicist physicist. So thank you for joining us. John Martinis:Well, that’s very kind. Not everyone feels that way, but I appreciate the kind words. Brian Keating:You’re now The 26th Nobel Prize winner I’ve had on the podcast. Every, every 9 multiples of 9, I write a book featuring wisdom, and I hope the, the 3rd version will come out with you in the coming years. Well, I’ll let you know about how that progresses, but I wanna take us to that October morning last year. I was teaching quantum mechanics that day, advanced quantum mechanics with perturbation theory, and I said, a guy I know just won the Nobel Prize. What was that like? What was the, what was the 2nd thing that went through your mind when you got the phone call at whatever time it was? John Martinis:Well, I actually didn’t get the phone call. The phone is way across the house, but my wife was up late reading and she heard the phone ringing, but she figured she’d get it in the next day. She looked at her email and there was a bunch of congratulations. So she knew about it, but she knows that I need my sleep, especially, you know, the next day when you, you know, you have to be on the whole day. So she waited till about 6 o’clock. There were reporters who showed up soon after that. So it was actually better because my wife woke me up, but she was very clever about it. She tapped me on the shoulder and said there were reporters coming over. John Martinis:And I realized, oh, it’s the beginning of October. So I opened my computer and, and, you know, saw Let me share something a tiny bit personal. There are things called Nobel symposiums where they look at a field and see if the field is noteworthy and look at people in the, you know, leaders in the field and the like. So you kind of understand a little bit that you might be on some kind of list. Okay. For me, that’s the biggest honor because the Nobel Prize is just so crazy unlikely, right? Just being invited to that is really very special. And And then for some years, you know, I’d wake up in beginning October. It’s like, oh, okay. John Martinis:And it’s just so wrong to be disappointed by this because, so after the years, you know, I just stopped kind of, I knew, well, it’s beginning October, but I stopped really thinking about it. That’s why my wife didn’t know. We didn’t talk about this at all. Right. Because it’s just, okay. You know, whatever happens and it’s actually better that way. Yeah. Brian Keating:There’s a joke I sometimes will, you know, pull out on October 1st at 2 in the morning. I’ll say, I’m working on my best, you know, Swedish accent to cause somebody I don’t like a heart attack. We are calling from the Swedish Royal Academy. John Martinis:Yeah, I’ve heard people get pranks like that. Brian Keating:Well, yours isn’t a prank. And the only thing that frustrated me is that I was talking about, you know, quantum tunneling, but I was talking about single electron tunneling and even nuclear tunneling. So you wanted for macroscopic tunneling. What was the impetus for— obviously you didn’t set out to win a Nobel Prize. Talk us through the history of why you thought macroscopic effects would manifest themselves instead of just the already mysterious microscopic tunneling effects? John Martinis:Yeah. So this experiment in line of research was very much motivated by Anthony Leggett. You can just go back to the Schrödinger cat paradox where you do a microscopic atom decay that’s connected up. So the atom decays, it kills the cat, and then, you know, okay, before you open the box, is the cat in some dead and alive state? And okay, I think there, for me, there are ways to answer this that are very sensible. But Leggett pointed out that there actually is no experimental evidence that macroscopic entities, especially a cat, can obey quantum mechanics. And he said, there’s— if there’s no evidence, we should be looking for evidence as a way to test quantum mechanics. And he came up with the idea of these superconducting circuits where you have a macroscopic number of electrons that are tunneling through the junction. For me, you know, as a young student, you look at quantum mechanics, it’s really wonderful. John Martinis:My personal hobby was electronics. I joined John Clarke’s group because he was doing things on quantum noise. Okay. And thinking about this. And for me, it was the most natural experiment to want to do. I’m surprised that there weren’t 30 other groups doing it. At the point now, everyone can understand that. But back in the mid-’80s, the idea of quantum information and doing these tests wasn’t as popularized as, as it is now. John Martinis:So for me, it was the most fascinating experiment. I thought it could be done cleanly. You know, obviously John Clarke, you know, was moving in this direction. Michel Devoreg came over. And for me, it was, you know, a perfect thesis experiment. And I’m going to say it was also very groundbreaking because Then John’s group, you know, we understood how to measure noise in these devices, but

Fermilab’s Scott Dodelson on Cosmology’s Crisis

Fermilab’s Scott Dodelson on Cosmology’s Crisis https://www.youtube.com/watch?v=Bal1uzzZXqI Transcript Scott Dodelson:What we’ve done in order to make the story of the Universe work is introduce dark matter, introduce inflation, introduce dark energy, and none of this has been found in the lab. How many free passes do we get? Exploring the tension, killing the model is my dream. Brian Keating:That’s Scott Dodelson. He runs the Cosmic Physics Division at Fermilab. He teaches at the University of Chicago, and if you’ve ever taken a graduate cosmology class, you probably took it from Scott. He also spent 10 years running the sharpest test anyone has ever made of the Standard Model of cosmology, the model he helped build. The answer came back 2.5 sigma off. Close enough to call it a triumph, not close enough to stop staring at it in disbelief. I’m Brian Keating. This is Into the Impossible. Brian Keating:What is new in the field of dark energy? Scott Dodelson:Yeah. Brian Keating:Before we go any further. Scott Dodelson:Well, since you’ve been in the field, we’ve had this fiducial model of cosmology. I guess you— well, you helped establish it really. So it’s called Lambda CDM. I’ve heard you talk about it. And now there you’ve had Kyle and other people on saying that it’s under stress. So the fundamental question that I’m interested in is not— or one of the questions I’m interested in is how will we change our mind, if we will? That is, we have this pretty simple model, and then there are these data points which are saying, oh, this doesn’t work. It doesn’t work. Oh, maybe it doesn’t work. Scott Dodelson:And so we have to digest that, and everyone digests it in their own way. And then how are we going to collectively— land on another model. And so I know people have written books about this, Thomas Kuhn’s Structure of Scientific Revolutions, but we’re living through that time now. So I’m kind of— Brian Keating:And maybe in many ways with AI, we’ll get to that later. Scott Dodelson:Yeah, right. So it’s not, and of course it’s not just in cosmology. So cosmology is the lens through which I can understand stuff. But as you say, in society in general, we’re losing faith in institutions. So we don’t know which institutions we’re gonna land on and which to believe, which to trust. So I think it’s kind of an important question. And so I’ve been trying to explore it in this little corner of our world, cosmology, which is in some ways the simplest thing we do. It’s very hard to be a parent, to be a spouse, to be a friend, but cosmology’s really easy ’cause there’s no people and it’s an easier thing. Scott Dodelson:So that’s why I’ve been trying to explore it in that context. For about 10 years, I was heavily involved in this project called DES, the Dark Energy Survey, and that started taking data in 2012, and as you know, It takes an enormous amount of time to process and analyze this data. So we only put out our final results a few months ago. So that’s what’s been occupying me for the last 10 years or so. Brian Keating:Talk about the connection between, you know, the type of science that I do, which is the first light in the universe, the cosmic microwave background. I’ve talked a lot about that. I’ve talked less about the kind of science that DES does, though, with the exception of, you know, conversations with people like Kyle and others. But talk about what was DES? What, you know, again, it’s somewhat strange. Not only are you not using, you know, particle detectors and whatnot, but you’re using optical telescopes, right? So what does DES do? What is it comprised of? You mentioned how long it took, but what really went into that? How much— what’s the portfolio diversification, you know, between theory, which is what you do, experimental hardware, observations, big data, machine learning? What are the different ingredients in DES? And first of all, what does it stand for? Scott Dodelson:DES stands for the Dark Energy Survey. The detectors on the telescope, which was in Chile, are made of silicon. And so they leveraged a lot of the silicon technology that Fermilab was always already an expert in. So that’s one piece of it. The camera itself was built by Fermilab. So that’s a tremendous contribution. Then there’s the whole data processing thing. And again, in that realm, high-energy physics has a huge advantage because they’re used to processing tremendous amounts of data. Scott Dodelson:So there’s those 2 pieces. And then there’s the analysis, which takes us an enormous amount of time. And that piece is ongoing. It’s really thinking back to when it was first started. We thought of it in one way, and all our analysis tools have evolved partially due to AI. So that’s been kind of interesting to be a part of. But getting back to the science part of it, there’s a strong connection between what you do and what I do in the sense that one of the things you do is you measure in the cosmic microwave background the fluctuations. So I don’t know if people can see this, but there are small hot and cold spots in this microwave background. Scott Dodelson:Basically what that means is the temperature, say on this spot over here, was a little bit about 1 part in 10,000 higher than the temperature over here. All that means is there were more photons there, and because there were more photons there, because the photons were also talking to the electrons and protons, there were more electrons and protons there. So if you go back to the early universe, what you have provided us with is a snapshot of the early universe, and we know what it looks like. It was incredibly homogeneous, so the universe was basically the same

Faith & the Physicist: Aliens, Astrobiology, String Theory, and the Search for Meaning

Faith & the Physicist: Aliens, Astrobiology, String Theory, and the Search for Meaning Transcript Brian Keating:My 2 friends, Professor Adam Frank and Professor Niayesh Afshordi of respectively University of Rochester and Perimeter Institute, University of Waterloo. How are you boys? Adam Frank:Doing well. Niayesh Afshordi:Yeah, I’m doing great. Thank you. Thanks for having us. Brian Keating:Smoke’s not too bad for you guys, I hope, on the East Coast. You know, we’re usually the ones with the smoky forest fires, but you guys have all the fun this summer, it seems like. We’re enjoying a characteristic day here in Southern California. Adam Frank:It’s been clear for a bit. It was, it was a couple weeks ago that we had the smoke, so right now it’s okay. Brian Keating:Well, the reason we’re here today is because you guys are 2 of the most interesting, um, not only scientists and what you do, but you guys see it as your mission, perhaps the way that I do, that we scientists have a moral obligation to share what we do with the public because they pay our salaries. I don’t even care if you’re privately funded like I am, pretty much. We’re all serving at the, you know, kind of gratitude or gratitude towards the public, and they’re our boss. And you guys in particular do, you know, heroic work in communicating real science without pulling punches to real people without dumbing it down. And I just wanna acknowledge you guys for doing that. And maybe just start with that. What do you think about this statement? I’ll start with Nayesh. I feel like what we do as scientists is important, but sometimes it’s not significant. Brian Keating:In other words, I’m searching for some ancient photons. Maybe they’ll be, you know, important to understand the primordial universe. Niyash, you also study the primordial universe, but also high-energy particles, forces, and fields. Adam studies the existence of life throughout the cosmos. None of these things affect, you know, Joe Six-Pack here, you know, in El Cajon, or Joey Bag of Donuts over there in Rochester or Toronto. So what do you see as our obligation, Niyash? And then Adam, what is our obligation as citizens Well, that’s an excellent question. Niayesh Afshordi:And it’s— I want to say that we are kind of like the canary in the coal mine that we— I mean, the society is this huge, humongous thing that operates and requires a certain set of skills. And in particular, we need to kind of foresee what’s, what’s coming down the road, how we could survive things like catastrophes, pandemics, climate, uh, climate catastrophes, all sorts of things, bad things that could happen. And also the good things can potentially happen— artificial intelligence, uh, technology. All of these things are things that, I mean, the society needs. But to get there, to avoid bad things and get the good things, there is a method to it, right? You have to learn from your mistakes and build upon your successes. And really, if you want to summarize this, this is the scientific method, right? You want to learn from experience and build upon that. You don’t repeat your mistakes, build a model, a model that works. If the model doesn’t work, you modify it, you find a better model. Niayesh Afshordi:If the model works, you fortify it and you kind of make it better and better. And that’s what science does. My idea is that what we do, even though we look at very abstract things, is that we are really perfecting the scientific method in the corners that are— it hasn’t been really pushed and explored before, in places where the scientific method may not be developed yet. But then when we develop it, it’s ready to be used. It’s something that everyone else can plug and play for their Nintendos or for iPhones. Eventually, that’s going to come down the line. But somebody should start basically developing this method somewhere. And I think we are kind of the pioneers that are doing it for the first time. Brian Keating:Yeah, you just left off the highest form of technology, which is the podcast, right, Adam? Adam, you, uh, and I, I want to recognize, uh, you, you’ve, you’ve been writing for longer, uh, than either Naish and or myself in the public and communicating for some, for some, uh, just incredible content that you put out. Um, but you too are also a working practicing scientist like Naish, and, and I’m curious how you divide your time. You just received an enormous and well-deserved grant from the Templeton Foundation. to really explore these ideas that I quite frankly think you are one of the major expositors of and certainly among the best. So how do you view your time between splitting it, you know, between popularization, shall we say, communication, which is a form of scientific marketing? I think it’s good to be honest about it. Niayesh Afshordi:Sure. Brian Keating:And it redounds to our benefit, but it should redound to the public’s benefit. So how do you divide that? ‘Cause I know my first book was being written, I asked my department chair, who’s the son of a Nobel Prize winner, Nikolai Basov, co-invented the laser. I said, you know, so I get some time off, I’m writing a book, right? And Dmitry, his son, said, no, but we will try not to punish you for writing a book. Adam Frank:That’s an apt way of putting it. Yeah. Brian Keating:How do you view your kind of— how do you break down your time? Take me through a day in the life of Adam Frank, Professor Adam Frank. Yeah. Adam Frank:You know, I find them very— they’re interwoven, right? And, you know, I’ve I always tell young students, doing this kind of science communication is super important, but it’s more important that you have your scientific career, right? I mean, especially when you’re

Annie Jacobsen: How One Lab Leak Ends the World in 6 Days

Annie Jacobsen: How One Lab Leak Ends the World in 6 Days https://www.youtube.com/watch?v=zScFJ7BTcuo Transcript Annie Jacobsen:COVID had a 1% death rate. 1%. Pneumonic plague, you get it and you don’t get antibiotics inside of 24 hours, you’re dead. With 100% case fatality rates. Brian Keating:Is it up to us? And what can we do? Annie Jacobsen:I’m talking to presidential advisors, secretaries of defense, STRATCOM commanders, epidemiologists, directors of Operation Warp Speed, people who have their hand on the pulse. He gave me this line which just floored me. He said, you know, Annie, in a large-scale biological attack, There’s nothing you can really do to prevent it. You can only handle mitigating extermination. Essentially, a kid in a basement, with the help of a computational system, an AI system like Claude, could potentially make this kind of bioweapon. And that’s the fundamental danger. Bio is very, very, very different. I take readers from outbreak to anarchy in 6 days. Annie Jacobsen:Instead of being home under the covers, not infecting people, you’re out disco dancing… Brian Keating:Hello. Annie Jacobsen:…in a very crowded, crowded place, and you’re essentially in the middle of a superspreader event. That’s straight-up dark-hearted evil. If there is a lab leak, if the outbreak can be shut down in the first 12 to 24 hours, you can actually prevent the dystopian ending. But once it’s 24 hours, once it’s 48 hours, 96 hours, it’s out of the bag. Brian Keating:What scenario scares you more, nuclear war or biological war? Quick thing before we start. Most of you watching this aren’t subscribed. If you’ve ever gotten something out of one of my conversations, subscribing is a single thing that decides whether guests like Andy say yes to the next pitch to the episode. The scenario book that you’ve written this time is also kind of in a similar genre as, as your previous book, which is just some of the— I call it, um, hard science fiction. You called it science fact. I agree, except up to the point when There’s actually a disaster that’s going to take place in this book. And it’s very hard to talk about books that are A, embargoed, and B, I don’t want to give away too much of this. Annie Jacobsen:You know, I sometimes say in my tweets and such, spoiler alert, it has a dystopian ending. Brian Keating:That’s right. And for those, you know, 10 of you out there who haven’t read Nuclear War, you will have 2 treats in store for you because these books are both incredibly historically accurately researched. You’re a journalist, you’re, you’re, you’re trained as a journalist, you actually do the hard work of being a journalist, but you see it from the eye of a storyteller. You write for television. I guess the first place I’d like to start is, yeah, with, with what you’re not supposed to do, which is to judge a book by its cover. So I want you to take us through this incredible new book, Biological War: A Scenario. So tell us about the COVID the title, and the subtitle, which is just a scenario. Annie Jacobsen:Well, it’s a beautiful cover, and I have to give credit to the art department for that. And you’ve been a a lot more places, a lot more radical places around the globe than I have. You’ve been to a continent I haven’t been. Yet. Brian Keating:Yeah, we may take you there. Annie Jacobsen:Yeah. You know, Biological War: A Scenario follows in the vein of Nuclear War: A Scenario. Not that I expected to write a follow-on book, although when I was reporting Nuclear War, a number of my sources said to me, Annie, you have to look at bio next. These are people who work in counterproliferation, a fancy word for God forbid, you know, we need to make sure that this kind of an existential threat doesn’t happen. And, you know, they’re similar in terms of the title and the front, but oh my goodness, they are different millenials. They are different scenarios. One note on your like science fiction versus something. In fact, what I like to say is it’s, you know, it’s hypothetical. Annie Jacobsen:It’s a hypothetical scenario. based on wargaming that is done at the Pentagon. And you will note, I list my sources in the front of the book, not the back. Brian Keating:Yes. Annie Jacobsen:My first 6 books have the sources in the back, but I want readers to realize, because I’m now writing scenario, that this is who she spoke with. And so you realize I’m talking to former presidential advisors, secretaries of defense, StratCom commanders, epidemiologists, directors of Operation Warp Speed, people who have their hand on the pulse, if you will. And one of the individuals in this book I was fortunate to interview was Colonel, retired Colonel Tom Earhart, who worked in the Pentagon for a very famous office there called the Office of Net Assessment inside the Office of the Secretary of Defense. It was run for decades by a guy called Andy Marshall. They called him the Wizard. Brian Keating:Hmm. Annie Jacobsen:And the wargaming that was done in there is deeply classified, but Colonel Earhart helped me to understand what What it is, how it works. And most importantly, he gave me this line, which just floored me and became a lot of the scenario, which is when he said, you know, Annie, in a large-scale biological attack, there’s nothing you can really do to prevent it. You can only handle mitigating extermination. That freaked me out. And that became a real locus of this book. Yeah. Brian Keating:compare the two. I know that’s like asking you to compare your children, although when I do that with my mom, she’ll say, oh, it’s like comparing my left hand to my right hand. I say, Mom, you’re left-handed. I mean, it’s pretty obvious which one you like better. But when you