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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

Keach Hagey: Keach Hagey, author of The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, explores the rise of Sam Altman and the founding of OpenAI, which launched in 2015 as a nonprofit research lab aimed at developing artificial general intelligence safely. Altman partnered with Greg Brockman and lead scientist Ilya Sutskever, securing initial billion-dollar commitments from major players such as Elon Musk and Peter Thiel. The narrative follows Altman's trajectory from a brilliant student at John Burroughs School to a Stanford dropout who founded the startup Loopt. Though Loopt was considered a relative failure, Altman's charismatic storytelling and investment prowess eventually led him to succeed Paul Graham as president of Y Combinator. As OpenAI's needs for computational power grew, the organization transitioned into a complex for-profit structure, leading to a power struggle that saw Musk depart. The account highlights a pivotal 2023 crisis in which the board fired Altman over concerns regarding his transparency, only for him to be reinstated after a massive staff revolt. Throughout, the book balances Altman's unwavering optimism for the future against stark warnings from AI godfathers about the potential existential risks of unaligned artificial intelligence. (1)

In a lab in the Rocky Mountains, a group of scientists is hard at work trying to find answers across biology, physics, and engineering: They're looking into things like the hydrodynamics of insect pee and the physics of sheepdog herding, and working to designing robots that resemble squid and hearing aids that only cost a dollar. These endeavors may seem unrelated, but they're glued together by bioengineer Saad Bhamla's philosophy of science driven by curiosity, and how to make the most of the lab's time. He joins Host Flora Lichtman to talk about what led him to this approach. Guest: Dr. Saad Bhamla is an associate professor of chemical and biomolecular engineering at the University of Colorado Boulder. Transcripts for each episode are available within 1-3 days at sciencefriday.com.   Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

About this episode:   Telling a good joke is a science—it requires gathering data, thoughtful analysis, and constant experimentation to get it right. A good joke can also help us better communicate about science. In this episode: A stand-up comedian with a research background explains why comedy is a useful tool in science communication and shares tips for using humor to illustrate academic concepts. Guest:  Sarah Adelman, MPH, is a stand-up comic, screenwriter, and former scientist. She is also the creator, host, and executive producer of the original social media series "BLOW MY MIND." Host:  Lindsay Smith Rogers, MA, is the producer of the Public Health On Call podcast, an editor for Expert Insights, and the director of content strategy for the Johns Hopkins Bloomberg School of Public Health.  Show links and related content:  A Scientist Walks Into a Bar…—Nautilus Not Just Funny After All: Sarcasm as a Catalyst for Public Engagement With Climate Change—Science Communication Can a Joke Make Science More Trustworthy?—Journal of Science Communication Specialized terminology reduces the number of citations of scientific papers—Proceedings of the Royal Society B: Biological Sciences Why Do We Believe Misinformation?—Public Health On Call (December 2025) Transcript information: Looking for episode transcripts? Open our podcast on the Apple Podcasts app (desktop or mobile) or the Spotify mobile app to access an auto-generated transcript of any episode. Closed captioning is also available for every episode on our YouTube channel. Contact us: Have a question about something you heard? Looking for a transcript? Want to suggest a topic or guest? Contact us via email or visit our website. Follow us: @‌PublicHealthPod on Bluesky @‌PublicHealthPod on Instagram @‌JohnsHopkinsSPH on Facebook @‌PublicHealthOnCall on YouTube Here's our RSS feed Note: These podcasts are a conversation between the participants, and do not represent the position of Johns Hopkins University.

(Jun 29, 2026) The Adirondack Watershed Institute, a research lab at Paul Smith's College, was awarded $1 million in state funding. New York Assembly Speaker Carl Heastie came to deliver the good news in person. Also: The Plattsburgh YMCA's child care center is being sued for negligence over allegations of child abuse.

The Founding of OpenAI. Guest Author: Keach Hagey. In this opening segment, Keach Hagey discusses the January 2016 founding of OpenAI as a nonprofit research lab. Key figures included co-founder Greg Brockman and chief scientist Ilya Sutskever, a renowned researcher whose recruitment from Google signaled the lab's potential. Backed by a billion-dollar commitment from Elon Musk, Peter Thiel, and Jessica Livingston, the project was designed as a safe, non-commercial counterweight to Google's DeepMind. Operating initially out of Brockman's apartment, the team aimed to achieve Artificial General Intelligence (AGI) for the benefit of humanity. The technical foundation relied heavily on GPUs—hardware originally designed for video games—which proved essential for training the deep learning neural networks necessary for their research. This era was characterized by an ambitious, "pirate" spirit funded through YC Research to explore radical ideas outside the profit motive. 1JANUARY 1931

This episode I'm joined by Dr Peter Lee, Head of Research at Microsoft, to explore the ideas shaping the future of technology. Leading 14 research laboratories globally and overseeing a research and development program that receives more than 10% of Microsoft's overall budget, Dr Lee shares his journey to the top of one of the world's largest innovation engines. From AI tools transforming early cancer detection and predicting protein structures in hours (instead of years!), to discovering new battery materials and improving weather forecasting through deep learning, the conversation reveals how research is moving from possibility to real-world impact. We also explore Microsoft's investment in open-ended innovation, research not driven by immediate products, but by imagining entirely new experiences and technologies. Encrypted Spaces - Microsoft Research | Microsoft Research

Ever wonder why so many undergrads stumble into research by complete accident or end up just doing the grunt work without ever understanding the bigger picture? In this episode, we chat with Robert, a cognitive scientist and behavioral psychophysics researcher at NYIT (who studies X-rays in some seriously cool ways!). Robert is on a mission to completely change how undergraduate research works. He's built a scalable, student-first lab system that gives undergrads and pre-med students real, meaningful experience without draining a PI's most precious resource: time. By using a brilliant peer-to-peer training model, Robert frees himself up to focus on what actually matters, mentoring students on their career goals and helping them map out exactly what they need for their resumes and med school applications. From tracking skills on a "recommendation letter checklist" to securing funding for international conferences, Robert's system is a massive win-win. We discuss why real research is way more creative than standard science classes, how he tracks down graduated students to ensure they get the authorship credit they deserve, and how his lab is using social media to show the world what undergrads are truly capable of. Check out this cool episode to hear how we can lift students up without bringing faculty down!

Let us know what you think about this episode and share it with a friend!Success can go to your head. Failure can go to your heart. And if you're building something from scratch, it's easy to let the business decide who you are.We sit down with Brett Smith, Executive Director of the Center for LIFE at Miami University (Leading the Integration of Faith and Entrepreneurship), to dig into what founders rarely say out loud: entrepreneurship is a tough, lonely sport that can amplify stress, shame, and identity swings. Brett shares what his research reveals about the “high highs and low lows” of entrepreneurial life and why a founder's identity often rises and falls right along with revenue, funding, and momentum.Then we get practical. Brett explains how a relational identity with God can act as a stabilizing counterbalance to entrepreneurial identity, affirming you in the lows and humbling you in the highs. We also unpack why success can be just as destabilizing as failure, how faith can shape decision making when the information is ambiguous, and why translating academically rigorous research into everyday language actually matters for entrepreneurs, investors, and teams.Finally, we point you to free tools through Faith Driven Entrepreneur's Research Insights and share where to learn more about the Center for LIFE, including resources on faith-driven entrepreneurship and social entrepreneurship. If this conversation helps, subscribe, share it with a founder friend, and leave a review so more people can find it.Brett Smith Bio:Brett R. Smith, Ph.D. is the Cintas Endowed Chair of Entrepreneurship, Founding Director, Center for Social Entrepreneurship, and Founding Research Director, Leading the Integration of Faith & Entrepreneurship (L.I.F.E.) Research Lab at Miami University in Oxford, Ohio. His research interests focus on social and faith-based entrepreneurship. His research has been featured in leading academic journals.Learn more and contact Brett at: https://lifemiamioh.com/ Subscribe to the Pivotal People newsletter for new episodes, giveaways and more: https://stephanienelson.com/newsletter/ Learn more at StephanieNelson.comFollow us on Instagram  @stephanie_nelson_cmFollow us on Facebook at CouponMomOrder Stephanie's book Imagine More: Do What You Love, Discover Your Potential 

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Keach Hagey recounts the January 2016 founding of OpenAI in San Francisco, initially established as a modest nonprofit research lab in Greg Brockman's apartment. Co-founded by Sam Altman, Brockman, and chief scientist Ilya Sutskever, the organization aimed to develop artificial general intelligence (AGI) safely outside of profit motives. Major initial backers included Elon Musk and Peter Thiel, who sought to create a counterweight to Google's DeepMind. The discussion explains how neural networks utilize Nvidia's GPUs—originally designed for video games—to mimic human thought, forming the technical foundation for the current AI race. (1/4)MARCH 1959

Britain has a proud history of advanced railway research. It was British Rail who conceived the hugely successful High Speed Train. The BR Research Centre then came up with the brilliant tilting train design of the Advanced Passenger Train until it was scrapped, only for it to reappear years later, when it was sold back to us by a French Italian consortium in the form of the Pendolino. The Railway Technical Centre in Derby was a global centre of excellence. And then at privatisation, we rather lost the plot. The research arm of BR was sold, and research and development became fractured.Fortunately though, the story did not end there and Green Signals recently had an exclusive behind the scenes visit to the University of Southampton where Professor William Powrie CBE and his colleagues are undertaking some amazing research that is not only improving our knowledge, but helping to save hundreds of millions of pounds as well.Membership: If you want to see even more from Green Signals, including exclusive content, become a member and support the channel further too.YouTube -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@GreenSignals/join⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/GreenSignals⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Green Signals: Website -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.greensignals.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Merchandise - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://greensignals.etsy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.greensignals.org/#mailing-list⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow: X (Twitter) -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/greensignallers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/green-signals-productions-ltd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You can view our legal disclaimer, copyright information and privacy policy here - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.greensignals.org/legal/⁠

When 60,000 attendees descend on Tokyo Big Sight April 27–29, the headline numbers are hard to ignore: 750 startup exhibitors, 151 sessions, city leaders from 49 countries. But the stat that tells you what kind of event this actually is? It's 10,000 facilitated business meetings — brokered, booked, and tracked before most attendees even land. Here's the link to the article. Also, founded by an OSU researcher, NeoCognition is developing AI agents that can become experts in any domain. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Wie hat dir die Folge gefallen?Gut

Descend into the stillness of the ocean's depths with Deep Sea Research Lab, an 8-hour immersive soundscape blending cinematic deep ocean ambience with powerful 4 Hz delta wave binaural beats designed for deep, restorative sleep. Surrounded by the vast pressure of the deep sea, low-frequency underwater drones and distant aquatic textures create a hollow, expansive atmosphere that quiets the mind and blocks out distractions. Beneath it all, 4 Hz delta waves gently guide your brain into the deepest stages of non-REM sleep, supporting healing, recovery, and uninterrupted rest. -- ✨ Support the show with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium (Ad-Free)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

10-year-old Sofia hosts a friendly conversation with her guest Shania, a 9-year-old from Mumbai, about what sleep looks like in their day-to-day lives and compare bedtime routines.Full show notes and links: https://www.ucl.ac.uk/ioe/news/2026/mar/childrens-sleep-routines-bedtimes-dreams-and-how-sleep-affects-mood-and-schoolPodcast produced by UCL Sleep Education Research Lab.

High school teacher Mustafa Sakarwala speaks with Nandini Adusumilli (PhD student at Sleep and Education Research Lab at UCL) about why sleep is a core foundation for children's and teenagers' attention, learning, and exam recall.Full show notes and links: https://www.ucl.ac.uk/ioe/news/2026/mar/sleep-memory-and-academic-performance

Al's on the mic as British Science Week kicks off today — ten days of pure “go on then, show me how it works” energy across London and the UK. Then the government backs a new fundamental AI research lab, aiming for proper long-term breakthroughs, not just flashy demos. After that, Cambridge researchers give robots a better sense of touch with graphene-based “artificial skin”… and scientists unveil a half-Möbius molecule that sounds like sci-fi but lands in Science anyway. We're finishing with a London phone launch from Nothing — plus a quick gaming nod for your weekend queue. Hosted on Acast. See acast.com/privacy for more information.

Dr. Sean Freeder shares the latest UNF statewide poll data, including attitudes on governor & senate candidates, immigration policies, property taxes, and more.

The Wisconsin Historical Society is safeguarding the stories of being Black in Madison by including the SoulFolk Collective's oral history collection in its archive. The collective is a research lab in UW-Madison's Department of African American Studies that does research projects centering the Black experience , including mapping Black-affirming spaces in the city. .  To learn more, host Bianca Martin speaks with Dr. Jessica Stovall and Angela Fitzgerald about the Black Madison Archive and what is next for the collective.   Shout out that the collection is at WI historical society! This episode originally aired September 16, 2025. Learn more about the sponsors of this February 24th episode: Wisconsin Chamber Orchestra Dane County Humane Society Taskrabbit Looking to advertise on City Cast Madison? Check out our options for podcast and newsletter ads.

Recently funded and aiming for sustainable retention? Intro chat (no sales pitch): professorgame.com/chat What if your research team worked like a raid party? Raul Mora shares how bringing gamer language and MMORPG structures into academia boosted clarity, motivation, and long-term commitment. This conversation explores community design, role-based engagement, and why listening to gamers is the most underrated retention strategy in education. Raúl is a professor at Universidad Pontificia Bolivariana in Medellín, Colombia, now also teaching remotely from Trondheim, Norway. He's been in education for over 30 years, including time as a school and English teacher and as a college professor. His research explores second language literacy practices in the city, digital spaces, and schools. Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia.   Guest Links and Info Webs: Guest: elpatronhimself.net Research Lab: lslp.org LinkedIn: Raúl Mora Instagram: @lslplegion TikTok: @lslplegion Bluesky: bsky.app/profile/elpatronhimself.bsky.social Book: Understanding Second Language Users as Gamers   Links to episode mentions: Proposed guest: Antero Garcia Recommended book: What Video Games Have to Teach Us by James P. Gee Favorite game: Mortal Kombat   Lets's do stuff together! Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

In this episode of Why I Teach, Provost Kimberly D. McCorkle sits down with Dr. Aaron Polichnowski, associate professor in the Department of Biomedical Sciences at ETSU's Quillen College of Medicine and recipient of the university's 2025 Distinguished Faculty Award in Research. A nationally recognized expert in hypertension and chronic kidney disease, Dr. Polichnowski shares how curiosity-driven research, teaching medical students, and mentoring future scientists are deeply interconnected—and why helping students ask the right questions is at the heart of his work. Download an accessible transcript file. Listen to more episodes of “Why I Teach,” where Dr. Kimberly D. McCorkle explores stories of impact and success of ETSU faculty. Subscribe at https://why-i-teach-conversation-with-etsu-faculty.podbean.com/.   ETSU College of Medicine: https://www.etsu.edu/com/ Department of Biomedical Sciences: https://www.etsu.edu/com/dbms/ ETSU Health: www.etsuhealth.org

Rene Thomas Folse, JD, Ph.D. is the host for this edition which reports on the following news stories: Pension Denied for Injured Deputy's Unreasonable Refusal of Surgery. Court Denies Carriers Suit Against Attorneys for Worker's Fraudulent Claim. Admin Remedies Not Required for Firefighter's Whistleblower Action. Law Violation Not Required for Application of Whistleblower Protections. Superior Court Judge Resigns & Pleads Guilty to Defrauding SIBTF. S.F. City Official to Serve 3 Years for $600K Work Comp Fund Theft. Proposed New Law Takes Aim at Insurance Company Conduct. Research Lab to Pay $1M for Controlled Substances Violations.

This show has been flagged as Clean by the host. In our next look at the game mechanics for Civilization V we examine the topic of Science and how to win a Science victory. This is something that has been in Civilization from the very beginning, but in Civilization V there are some changes worth addressing. Playing Civilization V, Part 7 Science In most respects this is not all that different in Civ 5. Most of the techs are the same, there is a tech tree that is pretty similar, and you need to keep up in Science for any victory condition you are seeking. You may want to just beat your enemies into submission, but if you are using Chariots while they have Tanks, you aren't going to have success. But also it is obvious that if you are going for a Science victory, you need to really focus on this. So many of these tips should be followed for any victory condition, but should be mandatory if you are going for a Science victory. The mechanics of researching technologies is that you have to accumulate a certain amount of Science to discover a new technology, but this amount goes up over time, so you have be continuously looking to increase your output of Science to keep up. for instance, one of your first Techs would be Pottery, which has a cost of 35 Science. But in your Capital city you get 3 Science from your Palace, and let's say you have a population of 2, so you are generating 5 Science per turn. That means you will research Pottery in 7 turns. But the Education tech costs 485 Science, Astronomy costs 780, Scientific Theory costs 1650, Plastics 4700, and Particle Physics 6000. These are all key techs to advance your Science to a Science Victory. So you can see that you need to be continually increasing your Science. To start with, Population=Science. You get one Science for every one point of population. That does not, however, mean that you need to have a lot of cities to get there. 4-5 well developed cities are quite sufficient, and adding more cities can cause Unhappiness problems. Since higher population itself can cause Unhappiness there is no good reason to add to the problem. Buildings The next boost you can give to Science is by building city improvements. The first, which comes early in the game, is the Library, which is available once you research Writing. A Library boosts the Science output of a city by one Science for every two citizens (roughly a 50% boost, rounded down), so building those early pays off. Because advancing through the tech tree is a process of accumulating Science, the earlier you can get these boosts the better. The other population-based boost is the Public School (available when you research Scientific Theory), which also boosts Science by one for every two citizens, and also offers a Specialist slot for a Science Specialist. And since more population means more Science, the Granary (available when you research Pottery) is a good building because it helps to grow your population. There is one other building worth mentioning which is the Observatory (available when you discover Astronomy). It doesn't depend on population, but on location. You have to have a city that is located directly next to a Mountain to build this, but it adds 50% to the Science output of the city. Mountains are otherwise useless (unless you are the Incas), but if you want a Science boost and happen to see good location (the ideal spot is an isolated mountain that is not part of a mountain range so you don't lose farming and mining production) this can be great boost. Scientist Specialists You can at a certain point take some of your citizens out of the farming and mining and turn them into Specialists, but you have to have a slot for them, and those slots come in buildings as well. We've already mentioned Public Schools providing one slot. Universities (available when you discover Education) provide 2 slots, as well as boosting the city output of Science by 33%. The other Science building, which comes late in the game, is the Research Lab (available when you discover Plastics) which adds another Specialist slot, plus 4 Science, and then adds 50% to the Science Output of the city. It comes too late to help much in most of the Tech Tree, but is essential to research the Space techs, which are very expensive. Wonders The first one to try for is the Great Library. It gives you a free Library in the city, +3 Science per turn, and a free tech. Use the free tech to get an expensive tech like Philosophy. Oracle provides 1 Great Person Point per turn towards a Great Scientist. Hanging Gardens provides +6 Food per turn (boosting your population), and a free Garden which boosts your Great Person Points by 25%. Leaning Tower of Pisa increases your Great Person Points by 25% in all cities, plus a free Great Person of your choice when you build it. Porcelain Tower gives you +50% from Research Agreements, plus a free Great Scientist. and Hubble Space Telescope provides two Great Scientists, a free Spaceship Factory in the city where it was built, and +25% production for spaceship parts. All of the above are World Wonders, which means you are in competition with other players to build them, and only one player can be successful in each case, so you won't get them all. You can sometimes rush a World Wonder by “chopping”, i.e. using your workers to cut down Forests for added production, but you need to have high production cities to build Wonders in general. There is one National Wonder to focus on, though, the National College. Every player can build their own version of any National Wonder. The National College can be built only when you have a Library in every one of your cities. Your strategy should be to build it as soon as possible, so don't build more than 3-4 cities before you get to this. It gives you +3 Science, plus an increase of 50% in the Science output of the city you build it in. Great Scientists As you work on your Science you will accumulate Great Person Points towards getting a Great Scientist. Some wonder produce Great Person Points, and all of your Science Specialists produce Great Person Points as well. As these add up you will suddenly see a Great Scientist appear. In the early game, the best thing to do is use this Great Scientist to build an Academy. Move the GS to any tile within your city and create the Academy there. It will yield at least +8 Science, bu there are also modifiers that can add to that. The alternative which is better later in the game is to use the Great Scientist to get a free Tech discovery. The reason is that early in the game that +8 Science is very significant, and it can accumulate over time. Combine that with things like an Observatory and a University that increase the city output and it can add up nicely over time. But by perhaps the Medieval Era, and certainly the Renaissance Era, you start running out of time for that accumulation. Meanwhile, the techs have gotten so expensive that a free Tech is the better option. Research Agreements These become available once you research Education. You have to have a Declaration of Friendship with the other player to create one. You each put a certain amount of gold into the pot to fund the research, and after a period of time (usually 30 turns) you each get an amount of Science from it. The way it is calculated is based on the partner that produced the least amount of Science during the agreement. From a science standpoint if you are ahead in Science it probably won't benefit you to enter into the agreement. But it does build your relationship with the other player so I wouldn't avoid them altogether. If you are behind in Science it can help you, of course. Policies and Ideologies Given that you should probably be building tall (4-5 cities) instead of wide (8-12 cities), it makes sense to start out with Tradition instead of Liberty. But once you get to the Renaissance you will want to enable the Rationalism tree to maximize your Science. When you get to Ideologies, you have a choice to make. Ignore Autocracy as that is not a Science-oriented choice. If you have 3-5 cities, Freedom is the best Ideology because Specialists require less food (Civil Society), and have reduced Unhappiness (Universal Suffrage). With a wide strategy (more than 5 cities) Order starts to look better. Getting Worker's Faculties will give +25% Science from every Factory. Exploration and Techs Exploration is generally a good idea for a variety of reasons, but one to focus on here is the effect of meeting other players. In the first place, if you find other players who have researched techs you do not yet have, you can trade for them. You do this whenever possible. Remember, the other players will all be trading with each other anyway, so if you don't participate you will simply fall behind. If you have a nice tech and can trade it to just two other players, you will jump up two techs along the tech tree, and that can be huge. If you hold onto it as a secret, some other player will research it, and they will trade it and get that boost instead. So trade whenever you can. Another advantage is that when you discover that another player has a tech you don't have yet, your cost to research it goes down. Trade This is the next Science boost we will cover. when you set up a trade route with either another player or a City-State, one of the benefits can be an increase to your Science. The main benefit of trade routes is money, at least the way I play, so I will always start by looking for the best addition to my Treasury, but if I can choose between equivalent monetary rewards but one trade route offers more Science I might prefer that if I am going for a Science victory. Choosing an Empire There are many Empires you can play, and some of them are oriented to a Science victory. The two obvious choices are Babylon and Korea. Babylon gets a free Great Scientist when you discover Writing, which is very early, so you should use it to put down an Academy. And it earns Great Scientists 50% faster. Korea's advantage comes from +2 Science from all specialists and from all Great Person tile improvements, plus you get a tech boost each time a Science building or wonder is built in the Capital. Of course, you can win a Science victory with any Empire if you are careful about leveraging your Empire's strengths. For example, Venice and Portugal can rake in the gold in huge amounts, and you can buy a lot of stuff that way. Or with the Celts you generate a ton of Faith, and that can be used to buy buildings and Great Scientists with the right Social Policies. Conclusion This is just a quick overview of the Science path, and there is always more to learn. If you really want to dive into the Science options and get a Science Victory, the Civilization Fanatics site has a pretty good strategy guide at https://forums.civfanatics.com/threads/science-victory-guide-any-difficulty.530940/. Links: https://forums.civfanatics.com/threads/science-victory-guide-any-difficulty.530940/ https://www.palain.com/gaming/civilization-v/playing-civilization-v-part-7/ Provide feedback on this episode.

FOUNDING OPENAI Colleague Keach Hagey, The Optimist. In 2016, Sam Altman, Greg Brockman, and Ilya Sutskever founded OpenAI as a nonprofit research lab to develop safe artificial general intelligence (AGI). Backed by investors like Elon Musk and Peter Thiel, the organization aimed to be a counterweight to Google's DeepMind, which was driven by profit. The team relied on massive computing power provided by GPUs—originally designed for video games—to train neural networks, recruiting top talent like Sutskever to lead their scientific efforts. NUMBER 13 1955

This is a special episode, highlighting a session from ELC Annual 2025! OpenAI evolved from a pure research lab into the fastest-growing product in history, scaling from 100 million to 700 million weekly users in record time. In this episode, we deconstruct the organizational design choices and cultural bets that enabled this unprecedented velocity. We explore what it means to hire "extreme generalists," how AI-native interns are redefining productivity, and the real-time trade-offs made during the world's largest product launches. Featuring Sulman Choudhry (Head of ChatGPT Engineering) and Samir Ahmed (Technical Lead), moderated by Lawrence Bruhmeller (Eng Management @ Sigma). ABOUT SULMAN CHOUDHRYSulman leads ChatGPT Engineering at OpenAI, driving the development and scaling of one of the world's most impactful AI products. He pushes the boundaries of innovation by turning cutting‑edge research into practical, accessible tools that transform how people interact with technology. Previously at Meta, Sulman founded and scaled Instagram Reels, IGTV, and Instagram Labs, and helped lead the early development of Instagram Stories.He also brought MetaAI to Instagram and Messenger, integrating generative AI into experiences used by billions. Earlier in his career, Sulman was on the founding team that built and launched UberEATS from the ground up, helping turn it into a global food delivery platform. With a track record of marrying technical vision, product strategy, and large‑scale execution, Sulman focuses on building products that meaningfully change how people live, work, and connect.ABOUT SAMIR AHMEDSamir is the Technical Lead for ChatGPT at OpenAI, where he currently leads the Personalization and Memory efforts to scale adaptive, useful, and human-centered product experiences to over 700 million users. He works broadly across the OpenAI stack—including mobile, web, services, systems, inference, and product research infrastructure.Previously, Samir spent nine years at Snap, working across Ads, AR, Content, and Growth. He led some of the company's most critical technical initiatives, including founding and scaling the machine learning platform that powered nearly all Ads, Content, and AR workloads, handling tens of billions of requests and trillions of inferences daily.ABOUT LAWRENCE BRUHMELLERLawrence Bruhmuller has over 20 years of experience in engineering management, much of it as an overall head of engineering. Previous roles include CTO/VPE roles at Great Expectations, Pave, Optimizely, and WeWork. He is currently leading the core query compiler and serving teams at Sigma Computing, the industry leading business analytics company.Lawrence is passionate about the intersection of engineering management and the growth stage of startups. He has written extensively on engineering leadership (https://lbruhmuller.medium.com/), including how to best evolve and mature engineering organizations before, during and after these growth phases. He enjoys advising and mentoring other engineering leaders in his spare time.Lawrence holds a Bachelors and Masters in Mathematics and Engineering from Harvey Mudd College. He lives in Oakland, California, with his wife and their three daughters. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:From research lab to record-breaking product: Navigating the fastest growth in history (4:03)Unpredictable scaling: Handling growth spurts of one million users every hour (5:20)Cross-stack collaboration: How Android, systems, and GPU engineers solve crises together (7:06)The magic of trade-offs: Aligning the team on outcomes like service uptime vs. broad availability (7:57)Why throwing models "over the wall" failed and how OpenAI structures virtual teams (11:17)Lessons from OpenAI's first intern class: Why AI-native new grads are crushing expectations (13:41)Non-hierarchical culture: Using the "Member of Technical Staff" title to blur the lines of expertise (15:37)AI-native engineering: When massive code generation starts breaking traditional CI/CD systems (16:21)Asynchronous workflows: Using coding agents to reduce two-hour investigations to 15 minutes (17:35)The mindset shift: How rapid model improvements changed how leaders audit and trust code (19:00)Predicting success: "Vibes-based" decision making and iterative low-key research previews (20:43)Hiring for high variance: Why unconventional backgrounds lead to high-potential engineering hires (22:09) LINKS AND RESOURCESLink to the video for this sessionLink to all ELC Annual 2025 sessions This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This festive charity debate asks a question nobody saw coming but everyone had an opinion on. Would Santa Claus make a good principal investigator? Recorded live in the Dementia Researcher Community, this Christmas special brings humour, sharp thinking, and real reflections on leadership, research culture, ethics, and academia. -- The debate is hosted by Adam Smith and Dr Anna Volkmer. Speaking for the motion is Rebecca Williams, PhD researcher exploring FTD and apathy. Speaking against the motion is Dr Connor Richardson, Research Fellow working in data science, epidemiology, and machine learning in dementia research. Through opening statements, rebuttals, and audience questions, the discussion ranges from logistics and mentorship to ethics, transparency, wellbeing, and what good leadership really looks like in research. While lighthearted on the surface, the debate reveals some very familiar academic tensions beneath the tinsel. Vote now:

Anyone who owns or has driven a new car lately might have come across the annoying beeps and bongs that happen when you get near the speed limit or look too long at the radio, or perhaps get close to the edge of your lane on the road. These safety features are known as ADAS - but they are often annoying. We catch up with Shawn Ticehurst from NRMA Insurance's Research Lab about what they are doing to research the feelings of drivers toward these systems. Plus, we've driven the all-electric Kombi - the VW ID.Buzz, and Ben joins us to talk about his classic wagon as he considers an electric wagon. Get in touch, text or WhatsApp to 0477 657 657.

Felipe Araujo discusses Lehigh's Behavioral Research Lab and some of the studies that have come out of it.Learn more about Felipe Augusto de AraujoDanny Zane's marketing researchThe book NudgeLearn more about the Behavioral Research Lab

While the Bitcoin Core v30 vs Knots debate went on, Paul Sztorc was hard at work building the Drivechains software. However, this doesn't meant that he doesn't have any opinions about the recent events and their greater significance. Time stamps: 00:01:30 - Welcome to the Bitcoin Takeover Podcast and Paul's Second Appearance This Season 00:02:28 - Bitcoin Civil War: Core v30 vs Knots Debate 00:03:02 - Nick Szabo Joining the Debate and Ties to Samson Mow's Company 00:04:11 - Op Return Limit Silliness and Historical Bitcoin Uncensored Humor 00:05:21 - Shift in Bitcoin Community: From Subversive to Suit Coiner Route 00:06:02 - Resistance Money, Privacy, and Black/Gray Markets 00:09:18 - Rambling Due to Sickness/Jetlag and Software Demo Tease 00:09:29 - LayerTwo Labs Software Overview and Download Instructions 00:11:55 - Tweet on Turning Transactions into JPEGs 00:13:00 - Bit Window Software Explanation vs Bitcoin Knots 00:15:31 - Bit 300 Activator vs Bitcoin Core Pull Requests 00:17:25 - No More Soft Forks in Bitcoin and Activation Challenges 00:18:23 - CTV Almost Activated, Shift to Filter Debates 00:19:10 - Bitcoin's Potential Death from Complacency 00:19:36 - Derangements of Bitcoin Post and Lightning as Sacred Cow 00:20:02 - Tabconf Presentation on Lightning Network Issues 00:22:52 - Lightning Network Blackpill Article and Updates 00:24:40 - Lightning Cult and Misunderstandings 00:26:24 - Promoting Chaumian Ecash and Human Rights Foundation 00:27:03 - Funding R&D: Ecological Impacts and Bad Ideas 00:29:02 - Maginot Line Example: Funding Bad Defenses 00:30:16 - Drivechains Activation on Litecoin Progress 00:31:58 - Next Tests: Testnet 4 and Forknet Realism 00:32:58 - Litecoin Activation Process and Differences 00:34:49 - Dogecoin Merged Mining and Potential Activation 00:35:26 - Sky Doge as First Drivechain Chain 00:36:50 - Bit Window Advantages Over Knots 00:37:25 - Views on Mining Pools and Game Theory 00:40:05 - Critique of Luke Dash Jr.'s Ideas on Pools 00:41:05 - Transaction Relay Policies and Miner Incentives 00:41:47 - Emotional Manipulation in Debates 00:43:13 - Raising Funding and Different Approaches vs Luke 00:44:02 - Marketing: Funny Videos and Memes 00:45:05 - Luke Dash Jr.'s Character and Expertise 00:46:05 - Experts vs Elites in Bitcoin 00:48:08 - Testing LayerTwo Labs Software on OSes 00:48:38 - Windows/Linux Discussion and Preferences 00:59:30 - ZK Rollups and Data Availability on Bitcoin (Post-Truncation) 01:00:06 - Non-Miner L2s Have No Future 01:01:47 - Drivechain as Minimal, Optional Soft Fork 01:02:55 - Tom Cruise Party Analogy for Bitcoin Upgrades 01:03:08 - Derangements of Bitcoin Post Recommendation 01:03:50 - Viewer Comment: Layer 2s Bad, More Altcoins Needed 01:04:03 - Importance of Competition in Crypto 01:07:02 - Altcoins as Regression Due to Switching Costs 01:08:33 - Sponsor Plugs: LayerTwo Labs, SideShift, Bitcoin.com, NoOnes 01:09:07 - Questions: Why Zcash for Fungibility Sidechain? DAGs? 01:13:35 - Prediction Market L2 and Ambitious Design 01:14:24 - Truthcoin History and Inspiration for Others 01:16:34 - Robin Hanson as Prediction Market OG 01:17:23 - Zcash vs Monero: Code Forks and Privacy Comparison 01:21:30 - Privacy via Decoys vs Pools in Zcash/Monero 01:24:10 - Zcash Advantages for Sidechains 01:26:30 - Zcash Drawbacks Ideal for Sidechain Rollovers 01:27:42 - Zcash as Research Lab for Bitcoin/Ethereum 01:28:07 - R&D as Creative Endeavor Over Funding 01:30:19 - Zcash Launch Story and Roger Ver's Influence 01:31:08 - Truthcoin Parallels and Sidechain Intent 01:32:03 - Maximalist Sacrifices for Bitcoin 01:32:31 - Blockstream's Failure and Altcoin Rise 01:34:29 - Thoughts on DAGs (e.g., Kaspa, Quai) 01:36:20 - SPV Importance and Satoshi's Vision 01:39:05 - SPV Balances Security and Convenience 01:39:59 - DAGs Not Solving Key Scaling Problems 01:42:42 - Shitcoin Definition: Outdated Dichotomy 01:43:53 - Optimism for Bitcoin's Future Choice 01:45:32 - One Coin to Rule Them All Analogy 01:45:38 - Thanks, Wrap-Up, and Jay Berg Recommendation 01:46:54 - Future Live Demo Tease 01:47:16 - Join Drivechain Insiders Telegram Group 01:48:41 - Closing and Thanks

In this episode of Mission Matters, host ⁠Adam Torres⁠ talks with ⁠Csilla Ari D'Agostino, Ph.D⁠., Founder and CEO of Audacious Nutrition. Dr. D'Agostino shares her inspiring journey from studying ketones in a research lab to building a company that brings cutting-edge nutrition science directly to consumers. Follow Adam on Instagram at ⁠https://www.instagram.com/askadamtorres/⁠ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: ⁠https://missionmatters.lpages.co/podcastguest/⁠ Visit our website: ⁠https://missionmatters.com/⁠ More FREE content from Mission Matters here: ⁠https://linktr.ee/missionmattersmedia⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Flint raised a $5 million seed round led by Accel, with participation from Sheryl Sandberg's fund, Sandberg Bernthal Venture Partners, and prior investor Neo. Also, Coco Robotics is working toward automating its fleet of delivery robots using its millions of miles of collected data. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Over three years after her first appearance (Episode 18), Kim Bryan returns to the Talent Intelligence Collective podcast to discuss her evolution from leading a global TI team of 120 at its peak to launching AMS's Research Lab. In this wide-ranging conversation, Kim shares insights from analysing around 400,000 hiring records spanning just under 100 countries from 2020 to 2025 and reveals what's really driving offer declines (spoiler: it's not always about money).What We CoverAI & Employment - Examining Stanford's "Canaries in the Coal Mine" study and why the "AI is replacing entry-level workers" narrative might be correlation, not causation. The real impact on software development and customer support roles, and why businesses still don't understand where to apply AI effectively.ONS Labour Force Survey Crisis - UK response rates dropped from under 50% in 2016 to around 20% now, whilst the US maintains 68%. Critical national decisions are being made on inadequate data due to funding and skills mismatches.Evolution of TI at AMS - How talent intelligence moved from "add-on service" to embedded across all client work. The shift to self-service models, introduction of Insights and Intelligence Partners, and the ongoing data literacy challenge.Offer Declines Research - Key findings: 15% increase in time-to-hire when offers are declined. Compensation wasn't the dominant reason—personal factors, hiring process issues, and flexibility matter more than expected. Sales roles showed highest volatility; project management roles surprisingly volatile due to change management demand. The critical finding: recruiter-candidate relationships matter more than process automation.Education Revolution - Oxford research showing AI sector prioritises skills over formal education. Why universities haven't fundamentally changed since post-Industrial Revolution, and the return of apprenticeships and practical training.Key Quote"Despite all of the tech advances and all of the different strategies you can apply, the biggest difference that you can make to your process is still through your people. Post-offer engagement can be the difference between an offer being accepted and being declined."Practical Tips for TA LeadersGive Yourself Creative Space - Stop firefighting long enough to actually plan aheadInvest in Your People - Find time to develop your team, not just extract from themFind Something Outside Work - Your professional performance depends on your personal wellbeingComing from AMS Research LabThe Great Flattening (declining management layers)Skills mismatch: Are universities preparing students for tomorrow's jobs? (publishing soon)Stores to supply chains: How holiday hiring is changingEU Pay Transparency Directive analysisIndustry deep dives and labour market overviewsComprehensive TA metrics benchmarking (2026)About Kim BryanKim Bryan is the Global Head of Research at AMS, where she leads their Research Lab think tank. She's been with AMS for nearly 10 years in this stint (and worked there previously too, making it nearly two decades total). She previously looked after talent intelligence for AMS and managed a global team of 120 at its peak. Her varied career spans insurance and a mix of numbers and people work, making her ideally suited to the intelligence and insights space.Resources MentionedAMS Research Lab Report: "Offer Declines and Dropouts"Stanford Digital Economy Lab: "Canaries in the Coal Mine: Six Facts About the Recent Employment Effects of Artificial Intelligence"Beyond the Buzz Report on AI SkillsOxford Internet Institute & University of Oxford: Research on AI sector prioritising skills over formal educationOffice for National Statistics Labour Force SurveyAs ever - big thanks to our sponsors: ⁠⁠⁠https://lightcast.io⁠⁠⁠

Ben Pascoe visits the pop-up teahouse at Research lab, an experimental space where diverse intersect and they attempt to develop at holistic approach to the co-evolution of human consciousness and AI. He talks with founders/organizers Roman Pii Wagner, Peter Kašpar, and Andrea Kutlikova about AI, community building, keeping young people at home and doing business in Slovakia today.

Episode 140: In this episode of Critical Thinking - Bug Bounty Podcast Justin and Joseph give an update from The Crit Research Lab, as well as some writeups on postMessage vulnerabilities, Cookie Chaos, and more.Follow us on X at: https://x.com/ctbbpodcastGot any ideas and suggestions? Send us feedback at info@criticalthinkingpodcast.ioShoutout to YTCracker for the awesome intro music!====== Links ======Follow your hosts Rhynorater and Rez0====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord!Get some hacker swag here!====== This Week in Bug Bounty ======Cross-site request forgeryHackerOne New Milestone ProgramEmail santerra.holler@bugcrowd.com for media opportunities====== Resources ======Exploiting Web Worker XSS with BlobsCritical Research LabRez0's TweetCVE-2022-21703: cross-origin request forgery against GrafanaConversation about Forcing Quirks ModeAI Busniess Logic & POC or GTFOHunting postMessage Vulnerabilities – Part 1Hunting postMessage Vulnerabilities – Part 2Executive OffenseCookie Chaos: How to bypass Host and Secure cookie prefixes====== Timestamps ======(00:00:00) Introduction(00:05:48) Crit Research Update(00:13:00) Encouragement & Collaboration(00:19:37) Cross-origin request forgery & Anthropic's web fetch(00:29:17) Quirks Mode, AI Business Logic & POC or GTFO(00:44:21) Hunting postMessage & Claude Code browserbase(00:51:25) Community story, Executive Offense, & Cookie Chaos

You are in for a dose of inspiration in this episode of Raise the Line as we introduce you to a rare disease patient who was a leading force in establishing the diagnosis for her own condition, who played a key role in launching the first phase three clinical trials for it, and who is now coordinating research into the disease and related disorders at one of the nation's top hospitals. Rebecca Salky, RN, was first afflicted at the age of four with MOGAD, an autoimmune disorder of the central nervous system that can cause paralysis, vision loss and seizures. In this fascinating conversation with host Lindsey Smith, Rebecca describes her long and challenging journey with MOGAD, her work at the Neuroimmunology Clinic and Research Lab at Massachusetts General Hospital, and the importance of finding a MOGAD community in her early twenties. “There's a sense of power and security when you have others on your side. You're not alone in this journey of the rare disease,” she explains. Be sure to stay tuned to learn about Rebecca's work in patient advocacy, her experience as a nurse, and the three things she thinks are missing in the care of rare disease patients as our Year of the Zebra series continues.Mentioned in this episode:The MOG ProjectNeuroimmunology Clinic & Research Lab at Mass General If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/raisethelinepodcast

Nvidia's research lab developed the technology that took the company from a video game GPU startup to a $4 trillion-dollar company. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Alyssa Parten, PhD, CSCS, is a Clinical Assistant Professor of Kinesiology at The University of Alabama. Her research centers on resistance training and female physiology, with a focus on strategies to enhance female exercise performance and how resistance training may impact female long-term health. Dr. Alyssa Parten shares her expertise on female physiology and resistance training, challenging conventional wisdom about menstrual cycle-based training while advocating for personalized, auto-regulated approaches instead.• Clinical assistant professor of kinesiology researching resistance training in female physiology• Competitive powerlifter with a 292.5 lb squat, 187.5 lb bench press, and 375 lb deadlift at 138 lbs bodyweight• Research found no significant metabolic differences between follicular and luteal phases• Auto-regulation through RPE is more effective than strict cycle-based training programs• Normal menstrual cycle length ranges from 24-39 days, with significant individual variation• Traditional powerlifting and bodybuilding training are both effective for female physiology• Leading FEMPOWER research team studying women-specific training adaptations• Current projects examine post-activation performance enhancement protocols for women• Future research will explore resistance training benefits during perimenopause and menopause

In today's newscast, a look at the downstream effects of changes to federal grant requirements since President Trump took office.

Scientific research is the foundation of many innovative solutions in any field. Did you know that Dynatrace runs its own Research Lab within the Campus of the Johannes Kepler University (JKU) in Linz, Austria - just 2 kilometers away from our global engineering headquarter? What started in 2020 has grown to 20 full time researchers and many more students that do research on topics such as GenAI, Agentic AI, Log Analytics, Procesesing of Large Data Sets, Sampling Strategies, Cloud Native Security or Memory and Storage Optimizations.Tune in and hear from Otmar and Martin how they are researching on the N+2 generation of Observability and AI, how they are contributing to open source projects such as OpenTelemetry, and what their predictions are when AI is finally taking control of us humans!To learn more about their work check out these links:Martin's LinkedIn: https://www.linkedin.com/in/mflechl/Otmar's LinkedIn: https://www.linkedin.com/in/otmar-ertl/Dynatrace Research Lab: https://careers.dynatrace.com/locations/linz/#__researchLab

Send us a textCan we take lessons from one location and expect similar results in another location? How does replication strengthen geographic research? Today's guest, Dr. Peter Kedron, an expert in validating geographic research, shares how he thinks about how learning about one location can translate to another location.From the Spatial Pattern Analysis and Research Lab.This episode is produced, edited, and distributed by Lizzy Schattle.Music by Arnav Srivastav.

Members of the Department of Government Efficiency have made their way into the National Science Foundation, as grants throughout the agency are being terminated. Three DOGE affiliates are currently listed as working in the Office of the Director at NSF, according to multiple sources within the agency: Luke Farritor, a former SpaceX intern and AI engineer who has shown up at other agencies DOGE has entered; Rachel Riley, a former McKinsey consultant who has also appeared at the Department of Health and Human Services; and Zachary Terrell. As part of the arrangement, Farritor has a “Budget, Finance, and Administration” clearance, which a source said allows him to view and modify the agency's funding opportunity system. Farritor and Terrell are listed in an agency directory as consultants. On April 18, NSF published a statement that it was terminating grants and awards that don't align with the administration's priorities, including those related to diversity, equity and inclusion (DEI) and misinformation and disinformation. Alexis Bonnell has stepped down from her positions at the Air Force Research Laboratory and transitioned to a new job at OpenAI, the company responsible for the development of ChatGPT. In 2023, Bonnell was tapped to serve as AFRL's first-ever chief information officer and director of the laboratory's Digital Capabilities Directorate, where she led the lab's information technology strategy and overall modernization efforts. According to a Tuesday post on LinkedIn, Bonnell is now working at OpenAI as a partnership manager, a position she took on in March. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

In the second episode of the series "Between Two Ears", I share my recent trip to Iowa City to participate in a groundbreaking misophonia research study led by Dr. Sukhbinder Kumar — a continuation of his earlier work on mirror neurons and the motor basis of misophonia. This new study explores the social context of trigger reactions and involved time in an MRI chamber while exposed to common misophonic triggers.I talk about what it was like to undergo the study, the misophonic challenges(!), and why I believe it was worth it — not just for science, but for personal growth and understanding. I also reflect on meeting Dr. Kumar in person, our conversation about the deeper roots of misophonia, and why this research made me hopeful for the future. I hope to have Dr. Kumar on a regular episode of the podcast in the future!If you're in the area or able to travel, I encourage you to consider participating in studies like this. They matter.Photos and more on social. Here's a link to the lab: https://interoception.lab.uiowa.edu/misophonia-research   -----Web: https://misophoniapodcast.comOrder "Sounds like Misophonia" - by Dr. Jane Gregory and ISupport the podcast at https://misophonia.shopEmail: hello@misophoniapodcast.comSend me any feedback! Also, if you want some beautiful podcast stickers shoot over your address.YouTube channel (with caption transcriptions)Social:Instagram - @misophoniapodcastFacebook - misophoniapodcastTwitter/X - @misophoniashowSoQuiet - Misophonia Advocacyhttps://soquiet.orgSupport the show

Birgitt Boschitsch is the co-founder and CEO of spotLESS Materials, an advanced materials company and Penn State spinoff commercializing highly repellent anti-fouling coatings.  In this episode, Birgitt shares how her passion for helping others through research led to her co-founding spotLESS Materials. She discusses her formative experiences, academic challenges, and the pivotal moments that shaped her career path, including her time at Princeton and her decision to pursue graduate studies at Penn State.  She discusses the original inspiration behind the idea for a toilet coating that repels sticky waste, as well as the challenges of transitioning from lab work to startup life. She shares the importance of community support and the impact of media exposure on sales.

Birgitt Boschitsch is the co-founder and CEO of spotLESS Materials, an advanced materials company and Penn State spinoff commercializing highly repellent anti-fouling coatings.  In this episode, Birgitt shares how her passion for helping others through research led to her co-founding spotLESS Materials. She discusses her formative experiences, academic challenges, and the pivotal moments that shaped her career path, including her time at Princeton and her decision to pursue graduate studies at Penn State.  She discusses the original inspiration behind the idea for a toilet coating that repels sticky waste, as well as the challenges of transitioning from lab work to startup life. She shares the importance of community support and the impact of media exposure on sales.

François Chollet discusses the outcomes of the ARC-AGI (Abstraction and Reasoning Corpus) Prize competition in 2024, where accuracy rose from 33% to 55.5% on a private evaluation set. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? They are hosting an event in Zurich on January 9th with the ARChitects, join if you can. Goto https://tufalabs.ai/ *** Read about the recent result on o3 with ARC here (Chollet knew about it at the time of the interview but wasn't allowed to say): https://arcprize.org/blog/oai-o3-pub-breakthrough TOC: 1. Introduction and Opening [00:00:00] 1.1 Deep Learning vs. Symbolic Reasoning: François's Long-Standing Hybrid View [00:00:48] 1.2 “Why Do They Call You a Symbolist?” – Addressing Misconceptions [00:01:31] 1.3 Defining Reasoning 3. ARC Competition 2024 Results and Evolution [00:07:26] 3.1 ARC Prize 2024: Reflecting on the Narrative Shift Toward System 2 [00:10:29] 3.2 Comparing Private Leaderboard vs. Public Leaderboard Solutions [00:13:17] 3.3 Two Winning Approaches: Deep Learning–Guided Program Synthesis and Test-Time Training 4. Transduction vs. Induction in ARC [00:16:04] 4.1 Test-Time Training, Overfitting Concerns, and Developer-Aware Generalization [00:19:35] 4.2 Gradient Descent Adaptation vs. Discrete Program Search 5. ARC-2 Development and Future Directions [00:23:51] 5.1 Ensemble Methods, Benchmark Flaws, and the Need for ARC-2 [00:25:35] 5.2 Human-Level Performance Metrics and Private Test Sets [00:29:44] 5.3 Task Diversity, Redundancy Issues, and Expanded Evaluation Methodology 6. Program Synthesis Approaches [00:30:18] 6.1 Induction vs. Transduction [00:32:11] 6.2 Challenges of Writing Algorithms for Perceptual vs. Algorithmic Tasks [00:34:23] 6.3 Combining Induction and Transduction [00:37:05] 6.4 Multi-View Insight and Overfitting Regulation 7. Latent Space and Graph-Based Synthesis [00:38:17] 7.1 Clément Bonnet's Latent Program Search Approach [00:40:10] 7.2 Decoding to Symbolic Form and Local Discrete Search [00:41:15] 7.3 Graph of Operators vs. Token-by-Token Code Generation [00:45:50] 7.4 Iterative Program Graph Modifications and Reusable Functions 8. Compute Efficiency and Lifelong Learning [00:48:05] 8.1 Symbolic Process for Architecture Generation [00:50:33] 8.2 Logarithmic Relationship of Compute and Accuracy [00:52:20] 8.3 Learning New Building Blocks for Future Tasks 9. AI Reasoning and Future Development [00:53:15] 9.1 Consciousness as a Self-Consistency Mechanism in Iterative Reasoning [00:56:30] 9.2 Reconciling Symbolic and Connectionist Views [01:00:13] 9.3 System 2 Reasoning - Awareness and Consistency [01:03:05] 9.4 Novel Problem Solving, Abstraction, and Reusability 10. Program Synthesis and Research Lab [01:05:53] 10.1 François Leaving Google to Focus on Program Synthesis [01:09:55] 10.2 Democratizing Programming and Natural Language Instruction 11. Frontier Models and O1 Architecture [01:14:38] 11.1 Search-Based Chain of Thought vs. Standard Forward Pass [01:16:55] 11.2 o1's Natural Language Program Generation and Test-Time Compute Scaling [01:19:35] 11.3 Logarithmic Gains with Deeper Search 12. ARC Evaluation and Human Intelligence [01:22:55] 12.1 LLMs as Guessing Machines and Agent Reliability Issues [01:25:02] 12.2 ARC-2 Human Testing and Correlation with g-Factor [01:26:16] 12.3 Closing Remarks and Future Directions SHOWNOTES PDF: https://www.dropbox.com/scl/fi/ujaai0ewpdnsosc5mc30k/CholletNeurips.pdf?rlkey=s68dp432vefpj2z0dp5wmzqz6&st=hazphyx5&dl=0

Sarah returned to her Real World house and wasn't prepared for how emotional it would make her, and now she's considering why the experience brought out so many feelings. We talk about "wet dog shake," what causes it, and why some mammals have that response to certain stimuli, while others, like cats and mice, don't. We learn about dozens of monkeys who escaped from a research lab, and debate whether we're on team monkeys or team science. Either way, those monkeys are clever and free. We discuss a man who uses a medical voice board to communicate and how he modified the device to make it more personal and reflective of his identity. We consider which accents we like in the world and which grate on our nerves, and why so many people hate the sound of their own voice. Sarah explains how chocolate exposed racism within the movie industry, and how things have improved since the 70s. Plus, we discuss the Sweet Bobby documentary where a woman is catfished by someone for a decade, and we debate how someone could allow that to happen, why it could happen to anyone, and why there wasn't much punishment for the person who tricked her.Listen to more podcasts like this: https://wavepodcastnetwork.comJoin our Candy Club, shop our merch, sign-up for our free newsletter, & more by visiting The Brain Candy Podcast website: https://www.thebraincandypodcast.comConnect with us on social media:BCP Instagram: https://www.instagram.com/braincandypodcastSusie's Instagram: https://www.instagram.com/susiemeisterSarah's Instagram: https://www.instagram.com/imsarahriceBCP on X: https://www.x.com/braincandypodSponsors:Get an exclusive 20% off your first order at https://thrivecausemetics.com/BRAINCANDYhis episode is sponsored by BetterHelp. Visit https://www.betterhelp.com/braincandy today to get 10% off your first month.Get ten dollars off any order! Enjoy free shipping when you subscribe. Go to https://nutrafol.com and enter the promo code BRAINCANDYGIFTFor 50% off your first order, head to https://www.smalls.com/BRAINCANDY and use code BRAINCANDYSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Bob Stein, Atari's Encyclopedia Project   Bob Stein worked at Atari Research for 18 months beginning in 1981. He was hired by Alan Kay. He worked almost exclusively on an encyclopedia project, a potential collaboration between Atari and Encyclopaedia Britannica that never went anywhere.   I learned about Bob after he uploaded an item called The Atari Drawings to Internet Archive. It's a collection of nine colorful pencil drawings, drawn in 1982 by Disney animator Glen Keane. The drawings depict futuristic scenarios where people use a computerized encyclopedia to get information: for instance, "An earthquake wakes a couple in the middle of the night. The Intelligent Encyclopedia, connected to an online service, informs them of the severity of the earthquake and makes safety tips readily available." and "A mother and her children looking into a tidepool in Laguna ask the Intelligent Encyclopedia about the plants and animals that they see."   Bob described the collection of art in his introduction to the document:   "In 1982 executives from Warner, Inc., Atari's parent company, were scheduled to visit the Research Lab where the Encyclopedia Project was located. Brenda Laurel and I came up with these scenarios to give the execs a sense of what we were working toward. The drawings were made by Disney animator, Glen Keane.   When you look at these, remember they were made 16 years before Google and 12 years before Yahoo, even 8 years before the earliest web-based search engines.   That said, one of the most interesting things about these scenarios as seen today, is that with the exception of the image of the architect and the teacher none of them indicated any inkling that the most important element of the web to come was that it would bring people into contact with each other. What we see here is almost entirely people accessing content from a central server, no sense that we would be communicating with each other or uploading our own contributions to the collective culture. My own explanation for this lapse focuses on the print-era mentality that saw readers purely as consumers of content."   Bob saved and scanned a large number of materials from his time at Atari, and uploaded them to Internet Archive. In addition to the scans of Keane's Atari Drawings, the documents include memos about the encyclopedia project and a transcript of a 1982 seminar for Atari Research featuring Charles Van Doren. Check the show notes for those links.   After Atari, Bob was co-founder of The Criterion Collection, which restores and distributes important classic films; and co-founder of The Voyager Company, the first commercial multimedia CD-ROM publisher. In 2004, he co-founded The Institute for the Future of the Book, a think tank "investigating the evolution of discourse as it shifts from printed pages to networked screens."   This interview took place December 16, 2023.   Video version of this interview at YouTube   The Atari Drawings   ANTIC Interview 420 - Brenda Laurel, Atari Research   Whither The Encyclopedia Project - Atari Encyclopedia Project memos   Back to the Future -- In honor of Encyclopedia Britannica giving up its print edition (Wayback machine)   Stein Kay Atari Memos Pt 1   Stein Kay Atari Memos Pt 2   Exchange With Steve Weyer And J. David Bolter 1983   Hadley Letter 1980-12-01   Atari...Ifugao Question Journal, Michael Naimark   CVD Atari Seminar 20 December 1982   Encyclopedia And The Intellectual Tools Of The Future . . . November 1981   Bob Stein Archives at Stanford   The Digital Antiquarian — Bob Stein and Voyager   Charles Van Doren in Wikipedia   Bob Stein wants to change how people think about the book (2010)

There's a boring red brick building in the middle of Yale University's campus. If you walk past it you might think it's an administrative office. It looks - boring.  But if you look closely - there are 75 CCTV cameras pointed directly at the building. Covering every square inch of the exterior. Why would they need so much security for a university building? It is the Animal Research Laboratory. It houses over 4000 research mice and has tens of millions of dollars of research experiments being conducted inside the brick walls.  It is one of the most secure buildings on campus.  Till a brilliant Yale graduate student disappears inside.  She walks in.  Never walks out.  She is nowhere to be found in the building - or at least that's what the authorities initially thought.  But have they checked the walls? Full Source Notes: rottenmangopodcast.com To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

This Creepypasta scary story is from the creepypasta website, written by Bryan A Young, make sure to check out the original story and support the author: "I Worked at a Top Secret Government Research Lab, I Need to Share My Journals" https://www.creepypasta.com/i-worked-at-a-top-secret-government-research-lab-i-need-to-share-my-journals/ Learn more about your ad choices. Visit megaphone.fm/adchoices