Podcasts about nobel laureates

Editor's note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It's a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.Along the way, we discuss:* Why symmetry and equivariance matter in deep learning* The tradeoff between scale and inductive bias* The deep mathematical links between diffusion models and stochastic thermodynamics* Why materials—not software—may be the real bottleneck for AI and the energy transition* What it actually takes to build an AI-driven materials platformMax reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.Full Video EpisodeTimestamps* 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer* Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.* 00:00:44 – From Quantum Gravity to AI for Materials* Brandon frames Max's career arc: VAE pioneer → equivariant GNNs → materials startup founder.* 00:01:34 – Curiosity vs Impact: How His Motivation Evolved* Max explains the shift from pure theoretical curiosity to climate-driven impact.* 00:02:43 – Why CaspAI Exists: Technology as Climate Strategy* Politics struggles; technology scales. Why materials innovation became the focus.* 00:03:39 – The Thread: Physics → Symmetry → Machine Learning* How gauge symmetry, group theory, and relativity informed equivariant neural networks.* 00:06:52 – AI for Science Is Exploding (Not Emerging)* The funding surge and why AI-for-Science feels like a new industrial era.* 00:07:53 – Why Now? The Two Catalysts Behind AI for Science* Protein folding, ML force fields, and the tipping point moment.* 00:10:12 – How Engineers Can Enter AI for Science* Practical pathways: curriculum, workshops, cross-disciplinary training.* 00:11:28 – Why Materials Matter More Than Software* The argument that everything—LLMs included—rests on materials innovation.* 00:13:02 – Materials as a Search Engine* The vision: automated exploration of chemical space like querying Google.* 01:14:48 – Inside CuspAI: The Platform Architecture* Generative models + multi-scale digital twin + experiment loop.* 00:21:17 – Automating Chemistry: Human-in-the-Loop First* Start manual → modular tools → agents → increasing autonomy.* 00:25:04 – Moonshots vs Incremental Wins* Balancing lighthouse materials with paid partnerships.* 00:26:22 – Why Breakthroughs Will Still Require Humans* Automation is vertical-specific and iterative.* 00:29:01 – What Is Equivariance (In Plain English)?* Symmetry in neural networks explained with the bottle example.* 00:30:01 – Why Not Just Use Data Augmentation?* The optimization trade-off between inductive bias and data scale.* 00:31:55 – Generative AI Meets Stochastic Thermodynamics* His upcoming book and the unification of diffusion models and physics.* 00:33:44 – When the Book Drops (ICLR?)TranscriptMax: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known, as possible even. It's a bit hard to program because you have to do all these experiments. Those are quite bulky, it's like a very large thing you have to do. But in a way it is a computation and that's the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in.[01:00:44:14 - 01:01:34:08]Brandon: Yeah, it's a pleasure to have Max Woehling as a guest today. Max has done so much over his career that I've been so excited about. If you're in the deep learning community, you probably know Max for his work on variational autocoders, which has literally stood the test of prime or officially stood the test of prime. If you are a scientist, you probably know him for his like, binary work on graph neural networks on equivariance. And if you're a material science, you probably know him about his new startup, CASPAI. Max has a long history doing lots of cool problems. You started in quantum gravity, which is I think very different than all of these other things you worked on. The first question for AI engineers and for scientists, what is the thread in how you think about problems? What is the thread in the type of things which excite you? And how do you decide what is the next big thing you want to work on?[01:01:34:08 - 01:02:41:13]Max: So it has actually evolved a lot. In my young days, let's breathe, I would just follow what I would find super interesting. I have kind of this sensor. I think many people have, but maybe not really sort of use very much, which is like, you get this feeling about getting very excited about some problem. Like it could be, what's inside of a black hole or what's at the boundary of the universe or what are quantum mechanics actually all about. And so I follow that basically throughout my career. But I have to say that as you get older, this changes a little bit in the sense that there's a new dimension coming to it and there's this impact. Going in two-dimensional quantum gravity, you pretty much guaranteed there's going to be no impact on what you do relative, maybe a few papers, but not in this world, this energy scale. As I get closer to retirement, which is fortunately still 10 years away or so, I do want to kind of make a positive impact in the world. And I got pretty worried about climate change.[01:02:43:15 - 01:03:19:11]Max: I think politics seems to have a hard time solving it, especially these days. And so I thought better work on it from the technology side. And that's why we started CaspAI. But there's also a lot of really interesting science problems in material science. And so it's kind of combining both the impact you can make with it as well as the interesting science. So it's sort of these two dimensions, like working on things which you feel there's like, well, there's something very deep going on here. And on the other hand, trying to build tools that can actually make a real impact in the world.[01:03:19:11 - 01:03:39:23]RJ: So the thread that when I look back, look at the different things that you worked out, some of them seem pretty connected, like the physics to equivariance and, yeah, and, uh, gravitational networks, maybe. And that seems to be somewhat related to Casp. Do you have a thread through there?[01:03:39:23 - 01:06:52:16]Max: Yeah. So physics is the thread. So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity. So that is really the frontier. There's also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space to play an enormously important role. And this goes all the way to gauge symmetries as well. And so applying these kinds of symmetries to, uh, machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem. I did this with Taco Cohen and Taco was the main driver behind this, went all the way from just simple, like rotational symmetries all the way to gauge symmetries on spheres and stuff like that. So, and, uh, Maurice Weiler, who's also here, um, when he was a PhD student, he was a very good student with me, you know, he wrote an entire book, which I can really recommend about the role of symmetries in AI and machine learning. So I find this a very deep and interesting problem. So more recently, so I've taken a sort of different path, which is the relationship between diffusion models and that field called stochastic thermodynamics. This is basically the thermodynamics, which is a theory of equilibrium. So but then formulated for out of equilibrium systems. And it turns out that the mathematics that we use for diffusion models, but even for reinforcement learning for Schrodinger bridges for MCMC sampling has the same mathematics as this theoretical, this physical theory of non-equilibrium systems. And that got me very excited. And actually, uh, when I taught a course in, um, Mauschenberg, uh, it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences Ames. And I turned that into a book site. Two years later, the book was finished. I've sent it to the publisher. And this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics. So it's always some kind of, I don't know, I find physics very deep. I also think a lot about quantum mechanics and it's, it's, it's a completely weird theory that actually nobody really understands. And there's a very interesting story, which is maybe good to tell to connect sort of my PZ back to where I'm now. So I did my PZ with a Nobel Laureate, Gerard the toft. He says the most brilliant man I've ever met. He was never wrong about anything as long as I've seen him. And now he says quantum mechanics is wrong and he has a new theory of quantum mechanics. Nobody understands what he's saying, even though what he's writing down is not mathematically very complex, but he's trying to address this understandability, let's say of quantum mechanics head on. And I find it very courageous and I'm completely fascinated by it. So I'm also trying to think about, okay, can I actually understand quantum mechanics in a more mundane way? So that, you know, without all the weird multiverses and collapses and stuff like that. So the physics is always been the threat and I'm trying to apply the physics to the machine learning to build better algorithms.[01:06:52:16 - 01:07:05:15]Brandon: You are still very involved in understanding and understanding physics and the worlds. Yeah. And just like applications to machine learning or introducing no formalisms. That's really cool.[01:07:05:15 - 01:07:18:02]Max: Yes, I would say I'm not contributing much to physics, but I'm contributing to the interface between physics and science. And that's called AI for science or science or AI is kind of a super, it's actually a new discipline that's emerging.[01:07:18:02 - 01:07:18:19]Speaker 5: Yeah.[01:07:18:19 - 01:07:45:14]Max: And it's not just emerging, it's exploding, I would say. That's the better term because I know you go from investments into like in the hundreds of millions now in the billions. So there's now actually a startup by Jeff Bezos that is at 6.2 billion sheep round. Right. Insane. I guess it's the largest startup ever, I think. And that's in this field, AI for science. It tells you something that we are creating a new bubble here.[01:07:46:15 - 01:07:53:28]Brandon: So why do you think it is? What has changed that has motivated people to start working on AI for science type problems?[01:07:53:28 - 01:08:49:17]Max: So there's two reasons actually. One is that people have been applying sort of the new tools from AI to the sciences, which is quite natural. And there's of course, I think there's two big examples, protein folding is a big one. And the other one is machine learning forest fields or something called machine learning inter-atomic potentials. Both of them have been actually very successful. Both also had something to do with symmetries, which is a little cool. And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right, or multimedia applications into something that could actually make a very positive impact in society like health, drug development, materials for the energy transition, carbon capture. These are all really cool, impactful applications.[01:08:50:19 - 01:09:42:14]Max: Despite that, the science and the kind of the is also very interesting. I would say the fact that these sort of these two fields are coming together and that we're now at the point that we can actually model these things effectively and move the needle on some of these sort of science sort of methodologies is also a very unique moment, I would say. People recognize that, okay, now we're at the cusp of something new, where it results whether the company is called after. We're at the cusp of something new. And of course that always creates a lot of energy. It's like, okay, there's something, it's like sort of virgin field. It's like nobody's green field. Nobody's been there. I can rush in and I can sort of start harvesting there, right? And I think that's also what's causing a lot of sort of enthusiasm in the fields.[01:09:42:14 - 01:10:12:18]RJ: If you're an AI engineer, basically if the people that listen to this podcast will be in the field, then you maybe don't have a strong science background. How does, but are excited. Most I would say most AI practitioners, BM engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry college, maybe even graduate school that have been working or are starting out. How does somebody who is not a scientist on a day-to-day basis, how do they get involved?[01:10:12:18 - 01:10:14:28]Max: Well, they can read my book once it's out.[01:10:16:07 - 01:11:05:24]Max: This is basically saying that there is more, we should create curricula that are on this interface. So I'm not sure there is, also we already have some universities actual courses you can take, maybe online courses you can take. These workshops where we are now are actually very good as well. And we should probably have more tutorials before the workshop starts. Actually we've, I've kind of proposed this at some point. It's like maybe first have an hour of a tutorial so that people can get new into the field. There's a lot out there. Most of it is of course inaccessible, but I would say we will create much more books and other contents that is more accessible, including this podcast I would say. So I think it will come. And these days you can watch videos and things. There's a huge amount of content you can go and see.[01:11:05:24 - 01:11:28:28]Brandon: So maybe a follow-up to that. How do people learn and get involved? But why should they get involved? I mean, we have a lot of people who are of our audience will be interested in AI engineering, but they may be looking for bigger impacts in the world. What opportunities does AI for science provide them to make an impact to change the world? That working in this the world of pure bits would not.[01:11:28:28 - 01:11:40:06]Max: So my view is that underlying almost everything is immaterial. So we are focusing a lot on LLMs now, which is kind of the software layer.[01:11:41:06 - 01:11:56:05]Max: I would say if you think very hard, underlying everything is immaterial. So underlying an LLM is a GPU, and underlying a GPU is a wafer on which we will have to deposit materials. Do we want to wait a little bit?[01:12:02:25 - 01:12:11:06]Max: Underlying everything is immaterial. So I was saying, you know, there's the LLM underlying the LLM is a GPU on which it runs. In order to make that GPU,[01:12:12:08 - 01:12:43:20]Max: you have to put materials down on a wafer and sort of shine on it with sort of EUV light in order to etch kind of the structures in. But that's now an actual material problem, because more or less we've reached the limits of scaling things down. And now we are trying to improve further by new materials. So that's a fundamental materials problem. We need to get through the energy transition fast if we don't want to kind of mess up this world. And so there is, for instance, batteries. That's a complete materials problem. There's fuel cells.[01:12:44:23 - 01:13:01:16]Max: There is solar panels. So that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light, where now we're at, I don't know, maybe 22 or something. So these are huge changes all by material innovation.[01:13:02:21 - 01:13:47:15]Max: And yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you're doing is a material problem. And so I think it's just very nice to work on this very, very foundation. And also because I think this is maybe also something that's happening now is we can start to search through this material space. This has never been the case, right? It's like scientists, the normal way of working is you read papers and then you come up with no hypothesis. You do an experiment and you learn, et cetera. So that's a very slow process. Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they're in the universe, but all of them.[01:13:48:21 - 01:14:42:01]Max: And we can make this kind of fully automated. That's the hope, right? We can just type, it becomes a tool where you type what you want and something starts spinning and some experiments get going. And then, you know, outcome list of materials and then you look at it and say, maybe not. And then you refine your query a little bit. And you kind of do research with this search engine where a huge amount of computation and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this. I find this a very, very promising view of how we can sort of build a much better sort of materials layer underneath almost everything. And also more sustainable materials. Our plastics are polluting the planet. If you come up with a plastic that kind of destroys itself, you know, after, I don't a few weeks, right? And actually becomes a fertilizer. These are things that are not impossible at all. These things can be done, right? And we should do it.[01:14:42:01 - 01:14:47:23]RJ: Can you tell us a little bit just generally about CUSBI and then I have a ton of questions.[01:14:47:23 - 01:14:48:15]Speaker 5: Yeah.[01:14:48:15 - 01:17:49:10]Max: So CUSBI started about 20 months ago and it was because I was worried about I'm still worried about climate change. And so I realized that in order to get, you know, to stay within two degrees, let's say, we would not only have to reduce our emissions to zero by 2050, but then, you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that's about half the rate that we now emit it. And that is a unsolved problem. But if we don't solve it, two degrees is not going to happen, right? It's going to be much more. And I don't think people quite understand how bad that can be, like four degrees, like very bad. So this technology needs to be developed. And so this was my and my co-founder, Chet Edwards, motivation to start this startup. And also because, you know, we saw the technology was ready, which is also very good. So if you're, you know, the time is right to do it. And yeah, so we now in the meanwhile, we've grown to about 40 people. We've kind of collected 130 million investment into the company, which is for a European company is quite a lot. I would say it's interesting that right after that, you know, other startups got even more. So that's kind of tells you how fast this is growing. But yeah, we are we are now at the we've built the platform, of course, but it's for a series of material classes and it needs to be constantly expanded to new material classes. And it can be more automated because, you know, we know putting LLMs in as the whole thing gets more and more automated. And now we're moving to sort of high throughput experimentation. So connecting the actual platform, which is computational, to the experiments so that you can get also get fast feedback from experiments. And I kind of think of experiments as something you do at the end, although that's what we've been doing so far. I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right, which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known as possible, even. It's a bit hard to program because you have to do all these experiments. Those are quite, quite bulky. It's like a very large thing you have to do. But in a way, it is a computation. And that's the way I want to see it. So I want to you can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in. And that's the vision we have. We don't say super intelligence because I don't quite know what it means and I don't want to oversell it. But I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.[01:17:49:10 - 01:18:01:02]Brandon: That actually brings up a question I wanted to ask you. First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?[01:18:01:02 - 01:20:47:22]Max: Yeah, I think it's been surprisingly, it's not rocket science, I would say. It's not rocket science in the sense of the design and basically the design that, you know, I wrote down at the very beginning. It's still more or less the design, although you add things like I wasn't thinking very much about multi-scale models and as the common are rated that actually multi-scale is very important. And the beginning, I wasn't thinking very much about self-driving labs. But now I think, you know, we are now at the stage we should be adding that. And so there is sort of bits and details that we're adding. But more or less, it's what you see in the slide decks here as well, which is there is a generative component that you have to train to generate candidates. And then there is a digital twin, multi-scale, multi-fidelity digital twin, which you walk through the steps of the ladder, you know, they do the cheap things first, you weed out everything that's obviously unuseful, and then you go to more and more expensive things later. And so you narrow things down to a small number. Those go into an experiment, you know, do the experiment, get feedback, etc. Now, things that also have been more recently added is sort of more agentic sort of parts. You know, we have agents that search the literature and come up with, you know, actually the chemical literature and come up with, you know, chemical suggestions for doing experiments. We have agents which sort of autonomously orchestrate all of the computations and the experiments that need to be done. You know, they're in various stages of maturity and they can be continuously improved, I would say. And so that's basically I don't think that part. There's rocket science, but, you know, the design of that thing is not like surprising. What is it's surprising hard to actually build it. Right. So that's that's the thing that is where the moat is in the data that you can get your hands on and the and actually building the platform. And I would say there's two people in particular I want to call out, which is Felix Hunker, who is actually, you know, building the scientific part of the platform and Sandra de Maria, who is building the sort of the skate that is kind of this the MLOps part of the platform. Yeah. And so and recently we also added sort of Aaron Walsh to our team, who is a very accomplished scientist from Imperial College. We're very happy about that. He's going to be a chief science officer. And we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important. In print, it's so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with sort of the domain experts, which are the companies typically. So we always we only start to invest in the direction if we find a good industrial partner to go on that journey with us.[01:20:47:22 - 01:20:55:12]Brandon: Makes a lot of sense. Over the evolution of the platform, did you find that you that human intervention, human,[01:20:56:18 - 01:21:17:01]Brandon: I guess you could start out with a pure, you could imagine two directions when you start up making everything purely automatic, automated, agentic, so on. And then later on, you like find that you need to have more human input and feedback different steps. Or maybe did you start out with having human feedback? You have lots of steps and then like kind of, yeah, figure out ways to remove, you know,[01:21:17:01 - 01:22:39:18]Max: that is the second one. So you build tools for you. So it's much more modular than you think. But it's like, we need these tools for this application. We need these tools. So you build all these tools, and then you go through a workflow actually in the beginning just manually. So you put them in a first this tool, then run this to them or this with sithery. So you put them in a workflow and then you figure out, oh, actually, you know, this this porous material that we are trying to make actually collapses if you shake it a bit. Okay, then you add a new tool that says test for stability. Right. Yeah. And so there's more and more tools. And then you build the agent, which could be a Bayesian optimizer, or it could be an actual other them, you know, maybe trained to be a good chemist that will then start to use all these tools in the right way in the right order. Yeah. Right. But in the beginning, it's like you as a chemist are putting the workflow together. And then you think about, okay, how am I going to automate this? Right. For one very easy question you can ask yourself is, you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation has to go to somebody who knows DFT. And so could you start to automate that away, which is like, okay, make it so user friendly, so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it's a good outcome, etc. So you start to automate smaller small pieces and bigger pieces, etc. And in the end, the whole thing is automated.[01:22:39:18 - 01:22:53:25]Brandon: So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and less so trying to create an automated process.[01:22:53:25 - 01:23:22:01]Max: I think it's this is sort of the same where you're saying because, yes, we want to automate, yeah, but we don't see something very soon where the chemists and the domain expert is out of the loop. Yeah, but it but it's a retreat, right? It's like, okay, so first, you need an expert to tell you precisely how to set the parameters of the DFT calculation. Okay, maybe we can take that out. We can maybe automate that, right? And so increasingly, more of these things are going to be removed.[01:23:22:01 - 01:23:22:19]Speaker 5: Yeah.[01:23:22:19 - 01:24:33:25]Max: In the end, the vision is it will be a search engine where you where somebody, a chemist will type things and we'll get candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right? And so the vision of a completely dark lab, where you can close the door and you just say, just, you know, find something interesting and then it will it will just figure out what's interesting and we'll figure out, you know, it's like, oh, I found this new material to blah, blah, blah, blah, right? That's not the vision I have. He's not for, you know, a long time. So for me, it's really empowering the domain experts that are sitting in the companies and in universities to be much faster in developing their materials. And I should say, it's also good to be a little humble at times, because it is very complicated, you know, to bring it to make it and to bring it into the real world. And there are people that are doing this for the entire lives. Yeah. Right. And it's like, I wonder if they scratch their head and say, well, you know, how are you going to completely automate that away, like in the next five years? I don't think that's going to happen at all.[01:24:35:01 - 01:24:39:24]Max: Yeah. So to me, it's an increasingly powerful tool in the hands of the chemists.[01:24:39:24 - 01:25:04:02]RJ: I have a question. You've talked before about getting people interested based on having, you know, sort of a big breakthrough in materials, incremental change. I'm curious what you think about the platform you have now in are sort of stepping towards and how are you chasing the big change or is this like incremental or is there they're not mutually exclusive, obviously, but what do you think about that?[01:25:04:02 - 01:26:04:27]Max: We follow a mixed strategy. So we are definitely going after a big material. Again, we do this with a partner. I'm not going to disclose precisely what it is, but we have our own kind of long term goal. You could call it lighthouse or, you know, sort of moonshot or whatever, but it is going to be a really impactful material that we want to develop as a proof point that it can be done and that it will make it into the into the real world and that AI was essential in actually making it happen. At the same time, we also are quite happy to work with companies that have more modest goals. Like I would say one is a very deep partnership where you go on a journey with a company and that's a long term commitment together. And the other one is like somebody says, I knew I need a force field. Can you help me train this force field and then maybe analyze this particular problem for me? And I'll pay you a bunch of money for that. And then maybe after that we'll see. And that's fine too. Right. But we prefer, you know, the deep partnerships where we can really change something for the good.[01:26:04:27 - 01:26:22:02]RJ: Yeah. And do you feel like from a platform standpoint you're ready for that or what are the things that and again, not asking you to disclose proprietary secret sauce, but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs?[01:26:22:02 - 01:28:40:01]Max: What I find interesting about this field is that every time you build something, it's actually immediately useful. Right. And so unlike quantum computing, which or nuclear fusion, so you work for 20, 30, 40 years and nothing, nothing, nothing, nothing. And then it has to happen. Right. And when it happens, it's huge. So it's quite different here because every time you introduce, so you go to a customer and you say, so what do you need? Right. So we work, let's say, on a problem like a water filtration. We want to remove PFAS from water. Right. So we do this with a company, Camira. So they are a deep partner for us. Right. So we on a journey together. I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it's us who will help them with training, having a new message. And in that kind of interface, these interactions, something beautiful will happen and that will have to happen first before this field will really take off, I think. And so in the sense that it's not a bubble, let's put it that way. So that's people see that as actual real what's happening. So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say, and I would I would hope we will have this new sort of breakthrough material before, you know, everything is completely automated because that will take a while. And also it is very vertical specific. So it's like completely automating something for problem A, you know, you can probably achieve it, but then you'll sort of have to start over again for problem B because, you know, your experimental setup looks very different in the machines that you characterize your materials look very different. Even the models in your platform will have to be retrained and fine tuned to the new class. So every time, you know, you have a lot of learnings to transfer, but also, you know, the problems are actually different. And so, yes, I would want that breakthrough material before it's completely automated, which I think is kind of a long term vision. And I would say every time you move to something new, you'll have to start retraining and humans will have to come in again and say, okay, so what does this problem look like? And now sort of, you know, point the the machine again, you know, in the new direction and then and then use it again.[01:28:40:01 - 01:28:47:17]RJ: For the non-scientists among us, me included a bit of a scientist. There's a lot of terminology. You mentioned DFT,[01:28:49:00 - 01:29:01:11]RJ: you equivariance we've talked about. Can you sort of explain in engineering terms or the level of sophistication and engineering? Well, how what is equivariance?[01:29:01:11 - 01:29:55:01]Max: So equivariance is the infusion of symmetry in neural networks. So if I build a neural network, let's say that needs to recognize this bottle, right, and then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle. Well, actually, the input that represents a rotated bottle is actually rotated bottle. It just doesn't understand that. Right. If you build equivariance in basically once you've trained it in one orientation, it will understand it in any other orientation. So that means you need a lot less data to train these models. And these are constraints on the weights of the model. So so basically you have to constrain the way such data to understand it. And you can build it in, you can hard code it in. And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations. I can graph neural network, their permutations and then physics, of course, as many more of these groups.[01:29:55:01 - 01:30:01:08]RJ: To pray devil's advocate, why not just use data augmentation by your bottle is in all the different orientations?[01:30:01:08 - 01:30:58:23]Max: As an option, it's just not exact. It's like, why would you go through the work of doing all that? Where you would really need an infinite number of augmentations to get it completely right. Where you can also hard code it in. Now, I have to say sometimes actually data augmentation works even better than hard coding the equivariance in. And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated. And so it's harder to find good minima. So there is also a complicated interplay, I think, between the optimization process and these constraints you put in your network. And so, yeah, you'll hear kind of contradicting claims in this field. Like some people and for certain applications, it works just better than not doing it. And sometimes you hear other people, if you have a lot of data and you can do data augmentation, then actually it's easier to optimize them and it actually works better than putting the equivariance in.[01:30:58:23 - 01:31:07:16]Brandon: Do you think there's kind of a bitter lesson for mathematically founded models and strategies for doing deep learning?[01:31:07:16 - 01:31:46:06]Max: Yeah, ultimately it's a trade-off between data and inductive bias. So if your inductive bias is not perfectly correct, you have to be careful because you put a ceiling to what you can do. But if you know the symmetry is there, it's hard to imagine there isn't a way to actually leverage it. But yeah, so there is a bitter lesson. And one of the bitter lessons is you should always make sure your architecture is scale, unless you have a tiny data set, in which case it doesn't matter. But if you, you know, the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.[01:31:47:10 - 01:31:55:01]RJ: Can you talk a little bit about your upcoming book and tell the listeners, like, what's exciting about it? Yeah, I should read it.[01:31:55:01 - 01:33:42:20]Max: So this book is about, it's called Generative AI and Stochastic Thermodynamics. It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just moving around and relaxing to the ground state, or that you can control to have certain, you know, be in a certain state, the mathematics of these two is actually identical. And so that's fascinating. And in fact, what's interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for machine learning a long time ago. And there's also Carl Friston's work on free energy principle and active entrance. But now we've related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems, like fluctuation theorems, which we don't typically talk about, but we can learn a lot from. And I think it's just it can sort of now start to cross fertilize. When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that's out there, developed by these very smart physicists, and say, okay, what can we take from here that will make our algorithms better? At the same time, we can use our models to now help the scientists do better science. And so it becomes a beautiful cross-fertilization between these two fields. The book is rather technical, I would say. And it takes all sorts of things that have been done as stochastic thermodynamics, and all sorts of models that have been done in the machine learning literature, and it basically equates them to each other. And I think hopefully that sense of unification will be revealing to people.[01:33:42:20 - 01:33:44:05]RJ: Wait, and when is it out?[01:33:44:05 - 01:33:56:09]Max: Well, it depends on the publisher now. But I hope in April, I'm going to give a keynote at ICLR. And it would be very nice if they have this book in my hand. But you know, it's hard to control these kind of timelines.[01:33:56:09 - 01:33:58:19]RJ: Yeah, I'm looking forward to it. Great.[01:33:58:19 - 01:33:59:25]Max: Thank you very much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

In this bonus episode, Nobel Prize-winning economist Daron Acemoglu joins Sam to challenge some of the most common assumptions about artificial intelligence's future. Drawing on his book Power and Progress, Daron argues that technology doesn't have a fixed destiny — and that today's choices will determine whether AI boosts workers or simply accelerates automation and inequality. He makes a case for focusing on new tasks that complement human skills, rather than replacing them, and warns that current incentives push AI toward centralization and automation by default. The conversation tackles productivity myths, reliability risks, and why regulation should proactively steer AI toward social good. Read the episode transcript here. Guest bio: Daron Acemoglu is an institute professor at MIT, faculty codirector of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work, and a research affiliate at MIT's newly established Blueprint Labs. He is an elected fellow of the National Academy of Sciences, American Philosophical Society, the British Academy of Sciences, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists. He is also a member of the Group of Thirty. He has authored six books, including Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity with Simon Johnson. His work in economics has been recognized around the world, notably with the Nobel Prize in economic sciences, along with co-laureates Johnson and James A. Robinson, in 2024. *Please take our listener survey: ⁠⁠mitsmr.com/podcastsurvey⁠⁠ It's short — we promise! — and all respondents will receive a free MIT SMR article collection, "Maximizing the Value of Generative AI." Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials. ME, MYSELF, AND AI® is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.

Crispr's ability to cut genetic code like scissors has just started to turn into medicines. Now, gene editing pioneer Jennifer Doudna wants to build an entire ecosystem to bring these treatments mainstream. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Nobel Laureate Paul Krugman, a City University of New York professor, says President Donald Trump's tariffs are a sales tax and are slightly contractionary. He speaks on "Bloomberg The Close."See omnystudio.com/listener for privacy information.

How did we go from digital computers to AI seemingly everywhere? Neil deGrasse Tyson, Chuck Nice, & Gary O'Reilly dive into the mechanics of thinking, how AI got its start, and what deep learning really means with cognitive and computer scientist, Nobel Laureate, and one of the architects of AI, Geoffrey Hinton. Subscribe to SiriusXM Podcasts+ to listen to new episodes of StarTalk Radio ad-free and a whole week early.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

HEALTH NEWS Bioactive polyphenolic compounds and in vitro anti-degenerative property-based pharmacological propensities of germplasms of Amaranth Magnesium lower fasting blood sugar in older adults Inactivity linked to up to 10% of type 2 diabetes complications Could Sugary Drinks Be Fueling the Rise in Teen Anxiety? Exercise-induced activation of neurons mediates improvements in endurance   Clips For Today  Increasing Attacks on Francesca Albanese Presage a New Dark Age - Chris Edges  Dr. Luc Montagnier, Nobel Laureate, final interview before being FOUND DEAD 6 days later. Jeffrey Epstein: The Autopsy Details No One Is Talking About - Lauren Mortician 

47 years ago today, Iran was declared an Islamic republic, after a year-long revolution that toppled a Shah and sent shockwaves throughout the world. And this anniversary sees the regime in its fiercest fight for survival yet. Last month, hundreds of thousands of Iranians rose up in protest, demanding change, before authorities brutally cracked down. The government itself admits to more than 3,000 deaths, but the real number could be in the tens of thousands, according to human rights groups. The violence and intimidation continue even in prison. Detained Nobel Peace Prize winner Narges Mohammadi has been viciously assaulted, according to the Nobel Committee, whose leader Jørgen Frydnes joins from Oslo.  Also on today's show: Former Danish Prime Minister Anders Fogh Rasmussen; Sara Khaki & Mohammadreza Eyni, co-directors of Oscar-nominated Iranian documentary "Cutting Through Rocks"; Scott Galloway, professor at the NYU Stern School of Business    Learn more about your ad choices. Visit podcastchoices.com/adchoices

We all love the thrill of winning - the house, the promotion, the deal. But as Nobel laureate Richard Thaler explains, some of our biggest “wins” are actually the moments we set ourselves up to lose. Thaler breaks down why we overbid, overpay, and talk ourselves into choices we regret. And he shares simple tricks to help you catch yourself before you make a mistake you can't undo.

Litfest 2026 served as another inspiring occasion that turned the spotlight on the island's creatives. The annual literary festival is held in observance of Nobel Laureates month. This year's day-long event included a midday cultural and artistic showcase.

I can't believe we're already through the first month of 2026, but here we are. This month, Ian started us off with László Krasznahorkai's: Sátántangó. The Hungarian author was last year's Nobel Laureate, so we decided that we'd better take a look at his oeuvre. Sátántangó is a bleak novel that describes the lives of the people living on an "estate". The people lie, cheat, and steal from each other, wallowing in their own problems, until Irimiás, a man they thought dead returns. The residents think that he's going to better their lives, but Irimiás is a conman. Sátántangó is a difficult text, especially only having read it once, but upon discussing the text, had more to say than we thought. We hope you enjoy our discussion! Maybe you have your own theories about what is really going on in this book?    February's book is a classic: Wuthering Heights by Charlotte Brontë. Ronnie chose this one because Emerald Fennel's new movie!

In this episode of ET@Davos, ET’s Sruthijith KK speaks to Demis Hassabis, CEO of Google DeepMind and Nobel Laureate 2024, on the future of AI. The chess prodigy-turned scientist-turned-AI pioneer explains how DeepMind balances frontier research with a billion-user scale. Hassabis says Google’s Apple partnership followed direct model comparisons where Gemini prevailed; China is now only months behind the West but lacks frontier breakthroughs; and AGI could arrive within a decade, triggering “post-scarcity” abundance. He defends AI’s energy demands, citing AI-designed fusion and grid optimisation. From Transformers to AlphaFold, Hassabis argues Google pioneered modern AI but moved too slowly. His bottom line: within 5–10 years, machines will be doing original science. The stakes couldn’t be higher.You can follow Sruthijith K.K. on his social media: X and LinkedinCheck out other interesting episodes like: When Grinch Almost Stole Gig Workers' Christmas, How Will a Volatile ₹ Impact You in 2026?, How Quick Commerce is Triggering a Health Crisis for Gen Z, India’s Labour Law Reboot, Viral to Valuation: Building Women’s Cricket as a Brand and much more. Catch the latest episode of ‘The Morning Brief’ on The Economic Times Online, Spotify, Apple Podcasts, JioSaavn, Amazon Music and Youtube.See omnystudio.com/listener for privacy information.

 If you think about it virtually all major public policy issues involve the application of science.  How do we deal with global warming?  What limits can we put on the development of nuclear capability from a rogue nation that we are able to drag to a bargaining table?  How do we get consensus on a strategy to blunt the next airborne virus which starts with human and animal contact continents away?  Yet it would seem that the lens through which scientists look at problem solving and that of politicians is worlds apart.  So how does scientific input affect the ultimate resolution of some of the world’s most vexing problems?  Nobel Laureate, Dr. Peter Agre, attempts to answer that question in his new book, “Can Scientists Succeed where Politicians Fail?”  It’s interesting when you consider how we must rely on scientists to help craft policies to ameliorate problems that resulted from their own acumen.  The limiting of the potential of nuclear weapons may be the best example, as J. Robert Oppenheimer, the father of the nuclear bomb, realized early on.

For this episode, Robin examines the fundamental differences between liberalism and progressivism and why understanding this divide is critical for 2028.Key topics include: Why Republicans fall in line while Democrats fracture over purity tests, the data showing disruptive protest tactics reduce public support by 15%, and how protest votes intended to help Gaza enabled Trump's return and Gaza's devastation. With 31 Nobel laureates warning about fascism, Yale historians fleeing to Canada, and a Democracy Index rating the U.S. at 55/100 (authoritarian territory), ideological purity has become dangerous. With democracy itself at stake, coalition building must supersede purity politics.In this episode, Robin argues that while liberals and progressives share end goals, such as universal healthcare, climate action, workers' rights, racial justice...their strategic disagreements determine electoral outcomes. Compromise isn't betrayal; it's how democracy functions.Keywords: liberal vs progressive, democratic party divide, why democrats lose elections, electoral strategy 2028, progressive purity politics, coalition building, capitalism vs socialism, free speech vs cancel culture, identity politics, incrementalism vs revolution, protest tactics effectiveness, political compromise, Gaza protest votes, Republican electoral strategy, swing state politics, moderate vs progressive, political pragmatism, deplatforming debate, working class voters, political podcast, 2028 election analysis, authoritarianism warning, voting strategy, ideological purity critiqueSources: Pew Research (2021), Ruy Teixeira/The Liberal Patriot, Matthew Yglesias/The Atlantic, Ezra Klein/NYT, Democracy Index 2025, 31 Nobel Laureates fascism letterBecome a supporter of this podcast: https://www.spreaker.com/podcast/we-saw-the-devil-crime-political-analysis--4433638/support.Website: http://www.wesawthedevil.comPatreon: http://www.patreon.com/wesawthedevilDiscord: https://discord.gg/X2qYXdB4Twitter: http://www.twitter.com/WeSawtheDevilInstagram: http://www.instagram.com/wesawthedevilpodcast.

Nobel Laureate Dr. Omar Yaghi joins The Take after winning the 2025 Nobel Prize in Chemistry for developing metal-organic frameworks (MOFs), materials that can capture carbon and store hydrogen. Born to a Palestinian refugee family in Amman, Yaghi tells the story of how hardship shaped his imagination, from getting fresh water only once a week to inventing systems that pull water from desert air. In this episode: Dr. Omar Yaghi, Nobel Laureate in Chemistry, Professor at University of California, Berkeley and Atoco Founder Episode credits: This episode was produced by Chloe K. Li and Melanie Marich with Phillip Lanos, Spencer Cline, Tamara Khandaker, Kylene Kiang, Sarí el-Khalili and our host, Malika Bilal. It was edited by Noor Wazwaz and Sarí el-Khalili. The Take production team is Marcos Bartolomé, Sonia Bhagat, Spencer Cline, Sarí el-Khalili, Tamara Khandaker, Kylene Kiang, Phillip Lanos, Chloe K. Li, Melanie Marich, and Noor Wazwaz. Our host is Malika Bilal. Our engagement producers are Adam Abou-Gad and Vienna Maglio. Andrew Greiner is lead of audience engagement. Our sound designer is Alex Roldan. Our video editors are Hisham Abu Salah and Mohannad al-Melhem. Alexandra Locke is The Take’s executive producer. Ney Alvarez is Al Jazeera’s head of audio. Connect with us: @AJEPodcasts on X, Instagram, Facebook, and YouTube

It took longer than expected to get all of the details from Thursday’s big meeting between Maria Corina Machado and President Trump. First we heard the Nobel Laureate “presented” Trump with her medal, but it wasn’t until later in the evening we learned that not only had Trump happily accepted it, there is an incredible photo that documents the moment. Trump’s face is priceless and so is the reaction on X from the Nobel Committee.See omnystudio.com/listener for privacy information.

It took longer than expected to get all of the details from Thursday’s big meeting between Maria Corina Machado and President Trump. First we heard the Nobel Laureate “presented” Trump with her medal, but it wasn’t until later in the evening we learned that not only had Trump happily accepted it, there is an incredible photo that documents the moment. Trump’s face is priceless and so is the reaction on X from the Nobel Committee.See omnystudio.com/listener for privacy information.

It took longer than expected to get all of the details from Thursday’s big meeting between Maria Corina Machado and President Trump. First we heard the Nobel Laureate “presented” Trump with her medal, but it wasn’t until later in the evening we learned that not only had Trump happily accepted it, there is an incredible photo that documents the moment. Trump’s face is priceless and so is the reaction on X from the Nobel Committee.See omnystudio.com/listener for privacy information.

It took longer than expected to get all of the details from Thursday’s big meeting between Maria Corina Machado and President Trump. First we heard the Nobel Laureate “presented” Trump with her medal, but it wasn’t until later in the evening we learned that not only had Trump happily accepted it, there is an incredible photo that documents the moment. Trump’s face is priceless and so is the reaction on X from the Nobel Committee.See omnystudio.com/listener for privacy information.

If you appreciate my work and would like to support it: https://subscribestar.com/the-saad-truth https://patreon.com/GadSaad https://paypal.me/GadSaad To subscribe to my exclusive content on X, please visit my bio at https://x.com/GadSaad _______________________________________ This clip was posted on January 13, 2026 on my YouTube channel as THE SAAD TRUTH_1980: https://youtu.be/tYRL7TiMBiM _______________________________________ Please visit my website gadsaad.com, and sign up for alerts. If you appreciate my content, click on the "Support My Work" button. I count on my fans to support my efforts. You can donate via Patreon, PayPal, and/or SubscribeStar. _______________________________________ Dr. Gad Saad is a professor, evolutionary behavioral scientist, and author who pioneered the use of evolutionary psychology in marketing and consumer behavior. In addition to his scientific work, Dr. Saad is a leading public intellectual who often writes and speaks about idea pathogens that are destroying logic, science, reason, and common sense.  _______________________________________

We always love a chance to hear from someone who's been investing in climate for a long time. Daniel Weiss fits the bill. His firm, Angeleno Group, was founded in 2001 and since then has led or co-led over $3 billion into clean energy and climate solutions. Daniel and the Angeleno Group also surround themselves with accomplished leaders that bring true global expertise. Their advisory board includes a former US Secretary of Energy, former Secretary of Treasury, UN Ambassador, Nobel Laureate, and several other top scholars and industry leaders.Lean in closely for this conversation and learn from Daniel's perspective. What we heard was somewhat surprising: that despite the headwinds coming from Washington and rippling around the world, strong deal flow, ever improving talent, and low valuations make this the best time in decades to invest in climate solutions. We spoke about Daniel's background, this unique moment in climate investing, Angeleno Group's thesis and recent investments, and much more. Lots to learn about and consider in this episode. Enjoy. On today's episode, we cover:02:30 – Daniel's Personal Climate Journey05:30 – From Law to Climate Investing & Founding Angeleno Group07:39 – World Resources Institute (WRI) & Global Systems Change12:29 – Optimism & “The New Global Possible”13:21 – Building Angeleno Group Through Turbulent Times14:36 – Check Sizes, Stages & How Angeleno Invests15:18 – Evolution of Climate Investing & Why 2025 Is So Compelling19:56 – Megatrends: Load Growth, AI & Energy Security22:27 – Angeleno's Advisory Board & Why It Matters24:19 – Angeleno Group's Investment Thesis25:18 – Example Investments: Software for the Grid & Wildfire Risk29:08 – Headwinds in Climate Tech: Fundraising & Exits33:05 – Scaling Climate Finance & Global Opportunity34:35 – Climate Week NYC & Hope from the Next Generation36:33 – Closing Thoughts Resources MentionedAngeleno GroupWorld Resources Institute (WRI)Book: The New Global...

What if AI is repeating the same mistakes society made during the Industrial Revolution? In this episode of Technovation, Peter is joined by Nobel Prize Laureate in Economics and Ronald A. Kurtz Professor of Entrepreneurship at the MIT Sloan School of Management Simon Johnson. Throughout their conversation, they explore why automation has historically failed to deliver shared prosperity and why artificial intelligence may be following the same path. Drawing on centuries of economic history, Johnson explains how mechanization once displaced workers faster than new jobs were created, fueling inequality and social unrest. Together, they discuss what today's AI leaders must learn from history, why institutions matter more than technology alone, and how workforce anxiety is an early warning sign of deeper structural problems. Key topics include: Automation vs. job creation AI's impact on entry-level and knowledge work Workforce polarization and regional inequality Lessons from the Industrial Revolution for today's leaders What it takes to align innovation with shared prosperity

In this extraordinary episode of Gateways to Awakening, Yasmeen sits down with evolutionary astrologer and Vastu gemstone master Tashi Powers, whose decades of experience reading charts for luminaries, from the Rolling Stones to Brad Pitt to Nobel Laureates, have made her one of the most respected astrologers in the world. Together, Yasmeen and Tashi explore the hidden architecture of reality through Vastu, gemstone astrology, and evolutionary astrology—revealing how our homes, bodies, and birth charts are encoded with cosmic intelligence.“Your home is a living organism. When you align your space with your planetary blueprint, the universe begins to conspire with you—fast.” — Tashi Powers“These remedies aren't symbolic. They're vibrational technologies that have been working for 10,000 years.” - Tashi Powers Tashi explains how to personalize Vastu using your birth chart, how gemstones communicate with planetary frequencies, and why your home is a living organism that can either harmonize or distort your destiny. She shares astonishing stories about space clearing, planetary remedies, and the Deva realm, and breaks down how colors, deities, sacred geometry, and gemstones can transform your relationships, career, creativity, and emotional harmony.This episode also dives into:- How Vastu aligns your home with your planetary blueprint- Why gemstones work—and how to use them for love, money, empowerment, and protection- How the days of the week correspond to planetary energies- What evolutionary astrology reveals about your karma, purpose, and soul path- The power of the moon, Venus gates, and the sacred geometry of the cosmos- How to work with natural law, intuition, and the seed cycle of the zodiac- The importance of the galactic center and why some souls come in with a “galactic mission”Yasmeen and Tashi also announce the relaunch of their personalized Vastu app, Vastu Feng Shui, which turns an ancient $500-$2,000 consultation into a modern, intuitive tool for anyone to use—complete with personalized remedies for love, health, prosperity, and emotional harmony.This is a mystical, grounded, highly practical conversation for anyone ready to understand their space, their chart, and their energy in a completely new way.Learn more at:VastuFengShuiHarmony.comEnlighteningTimes.comInstagram: @tashiastrodikini @yasmeenturayhi @vastushaktifengshuiTune in to Gateways to Awakening for more conversations with leading thinkers, creators, and spiritual pioneers shaping the future of consciousness. For more from me: follow my writing on Substack (substack.com/@therealyasmeent), find me on Instagram @TheRealYasmeenT, or visit InnerKnowingSchool.com.

A loss in Jason's family, RIP Rob Reiner -- the latest on the investigation into his and his wife, Michelle's death, myTalk Loves Local: MSP, and Alexis shares details about the Nobel Laureate dinner See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AP correspondent Karen Chammas reports on the long awaited appearance of Venezuelan opposition leader María Corina Machado a day after she was announced as the winner of the Nobel Peace Prize.

AP correspondent Ed Donahue reports on the status of a Nobel Prize winner.

Send us a textThis episode looks at two literary giants and includes their writing tips. It also features the only writer in our entire series our host, Randal Wallace,  actually met, Maya Angelou. Mrs. Morrison was at the time of her passing, America's only living Nobel Laureate for writing. This is an inspiring episode for any aspiring authors and for everyone else too. Toni MorrisonToni Morrison advised writers to embrace the revision process, to write without considering an audience, and to trust the stories that demand to be told. Her writing method was deeply personal, relying on a pre-dawn ritual and an intimate focus on her characters. Begin with self-authorshipWrite the book you want to read. Ignore the "white gaze."  for honesty and truth rather than for applause.Write for the characters, not an audience. Trust the creative processFind your ideal creative space.Start with an image. Be open to what your writing tells you.Embrace revision and growthRevision is where the real work begins. Know the difference between revision and "fretting." Recognize missed opportunities. Maya AngelouMaya Angelou emphasized discipline, emotional truth, and mastery of language as essential for writers. Her own writing process was a dedicated ritual that supported her creative work. Embrace a disciplined routineAngelou held great respect for the craft of writing and maintained a consistent, structured process.Create a separate workspaceWrite consistentlyEdit and reviseWrite from the heartFor Angelou, the goal of writing was to reach the reader's heart and help them feel connected to the shared human experience.Tell the truth, not just the factsShare your story to help others : She said, "A bird doesn't sing because it has an answer. It sings because it has a song".Move beyond bitterness. Master the craft of languageAngelou believed that creative inspiration was nothing without the discipline to master one's tools.Use words to create emotion Take familiar words and make them new: Engage the sensesBelieve in your creative capacityAngelou taught that creativity is an endless resource that only grows through use.Creativity is a muscle: She famously stated, "You can't use up creativity. The more you use, the more you have".Dare to be creative: Make writing a necessity:   Questions or comments at , Randalrgw1@aol.com , https://twitter.com/randal_wallace , and http://www.randalwallace.com/Please Leave us a review at wherever you get your podcastsThanks for listening!!

When UC Berkeley Professor Randy Schekman was 12, he scooped up a jar of pond scum and examined it under his toy microscope.“I just could not believe the world that was revealed,” he said during a campus event earlier this month. “This complex set of creatures that you can't see with your naked eye, and yet are moving and somehow mechanically independent, and able to do amazing things. And this was so fascinating.”Schekman went on to become a professor of molecular and cell biology at Berkeley and win the Nobel Prize in Physiology or Medicine in 2013 for his discovery of how yeast membranes work. His research has led to advances in food and fuel production, as well as life-saving drugs and vaccines. In this Berkeley Talks episode, Schekman explains the molecular building blocks that define who we are, the cellular processes that drive health and illness, and how curiosity-driven research leads to revolutionary insights into disease and opens doors to new possibilities for medicine and human health.This lecture, which took place on Nov. 7, was sponsored by UC Berkeley's Osher Lifelong Learning Institute. Watch a video of Schekman's talk.Listen to the episode and read the transcript on UC Berkeley News (news.berkeley.edu/podcasts/berkeley-talks).Music by HoliznaCC0.UC Berkeley photo by Elena Zhukova. Hosted on Acast. See acast.com/privacy for more information.

Episode overviewJohn Martinis, Nobel laureate and former head of Google's quantum hardware effort, joins Sebastian Hassinger on The New Quantum Era to trace the arc of superconducting quantum circuits—from the first demonstrations of macroscopic quantum tunneling in the 1980s to today's push for wafer-scale, manufacturable qubit processors. The episode weaves together the physics of “synthetic atoms” built from Josephson junctions, the engineering mindset needed to turn them into reliable computers, and what it will take for fabrication to unlock true large-scale quantum systems.Guest bioJohn M. Martinis is a physicist whose experiments on superconducting circuits with John Clarke and Michel Devoret at UC Berkeley established that a macroscopic electrical circuit can exhibit quantum tunneling and discrete energy levels, work recognized by the 2025 Nobel Prize in Physics “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” He went on to lead the superconducting quantum computing effort at Google, where his team demonstrated large-scale, programmable transmon-based processors, and now heads Qolab (also referred to in the episode as CoLab), a startup focused on advanced fabrication and wafer-scale integration of superconducting qubits.Martinis's career sits at the intersection of precision instrumentation and systems engineering, drawing on a scientific “family tree” that runs from Cambridge through John Clarke's group at Berkeley, with strong theoretical influence from Michel Devoret and deep exposure to ion-trap work by Dave Wineland and Chris Monroe at NIST. Today his work emphasizes solving the hardest fabrication and wiring challenges—pursuing high-yield, monolithic, wafer-scale quantum processors that can ultimately host tens of thousands of reproducible qubits on a single 300 mm wafer.Key topicsMacroscopic quantum tunneling on a chip: How Clarke, Devoret, and Martinis used a current-biased Josephson junction to show that a macroscopic circuit variable obeys quantum mechanics, with microwave control revealing discrete energy levels and tunneling between states—laying the groundwork for superconducting qubits. The episode connects this early work directly to the Nobel committee's citation and to today's use of Josephson circuits as “synthetic atoms” for quantum computing.From DC devices to microwave qubits: Why early Josephson devices were treated as low-frequency, DC elements, and how failed experiments pushed Martinis and collaborators to re-engineer their setups with careful microwave filtering, impedance control, and dilution refrigerators—turning noisy circuits into clean, quantized systems suitable for qubits. This shift to microwave control and readout becomes the through-line from macroscopic tunneling experiments to modern transmon qubits and multi-qubit gates.Synthetic atoms vs natural atoms: The contrast between macroscopic “synthetic atoms” built from capacitors, inductors, and Josephson junctions and natural atomic systems used in ion-trap and neutral-atom experiments by groups such as Wineland and Monroe at NIST, where single-atom control made the quantum nature more obvious. The conversation highlights how both approaches converged on single-particle control, but with very different technological paths and community cultures.Ten-year learning curve for devices: How roughly a decade of experiments on quantum noise, energy levels, and escape rates in superconducting devices built confidence that these circuits were “clean enough” to support serious qubit experiments, just as early demonstrations such as Yasunobu Nakamura's single-Cooper-pair box showed clear two-level behavior. This foundational work set the stage for the modern era of superconducting quantum computing across academia and industry.Surface code and systems thinking: Why Martinis immersed himself in the surface code, co-authoring a widely cited tutorial-style paper “Surface codes: Towards practical large-scale quantum computation” (Austin G. Fowler, Matteo Mariantoni, John M. Martinis, Andrew N. Cleland, Phys. Rev. A 86, 032324, 2012; arXiv:1208.0928), to translate error-correction theory into something experimentalists could build. He describes this as a turning point that reframed his work at UC Santa Barbara and Google around full-system design rather than isolated device physics.Fabrication as the new frontier: Martinis argues that the physics of decent transmon-style qubits is now well understood and that the real bottleneck is industrial-grade fabrication and wiring, not inventing ever more qubit variants. His company's roadmap targets wafer-scale integration—e.g., ~100-qubit test chips scaling toward ~20,000 qubits on a 300 mm wafer—with a focus on yield, junction reproducibility, and integrated escape wiring rather than current approaches that tile many 100-qubit dies into larger systems.From lab racks of cables to true integrated circuits: The episode contrasts today's dilution-refrigerator setups—dominated by bulky wiring and discrete microwave components—with the vision of a highly integrated superconducting “IC” where most of that wiring is brought on-chip. Martinis likens the current state to pre-IC TTL logic full of hand-wired boards and sees monolithic quantum chips as the necessary analog of CMOS integration for classical computing.Venture timelines vs physics timelines: A candid discussion of the mismatch between typical three-to-five-year venture capital expectations and the multi-decade arc of foundational technologies like CMOS and, now, quantum computing. Martinis suggests that the most transformative work—such as radically improved junction fabrication—looks slow and uncompetitive in the short term but can yield step-change advantages once it matures.Physics vs systems-engineering mindsets: How Martinis's “instrumentation family tree” and exposure to both American “build first, then understand” and French “analyze first, then build” traditions shaped his approach, and how system engineering often pushes him to challenge ideas that don't scale. He frames this dual mindset as both a superpower and a source of tension when working in large organizations used to more incremental science-driven projects.Collaboration, competition, and pre-competitive science: Reflections on the early years when groups at Berkeley, Saclay, UCSB, NIST, and elsewhere shared results openly, pushing the field forward without cut-throat scooping, before activity moved into more corporate settings around 2010. Martinis emphasizes that many of the hardest scaling problems—especially in materials and fabrication—would benefit from deeper cross-organization collaboration, even as current business constraints limit what can be shared.Papers and research discussed“Energy-Level Quantization in the Zero-Voltage State of a Current-Biased Josephson Junction” – John M. Martinis, Michel H. Devoret, John Clarke, Physical Review Letters 55, 1543 (1985). First clear observation of quantized energy levels and macroscopic quantum tunneling in a Josephson circuit, forming a core part of the work recognized by the 2025 Nobel Prize in Physics. Link: https://link.aps.org/doi/10.1103/PhysRevLett.55.1543“Quantum Mechanics of a Macroscopic Variable: The Phase Difference of a Josephson Junction” – J. Clarke et al., Science 239, 992 (1988). Further development of macroscopic quantum tunneling and wave-packet dynamics in current-biased Josephson junctions, demonstrating that a circuit-scale degree of freedom behaves as a quantum variable. Link (PDF via Cleland group):

James sits down with astrophysicist Brian Keating for a candid, useful tour through three hot zones: how to think about AI (and where it actually helps), what's broken in higher ed and admissions right now, and why outsourcing your mood to politics is a losing strategy. You'll hear first-hand stories (from UC San Diego classrooms to New York City politics), specific ways James and Brian really use AI daily, and a simple framework for protecting your attention and happiness—even when everything feels polarized. What You'll Learn: How universities can leverage AI-guided curiosity to revolutionize learning, according to James Altucher's vision for "Altucher University." Why mastering communication skills—writing, speaking, negotiating—is crucial for career success, and why these skills are often neglected in traditional education. Firsthand insights into how Brian Keating and James Altucher use AI daily for research, problem-solving, and creativity, along with practical examples from their personal and professional lives. The economic and philosophical debates around AI's actual impact on industries, jobs, and the broader GDP, including its use in coding, media, and even farming. The limitations of AI and large language models in science and creative work, and why critical thinking and prompt engineering remain essential—even as technology evolves. Timestamped Chapters: 00:00 "AI Clarifies Venezuela Questions" 05:59 Venezuela News Omission 07:45 Frustrating Academia Raise Policy 11:54 Collaboration and Engagement Terms 14:23 "Ideas Overload Dilutes Impact" 19:11 Economic Efficiency Benefits All 19:49 Automation's Effect on Jobs 23:43 "Decentralized AI Competition" 27:09 "AI's Rapid Growth" 31:39 Copyright Limits Creativity 33:17 AI Book Recommendations 38:38 "AI Won't Replace Writers" 41:01 "Dumb Takes by Geniuses" 44:39 Content Overload Shift 47:47 Self-Publishing Outperforms Traditional 49:05 Dying Publishing Model 54:21 "Nobel Laureates' Impact Explained" 57:49 "Epstein, Trump, Wishcasting" 59:37 "Thrills Free on Pluto TV" Additional resources:

Here in Episode #41, podcast host Dr. Jerry Workman speaks with Sunil Mehrotra and Doug Modlin, who are the project leaders of the Albert Michelson Exhibit at the Angels Camp Museum located in Angels Camp, California. They will be discussing the life and times of Albert Michelson and the creation of the Albert Michelson Angels Camp Museum Exhibit. Albert A. Michelson was the first Nobel Laureate in the sciences from the United States and the first physicist to accurately measure the speed of light, the size of stars, and more. References and Further Information for Albert A. Michelson's Life and Angels Camp Museum Exhibit Life and Scientific Contributions (1) The Albert Michelson website landing page. https://albertmichelson.com/ (accessed 2025-09-30). (2) Michelson live interview film YouTube link: https://youtu.be/gQoNnu0n2lk (accessed 2025-09-30). (3) Workman, J., Jr. Albert A. Michelson: A Pioneer of Interferometry and Precision Optical Spectroscopy. Spectroscopy 2025, 40 (6), 22–26. https://doi.org/10.56530/spectroscopy.tz5770i4. (4) National Academy of Sciences (USA) Biographical Memoir of Albert Abraham Michelson PDF document. Available at: https://drive.google.com/file/d/1jd-yeTdj8y0kQVENLZkCXe8uoF3RX84E/view?usp=sharing (accessed 2025-09-30). (5) Albert A. Michelson, Recent advances in spectroscopy, Nobel Lecture, December 12, 1907. Available at: https://drive.google.com/file/d/1GeXXxuCoLbWVIfG8wS1XJ-kpY0ajBXEz/view?usp=sharing (accessed 2025-09-30). (6) Michelson featured in US Navy (USN) history video, YouTube link: https://youtu.be/-CbrVa9SrCI (accessed 2025-09-30). (7) Edna and Albert Michelson Family History. Available at: https://docs.google.com/document/d/1Kk_HpxnKjKbtHx9SPozM-jDbVFKc9L-_/edit?usp=sharing&ouid=100533588463446467177&rtpof=true&sd=true (accessed 2025-09-30). Albert Michelson exhibit at Angels Camp Museum (8) Albert Michelson exhibit unveiled at Angels Camp Museum article. Available at: https://drive.google.com/file/d/1VMjhYta7qfJbq95lKW2ftqPglJqCdhDO/view?usp=sharing (accessed 2025-09-30). (9) Albert Michelson Exhibit Inspires Young Minds at Angels Camp Museum Link: https://new.thepinetree.net/?p=179327 (accessed 2025-09-30). (10) Mark Twain Elementary Students Tour the Albert Michelson Exhibit YouTube link: https://youtu.be/hwpe-wkHtOo (accessed 2025-09-30). (11) A teacher and a student touring Albert Michelson exhibit at the Angels Camp Museum YouTube link: https://youtu.be/vdVJDkretU4 (accessed 2025-09-30). (12) Albert Michelson Exhibit at the Angels Camp Museum-spectrometer description YouTube link: https://youtu.be/Ed_-JSq_4pg (accessed 2025-09-30). (13). Virtual tour of the Albert Michelson Exhibit at the Angels Camp Museum link: https://tourmkr.com/F1SYh8VcsS/43684268p&273.03h&90t (14). The Albert Michelson Education Project supports the Learning Center at the Albert Michelson Exhibit and the Albert Michelson Science Fair Awards. Be sure to indicate that your donation is intended for the Albert Michelson Education Project and use the following link: https://www.m-otm.org/contribute (15). The Michelson STEAM Scholarship supports local high school students in Calaveras County pursue their education in STEAM related disciplines. Be sure to indicate that your donation is intended for the Michelon STEAM Scholarship and use the following link: https://calaverascommunityfoundation.org/ways-to-give/

She is not only a Nobel Peace Prize laureate. She is not only one of the most visible human-rights defenders in Europe in recent decades. She is not only a tireless activist with profound empathy for others. She is also a thinker — someone who reflects deeply on the moral foundations of freedom and dignity. Our guest today is Oleksandra Matviychuk, a prominent Ukrainian human-rights defender and head of the Center for Civil Liberties, which was awarded the Nobel Peace Prize in 2022. In this episode, we discuss the moral ideas that hold Ukrainian society together. *** Host: Volodymyr Yermolenko, a Ukrainian philosopher, editor-in-chief of UkraineWorld, and president of PEN Ukraine. Explaining Ukraine is a podcast by UkraineWorld, an English-language media platform about Ukraine, run by Internews Ukraine. Listen on various platforms: https://li.sten.to/explaining-ukraine UkraineWorld: https://ukraineworld.org/en *** SUPPORT: You can support our work on https://www.patreon.com/c/ukraineworld Your help is crucial, as we rely heavily on crowdfunding. You can also contribute to our volunteer missions to frontline areas in Ukraine, where we deliver aid to both soldiers and civilians. Donations are welcome via PayPal at: ukraine.resisting@gmail.com. *** CONTENTS: 00:00 Nobel Peace Prize Laureate Oleksandra Matviichuk: On Freedom, Dignity, and War 02:24 "Not Nobel Peace Prize changed my life - the large-scale war has changed my life" 08:32 Torture, rape, enforced disappearances, filtration camps — the reality of Russian occupation 11:55 Why are Ukrainians not "ideal victims"? 15:57 The horror of Russian captivity: Ihor Kozlovskyi`s experience 19:44 Why is freedom existential for Ukrainians? 24:16 Ukrainian strength lies in the people's belief that their efforts matter 31:38 Over 170,000 registered Russian war crimes in Ukraine 32:18 Why is justice important now, not after the end of the war? 35:41 Why is the Russian war against Ukraine genocidal? 43:50 What gives Oleksandra Matviichuk hope today? *** The podcast episode is produced by UkraineWorld with the support of the Askold and Dir Fund as a part of the Strong Civil Society of Ukraine - a Driver towards Reforms and Democracy project, implemented by ISAR Ednannia, funded by Norway and Sweden. The contents of this publication are the sole responsibility of UkraineWorld and can in no way be taken to reflect the views the Government of Norway, the Government of Sweden and ISAR

Here is the GoFundMe link for Pearl that I mention in this episode: https://gofund.me/2aa4d537e Most people don't get enough sleep — and even a small deficit can take a big toll. Just 15 extra minutes a night can boost your health, focus, and mood more than you'd expect. This episode begins with a surprising look at how too little sleep quietly undermines your life — and how a little more can make all the difference. https://www.sleep.com/sleep-health/15-minutes-extra-sleep Simple beats complicated — in business, communication, and life. Yet most of us instinctively make things harder than they need to be. Marketing entrepreneur and educator Ben Guttmann, who's helped clients from the NFL to Nobel Laureates, reveals why simplicity is the ultimate superpower and how to harness it in your ideas, writing, and daily decisions. He's the author of Simply Put: Why Clear Messages Win—and How to Design Them (https://amzn.to/3udtVwz). You probably have pockets in nearly everything you wear — and yet, they're only about 500 years old. Where did they come from? Why are women's pockets so small? And what do they say about how people have lived through history? Hannah Carlson, a historian of clothing and author of POCKETS: An Intimate History of How We Keep Things Close (https://amzn.to/3SUzmef), reveals the surprisingly political, personal, and practical story of the humble pocket. Finally, anger isn't always destructive — used wisely, it can be one of your greatest motivators. Research shows that channeling anger toward a meaningful goal can actually help you focus and achieve more. I'll explain how to tap into the power of anger — without letting it take over. https://www.nbcnews.com/health/feeling-angry-may-help-people-achieve-goals-study-finds-rcna123611 PLEASE SUPPORT OUR SPONSORS! AG1: Head to ⁠⁠⁠https://DrinkAG1.com/SYSK ⁠⁠⁠ to get a FREE Welcome Kit with an AG1 Flavor Sampler and a bottle of Vitamin D3 plus K2, when you first subscribe!  AURA FRAMES: For a limited time, visit https://AuraFrames.com and get $45 off Aura's best-selling Carver Mat frames -named #1 by Wirecutter -by using promo code SOMETHING at checkout INDEED: Get a $75 sponsored job credit to get your jobs more visibility at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://Indeed.com/SOMETHING⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ right now! QUINCE: Give and get timeless holiday staples that last this season with Quince.  Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://Quince.com/sysk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for free shipping on your order and 365 day returns! DELL: It's time for Black Friday at Dell Technologies. Save big on PCs like the Dell 16 Plus featuring Intel® Core™ Ultra processors. Shop now at: ⁠ https://Dell.com/deals ⁠ NOTION: ⁠⁠Notion brings all your notes, docs, and projects into one connected space that just works . It's seamless, flexible, powerful, and actually fun to use! Try Notion, now with Notion Agent, at: ⁠https://notion.com/something⁠ PLANET VISIONARIES: In partnership with Rolex's Perpetual Planet Initiative, this… is Planet Visionaries. Listen or watch on Apple, Spotify, YouTube, or wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Nobel Laureate Paul Krugman joins Bloomberg to discuss the state of the economy amid the ongoing shutdown. He says the US has the worst consumer sentiment basically ever, and that the President's idea of sending out checks is a terrible idea and deeply irresponsible. He speaks with hosts Romaine Bostick and Katie Greifeld.See omnystudio.com/listener for privacy information.

You might think you know about Malala. But you'd be wrong. For so many years, she stood as a symbol of resistance: the teenage girl who was shot by the Taliban for insisting on the right to go to school and who later won the Nobel Prize at 17 for her efforts to make education available for everyone. But there was another story that existed behind the headlines: the story of a young woman who was only just understanding who she was. Now 28, Malala has published a new book, Finding My Way which describes some of that extraordinary journey. In this episode we discuss her panic attack after smoking a bong at Oxford (and how this retriggered undiagnosed PTSD), what friendship taught her, her views on marriage and how they've changed, as well as the sadness she carries for Afghanistan and all the women who are denied an education around the world. Plus: how she fell in love with a hot cricketer. This is such a powerful conversation and Malala is also funny, warm and incredibly wise. You will laugh. You might cry. But whatever happens, you'll emerge with a new perspective on life. ✨ IN THIS EPISODE: 00:00 Introduction 01:27 Recovery and Continued Education 05:12 College Life and First Experiences 07:03 Mental Health and Panic Attack 11:09 Academic Struggles and Social Life 17:48 Reflections on Friendship and Cultural Pressures 26:02 Reflecting on Nasin's (her cousin's) Struggles 27:49 Reflections on Life Choices 30:14 Marriage: A Journey of Doubts and Discoveries 31:47 Redefining Marriage Norms 34:36 Contemplating Motherhood 37:04 The Fall of Afghanistan to the Taliban 44:27 Global Crises and Personal Reflections

John Maytham speaks to Abdulrazak Gurnah, Tanzanian novelist and Nobel Laureate, about the significance of this symposium, the role of African literature in reclaiming histories, and how stories from the continent continue to redefine the world’s understanding of exile, identity, and belonging. Presenter John Maytham is an actor and author-turned-talk radio veteran and seasoned journalist. His show serves a round-up of local and international news coupled with the latest in business, sport, traffic and weather. The host’s eclectic interests mean the program often surprises the audience with intriguing book reviews and inspiring interviews profiling artists. A daily highlight is Rapid Fire, just after 5:30pm. CapeTalk fans call in, to stump the presenter with their general knowledge questions. Another firm favourite is the humorous Thursday crossing with award-winning journalist Rebecca Davis, called “Plan B”. Thank you for listening to a podcast from Afternoon Drive with John Maytham Listen live on Primedia+ weekdays from 15:00 and 18:00 (SA Time) to Afternoon Drive with John Maytham broadcast on CapeTalk https://buff.ly/NnFM3Nk For more from the show go to https://buff.ly/BSFy4Cn or find all the catch-up podcasts here https://buff.ly/n8nWt4x Subscribe to the CapeTalk Daily and Weekly Newsletters https://buff.ly/sbvVZD5 Follow us on social media: CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

A Nobel laureate on why we should sometimes trust scientists, and not politicians, to fix the futurePeter Agre won the Nobel Prize for Chemistry in 2003, but he's not interested in playing God. Or even know-it-all. “When Nobel Prize winners start predicting what the stock market would do, or who's going to win the World Series, they may be beyond their specialty,” he says. Yet in his new book, Can Scientists Succeed Where Politicians Fail?, Agre claims that scientists have succeeded in defusing international crises where politicians have failed. He uses the 2015 Iran nuclear accord as an example, arguing that it only happened because two MIT-trained physicists spoke the same scientific language and brought presents for each other's grandchildren. Then Trump canceled it. Now, with RFK Jr. running American health policy and the CDC “decimated,” he fears for catastrophe. Peter Agre may not quite be God. But he's about as close as we will get in our polarized and paranoid world. * Science diplomacy works when politicians deadlock. The 2015 Iran nuclear accord succeeded because two MIT-trained physicists—Ernest Moniz and Ali Akbar Salehi—could speak the same technical language and find common ground where politicians like John Kerry and Javad Zarif had reached a standstill. They started by bringing presents for each other's grandchildren.* Trump's cancellation of the Iran deal exemplifies political failure. After scientists brokered a successful nuclear agreement involving the P5+1 nations, Trump withdrew from it, believing the deal wasn't “tough enough.” The result: “we're back to round zero,” undermining years of scientific diplomacy.* The bipartisan consensus on science has collapsed. During the Sputnik era, Republicans and Democrats united to fund NASA and transform American science education. Today, that unity is gone—COVID politicized science, Fauci became a lightning rod, and the traditional respect for scientific expertise has eroded across the political spectrum.* RFK Jr.'s health policies reflect “a lack of fundamental understanding.” Agre warns that Kennedy's anti-vaccine stance and the decimation of the CDC under his leadership are “dangerous” and “counterintuitive.” Measles, virtually absent from the Western Hemisphere, is now returning without leadership response. Catastrophe, Agre suggests, is not a question of if but when.* Scientists must inform policy without becoming know-it-alls. Agre argues that scientists shouldn't make all decisions but must make information accessible to those in power. The challenge: maintaining credibility and trust in an era when Americans are increasingly skeptical of expertise, and when standing up for science risks becoming unavoidably political.Keen On America is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Last week, several Nobel laureates and high-profile celebrities cautioned that the threat of artificial intelligence is real, particularly regarding what's known as artificial superintelligence. Max Tegmark, head of The Future of Life Institute and a professor doing AI research at MIT, spoke to The World's Host Marco Werman about why experts — including him — are calling for urgent action. The post Nobel laureates sound the alarm over artificial superintelligence appeared first on The World from PRX.

Last week, several Nobel laureates and high-profile celebrities cautioned that the threat of artificial intelligence is real, particularly regarding what's known as artificial superintelligence. Max Tegmark, head of The Future of Life Institute and a professor doing AI research at MIT, spoke to The World's Host Marco Werman about why experts — including him — are calling for urgent action. The post Nobel laureates sound the alarm over artificial superintelligence appeared first on The World from PRX.

Joel Mokyr is a professor at Northwestern University, who — along with Philippe Aghion and Peter Howitt — won the Nobel prize in economics earlier this week. Today, Mokyr joins the program to discuss how major technological changes can boost economic growth — that is, if politics and institutions can adapt quickly enough. Plus, why the bankruptcies of First Brands and Tricolor Holdings are raising questions about private credit markets and big banks' exposure to them.

Joel Mokyr is a professor at Northwestern University, who — along with Philippe Aghion and Peter Howitt — won the Nobel prize in economics earlier this week. Today, Mokyr joins the program to discuss how major technological changes can boost economic growth — that is, if politics and institutions can adapt quickly enough. Plus, why the bankruptcies of First Brands and Tricolor Holdings are raising questions about private credit markets and big banks' exposure to them.

Episode 208: Nitric Oxide - The Missing Link in Thyroid & Chronic Disease with Dr. Nathan Bryan In this episode, Dr. Eric Balcavage is joined by nitric oxide researcher and author Dr. Nathan Bryan, whose new book The Secret of Nitric Oxide explores why this tiny signaling molecule may be the key to preventing and reversing chronic illness. Together, they dive deep into: What nitric oxide is and why it's foundational for human health. How nitric oxide is made in the body—through both enzymatic and dietary pathways. The connection between nitric oxide, inflammation, oxidative stress, and the Cell Danger Response (CDR). How nitric oxide influences thyroid hormone production, conversion (T4 → T3), and the rise of reverse T3. Why loss of nitric oxide is one of the earliest triggers in chronic disease, including Hashimoto's, cardiovascular disease, Alzheimer's, and more. Common disruptors of nitric oxide production—nutrient deficiencies, environmental toxins, poor lifestyle habits, and even fluoride exposure. Practical steps to restore nitric oxide production naturally for better thyroid, metabolic, and overall health. This is a powerful conversation that reframes nitric oxide not as just a cardiovascular molecule, but as a master regulator of cellular health, energy production, and thyroid physiology.

This episode is a first for the show - a repeat of a previously posted interview on The New Quantum Era podcast! I think you'll agree the reason for the repeat is a great one - this episode, recorded at the APS Global Summit in March, features a conversation John Martinis, co-founder and CTO of QoLab and newly minted Nobel Laureate! Last week the Royal Swedish Academy of Sciences made an announcement that John would share the 2025 Nobel Prize for Physics with John Clarke and Michel Devoret “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” It should come as no surprise that John and I talked about macroscopic quantum mechanical tunnelling and energy quantization in electrical circuits, since those are precisely the attributes that make a superconducting qubit work for computation.  The work John is doing at Qolab, a superconducting qubit company seeking to build a million qubit device, is really impressive, as befits a Nobel Laureate and a pioneer in the field. In our conversation we explore the strategic shifts, collaborative efforts, and technological innovations that are pushing the boundaries of quantum computing closer to building scalable, million-qubit systems. Key HighlightsEmerging from Stealth Mode & Million-Qubit System Paper:Discussion on QoLab's transition from stealth mode and their comprehensive paper on building scalable million-qubit systems.Focus on a systematic approach covering the entire stack.Collaboration with Semiconductor Companies:Unique business model emphasizing collaboration with semiconductor companies to leverage external expertise.Comparison with bigger players like Google, who can fund the entire stack internally.Innovative Technological Approaches:Integration of wafer-scale technology and advanced semiconductor manufacturing processes.Emphasis on adjustable qubits and adjustable couplers for optimizing control and scalability.Scaling Challenges and Solutions:Strategies for achieving scale, including using large dilution refrigerators and exploring optical communication for modular design.Plans to address error correction and wiring challenges using brute force scaling and advanced materials.Future Vision and Speeding Up Development:QoLab's goal to significantly accelerate the timeline toward achieving a million-qubit system.Insight into collaborations with HP Enterprises, NVIDIA, Quantum Machines, and others to combine expertise in hardware and software.Research Papers Mentioned in this Episode:Position paper on building scalable million-qubit systems 

From hiding, Venezuelan opposition leader Maria Corina Machado reacts to her Nobel Peace Prize, announced Friday, and tells NPR's Ayesha Rascoe why she dedicated the prize in part to President Trump.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

Back in the 1980s, when Shimon Sakaguchi was a young researcher in immunology, he found it difficult to get his research funded. Now, his pioneering work which explains how our immune system knows when and what to attack, has won him a Nobel prize.Sakaguchi, along with American researchers Mary Brunkow and Fred Ramsdell, were jointly awarded the 2025 Nobel prize in physiology or medicine for the work on regulatory T-cells, known as T-regs for short, a special class of immune cells which prevent our immune system from attacking our own body.In this episode Sakaguchi tells The Conversation about his journey of discovery and the potential treatments it could unlock.This episode was produced by Mend Mariwany, Katie Flood and Gemma Ware. Sound design and mixing by Michelle Macklem and theme music by Neeta Sarl. Read the full credits for this episode and sign up here for a free daily newsletter from The Conversation.If you like the show, please consider donating to The Conversation, an independent, not-for-profit news organisation.Metal-organic frameworks: Nobel-winning tiny ‘sponge crystals' with an astonishing amount of inner spaceNobel physics prize awarded for pioneering experiments that paved the way for quantum computersHow does your immune system stay balanced? A Nobel Prize-winning answerNobel medicine prize: how a hidden army in your body keeps you alive – and could help treat cancer

BUFFALO, NY - October 1, 2025 – Oncotarget is proud to announce that its Editor-in-Chief, Wafik S. El-Deiry, MD, PhD, FACP, will chair the WIN Symposium as the Oncology Track of the Advancing Precision Medicine (APM) Annual Conference held October 3–4, 2025, at the Pennsylvania Convention Center in Philadelphia. The WIN Consortium annual symposium featured as the Oncology Track of the APM Annual Conference 2025 unites global leaders in oncology, translational science, and precision medicine. This year's program features keynote lectures, multi-track sessions– WIN Symposium, Multi-Omics Integration and Precision Medicine Outside of Oncology– and networking opportunities designed to accelerate the translation of research into clinical practice. Highlights include: --A keynote at opening of the WIN Symposium in Philadelphia by William G. Kaelin, Jr., MD — 2019 Nobel Laureate. --Other luminaries in Oncology are speaking, including AACR President Lillian Siu, MD and AACR President-Elect Keith Flaherty, MD along with internationally recognized leaders in precision oncology. --A world-class precision oncology molecular tumor board and oral presentations from the most competitive abstracts are part of the program. --Multi-omics and disease-specific tracks spanning oncology, neurology, cardiovascular disease, rare disease, and infectious disease. --Opportunities for collaboration among scientists, clinicians, industry innovators, and policymakers. Registration is still open. Attendance is free for students, academic/government/non-profit participants, healthcare providers, and investors. The event provides CME credits. For full program details, visit the APM Annual Conference website. About WIN Consortium: WIN Consortium is a non-profit association headquartered in France. WIN was the first consortium that assembled all stakeholders of cancer care, from academia, industry, and patient advocates to work together across the globe. The WIN network assembles 34 world-class academic medical centers, industries, research organizations and patient advocates spanning 18 countries and 5 continents, aligned to launch trials to bolster Precision Oncology across the world. It was also the first organization to launch a N-of-One study using transcriptomics in addition to genomics to inform therapeutic choice in the WINTHER study. WIN is the organizer of the WIN symposia in Precision Oncology. To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh

In this conversation, David Peck interviews Lorena Luciano, the director of the documentary Nuns vs.The Vatican. They discuss the film's exploration of the sexual abuse of nuns by clergy, the importance of accountability, and the role of storytelling in advocating for social justice. Lorena shares her personal journey that led her to create the film, emphasizing the need for community support and the courage to speak out against injustices. The conversation highlights the film's themes of love, responsibility, and the power of truth in challenging institutions.Lorena Luciano was born and raised in Italy, where she graduated from the School of Law at Milan University, Lorena Luciano moved to New York City in 1996 to pursue her career in documentary filmmaking. In 1998 her first feature documentary on Italian iconoclast playwright-performer Dario Fo, a Nobel Laureate in Literature, entered the Venice Film Festival's official selection.Lorena is the recipient of several prestigious artist grants such as the MacArthur Foundation, the New York State Council on the Arts, the Ben & Jerry Foundation, and Chicken & Egg Pictures. Her media work focuses on social issues, the environment, the human rights as well as on the arts. Her films, winners of numerous awards, have been screened and distributed internationally.She lives in New York with her two children and her husband and film partner Filippo Piscopo.David Peck is a writer, speaker, and award-winning podcaster who works at the intersection of storytelling, social change, and meaningful dialogue. As the host of Face2Face and former host of Toronto Threads on 640 AM, he has published over 650 in-depth interviews with some of the world's most compelling thinkers, artists and storytellers, including Viggo Mortensen, Sarah Polley, Raoul Peck, Werner Herzog, Chris Hadfield, David Cronenberg, Gillian Anderson, Jason Issacs and Wade Davis. With a background in philosophy and international development, David brings a thoughtful, globally aware perspective to every conversation. He's a published author and experienced keynote speaker, known for creating spaces where complexity is welcomed and ideas come alive. Whether moderating panels, hosting live events, or speaking on issues ranging from ethics to media, David's work is grounded in a deep curiosity about people. At heart, he simply loves good conversation — and believes it's one of the best ways we grow, connect, and make sense of the world.For more information about David Peck's podcasting, writing and public speaking please visit his site here.F2F Music and Image Copyright: David Peck, ICBL and Face2Face. Used with permission. Hosted on Acast. See acast.com/privacy for more information.

The queens descend upon Pittsburgh for a bittersweet (but dishy) tribute for Ed Ochester (1939-2023).Please Support Breaking Form!Review the show on Apple Podcasts here.Aaron's STOP LYING is available from the Pitt Poetry Series.James's ROMANTIC COMEDY is available from Four Way Books.SHOW NOTES:For more about the weekend events and about Ed Ochester's impact on American poetry, read here and here and here.The Agnes Lynch Starrett Poetry Prize carries a cash award of $5,000 and publication by the University of Pittsburgh Press as part of the Pitt Poetry Series. Submissions are accepted March 1--April 30. For more about Southern Methodist University's Project Poetica, read here. Read more about the George Garrett Award for Outstanding Community Service in Literature here. Damon Young is a writer, critic, humorist, satirist, and (as he says on his website) "professional Black person." He's a co-founder and editor in chief of VerySmartBrothas—coined "the blackest thing that ever happened to the internet" by The Washington Post and recently acquired by Univision and Gizmodo Media Group to be a vertical of The Root—and a columnist for GQ. Visit his website at https://www.damonjyoung.comAccording to CruisingGays.com, the Cathedral of Learning's 2nd and 8th floor bathrooms were popular cruising spots. The International Poetry Forum launched in 1966 with a reading that featured Archibald MacLeish. Since then, alumni of the series include nine Nobel Laureates, 14 Academy Award recipients, 28 U.S. Poets Laureate, 39 National Book Award winners, and 47 Pulitzer Prize winners.Joy Priest is the author of HORSEPOWER (Pitt Poetry Series, 2020), selected by the 19th U.S. Poet Laureate Natasha Trethewey as the winner of the Donald Hall Prize for Poetry, and the editor of Once a City Said: A Louisville Poets Anthology (Sarabande, 2023). Visit her website here.Check out Pittsburgh's City of Asylum here: https://cityofasylum.orgMonroeville is about 15 miles east of Pittsburgh. Read Ed's poem titled "Monroeville"; several others can be found online at the Poetry Foundation here.Thanks to Nancy Krygowski and Jeffrey McDaniel and Terrance Hayes for putting together an incredible, moving weekend to a brilliant editor, mentor, and friend. We miss you, Ed.

The 2021 winner of the Nobel Prize in medicine, Ardem Patapoutian speaks of his love for science, why he wishes he had an MD, and the importance of getting out of the lab to inspire young people. This podcast is intended for healthcare professionals only. To read a transcript or to comment, visit: https://www.medscape.com/author/bob-harrington Nobel Prize in Physiology or Medicine 2021 https://pubmed.ncbi.nlm.nih.gov/39429349/ https://www.nobelprize.org/prizes/medicine/2021/press-release/ Piezo1 and Piezo2 are essential components of distinct mechanically activated cation channels https://pubmed.ncbi.nlm.nih.gov/20813920/ PIEZOs mediate neuronal sensing of blood pressure and the baroreceptor reflex https://doi.org/10.1126/science.aau6324 PIEZO Ion Channels in Cardiovascular Functions and Diseases https://doi.org/10.1161/CIRCRESAHA.123.322798 You may also like: Hear John Mandrola, MD, with his summary and perspective on the top cardiology news each week, on This Week in Cardiology https://www.medscape.com/twic Questions or feedback, please contact news@medscape.net

As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe In this episode, I speak with Nobel laureate Gerard 't Hooft, a theoretical physicist known for his work on the electroweak interaction and his radical ideas about quantum mechanics. To him, the universe is a cosmic pinball machine. Every ball follows a fixed path. No randomness. No mystery. We only invented quantum mechanics to cope with our ignorance. In his picture, there are no real numbers. No wave functions. No superposition. Just discrete states clicking forward, one after another, beneath everything we see. Join My New Substack (Personal Writings): https://curtjaimungal.substack.com Listen on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e Timestamps: - 00:00 - Why Quantum Mechanics is Fundamentally Wrong - 05:00 - The Frustrating Blind Spots of Modern Physicists - 11:27 - The "Hidden Variables" That Truly Explain Reality - 17:00 - The "True" Equations of the Universe Will Have No Superposition - 23:00 - Our Universe as a Cellular Automaton - 30:02 - Why Real Numbers Don't Exist in Physics - 39:14 - Can This Radical Theory Even Be Falsified? - 46:29 - How Superdeterminism Defeats Bell's Theorem - 58:19 - 't Hooft's Radical View on Quantum Gravity - 1:08:24 - Solving the Black Hole Information Paradox with "Clones" - 1:14:00 - What YOU Would Experience Falling Into a Black Hole - 1:20:17 - How 't Hooft Almost Beat a Nobel Prize Discovery Links Mentioned: - Gerard's site: https://webspace.science.uu.nl/~hooft101/ - Gerard's papers: https://inspirehep.net/authors/1019113 - Cellular Automaton Interpretation Of Quantum Mechanics [Book]: https://www.amazon.com/Cellular-Automaton-Interpretation-Mechanics-Fundamental/dp/3319823140 - David Wallace [TOE]: https://youtu.be/4MjNuJK5RzM - Emily Adlam & Jacob Barandes [TOE]: https://youtu.be/rw1ewLJUgOg - Roger Penrose [TOE]: https://youtu.be/sGm505TFMbU - Conway's Game Of Life: https://playgameoflife.com/ - Julian Barbour [TOE]: https://youtu.be/bprxrGaf0Os - Emily Adlam [TOE]: https://youtu.be/6I2OhmVWLMs - Sabine's video on Gerard: https://youtu.be/2kxoq5UzAEQ - Sabine Hossenfelder [TOE]: https://youtu.be/E3y-Z0pgupg - Tim Palmer [TOE]: https://youtu.be/vlklA6jsS8A - Carlo Rovelli [TOE]: https://youtu.be/hF4SAketEHY - Stephen Wolfram [TOE]: https://youtu.be/0YRlQQw0d-4 - Bernardo Kastrup & Sabine Hossenfelder [TOE]: https://youtu.be/kJmBmopxc1k - Tim Maudlin [TOE]: https://youtu.be/fU1bs5o3nss - Jacob Barandes [TOE]: https://youtu.be/wrUvtqr4wOs - Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 - Claudia De Rham [TOE]: https://youtu.be/Ve_Mpd6dGv8 - Neil Turok [TOE]: https://youtu.be/ZUp9x44N3uE - Latham Boyle [TOE]: https://youtu.be/nyLeeEFKk04 - David Kaiser [TOE]: https://youtu.be/_yebLXsIdwo - String Theory Iceberg [TOE]: https://youtu.be/X4PdPnQuwjY - Birth of Asymptotic Freedom [Paper]: https://www.sciencedirect.com/science/article/abs/pii/0550321385902068 - How To Become A Good Theoretical Physicist [Article]: https://www.goodtheorist.science/index.html SUPPORT: - Become a YouTube Member (Early Access Videos): https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Support me on Patreon: https://patreon.com/curtjaimungal - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 SOCIALS: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs Guests do not pay to appear. Theories of Everything receives revenue solely from viewer donations, platform ads, and clearly labelled sponsors; no guest or associated entity has ever given compensation, directly or through intermediaries. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

USA TODAY White House Reporter Davis Winkie breaks down what nuclear experts said last week about the current state of nuclear threats and what to do about it.Support for President Donald Trump's immigration policies fell in a new poll.USA TODAY National Immigration and Border Reporter Lauren Villagran discusses Louisiana's place as a major immigration detainer.Israeli fire killed 67 people seeking aid in Gaza.WNBA All-Stars make a CBA statement with 'Pay Us What You Owe Us' shirts.Please let us know what you think of this episode by sending a note to podcasts@usatoday.com.Episode Transcript available hereSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.