Podcasts about simulating

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Why treating the cell, not the protein, could turn chronic disease treatment into something closer to a cure. You've built single-cell pipelines that spit out clusters, p-values and target lists, but nothing that survives contact with the clinic. What if the clustering method itself is quietly leading you astray? Adam Freund is Founder and CEO of Arda Therapeutics, a biotech using single-cell sequencing to find the pathogenic cells driving chronic disease. He spent seven years as a Principal Investigator at Calico Life Sciences, building a research lab on the biology of ageing and helping grow the company from 15 to more than 200 people, and holds a PhD in Molecular and Cell Biology from UC Berkeley. You'll get a working model for how Arda's discovery engine turns single-cell and spatial transcriptomic data into causal cell targets. Adam explains why a common statistical shortcut in single-cell analysis produces disease signals that don't hold up and how cell depletion could replace daily dosing with a handful of treatments that reset the immune system. Ross and Adam cover how Arda finds pathogenic cell populations across hundreds of donors, why chi-squared tests on cell clusters can substitute cell count for donor count without anyone noticing, and how B-cell depletion therapies proved that removing a cell can beat blocking its pathway. This one is for data science leaders and computational biologists building single-cell pipelines, not listeners after a general intro to drug discovery. Key Takeaways - Chi-squared tests on cell clusters draw their statistical power from the number of cells, not the number of donors, so a single oversampled patient can produce the same p-value as a hundred-donor study. - Rituximab clears 100% of B cells from circulation yet does nothing for lupus because the disease-driving cells live in tissue, not blood, a lesson now shaping where Arda tests its own molecules. - Neighborhood analysis scores each cell by the donor identity of its nearest neighbours rather than forcing cells into predefined clusters, producing a continuous disease-enrichment map with no cluster boundaries. - When depleted cells regrow, they often come back without the trait that made them harmful in the first place, which means a handful of doses can hold a chronic disease in remission for months. Chapter Markers 00:00 Why cell depletion beats pathway blocking 01:05 Welcome Adam Freund to the show 01:30 From Calico Life Sciences to founding Arda 03:29 Why blocking one pathway rarely works 05:32 B-cell depletion as the proof of concept 08:14 Building a modular library of depletion tools 10:46 Single-cell sequencing removes the need for a hypothesis 11:43 Why clustering is a dial, not ground truth 15:24 The chi-squared trap in single-cell analysis 20:40 Neighbourhood analysis and donor-weighted scoring 23:44 Moving from enrichment to causality 26:32 Inside Arda's lead fibrosis program 30:33 Why solid tissue testing beats blood samples 34:25 Simulating depletion in spatial transcriptomic data 38:49 The case for intermittent dosing over daily pills 43:58 The data infrastructure behind Arda's platform 48:46 Where spatial and protein data are heading Useful Links & Resources - Adam Freund on LinkedIn: https://www.linkedin.com/in/adam-freund-0657654 - CorrDyn: https://corrdyn.com Connect With the Show - Host Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Host Ross Katz on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ If your team runs single-cell pipelines, how do you currently decide on the number of clusters, and have you ever checked whether your significance scales with donor count rather than cell count? Tell us in the comments; we're building a running list of data QA checks for biotech data science teams. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data.

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Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: www.holmbergpodcast.com, www.98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Great athletes don't train to play hurt. Train like the best and bring your best hour to the LSAT each day. Read more on our website. Email daily@lsatdemon.com with questions or comments. Watch this episode on YouTube!More LSAT Demon Resources.

If you enjoy this episode, please take out a subscription to 10 Percent True at: https://www.10percenttrue.com/pricing-plans/list.10PCT EP88 Dave “Khan” Carr – F-15, F-16, Weapons School & AggressorsDave “Khan” Carr joins 10 Percent True to discuss an extraordinary fighter career flying the F-15 Eagle and F-16 Viper.From Cold War intercepts over Alaska and Iceland, to the Fighter Weapons School, Red Flag Aggressors, and teaching the next generation of fighter pilots, Khan offers a rare insight into the reality of high-end air combat.This is a deep dive into how the USAF prepared to fight the Soviet Union, how air combat tactics evolved through the Cold War and beyond, and what it really takes to become a top-tier fighter pilot.Along the way, Khan shares stories of Arctic alert missions, Weapons School pressure, Aggressor training, massive Red Flag battles, Soviet threat replication, and how the Eagle and Viper compared in a fight.If you've ever wanted to understand what really happens behind the scenes in elite fighter aviation, this is one you won't want to miss.⸻0:00 Intro Teaser – Eagle vs Viper Differences2:20 A Quick Word from Steve4:04 Welcome Khan4:50 Khan Introduces Himself6:40 First Tour at Elmendorf – Flying the Eagle in Alaska8:38 Early Career Challenges and Encounters12:12 Discussing Losses in the Early Days14:08 Dealing with Loss15:34 Eyeing the Next Career Step – and Making It Happen17:17 Keflavik – CFTs and Diversions to Scotland19:47 Steve Geeks Out on CFTs21:14 The Mission at Keflavik – Bears, Tomcats and Lightnings (with Tankers)23:04 Honing Air-to-Air Skills as the Eagle Matured25:28 Dialling Up the AoA – “It Depends…”26:55 Rudder Use, When to Use It, and Evolving Tactics28:32 Weapons School – Selection, Work-Ups and Challenges32:40 A Memorable Weapons School Sortie – Vark Speed and Perfect Execution35:58 Why No “Super Squadrons”?37:24 The Benefits of Weapons School Graduation40:30 Expertise Across All Facets of the Mission41:52 Tyndall and the FTU44:35 Does It Get Better Than This?46:18 Eagle Culture – and Did It Change?49:52 Peak Performance or Room for Improvement? The Importance of COMMS54:12 Getting Granular – What It Takes to Make It in the Eagle Community56:34 Regrets About Missing Desert Storm?58:12 Joining the Aggressors59:48 Being a “True” Aggressor1:04:25 Gloves Off?1:06:10 Simulating the Threat as Accurately as Possible – Who Sees Who?1:09:48 Maintaining Situational Awareness1:10:55 Becoming a Threat System SME – The Process1:13:05 Expectation vs Reality as More Information Became Available1:15:50 “Natural” Bias?1:17:54 Views on Threat Advantages and Capabilities1:21:45 Eagle vs Viper Comparisons (Intro Teaser Topic)1:25:02 The “Bad Bob” (VX-9 F-14D) Encounter1:26:55 Toughest Opponent as an Aggressor?1:30:19 When Things Don't Go to Plan – Scaring Yourself1:34:10 Eating Shit as an Eagle Guy Flying Vipers?1:34:54 Twilight of a Career – Guard Life, MSIP A Models, NVGs and Iraq1:39:32 Young vs Old Eagle Driver1:42:14 Keeping Up with Evolving Tactics and Changes1:49:30 Fini Flight1:53:15 Do You Miss It?1:53:54 Thanks Khan – Till Next Time

In this episode, we sit down with Stefan Sobkowiak, permaculture educator and renowned Youtuber at The Permaculture Orchard, to explore how to design and manage holistic, diverse agricultural ecosystems. We dive deep into his guiding philosophy that “nothing beats easy,” his background in wildlife biology, and the catastrophic tent caterpillar outbreak that transformed his conventional apple orchard into a biodiversity lab. Stefan deconstructs Bill Mollison's three phases of abundance and introduces his innovative “grocery store” design concept, explaining how grouping companion trees by harvest date completely revolutionizes the pick-your-own farm experience. The conversation highlights the powerful ecological synergies of combining perennial fruit trees with nitrogen-fixing species, annual vegetables, and pastured poultry to naturally optimize soil fertility. Finally, we discuss his playful "magic model" for farm planning, his perspective on utilizing AI as a personalized learning tutor, and how his parent's experience during World War II instilled in him the importance of food security, resilience, and human relationships. Timestamps [00:00] Intro [05:16] Reflecting on Diego Footer and the profound early community legacy of Permaculture Voices.[08:52] "Nothing beats easy" - Choosing crops based on what grows like a weed.[15:33] The side quest from wildlife biology to landscape architecture to building functional animal habitats.[20:51] Professor Caterpillar - Surviving a catastrophic monoculture harvest and shifting to permaculture design.[30:22] Simulating a volcano: Unlocking high-yielding soil dynamics through compost and basalt rock dust.[45:31] Bill Mollison's blueprint: Replanting a diverse layout with nitrogen-fixing guilds.[56:07] Launching a low-cost nursery. [01:08:03] Deconstructing Mollison's three phases of abundance and tracking regional seed adaptations.[01:21:25] Seasonal taste cravings and how nutrient density triggers our instinctive diet.[01:42:10] Designing the "grocery store" layout to group crops by seasonal harvest dates.[02:05:1]1 Rapid Fire Q&A: Reading Sir Albert Howard, using AI as a tutor, and generational memory.SponsorsDubois Agrinovation: Get 10% off by choosing the promo code ‘MasterClass – Jean-Martin Fortier' when you create an account. Some exceptions apply. https://duboisag.com/Links/ResourcesStart Your Market Gardener Journey Here : https://themarketgardener.com/starthere/Market Gardener Institute:  https://themarketgardener.com Masterclass:  https://themarketgardener.com/courses/the-market-gardener-masterclass Newsletter:  https://themarketgardener.com/newsletterBlog:  https://themarketgardener.com/blog Books: https://themarketgardener.com/booksGrowers & Co: https://growers.coHeirloom: https://heirloom.ag/The Old Mill: https://www.espaceoldmill.com/en/Follow UsWebsite: http://themarketgardener.com Facebook: http://facebook.com/marketgardenerinstitute Instagram: http://instagram.com/themarketgardeners Guest Social Media LinksStefan:Youtube: https://www.youtube.com/c/stefansobkowiak Instagram: https://www.instagram.com/stefansobkowiak/ Website: https://miracle.farm/ Masterclass: https://permaculture.study/ JM:Instagram: https://www.instagram.com/jeanmartinfortierFacebook: https://www.facebook.com/jeanmartinfortier

Zach Ragan and Austin Stanley of The Big Orange Podcast preview the Tennessee Volunteers' schedule for 2026 and give their prediction on how the Vols' season will go. The Texas and LSU games jump off the charts but how will the Vols fair overall in each of their games in 2026 and what will their final record be at the end of the year? Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailYou can hit targets, run the tests, and still hear the same question at the end of a fitting: “What will it sound like when I leave your office?” That one line exposes the biggest challenge in hearing healthcare right now, turning great clinical data into real-world confidence.I'm Blaise Delfino, M.S. - HIS, and I recorded a different kind of listen this week: a narrated reading of an interview I co-authored with Dr. Dave Fabry and Madison Levine, BC-HIS on AudiologyOnline. We dig into ecological momentary assessment (EMA), a method that captures patient behaviors, emotions, and listening challenges in real time out in the world, and Symphonia, a tool that brings realistic listening scenarios into the clinic through simulated environments. They're not competing ideas. They're complementary ways to close the loop between what patients report in restaurants, meetings, church, and the car, and what we can demonstrate and adjust during an appointment.We also get specific about evidence-based hearing aid fitting. Real ear measurement and probe microphone verification remain essential for audibility and comfort, but they don't replicate the messy dynamics of daily listening. That's where speech-in-noise testing, expectation setting, and hands-on counseling matter. We talk about why two patients with similar audiograms can need very different personalization, and how demonstrating programs, noise management, directionality, and comfort settings in a simulated soundscape can make those choices finally “click,” especially for first-time users.If you care about audiology, hearing technology, patient-centered care, and better hearing aid outcomes, queue this up and listen through. Subscribe to Hearing Matters, share this with a colleague, and leave a review with your biggest real-world listening challenge.Connect with the Hearing Matters Podcast TeamEmail: hearingmatterspodcast@gmail.com Instagram: @hearing_matters_podcast Facebook: Hearing Matters Podcast

Winning feels good, but the greatest lessons in my life didn't come from winning—they came from losing.In this episode, my friend Mike and I talk about why I actually want my sons to experience failure. We discuss how losses shape character, why struggle is necessary for growth, and how the things that don't seem to be working are often quietly working on us.If my boys ever ask me, "Dad, why would you let me fail?" I hope this conversation helps them understand that many of life's greatest victories are built on the foundation of loss.https://youtu.be/9hjtf1dHIM4Instagram: @roycelaguertaWebsite: https://www.nvfitt.comEmail: hello@nvfitt.comSupport the Show: https://www.buzzsprout.com/862429/sup...Key Moments03:40 - Chapter 1: How do I train for losses?36:51 - Chapter 2: Simulating games where loses are common.49:18- Chapter 3: If the thing your doing is not working, it's working on you.Support the show

Ken Carman and Anthony Lima dive into a hypothetical 2026 sports landscape, debating if Kobe Altman can successfully bring LeBron James back to Cleveland. They analyze a simulated NBA offseason where the Lakers roster features Luka Doncic and Walker Kessler, while also reflecting on Johnny Manziel's Browns career. They also recap a fictional US Men's Soccer World Cup run and the Guardians' success against the Rangers. 01:52 - Kobe Altman's Cavs Strategy 06:55 - NBA Offseason Movement 11:40 - LeBron Return Rumors 17:12 - Why Manziel's Numbers Lie 26:08 - LeBron's Lakers Legacy 37:37 - Soccer And Guardians Recap 42:12 - Stephen A On Lakers

00:00 Intro00:54 Taiwan Begins Drill Simulating War Scenario02:16 China Sanctions U.S. Rare Earth, Defense Firms04:03 Senators Urge FCC to Probe China-Made Health Wearables05:06 President Trump to Visit China Again05:48 Beijing's New Law Threatens U.S. Citizens07:10 China Implements ‘Ethnic Unity Promotion' Law10:01 Cuban Intel Facilities Show Possible Links to China11:20 U.S. and Uzbekistan Launch Joint Investment Platform12:09 How Taiwanese People Are Preparing for a Chinese Attack12:52 Returning From Wall Street to Taiwan15:27 How China's Rise Put Taiwan in the Crosshairs17:31 Protecting Democracy Through Resilient Societies18:35 Why the World Can't Ignore Taiwan

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

Brianna Bruce of Livin' Life Adventures has guided Washington waters for 15 years and she fishes just about everything that swims. In this episode, she's back to share what makes Baker Lake sockeye one of the most unique and rewarding fisheries in the state, including the gear tweaks, troll speeds, and bait strategies that separate limits from empty coolers.In this episode:Why Baker Lake produces the biggest, brightest sockeye in Washington — and why the fish arrive in such good conditionThe troll speed mistake that kills most people's chances Why dodgers outperform flashers, and how leader length affects your hook-upsBait and scent strategies that are legal at Baker but not allowed at other sockeye lakesHow to fish the right depth Brianna's guiding philosophy: why the best trips aren't always about the fish countTimestamped Chapters:00:00 - Welcome and intro to Briana Bruce01:00 - Briana's background: a lifelong angler turned 15-year guide05:00 - Guide philosophy: making memories when the fish don't cooperate08:00 - Species and seasons: where Briana fishes year-round in Washington14:00 - Why Baker Lake is her favorite fishery in the state17:00 - 2025 season dates, limits, and run size forecast20:00 - The mistakes anglers make: troll speed, dodgers, leaders, and bait23:00 - Simulating a school and finding active fish25:00 - The one that got away storyKey Takeaways:Troll significantly slower than kokanee setupsShort leaders outperform long flasher-style leaders in this fisheryLive sand shrimp early season, cured coon shrimp mid-season, cured prawn chunks late seasonFish the 20–40 foot zone — deep marks are usually bull trout or inactive fishRun extra rods or dummy flashers to mimic a school and trigger more bitesBaker Lake opens July 11th tentatively this year with a starting limit of four fishThe run forecast Resources & Links:Briana Bruce / Livin' Life Adventures: livinlifeadventures.comBook a charter or connect: gofish@livinlifeadventures.com | Text: 206-714-2112Facebook / Instagram / TikTok / YouTube: search Livin' Life AdventuresReady to go deeper? Join the waitlist for Anglers Unlimited Gold membership at https://anglersunlimited.co/gold — get access to exclusive trainings like the full Baker Lake Sockeye session with Briana, plus expert seminars, step-by-step courses, fishing maps, and a community of 60+ anglers who want you to succeed.Fishing for a Reason is the Pacific Northwest saltwater fishing education podcast for new anglers and families who want to catch more salmon, halibut, lingcod, shrimp, and crab in Washington waters. Hosted by Jamie & Scott Propst from Anglers Unlimited, each episode delivers practical techniques, local knowledge, and expert insights to help you get off the couch and into the fish. Perfect for relocated professionals, military families, and boaters who are just getting into fishing.

Welcome to Standing Stone Kennels! Ethan sits down with Tessa — one of the most integral parts of the Standing Stone program — for a deep catch-up on her growth as a trainer, the dogs she's been developing, and the honest lessons she's learning along the way.In this episode:00:00 — Welcome & Introduction00:42 — Catching Up with Tessa: How She's Growing01:11 — Sky's Trained Retrieve — When a Dog Makes It Too Easy07:17 — The Nix Parallel: A Classic Trained Retrieve Trap08:03 — Walking Through the Trained Retrieve Steps12:02 — Toe Pressure vs. Collar Pressure: Where the Breakdown Happened14:00 — Training Table Rules & Funny Stories (Thunder & the Food Table)19:00 — Water Introductions: Tessa's Approach & Tips for Hesitant Dogs24:52 — Rave's Light Switch Moment27:24 — Growing as a Trainer: Removing the Helpful Hands28:43 — The Muscle Analogy — Why Stress Is Required for Growth32:37 — Healing Mechanics & Over-Accommodating Your Dog35:00 — The Danger of Teaching Cool Tricks to Hunting Dogs41:00 — NAVHDA Test Prep & What Uncle Rich Is Working Toward51:32 — Pheasant Tracking: Why We Changed Our Entire Approach54:57 — Simulating a Shot Pheasant for Tracking Development01:01:13 — Tessa's Goals Moving Forward01:02:20 — Women's Pheasant Hunt in South Dakota — Spots Available01:06:12 — Wrap UpSend Us Mail5919 W Pleasant Valley RdPretty Prairie, KS 67570LinksStep-By-Step Dog Training Course: https://www.standingstonesupply.com/coursesJoin our Patreon Community - https://bit.ly/SSK-PatreonOur Store - https://bit.ly/SSK-StoreSocial MediaFacebook: www.facebook.com/StandingStoneKennelsInstagram: www.instagram.com/standingstonekennels/Website: www.standingstonekennels.comEthan and Kat Pippitt are the proud owners of Standing Stone Kennels. They breed German Shorthaired pointers and train all types of dogs for the hunt and the home. Their training strategies are easy to follow and are flexible to meet the needs of individual dogs. They are avid outdoorsmen and when they aren't training dogs they spend their free time hunting all kinds of game across the United States.We use affiliate links to help support the channel. If you would like to support Standing Stone content we appreciate you using the links in the description of this video.Subscribe to our channel here: http://bit.ly/2Dyy9DW

In this episode, I sit down with Prukalpa Sankar, the founder of Atlan, to discuss the missing piece that makes artificial intelligence actually useful in the enterprise: context. We dive deep into building the "second brain" of a company, the reality of agent development, and how to transition a traditional business into an AI-native organization. If you're looking to understand why your AI agents are getting abandoned in testing hell or how the roles of data and engineering are fundamentally shifting, this is the conversation for you. As always, we keep it practical and grounded. No hype, just education from the front lines of data architecture.What an Enterprise Context Layer Actually Is (Prukalpa's new article): https://www.linkedin.com/pulse/what-enterprise-context-layer-actually-prukalpa--avdqc/?trackingId=kq8lIdYdRnKsHu%2BdREYB3Q%3D%3DTimestamps01:15 - The missing half of AI: Contextual intelligence 02:15 - Reverse engineering business context and the second brain 05:06 - Escaping testing hell and hitting the 80% accuracy threshold for agents 07:54 - Simulating context for analytics use cases 11:34 - Does data quality matter for AI agents? 15:37 - Capturing tacit knowledge and human expertise 21:08 - The organizational chart of the future and "E-shaped" humans 26:26 - How Atlan transformed into a completely AI-native company 34:22 - Banning engineers from coding and the new mental model for work 39:05 - Societal resistance, historical context, and embracing technological change 46:00 - Optimism, childlike curiosity, and the path forward

For episode 743 of the BlockHash Podcast, host Brandon Zemp is joined by Andrej Bencic, CEO and Co-Founder of Tenderly, the simulation company for onchain institutions. An engineer by background, he co-founded Tenderly in 2018 and has spent the last eight years building it into the operational layer beneath crypto's most sophisticated protocols, enabling engineering, finance, and risk teams to model every onchain action against the live system before any capital or customer is exposed.  

Episode 374 Google DeepMind is simulating entire worlds using AI - that can be interacted with in real time. “World models” simulate the environment and physics of the real world. And DeepMind's Genie 3 model allows people to create these worlds with basic image and text prompts. The idea is not just to allow people to explore these worlds, but to serve as a testbed for AI agents to learn how to interact with the world before they are deployed in humanoid robotic bodies.  Could this be the next big step towards artificial general intelligence (AGI)? Joshua Howgego speaks to Jack Parker Holder, Research Director at Google DeepMind, about the latest developments. To read more about these stories, visit https://www.newscientist.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this special episode of Focus Check, Johnnie sits down with our guests David Kellermann and Viola Evang, the co-founders of Glaswerk Optics – a German lens manufacturer developing high-end 2x anamorphic cinema lenses. Tune in to this candid conversation about their seven-year journey: from a client arriving with a box of old projection lenses, to engineering some of the most meticulously designed anamorphic glass currently in development. The Glaswerk ONE and ONE+ prime sets are on the verge of shipping, and the story behind them is anything but straightforward.   (00:00) Introduction (00:45) Meet David and Viola – filmmakers turned lens makers (05:30) The moment a client's box of projection lenses changed everything (09:37) Going full frame and funding the project (13:08) The decision to go high-end: no compromises (16:36) First prototype at Cine Gear LA 2019 – the last-screw story (20:19) The development team: optical designers, mechanics, electronics (24:48) The ONE and ONE+: lens design, weight, and bokeh (29:24) Flares, coatings, and customisation (34:00) Why so long? Perfectionism, COVID, and metadata (35:52) Clients, pricing, and who buys these lenses (37:12) DCS metadata: why Glaswerk chose a different standard (44:31) Simulating lenses in 3D: Lens Sim plugin and Hübner Photonics (43:01) Final thoughts on the ONE / ONE+ before launch (44:31) Shipping a cinema lens in 2026: the market has changed (49:17) Could Glaswerk make lower-end lenses one day? (52:37) Wrap-up ➡ Visit Glaswerk's website: https://glaswerk-optics.de/

Get the full, ad-free episode here: https://www.10percenttrue.com/pricing-plans/list10PCT EP88 – Dave “Khan” Carr | F-15 Eagle, F-16 Viper, Weapons School & AggressorsDave “Khan” Carr joins 10 Percent True to discuss an extraordinary fighter career flying both the F-15 Eagle and F-16 Viper.From Cold War intercepts over Alaska and Iceland to the pressure cooker of the USAF Fighter Weapons School, Red Flag, and Aggressor duty, Khan offers a rare behind-the-scenes look at elite fighter aviation.This episode explores how the USAF prepared to fight the Soviet Union, how air combat tactics evolved through the Cold War and beyond, and what it really took to become a top-tier fighter pilot.Along the way, Khan shares stories of Arctic alert missions, Keflavik intercepts, Weapons School work-ups, massive Red Flag battles, Soviet threat replication, and how the Eagle and Viper compared in a fight.If you've ever wondered what really happens behind the scenes in elite fighter aviation, this is one you won't want to miss.Timestamps2:20 A Quick Word from Steve 4:04 Welcome, Khan 4:50 Khan Introduces Himself 6:40 First Tour at Elmendorf – Flying the Eagle in Alaska 8:38 Early Career Challenges & Encounters 12:12 Discussing Losses in the Early Days 14:08 Dealing with Loss 15:34 Eyeing the Next Career Step – and Making It Happen 17:17 Keflavik – CFTs & Diversions to Scotland 19:47 Steve Geeks Out on CFTs 21:14 The Mission at Keflavik – Bears, Tomcats & Lightnings (with Tankers) 23:04 Honing Air-to-Air Skills as the Eagle Matured 25:28 Dialling Up the AoA – “It Depends…” 26:55 Rudder Use, Evolving Tactics & When to Use It 28:32 Weapons School – Selection, Work-Ups & Challenges 32:40 A Memorable Weapons School Sortie – Vark Speed & Perfect Execution 35:58 Why No “Super Squadrons”? 37:24 The Benefits of Weapons School Graduation 40:30 Expertise Across All Facets of the Mission 41:52 Tyndall & the FTU 44:35 Does It Get Better Than This? 46:18 Eagle Culture – Did It Change? 49:52 Peak Performance or Room for Improvement? The Importance of Comms 54:12 Getting Granular – What It Takes to Make It in the Eagle Community 56:34 Regrets About Missing Desert Storm? 58:12 Joining the Aggressors 59:48 Being a “True” Aggressor 1:04:25 Gloves Off? 1:06:10 Simulating the Threat Accurately – Who Sees Who? 1:09:48 Maintaining Situational Awareness 1:10:55 Becoming a Threat System SME – The Process 1:13:05 Expectation vs Reality as More Information Became Available 1:15:50 “Natural” Bias? 1:17:54 Views on Threat Advantages & Capabilities 1:21:45 Eagle vs Viper Comparisons (Intro Teaser Topic) 1:25:02 The “Bad Bob” (VX-9 F-14D) Encounter 1:26:55 Toughest Opponent as an Aggressor? 1:30:19 When Things Don't Go to Plan – Scaring Yourself 1:34:10 Eating Shit as an Eagle Guy Flying Vipers? 1:34:54 Twilight of a Career – Guard Life, MSIP A Models, NVGs & Iraq 1:39:32 Young vs Old Eagle Driver 1:42:14 Keeping Up with Evolving Tactics & Change 1:49:30 Fini Flight 1:53:15 Do You Miss It? 1:53:54 Thanks, Khan – Till Next Time

If you were just catching a flight you wouldn't have known... but employees at the Salt lake City Airport spent their morning simulating an emergency... so they're prepared when disaster strikes. Joining me live is airport spokeswoman Nancy Volmer.

In This Episode of Business Lunch: We discuss how to transform note-taking apps into AI-powered chief of staff systems that manage daily operations, leveraging architectural insights and local data storage for security and efficiency.Chapters:00:00 Introduction to AI as a Chief of Staff00:29 The Limitations of Current AI Tools01:25 Architectural Insights and the Obsidian System02:14 Building a Memory-Enabled AI System03:13 Why Plain Text Markdown Matters04:34 The Web of Interconnected Notes06:03 Traversing the Knowledge Network07:00 Simulating a Human Chief of Staff08:27 Automating System Setup with a Single Prompt09:22 Ensuring Transparent and Permanent Memory11:45 From Thinking to Acting: Automating Operations12:12 Command Line Interface and External Tools14:30 Remote Control and Autonomous Agents15:01 Security Risks of Fully Autonomous AI16:29 Mitigating Prompt Injection Attacks18:26 Balancing Capability and Security19:23 Reflections on AI and Business ManagementConnect with me on social:TikTok: Check out my TikTok HereInstagram: Check out my Instagram HereFacebook: Check out my Facebook HereLinkedIn: Check out my LinkedIn HereSubscribe to my YouTube

Simulating the Lottery 10 times as we prepare for the draft. Giving our NBA playoff update and reacting to NBA awards revealed. Talking news around the Jazz and their offseason. Talking local sports with Utah Mammoth and BYU. IG: tunedinjazz

Hour 3 of Scotty G. & The Coach with Scott Garrard and Tim LaComb. Mark Medina, NBA coverage for Fox Sports Radio NBA Draft Lottery Simulation Sadness for the end of the Mammoth season

https://teachhoops.com/ In this episode, we tackle the "October Plateau"—that frustrating reality where players work hard all summer only to show up in the fall with the exact same skill set. We pull back the curtain on elite performance environments like chess, music academies, and military war games to reveal the hidden architecture of growth. The secret? You have to stop asking your players to "prove it" when they should be "improving it." [0:00] The Psychology of Performance vs. Development Why players "self-protect" and play it safe when they feel judged. The "Chess Master" secret: Studying the mess instead of just playing the game. [08:15] The Lab: Where Messy is the Goal Defining The Lab mode: A zero-gravity environment for experimentation. Why "aggressive mistakes" are the primary metric of success in the off-season. The Coach's shift from "General" to "Scientist." [15:45] The Arena: Testing Under Fire Defining The Arena mode: Simulating the worst-case scenario. Using high-stakes, small-sided games to see if skills translate. Keeping the "Competitive Cauldron" alive without killing growth. [22:30] Implementing the 70/30 Split How to structure your summer hours: 70% Lab, 30% Arena. The power of "Naming the Mode" out loud to remove psychological barriers. Proving vs. Improving: Most practices fail because they blend these two. If a player thinks a missed layup in April affects their playing time in November, they will never try a new finishing move. The "October Plateau" is a Choice: If your players look the same year after year, it's a design flaw in your practice, not a lack of talent. Ditch the Whistle: During Lab time, your voice should be for encouragement, not correction. Save the whistle for the Arena to signal that the "score is live." Intent = Intensity: Deliberate practice is only possible when the intent of the rep is crystal clear to the player. THE RUNDOWNKEY TAKEAWAYS Learn more about your ad choices. Visit podcastchoices.com/adchoices

Long before Artemis II launched, NASA teams were running simulations to show how the rocket's exhaust plumes would interact with the air, water, and the launchpad.

PodChatLive 225: First reported case of two FHL tendons in the same foot, and accessory ossicles simulating fracturesContact us: getinvolved@podchatlive.comLinks from this episode:Two Distinct Flexor Hallucis Longus Muscles and TendonsSymptomatic Accessory Ossicle Near the Medial Malleolus Simulating Fracture Non-union

SummaryThis episode is for anyone following the quantum utility debate or curious about how quantum computers will actually contribute to scientific discovery. Arnab Banerjee — assistant professor at Purdue, guest scientist at Oak Ridge's Quantum Science Center, and one of the most-cited experimentalists working at the intersection of quantum materials and quantum computing — walks us through his career-spanning journey from growing magnetic crystals to programming qubits.You'll hear how Banerjee's frustration with classical tools that couldn't explain his own experimental data drove him to quantum computing, why a quantum spin liquid is like the vortex that forms when you throw a stone into water, and how his team used 50 qubits on IBM's Heron chip to reproduce the spectroscopic fingerprint of a real material — KCuF3 — matching data collected at Oak Ridge and the UK's ISIS neutron source. He also offers a nuanced assessment of where different quantum computing platforms excel, drawing on hands-on experience with IBM, QuEra, and D-Wave.What you'll learnWhat a quantum spin liquid actually is and why its collective behavior — like vortices on water — could enable naturally error-protected qubitsHow neutron scattering works as a quantum probe — using the neutron's own spin and de Broglie wavelength to reveal both atomic positions and energy levels simultaneouslyWhy Banerjee's team chose to benchmark quantum simulation against known experimental data first before tackling classically intractable problemsWhat the IBM Heron benchmarking paper actually showed — reproducing spinon excitations in KCuF3, a one-dimensional Heisenberg chain, with quantitative agreement to neutron dataHow different quantum computing modalities serve different materials science problems — IBM for fast, cheap operations on 2D lattices; trapped ions for all-to-all connectivity; D-Wave and QuEra for Ising-like HamiltoniansHow close we are to quantum advantage in materials simulation — Banerjee estimates 70-90 "good enough" qubits in 2D geometry could reach classically inaccessible regimesWhy Kitaev quantum spin liquids could provide a fundamentally different path to fault tolerance — topological protection from decoherence built into the material itself, not imposed through softwareResources & linksPapers & researchBenchmarking quantum simulation with neutron-scattering experiments (March 2026) — The news hook: IBM Heron processor reproduces real neutron scattering data from KCuF3. First direct validation of quantum simulation against experimental measurements of a real material. Proximate Kitaev quantum spin liquid behaviour in a honeycomb magnet (2016) — Banerjee et al., Nature Materials. The career-defining paper providing first experimental evidence for Kitaev spin liquid behavior in alpha-RuCl3. Discover Magazine Top 100 Stories (#18). Neutron scattering in the proximate quantum spin liquid alpha-RuCl3 (2017) — Banerjee et al., Science. Comprehensive neutron scattering study revealing fractional spinon excitations. Materials for quantum technologies roadmap (2025) — Applied Physics Reviews. Banerjee's roadmap paper on the pipeline from material discovery to quantum devices.Lessons from alpha-RuCl3 for atomically thin materials (Nov 2025) — What the decade-long study of alpha-RuCl3 teaches about 2D quantum materials.Guest & lab links Quantum Spins Laboratory, Purdue University — Banerjee's research groupORNL Profile: Traversing the Unknown, Befriending Uncertainty — Oak Ridge profile on Banerjee's research philosophy Purdue News: Keck Foundation Grant for Quantum Spin Liquids — $1.2M grant to probe Majorana bound states with optical techniquesCoverage of the IBM benchmarking work - IBM Newsroom: Quantum Computer Simulates Real Magnetic Materials — IBM's announcement of the benchmarking resultNature News: Quantum simulations verified by experiments for the first time — Nature's coverage of the milestoneOrganizations & facilities - DOE Quantum Science Center at Oak Ridge — $115M National Quantum Initiative center where Banerjee is a guest scientistSpallation Neutron Source, Oak Ridge — The neutron scattering facility central to Banerjee's experimental workISIS Neutron and Muon Source, Rutherford Appleton Lab — UK facility where part of the KCuF3 data was collectedKey quotes & insights"The entire electronic industry is built around trying to avoid quantum effects as much as possible. This is the time when we need to make quantum our friend instead of our enemy.""In a quantum spin liquid, the spin directions move collectively in dancing patterns that look extremely ordered — but if you take a snapshot, the individual spins feel completely random." — On why spin liquids are like vortices in water"A spin is a qubit is a spin." — On why quantum magnets and quantum processors are fundamentally the same physics"We need to know whether what we are doing really makes sense. That's what this experiment is about." — On why benchmarking against known results must come before tackling unsolved problems"I would like to simulate the entire standard model using a quantum computer." — When asked what problem he'd throw at an unlimited quantum computer Related episodesEp 6: Better Qubits Through Material Science with Nathalie DeLeon — The materials science perspective on improving qubit quality, from diamond color centers to surface physicsEp 13: The Mysterious Majorana with Leo Kouwenhoven — The topological quantum computing vision that Kitaev materials could enable through a different routeEp 74: Majorana Qubits with Chetan Nayak — Microsoft's engineered approach to topological protection — contrast with Banerjee's materials-first pathEp 25: Material Science with Houlong Zhuang at Q2B Paris — Using quan...

Today we talk about how to think about riding the trainer so you can be more prepared for the reality of the course. We talk about hills, wind, aero, cadence, and much more. We take a look at the last hour of your race and how you can work on being stronger as you come into T2. Do we all complicate specific workouts when the reality is we just have to be stronger both physically and mentally to deal with the demands of certain courses. We look at flat and windy along with the continually changing demands of a hilly course. Put a little more reality into your trainer rides.  Topics: Ramp rides Cadence and power Body shots and how to make them less impactful Your ability to close out a climb  Simulating headwinds and tailwinds Training your body to handle long rides Preparing for the real demands of the bike  Moving your ceiling Staying within yourself Making yourself more uncomfortable on a trainer Fighting through the wrong gear Putting your mind on the course while on the trainer Mike Tarrolly - mike@c26triathlon.com Robbie Bruce - robbie@c26triathlon.com 

In this solo episode, Michael Blank introduces a powerful strategy designed to help aspiring syndicators overcome the biggest obstacle in real estate: getting your first deal done. While most first deals take 6–12 months, Michael explains how you can dramatically accelerate your learning curve by running a “Live Sample Deal”—a simulated real-world acquisition that walks you through the entire process in just seven days.By analyzing a real property, speaking with brokers, building your team, and even negotiating an offer, you gain the experience and confidence needed to pursue actual deals. This practical exercise helps eliminate fear, expand your comfort zone, and move you significantly closer to closing your first multifamily investment.Key TakeawaysThe first deal changes everything — once you close a deal, brokers, investors, and opportunities start coming to you.Most first deals take 6–12 months, but you can accelerate your learning through a simulated “Live Sample Deal.”Confidence comes from action, not just education—walking through the full deal process builds real-world experience.Simulating a deal removes fear, especially the fear of making offers or raising capital.Running a sample deal can get you roughly 80% of the experience of doing a real transaction.Expanding your comfort zone is key—what once felt impossible (like a 50-unit deal) quickly becomes achievable after practicing the process.Connect with MichaelFacebookInstagramYouTubeTikTokResourcesTheFreedomPodcast.com Access the #1 FREE Apartment Investing Course (Apartments 101)Schedule a Free Strategy Session with Michael's Team of AdvisorsExplore Michael's Mentoring ProgramJoin the Nighthawk Equity Investor ClubReview the Podcast on Apple PodcastsSyndicated Deal AnalyzerGet the Book, Financial Freedom with Real Estate Investing by Michael BlankFor full episode show notes visit: https://themichaelblank.com/podcasts/session515/

Visualizing the interior of the human body has always presented a major challenge to caregivers. Most medical imaging techniques in use today were first introduced during the past fifty years. Today novel technologies for entertainment are being applied to healthcare. Michael Hollins of the University of Nebraska's iEXCEL Center joins the Futurists to explain how breakthroughs in imaging and simulation are used to train doctors, nurses, and first responders in the most advanced techniques in the US. For the first time in history, doctors can visualize their patients' complex metabolic systems at the molecular level. 

Simulating debt for fun?Alright "Surprise Me" button, what are you trying to tell us with your selection of this rogue-lite gambling sim developed by Panik Arcade? Do you want Tom and Chris to literally fall into debt? Or is this perhaps a cautionary tale so that we all learn a painful lesson virtually in order to avoid experiencing that fate in the real world? Admittedly, we think it's highly unlikely either of our hosts, or even any of our listeners, will find themselves locked in a room with a slot machine suspended over a deadly pit. But, as a metaphor, perhaps it works. Will it be successful in teaching Tom and Chris about the pitfalls of gambling, or will they find themselves booking trips to the casino? Let's lean on this lever to learn!What do you think? Let us know!Check out all our links here:https://linktr.ee/tc1h1dThanks for taking this ride with us :-)

David Kocher, Automotive Instructor at Kirkland Ranch Academy of Innovation, joins us to explain how he introduced a simulated flat rate pay plan to help students understand how flat rate works early in their careers. David walks us through his background, the ins and outs of the incentive-based system he's developing for his class, and how students are responding to the experience.Watch the video podcast on YouTubeAbout the EpisodeHost: Jay Goninen, WrenchWay, jayg@wrenchway.comGuest: David Kocher, Kirkland Ranch Academy of Innovation, dkocher@pasco.k12.fl.usLinks & ResourcesGet notified of new episodes --> Join our email listJoin the ASE Connects CommunityASE Connects brings shops, dealerships, and schools together in one structured network to strengthen the technician pipeline. By making it easier to connect, collaborate, and support students through job shadows, internships, and classroom engagement, ASE Connects helps schools build stronger programs and helps shops develop a more consistent, local source of future technicians. Learn more:ASE Connects Memberships for Shops & DealersASE Connects Memberships for Schools (Free!)Connect with us on social: Facebook Instagram X LinkedIn YouTube TikTok

Welcome to Nerd Alert, a series of special episodes bridging the gap between marketing academia and practitioners. We're breaking down highly involved, complex research into plain language and takeaways any marketer can use. In this episode, Elena and Rob explore how privacy first advertising changes digital marketing. They reveal that when individual tracking disappears, platforms must rely on user groups instead. This shifts advertising toward probabilistic targeting, like how TV has always worked. Topics covered: [01:00] "Reach, Measurement, Optimization and Frequency Capping and Targeted Online Advertising Under K Anonymity"[01:45] Privacy forces less tracking, more thinking[02:50] How K Anonymity groups users by shared traits[04:35] Simulating the trade-off between privacy and performance[06:00] Privacy pushes reach-first thinking  To learn more, visit marketingarchitects.com/podcast or subscribe to our newsletter at marketingarchitects.com/newsletter. Resources: Gao, Y., & Qiao, M. (2025). Reach measurement, optimization and frequency capping in targeted online advertising under k-anonymity. arXiv preprint arXiv:2501.04882. Get more research-backed marketing strategies by subscribing to The Marketing Architects on Apple Podcasts, Spotify, or wherever you listen to podcasts. 

Join us for the February edition of the Simulcast Journal Club, hosted by Vic Brazil and Jess Stokes-Parish. In this episode: Simulating clinical debriefing, psychological safety deep dive, leadership training with sim, and improving ‘code' documentation in EHRs using sim.    The February papers  Dewdney CJ, et al. Transfer of clinical debriefing from simulation to practice: exploring the barriers and enablers. Adv Simul (Lond). 2026.   Gormley G, Nestel D. Not just ‘what you say' but ‘how you say it': co-creating psychological safety through micro-communication skills in simulation-based education. J Healthc Simul. 2025 Oct 2.   Carn-Bennett E, Gan KH. Sim2Lead: A new era in leadership training for healthcare professionals. Simul Healthc. 2025;00(00):00–00.   Biesbroek S, et al. Using human factors and systems simulation to optimize the usability of a code documentation tool. Pediatr Emerg Care. 2026;00(00):000–000.     Another great month on Simulcast.  Happy listening  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Ansys technology partner Electro Magnetic Applications' (EMA) VP of Business Development, John Twerdok, and Lead Software Product Manager, Kevin-Druis Merenda, as well as PADT's LFEM Application & Support Engineer Tyler Buntin. They discuss challenges facing those working with advanced electromagnetics in the space industry, and how simulating radiation hardened electronics with Ansys can help provide solutions. If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

In this lively episode, host Dom is joined by Papi Platano and BDC as they dive into the excitement of the Royal Rumble season. The trio discusses their fantasy match predictions for the men's Royal Rumble, highlighting various wrestlers, including legends and newcomers. They explore the potential outcomes of the match, the significance of the Royal Rumble in leading to WrestleMania, and the evolving dynamics of wrestling characters, particularly focusing on the rise of talents like Oba Femi and the enduring legacy of established stars like AJ Styles and Drew McIntyre. The conversation is filled with humor, nostalgia, and insightful commentary on the wrestling industry. In this episode, the hosts dive deep into the excitement surrounding the Royal Rumble, discussing potential returns and matchups, particularly focusing on Chris Jericho's future and his impact on AEW. They explore the significance of legends like Bret Hart and the potential for dream matches, such as Kenny Omega versus Bret Hart, while also analyzing the current roster's dynamics and the evolution of characters like Jon Moxley and Adam Cole. The conversation shifts to the thrilling moments of the Rumble itself, highlighting standout performances and the strategic elements of the match, culminating in MJF's victory and what it means for his future in wrestling. The hosts express their enthusiasm for the younger talent and the direction of wrestling as they look ahead to the Elimination Chamber and WrestleMania.Chapters00:00 Introduction to the Royal Rumble Fantasy Match01:07 Discussing Potential Winners and Predictions04:14 Character Dynamics in WWE: Babyfaces vs Heels06:24 The Importance of Stamina and Luck in the Rumble09:17 Simulating the Royal Rumble Match19:17 The Rumble Begins: Surprising Eliminations20:30 Gunther's Dominance and Future Matchups23:10 Drew McIntyre Enters the Fray25:59 The Clash of Titans: Gunther vs. Drew27:59 Eddie Guerrero's Strategic Play29:52 Bret Hart's Impact and Legacy31:43 Dustin Rhodes: The Natural's Comeback33:09 Jericho's Impact on AEW33:40 Royal Rumble Speculations36:10 Dream Matches and Legacy37:54 The Unpredictability of Wrestling40:17 The Rise of New Stars47:12 Kenny Omega's Dominance51:02 MJF Enters the Rumble55:41 The Final Showdown58:03 Reflections on the Rumble and Future Events

Roblox creators Adam and Fedor break down their predictions for the Roblox platform in 2026...Join the GEEIQ Integration Network for free today: https://geeiq.short.gy/LastLevelChapters:(00:00) Intro(01:10) 1. Developer Involvement in Moderation(11:40) GEEIQ Integration Network for Roblox Creators (ad)(12:50) 2. Simulating the Real World(24:58) 3. Changes to the Homepage(36:15) 4. Esports Goes Big in 2026(41:06) 5. Platforms Follow Roblox's Lead on Safety(45:59) 6. New CCU Records in 2026?(54:58) OutroEpisode 12Sources:Roblox Creator Roadmap- https://create.roblox.com/roadmap4k textures example- https://x.com/kripytic/status/1985571621554254177/video/1Realistic showcase examples- https://x.com/ArtBlox_406/status/1814371443917889922- https://www.roblox.com/games/7721370704/Druids-Sanctuary-ShowcaseRoblox's SLIM LoD system- https://corp.roblox.com/newsroom/2025/12/introducing-roblox-slim-scalable-lightweight-interactive-modelsHosts:- Adam (BanTech): https://lastlevel.co.uk/adam- Fedor (LoadingL0n3ly): https://x.com/LoadingL0n3ly----------------------------Watch or listen wherever you get your podcasts.Visit https://lastlevel.co.uk/podcast for more.Join the Discord: https://discord.lastlevel.co.ukBeyond The Blox is produced by Seb Jensen for Last Level Studios.

Dr. Jennifer Edmunson explains what it takes to simulate Moon and Mars dust on Earth, and lessons learned from preparing to build habitats on other worlds.

In this live episode from the Florida Chamber Future of Florida Forum, Small Biz Florida host Tom Kindred sits down with Waymon Armstrong, founder and CEO of Engineering and Computer Simulations (ECS), a pioneer in using video game technology for military training. Armstrong shares his entrepreneurial journey, including how he left Lockheed Martin to start ECS nearly three decades ago, his early wins with SBIR grants, and how he grew his company into a global provider of modeling and simulation solutions for the Department of Defense. He discusses why Orlando is a hub for simulation and training, how government contracting became his primary growth engine, and the importance of Florida's SBDC and entrepreneurial ecosystem in helping ECS thrive. From training combat medics to advising small businesses to follow the money in federal contracting, Armstrong delivers powerful insights and inspiration. This podcast episode was recorded live at the Florida Chamber Future of Florida Forum hosted at the JW Marriott Orlando Bonnet Creek. This podcast is made possible by the Florida SBDC Network and sponsored by Florida First Capital. Connect with Our Guest: https://www.ecsorl.com

Can radical optimism about AI truly shape our future, or are we stuck in a cycle of doom-and-hype? This episode features an unfiltered debate with Wired co-founder Kevin Kelly on why most fears about artificial intelligence might be missing the bigger picture. Vibe Coding' Named Word of the Year By Collins Dictionary OpenAI CFO Says Company Isn't Seeking Government Backstop, Clarifying Prior Comment Montana Becomes First State to Enshrine 'Right to Compute' Into Law - Montana Newsroom Sam Altman's Worldcoin Project Struggles Toward Billion-User Ambition With 17.5 Million Sign-Ups Meta's chief AI scientist Yann LeCun reportedly plans to leave to build his own startup Exclusive: US Army to buy 1 million drones, in major acquisition ramp-up Facebook Dating Is a Surprise Hit For the Social Network - Slashdot 12 Things I've Heard Boomers Say That I Agree With 100% The FBI has subpoenaed the domain registrar of archive.today, demanding information about the owner of the archiving site as part of a criminal investigation How Similar Are Grokipedia and Wikipedia? What We Can Learn From Brain Organoids If the US Has to Build Data Centers, Here's Where They Should Go LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior No. 10's synthetic voters Tim Wu and Cory Doctorow's NPCs: Non-Player Consumers Eric Schmidt: This Is No Way to Rule a Country My torture for you Ohio State to hire 100 new faculty with AI expertise 'A frightening development': How AI-Articles are flooding the internet with fake news Internet Archive's legal fights are over, but its founder mourns what was lost YouTube TV deal reportedly hung up on ESPN pricing as Disney loses $30 million a week How people really use ChatGPT, according to 47,000 conversations shared online Tort Law museum visit Bread and Puppet Museum We're famous in Germany Brand new bridge Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Kevin Kelly Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: zapier.com/machines ventionteams.com/twit Melissa.com/twit agntcy.org

Can radical optimism about AI truly shape our future, or are we stuck in a cycle of doom-and-hype? This episode features an unfiltered debate with Wired co-founder Kevin Kelly on why most fears about artificial intelligence might be missing the bigger picture. Vibe Coding' Named Word of the Year By Collins Dictionary OpenAI CFO Says Company Isn't Seeking Government Backstop, Clarifying Prior Comment Montana Becomes First State to Enshrine 'Right to Compute' Into Law - Montana Newsroom Sam Altman's Worldcoin Project Struggles Toward Billion-User Ambition With 17.5 Million Sign-Ups Meta's chief AI scientist Yann LeCun reportedly plans to leave to build his own startup Exclusive: US Army to buy 1 million drones, in major acquisition ramp-up Facebook Dating Is a Surprise Hit For the Social Network - Slashdot 12 Things I've Heard Boomers Say That I Agree With 100% The FBI has subpoenaed the domain registrar of archive.today, demanding information about the owner of the archiving site as part of a criminal investigation How Similar Are Grokipedia and Wikipedia? What We Can Learn From Brain Organoids If the US Has to Build Data Centers, Here's Where They Should Go LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior No. 10's synthetic voters Tim Wu and Cory Doctorow's NPCs: Non-Player Consumers Eric Schmidt: This Is No Way to Rule a Country My torture for you Ohio State to hire 100 new faculty with AI expertise 'A frightening development': How AI-Articles are flooding the internet with fake news Internet Archive's legal fights are over, but its founder mourns what was lost YouTube TV deal reportedly hung up on ESPN pricing as Disney loses $30 million a week How people really use ChatGPT, according to 47,000 conversations shared online Tort Law museum visit Bread and Puppet Museum We're famous in Germany Brand new bridge Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Kevin Kelly Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: zapier.com/machines ventionteams.com/twit Melissa.com/twit agntcy.org

Can radical optimism about AI truly shape our future, or are we stuck in a cycle of doom-and-hype? This episode features an unfiltered debate with Wired co-founder Kevin Kelly on why most fears about artificial intelligence might be missing the bigger picture. Vibe Coding' Named Word of the Year By Collins Dictionary OpenAI CFO Says Company Isn't Seeking Government Backstop, Clarifying Prior Comment Montana Becomes First State to Enshrine 'Right to Compute' Into Law - Montana Newsroom Sam Altman's Worldcoin Project Struggles Toward Billion-User Ambition With 17.5 Million Sign-Ups Meta's chief AI scientist Yann LeCun reportedly plans to leave to build his own startup Exclusive: US Army to buy 1 million drones, in major acquisition ramp-up Facebook Dating Is a Surprise Hit For the Social Network - Slashdot 12 Things I've Heard Boomers Say That I Agree With 100% The FBI has subpoenaed the domain registrar of archive.today, demanding information about the owner of the archiving site as part of a criminal investigation How Similar Are Grokipedia and Wikipedia? What We Can Learn From Brain Organoids If the US Has to Build Data Centers, Here's Where They Should Go LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior No. 10's synthetic voters Tim Wu and Cory Doctorow's NPCs: Non-Player Consumers Eric Schmidt: This Is No Way to Rule a Country My torture for you Ohio State to hire 100 new faculty with AI expertise 'A frightening development': How AI-Articles are flooding the internet with fake news Internet Archive's legal fights are over, but its founder mourns what was lost YouTube TV deal reportedly hung up on ESPN pricing as Disney loses $30 million a week How people really use ChatGPT, according to 47,000 conversations shared online Tort Law museum visit Bread and Puppet Museum We're famous in Germany Brand new bridge Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Kevin Kelly Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: zapier.com/machines ventionteams.com/twit Melissa.com/twit agntcy.org

Can radical optimism about AI truly shape our future, or are we stuck in a cycle of doom-and-hype? This episode features an unfiltered debate with Wired co-founder Kevin Kelly on why most fears about artificial intelligence might be missing the bigger picture. Vibe Coding' Named Word of the Year By Collins Dictionary OpenAI CFO Says Company Isn't Seeking Government Backstop, Clarifying Prior Comment Montana Becomes First State to Enshrine 'Right to Compute' Into Law - Montana Newsroom Sam Altman's Worldcoin Project Struggles Toward Billion-User Ambition With 17.5 Million Sign-Ups Meta's chief AI scientist Yann LeCun reportedly plans to leave to build his own startup Exclusive: US Army to buy 1 million drones, in major acquisition ramp-up Facebook Dating Is a Surprise Hit For the Social Network - Slashdot 12 Things I've Heard Boomers Say That I Agree With 100% The FBI has subpoenaed the domain registrar of archive.today, demanding information about the owner of the archiving site as part of a criminal investigation How Similar Are Grokipedia and Wikipedia? What We Can Learn From Brain Organoids If the US Has to Build Data Centers, Here's Where They Should Go LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior No. 10's synthetic voters Tim Wu and Cory Doctorow's NPCs: Non-Player Consumers Eric Schmidt: This Is No Way to Rule a Country My torture for you Ohio State to hire 100 new faculty with AI expertise 'A frightening development': How AI-Articles are flooding the internet with fake news Internet Archive's legal fights are over, but its founder mourns what was lost YouTube TV deal reportedly hung up on ESPN pricing as Disney loses $30 million a week How people really use ChatGPT, according to 47,000 conversations shared online Tort Law museum visit Bread and Puppet Museum We're famous in Germany Brand new bridge Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Kevin Kelly Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: zapier.com/machines ventionteams.com/twit Melissa.com/twit agntcy.org

This week we talk about Mach 1, the Bell X-1, and the Concorde.We also discuss the X-59, the Tu-144, and Boom Supersonic.Recommended Book: Red Team Blues by Cory DoctorowTranscriptThe term “supersonic,” when applied to speed, refers to something moving faster than the speed of sound—a speed that is shorthanded as Mach 1.The precise Mach 1 speed of sound will be different depending on the nature of the medium through which an object is traveling. So if you're moving at sea level versus up high in the air, in the stratosphere, the speed of sound will be different. Likewise if you're moving through moist air versus dry air, or moving through water versus moving through syrup, different speed of sound, different Mach 1.In general, though, to give a basic sense of how fast we're talking here, if an object is moving at sea level through dry air at a temperature of 20 degrees celsius, which is 68 degrees fahrenheit, Mach 1 is about 768 miles per hour, which is about 1,126 feet per second, and 343.2 meters per second.It's fast! It's very fast. Again, this is the speed at which sound moves. So if you surpass the speed of sound, if you go supersonic, you will arrive faster than the sound you make while moving.Back in 1947, an experimental American plane called the Bell X-1 broke the sound barrier, surpassed Mach 1, reaching a speed of almost 1,000 miles per hour using a 6,000 pound thrust rocket propulsion system. A later version of the same rocket-powered plane, the Bell X-1A, which was basically the same vehicle, it just had more fuel capacity, allowing the rocket to burn longer, achieved 1,600 miles per hour in 1956.Prior to that, in 1943, British began working on a secret experimental aircraft called the Miles M.52, intending to build a plane capable of traveling 1,000 mph. Interestingly, this project was apparently the result of the British wanting to keep up with a supposed already existing German aircraft capable of achieving that speed, though it's now believed the intelligence that led the British to believe the Germans had a supersonic-capable plane was the result of a mistranslation—the Germans hit 1,000 km per hour, which is about 621 mph, and still subsonic.Though apparently a success in terms of research and innovation, the Miles M.52 project was cancelled in 1946, due partly to budgetary concerns, and partly because the new government didn't believe supersonic aircraft were practical, or maybe even feasible.After the existence of this project was revealed to the public, however, criticism for the cancellation mounted, and the design was translated into new, unmanned scale-model experimental versions of the plane which achieved controlled Mach 1.38 supersonic speeds, and both the design and research from this program was shared with the American company, Bell, and all that knowledge informed the development of the aforementioned Bell X-1 supersonic plane.Again, that successful Bell mission was flown in 1947, and in 1961, a Douglas jetliner, a commercial jet, broke the sound barrier during a controlled test dive, and that fed the development of an intended supersonic airliner in the US, though similar research being conducted elsewhere would bear more direct and immediate fruit.In the Soviet Union, a supersonic jetliner called the Tupolev Tu-144 entered service in 1968, and a jetliner co-developed by the British and French, the Concorde, began construction in 1965, and tallied its first flight in March of 1969.The Tu-144 was thus the world's first commercial supersonic airliner, by a few months, and it also became the first commercial transport to exceed Mach 2, twice the speed of sound, in 1970.The Tu-144 was plagued by reliability issues from the get-go, however, and while performing maneuvers at an air show in Paris in 1973, it disintegrated in midair, which—combined with its high operating costs reduced its long-term market viability, especially internationally. By the mid-1970s, it was primarily operating within the Soviet Union, and after a new variant of the jet crashed in 1978, the Tu-144 program was cancelled in 1983. Existing models continued to be use for niche purposes, like training space program pilots, and for a supersonic research program undertaken by NASA in the late-1990s, but the final Tu-144 flight was in mid-1999, and all surviving aircraft are now on display or in storage.The Concorde has a similar history. Original forecasts for the supersonic airliner market were optimistic, and while the craft seemed to be generally more reliable and less issue-prone than the Tu-144, and it enjoyed a period of fanfare and promotion, as a sort of luxury experience for folks crossing the Atlantic in particular, cutting travel times in half, a major crash in mid-2000, which killed all 109 occupants and four people on the ground, led to the suspension of service until late-2001, and all remaining Concorde aircraft were retired in 2003—about 20 of them are on display throughout North American and Europe, as of the mid-2020s.The costs associated with operating Concorde aircraft, as with the Tu-144, were also quite high, and those costs and other complications led to the cancellation of a would-be supersonic jetliner competitor from Boeing, the 2707, in 1971, before it built any prototypes.What I'd like to talk about today is a renewed enthusiasm for supersonic passenger aircraft, and what's changed that might make supersonic transport a viable market, today.—In the United States, commercial aircraft are not allowed to fly at supersonic speeds. This is because the sonic booms generated by supersonic flight, which are shockwaves that work a bit like the crack of a bullwhip or the firing of a bullet, but much, much larger, can set off alarms, rattle or shatter windows, and generally create all sorts of chaos on the ground, even in areas not directly under the aircraft that's breaking the sound barrier.This was true even during the heyday of the Concorde: the craft was only allowed to travel at supersonic speeds over the ocean, because doing so over populated areas was such a pain, and in some cases, a danger.Sonic booms aren't the only reason supersonic aircraft like the Concorde failed to establish a long-term presence in the airline industry, but they're a big part of it. It's just really difficult to work around that kind of persistent issue.This is why a new experimental project by NASA, the X-59 Quesst, with two-s's, Quesst standing for Quiet SuperSonic Technology, is garnering so much attention. Built by Lockheed Martin, the X-59 is said to dramatically reduce the scale of sonic booms, instead producing what's been described as a sonic thump, its long, slender nose breaking up the pressure waves that otherwise build up and create that much larger, more impactful shock wave boom, and its engine is on top of the plane rather than underneath it, a design choice that sends the majority of remaining shock wave impacts upward toward the sky, rather than down toward the ground.The X-59 is still just an experimental jet. It's a single-seater, it's about twice as long as an F-16 fighter jet, and it can cruise at around 925 miles per hours, which is Mach 1.4.It's hoped that this new design will allow for the creation of future supersonic jetliners, though, as being able to traverse oceans twice as fast would bring massive economic benefits, in terms of shipping people, but also all kinds of goods. Being able to use these aircraft fully, at their full speed, over land and to and from any airport, would likewise make them more versatile and introduce new benefits and, hopefully, favorable economics.Worth noting here is that this jet is a descendent of that first Bell X-1 plane that broke the sound barrier in 1947; NASA's X-planes are innovative models meant to push the boundaries of what's currently possible, and the X-59 is just a more modern version of that initial X-1 conception in many ways.That said, the X-59 has only been successfully flown at low speeds and altitudes at this point. It got a lot of press at the end of October 2025 for successfully completing its first flight, which shows it can fly and land, which is good. But its inaugural flight stuck with a low altitude and just 240 miles per hour; really slow for a jet, and too low for a commercial airliner.The folks behind this project have also said that while they have every reason to believe this design will both work and create a far less impactful sonic boom, they don't yet know if that boom will actually be tolerable for people on the ground. Simulating such things is different from the experience of them, and they won't know until they power the thing all the way up and have it break the sound barrier whether the sonic thump will be barely noticeable and tolerable for folks near airports and flight paths, or if it will be better, but still not good enough to make this a viable alternative to existing jets.There are other entities working on similar things right now, including a company called Boom Supersonic that has already flown a piloted demonstration aircraft, the XB-1, at supersonic speeds—Mac 1.122, which is about 750 mph—at an altitude of over 35,000 feet; the first time a non-government-affiliated aircraft has done so.That was back in March of 2024, and the company plans to build a commercial supersonic aircraft that will carry between 64 and 80 passengers at Mach 1.7, on hundreds of global routes; they say they already have a large number of orders for this passenger aircraft they intend to build, and they say to begin with, they'll be able to produce 66 of them per year from their factory in North Carolina. They say that they'll have the first full-scale prototype of that passenger aircraft, called the Overture, in 2027, and they're aiming to put that craft into service beginning in 2029 or 2030.They're not the only private company aiming to produce supersonic aircraft for various purposes, either. The promise of moving people and things around the world, faster than most of today's options can manage, and in many cases far faster, is still tantalizing for many industries, so long as regulatory, safety, and technological hurdles can be traversed. For most of these private companies, their innovation seems to be mostly in price and scale, not reducing the boom, but some have also claimed that their sonic booms are more moderated; there's also a good chance findings from the NASA X project will translate over to the commercial world in due time, if these companies survive, blending those innovations.It's an interesting moment in this space, then, in part because it seems like supersonic flight is appealing again, to some, at least, after a long period of dashed hopes—that dashing partly the consequence of flaws in earlier models, and headline-grabbing crashes that ruined a lot of appetites for the option.But also because we could see modern technologies, from sensors to propulsion systems to manufacturing capacities applied to this vehicle type, which could ease a lot of the issues that made the Concordes and Tu-144s non-workable the first time around, and could make this type of transport and travel cheaper, too, though probably not until mid-century at the earliest, according to current timelines.Show Noteshttps://arstechnica.com/space/2025/10/nasa-test-flight-seeks-to-help-bring-commercial-supersonic-travel-back/https://en.wikipedia.org/wiki/Sonic_boomhttps://www.wired.com/story/nasas-quiet-supersonic-jet-takes-flight/https://www.sofeminine.co.uk/back-in-4-years-your-london-new-york-time-slashed-by-3-hours-as-60-80-seat-supersonic-jet-nears/https://abcnews.go.com/US/wireStory/nasa-takes-step-closer-launching-quiet-supersonic-jets-127036299https://boomsupersonic.com/https://www.grc.nasa.gov/www/k-12/airplane/lowsup.htmlhttps://www.nasa.gov/aeronautics/supersonic-flight/https://www.spikeaerospace.com/https://en.wikipedia.org/wiki/Miles_M.52https://en.wikipedia.org/wiki/Bell_X-1https://en.wikipedia.org/wiki/Supersonic_aircrafthttps://en.wikipedia.org/wiki/Tupolev_Tu-144https://en.wikipedia.org/wiki/Concordehttps://en.wikipedia.org/wiki/Supersonic_transporthttps://en.wikipedia.org/wiki/Supersonic_speed This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

Hosted by Dr. Jacinta Delhaize, Dr. Tshiamiso Makwela & Dr. Daniel Cunnama. This episode of Cosmic Savannah features Professor James Chibueze, a distinguished professor at the University of South Africa, discussing his research on star formation using radio astronomy.   During the episode, Prof Chibueze discusses his journey to become a professional astronomer and how he got interested in radio astronomy. Prof Chibueze also gives some insight into his experience doing his PhD in Japan and even having to learn Japanese. James also discusses his work on studying young spinning stars which produce bipolar outflows.   To shed angular momentum, stars launch outflows of gas perpendicular to their accretion disc, typically from the north and south poles. Using high-resolution radio astronomy techniques, Prof Chibueze's research revealed that the ejected gas in these outflows is also spinning. This finding suggests that the outflowing gas carries away the star's angular momentum, allowing it to continue accreting material and grow.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Is the most effective way to understand real human behavior to simulate it first?Agility requires a willingness to test ideas that sound strange at first—like asking bots to act more human by narrowing their point of view, or treating synthetic personas as real sources of insight. But when applied correctly, this thinking unlocks entirely new ways to scale customer understanding.Today we're going to talk about how synthetic research is reshaping how we understand audiences—and how asking the right questions can make these insights feel far less synthetic and far more human.To help me discuss this topic, I'd like to welcome Mike Taylor, Founder & CEO of Ask Rally.Mike – welcome to the show! About Mike Taylor Mike Taylor is the CEO & Co-Founder of Rally. He built a 50 person marketing agency in the US and EU, has taught 300,000 students in online courses, and wrote a prompt engineering book for O'Reilly. Mike Taylor on LinkedIn: https://www.linkedin.com/in/mjt145/ Resources Ask Rally: https://www.askrally.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

Is the most effective way to understand real human behavior to simulate it first?Agility requires a willingness to test ideas that sound strange at first—like asking bots to act more human by narrowing their point of view, or treating synthetic personas as real sources of insight. But when applied correctly, this thinking unlocks entirely new ways to scale customer understanding. Today we're going to talk about how synthetic research is reshaping how we understand audiences—and how asking the right questions can make these insights feel far less synthetic and far more human. To help me discuss this topic, I'd like to welcome Mike Taylor, Founder & CEO of AskRally. Mike – welcome to the show! About Mike Taylor Mike Taylor is the CEO & Co-Founder of Rally. He built a 50 person marketing agency in the US and EU, has taught 300,000 students in online courses, and wrote a prompt engineering book for O'Reilly. Mike Taylor on LinkedIn: https://www.linkedin.com/in/mjt145/ Resources AskRally: https://www.askrally.com https://www.askrally.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep475-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/demis-hassabis-2-transcript CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Demis's X: https://x.com/demishassabis DeepMind's X: https://x.com/GoogleDeepMind DeepMind's Instagram: https://instagram.com/GoogleDeepMind DeepMind's Website: https://deepmind.google/ Gemini's Website: https://gemini.google.com/ Isomorphic Labs: https://isomorphiclabs.com/ The MANIAC (book): https://amzn.to/4lOXJ81 Life Ascending (book): https://amzn.to/3AhUP7z SPONSORS: To support this podcast, check out our sponsors & get discounts: Hampton: Community for high-growth founders and CEOs. Go to https://joinhampton.com/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Shopify: Sell stuff online. Go to https://shopify.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex AG1: All-in-one daily nutrition drink. Go to https://drinkag1.com/lex OUTLINE: (00:00) - Introduction (00:29) - Sponsors, Comments, and Reflections (08:40) - Learnable patterns in nature (12:22) - Computation and P vs NP (21:00) - Veo 3 and understanding reality (25:24) - Video games (37:26) - AlphaEvolve (43:27) - AI research (47:51) - Simulating a biological organism (52:34) - Origin of life (58:49) - Path to AGI (1:09:35) - Scaling laws (1:12:51) - Compute (1:15:38) - Future of energy (1:19:34) - Human nature (1:24:28) - Google and the race to AGI (1:42:27) - Competition and AI talent (1:49:01) - Future of programming (1:55:27) - John von Neumann (2:04:41) - p(doom) (2:09:24) - Humanity (2:12:30) - Consciousness and quantum computation (2:18:40) - David Foster Wallace (2:25:54) - Education and research PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips

Tim Sweeney is a legendary video game programmer, founder and CEO of Epic Games that created the Unreal Engine, Fortnite, Gears of War, Unreal Tournament, and many other groundbreaking and influential video games. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep467-sc See below for timestamps, and to give feedback, submit questions, contact Lex, etc. CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Tim's X: https://x.com/timsweeneyepic Epic Games: https://epicgames.com/ SPONSORS: To support this podcast, check out our sponsors & get discounts: Notion: Note-taking and team collaboration. Go to https://notion.com/lex MasterClass: Online classes from world-class experts. Go to https://masterclass.com/lexpod Shopify: Sell stuff online. Go to https://shopify.com/lex AG1: All-in-one daily nutrition drink. Go to https://drinkag1.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex OUTLINE: (00:00) - Introduction (08:25) - 10,000 hours programming (11:42) - Advice for young programmers (19:54) - Video games in the 80s and 90s (22:02) - Epic Games origin story (34:40) - Indie game development (40:34) - Unreal Engine (1:06:30) - Technical details of Unreal Engine (1:11:23) - Constructive solid geometry (1:17:21) - Dynamic lighting (1:21:51) - Volumetric fog (1:25:19) - John Carmack (1:27:05) - Evolution of Unreal Engine (1:33:21) - Unreal Engine 5 (1:44:32) - Creating realistic humans (1:53:41) - Lumen global illumination (1:58:11) - Movies (2:12:53) - Simulating reality (2:25:08) - Metaverse (2:27:44) - Fortnite (2:31:40) - Scaling (2:47:04) - Game economies (2:48:33) - Standardizing the Metaverse (2:56:46) - Verse programming language (3:18:19) - Concurrency (3:25:56) - Unreal Engine 6 (3:30:34) - Indie game developers (3:33:32) - Apple (3:48:12) - Epic Games Store (4:11:03) - Future of gaming (4:17:03) - Greatest games ever made (4:22:39) - GTA 6 and Rockstar Games (4:25:58) - Hope for the future PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips SOCIAL LINKS: - X: https://x.com/lexfridman - Instagram: https://instagram.com/lexfridman - TikTok: https://tiktok.com/@lexfridman - LinkedIn: https://linkedin.com/in/lexfridman - Facebook: https://facebook.com/lexfridman - Patreon: https://patreon.com/lexfridman - Telegram: https://t.me/lexfridman - Reddit: https://reddit.com/r/lexfridman