Podcasts about pnas

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

The more you've built, the harder it gets to want the next thing. You've crossed milestones a past version of you only dreamed of and instead of momentum you're finding yourself busy with the safe stuff, the tasks you've already mastered, while the thing that would actually move your practice forward keeps waiting.If you've been asking yourself, "Why am I not motivated?" this episode is for you. I've seen the pattern in myself and some of my most successful clients: the mundane, comfortable tasks robbing us of our next level goals. Today I'm showing you why the old fuel that got you here won't get you to the next level.We'll look at why the urgency that pulled you out of scarcity isn't available anymore and how hiding in mastery can look exactly like productivity. The strategies that built your first six figures were built for different stakes, and that mismatch is fixable.By the end of this episode, you'll choose the one move that matters this week for that next-level goal and know how to keep busyness at bay so you can make real progress. Tune in!Topics covered on why am I not motivated:Why the next goal feels harder to want than the last one, even when you're more resourced than everHow low motivation disguises itself as busyness: the mundane, already-mastered tasks that feel urgent but aren'tThe stages from resistance to boredom, and how hiding in mastery keeps you coasting instead of growingWhy the strategies that built your first six figures were built for different stakes, and why external accountability matters more now, not lessThe one awareness exercise that starts the shift: name the scary goal you're circling and the task you're substituting for itConnect with Felicia:Get my freebie & join the email list: The Magic SheetsInstagram: @the_bad_therapistWebsite: www.thebadtherapist.coachResources from this episode:Liberated Business: www.thebadtherapist.coach/liberatedbusinessIncome and Emotional Well-Being: A Conflict Resolved: the PNAS study by Killingsworth, Kahneman and Mellers behind the money and happiness research Felicia referencesRelated episodes:A Time Blocking Method for Therapists Ready to Build Their Next Offer [Ep 177]: the calendaring episode to revisit when you're ready to make the changeThe Five Growth Stages Every Successful Therapist Goes Through [Ep 171]: the stages episode that maps moving from resistance to mastery to boredomQuote:"You need different fuel to move you to your next level of goals, not just more of the old fuel, because that's just not gonna work anymore." - Felicia

Empathischen Menschen fällt die Erfüllung der Bedürfnisse anderer oft leichter als die eigene Selbstfürsorge.Kennst du das auch?Doch die Erfüllung der eigenen Bedürfnisse ist der Schlüssel zu✨ mehr Schöpferkraft und weniger Erschöpfung,✨ mehr Freude und weniger Frust,✨ mehr Wahrhaftigkeit und weniger Wut.Fest steht: Den sanften Weg dürfen feinfühlige Menschen auch mit sich selber gehen.Auch wenn das bedeutet, den Fokus vom außen an einen anderen Ort zu lenken.In dieser Folge teile ich drei Schritte mit dir, die dir helfen können, die eigene Bedürfniserfüllung zu reaktivieren. Das Schönste daran: Du selbst bist der Schlüssel!Viel Freude damit, denn:Auch du machst die Welt heller ✨!Links zur Folge:(Alle folgenden Links zuletzt eingesehen am 04.08.2026) Meine Folge vom 29.04.2026:'Was 2002 geschah und mich seither nicht losgelassen hat'⁠⁠⁠⁠hhttps://open.spotify.com/episode/1ppEkKxVYBiZmaTpOeyjMN?si=ek5Hu9x0TcyenNHot-QQiQ⁠Meine Folge zu der Übertragung von den Stimmungen anderer:'Energie-Management ist für feinfühlige Menschen oft noch wichtiger als Zeit-Management'https://open.spotify.com/episode/5bLkwSG6TyaJH4Mvnzb4AN?si=v8emoGsHRZiHIQPJcbSuDwMeine Folge zu der Praxis der Morgenseiten:'Wie Morgenseiten helfen, die Seele sprechen und 'baumeln' zu lassen'https://open.spotify.com/episode/6u8iusdYJF1fJzGKmGNVL2?si=rtkUYvd0Ru22fVUaw7azuAZur Wirkung der Freude:Studie der PNAS zur Sichtbarkeit von Emotionen im Körper (im Englischen 'happiness')https://www.pnas.org/doi/10.1073/pnas.1321664111Meine Buchempfehlung zur heutigen Folge (unbezahlte Werbung):'Die 1%-Methode' von James ClearIn eigener Sache ✨:Hier im Podcast und auf meinem YouTube-Kanal⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ @auchdumachstdieweltheller⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠erscheinen regelmäßig Solofolgen und auch Gespräche mit ganz unterschiedlichen Menschen, die den Alltag anderer ein Stück heller machen. Und für kleine Impulse zwischendurch folge mir doch einfach auch auf Instagram:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/yvonnemuellerbuergel/?hl=d⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Glitterstaub zu dir...✨Alles Liebe,Yvonne

Evidence of early plate tectonics Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Brad Peters explores what we can learn from the journey of sediments from the early Earth through the rock cycle. In this episode, we cover: •[00:00] Introduction. •[01:04] Geochemist Brad Peters tells us about the Marquesas Archipelago and why is it a place of interest for geologists. •[02:42] He recounts evidence suggesting that one component of the Marquesas lavas was recycled sediment from the early Earth. •[04:59] Peters walks us through the likely journey of this sediment from its origins to its eruption on the Marquesas Archipelago. •[06:24] He explains what this lava tells us about the tectonic processes that were active four billion years ago. •[07:43] Peters summarizes how this result is different than what we previously thought about the inception of plate tectonics. •[08:19] He talks about what the results mean for the story of the early Earth. •[09:13] He lists the caveats and limitations of the study. •[10:09] Conclusion. About Our Guest: Brad Peters Postdoctoral researcher ETH Zurich View related content here: https://www.pnas.org/doi/abs/10.1073/pnas.2500506123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Viagra's Cancer Signal, TV's Toll on Your Brain, Heat and Accelerated Aging, and a New Way to Flush Your Brain Explained Viagra's Active Ingredient May Block Cancer From Spreading A study published in Cancer Research from the Weizmann Institute of Science, in collaboration with Clalit and the U.S. National Cancer Institute, found that sildenafil, the active ingredient in Viagra, may interfere with cancer cells' ability to metastasize by disrupting how they regulate cholesterol, a resource tumors need to detach and invade new tissue. The team combined mouse models and human cell cultures with more than twenty years of medical records covering roughly five million people, and found better survival outcomes among about forty thousand cancer patients who had taken sildenafil before diagnosis. Researchers also flagged a possible combination effect with statins. Host Dave Asprey breaks down why this cheap, off-patent drug class deserves more attention than it's getting, and where it fits alongside his own longevity protocol. Source: https://www.healthline.com/health-news/viagra-may-stop-cancer-from-spreading-study ~~ Heavy TV Watching In Midlife Tied To Alzheimer's-Related Brain Changes A nearly twenty-four-year study published in Alzheimer's & Dementia followed more than 1,700 adults, tracking TV habits in their early fifties and scanning their brains in their mid-seventies. Men who watched more TV showed greater white matter damage and smaller frontal and occipital lobes, while women showed no such association. Critically, people with desk jobs that kept them sedentary all day did not show the same brain changes, and the TV association held even after adjusting for exercise levels. Host Dave Asprey unpacks why passive screen time, not sitting itself, may be the real driver here. Sources: https://www.the-independent.com/life-style/tv-time-linked-alzheimers-study-b3023054.html https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.71582 ~~ Ambient Heat Exposure Linked To Faster Biological Aging A Science Advances study of 3,686 older adults linked neighborhood-level heat index exposure to accelerated biological aging using DNA methylation clocks. Short-term heat exposure moved one clock significantly, while exposure sustained over a full year or six years moved all major clocks, with extreme-caution heat days tied to nearly a three-year jump in one aging measure and a five percent faster overall aging pace on another. Researchers point to inflammation and stress signaling as likely drivers. Host Dave Asprey explains why this is a fundamentally different kind of heat story than the sauna and cold-plunge conversation biohackers are used to. Source: https://www.science.org/doi/10.1126/sciadv.adr0616 ~~ Industrial Chicken Farming May Be Accelerating The Spread Of Drug-Resistant Bacteria A genomic analysis of nearly 2,800 bacterial samples from chickens and wild birds across thirty countries, published in PNAS and covered by The Guardian, found a roughly hundredfold increase in Campylobacter strain transitions between wild birds and farmed chickens since 1900. Global chicken populations have grown sevenfold since the 1960s to about 27 billion birds, and researchers identified genetic adaptations tied to antimicrobial resistance developing inside that population. Sixty to eighty percent of human Campylobacter infections trace back to raw chicken, with links to post-infectious IBS in some cases. Host Dave Asprey connects the dots between industrial farming density and the gut-health stakes for anyone who eats chicken. Sources: https://www.theguardian.com/food/2026/jul/27/industrial-chicken-farming-accelerating-spread-of-diarrhoea-bacteria-study-finds https://doi.org/10.1073/pnas.2609969123 ~~ VA Researchers Develop Breathing-Based "Brain Flush" For Parkinson's And Alzheimer's VA researchers led by Dr. Henry Lin found a way to trigger the brain's glymphatic clearance system, normally active during deep sleep, in people who are awake, using alternating puffs of five percent carbon dioxide and room air every thirty-five seconds. In a study of thirty Parkinson's patients and thirty-three healthy controls, a single thirty-minute session produced measurable increases in blood levels of beta-amyloid, alpha-synuclein, and other waste proteins associated with neurodegeneration. The VA's Technology Transfer Program is now developing a patent and prototype device. Host Dave Asprey explains why this gives breathwork protocols a real physiological mechanism instead of just a hunch. Source: https://www.research.va.gov/currents/0426-Exciting-new-treatment-being-developed-for-Parkinsons-Alzheimers.cfm ~~~ This episode is designed for biohackers, longevity enthusiasts, and high-performance listeners who want mechanism-level insights into an overlooked cancer drug repurposing story, the real difference between passive and active screen time, a new angle on heat as a biological stressor, the hidden gut-health cost of industrial poultry farming, and a breathing technique that may unlock your brain's own detox system. Host Dave Asprey connects clinical research, large-scale genomic data, and translational neuroscience into practical frameworks for improving longevity, gut health, and brain performance. New episodes every Tuesday, Thursday, Friday, and Sunday. Keywords: sildenafil cancer metastasis, Viagra cancer research, cholesterol cancer cells, PDE5 inhibitor longevity, TV Alzheimer's risk, screen time dementia, white matter brain aging, passive sedentary behavior brain, ambient heat epigenetic aging, DNA methylation clock, heat index biological aging, industrial chicken farming bacteria, Campylobacter antibiotic resistance, foodborne illness IBS, glymphatic system brain flush, CO2 breathing brain detox, Parkinson's Alzheimer's treatment, VA brain research, biohacking news 2026, Dave Asprey, The Human Upgrade Thank you to our sponsors! - Suppgrade Labs | Get real restorative sleep with Quiet Mode. Use code DAVE15 at shopsuppgradelabs.com. - Timeline | Visit timeline.com/dave to learn more about Mitopure and get 20% off your first order for a limited time. - iRestore | Reverse hair loss at www.irestore.com/DAVE and get exclusive savings on the iRestore Elite, use code DAVE Resources: • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Get My 2026 Biohacking Trends Report: https://daveasprey.com/2026-biohacking-trends-report/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15 • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 00:00 – Intro 00:19 – Story #1: Viagra & Cancer 02:01 – Story #2: TV Watching & Brain Aging 03:16 – Story #3: Heat Exposure & Biological Age 04:46 – Story #4: Chicken Farming 06:34 – Story #5: CO2 Therapy & Brain Detox See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

¿Es mejor ser optimista o pesimista?

This evening at sundown marks the onset of Tisha B'Av, a 25-hour period in the Jewish calendar which is the saddest of the year. We commemorate and remember the destruction of the First and Second Temples, among the most important events in Jewish history. These disasters led to the dispersal of the Jewish people for thousands of years. They never abandoned their dream of returning to their ancient homeland, but throughout the centuries of exile they were persecuted ruthlessly and relentlessly. In Spain. England. Throughout Europe. In the middle east. And today, we are clearly in the early stages of yet another era of extreme tumult. For Jews.In this episode I speak with Dr. Yiftach Shalev of the Israel Antiquities Authority. Just a few weeks ago, Dr. Shalev and his team discovered charred wooden beams used in the construction of a grand home adjacent to the First Temple, which was destroyed by the Babylonians. The parallels regarding what transpired during this time and on October 7 are eerie. And we are only able to understand the commonalities because of strides in scientific and forensic analysis. Think about it. For a wooden beam charred more than 2,000 years ago to be preserved so that it can be analyzed is extraordinary. What kind of wood was used? Where did it come from? Why did it not decay? Dr. Shalev analyses the detail to preserve and present the big picture. The attackers had a plan. They ignited concentrated fires that incinerated the Temple and adjacent buildings, with temperatures reaching 700 degrees Celsius. Just as Hamas did on October 7. They had a similar plan, going house to house, torching the living and murdered.Everything about this story is fascinating; the scientific advances that make this forensic archaeological work possible. And how much it enhances our understanding of the present, in the context of the past. This is more than archaeology. It is history, war, conquest, and political reality tumbled together. If nothing else listen to the introduction and watch the two minute video. You'll be hooked by then. Trust me. I have never been one to show great interest in “archaeological” stories. This changed everything.Podcast NotesDr. Yiftah Shalev is a leading field archaeologist and the Scientific Advisor for the Jerusalem District of the Israel Antiquities Authority. Over the course of his career, he has directed and participated in major excavations throughout Israel. He spent fifteen years excavating at Tel Dor, one of the most important archaeological sites on Israel's Mediterranean coast, before turning his primary research focus to Jerusalem.Since 2017, Shalev has co-led the Givati Parking Lot excavations in ancient Jerusalem. The excavations he directs have reshaped our understanding of the city's development from the Iron Age through the Persian and Hellenistic periods. Among the remarkable discoveries he has helped bring to light are a monumental rock-cut moat that once divided ancient Jerusalem, an elite public building destroyed during the Babylonian conquest of 586 BCE, the first decorated ivory panels discovered in Jerusalem, and vessels that once contained wine flavored with vanilla. He is also advancing a pioneering interdisciplinary project that uses cosmic-ray muons to investigate underground spaces and structures without excavation.Following the October 7, 2023 attacks, Shalev was called upon to take part in the emergency effort in the communities near the Gaza border, where he used archaeological methods as part of the search for missing persons.Shalev is a teaching fellow at Tel Aviv University and Jerusalem University College. He has published extensively on the archaeology of Jerusalem, ancient economy, and Mediterranean trade networks. His interdisciplinary research has appeared in leading journals, including PNAS, Scientific Reports, PLOS ONE, the Journal of Applied Physics, the Journal of Archaeological Science: Reports, Tel Aviv, and the Israel Exploration Journal. He is married, a father of three, and enjoys hiking and traveling with his family.Show your support for STLV at buymeacoffee.com/stateoftelavivState of Tel Aviv is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.stateoftelaviv.com/subscribe

Domestication of wheat Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, David Lordkipanidze recounts archaeological findings from Georgia expanding the history of bread wheat. In this episode, we cover: •[00:00] Introduction. •[00:51] Archaeologist and paleobotanist David Lordkipanidze tells about the archaeological sites where wheat samples were found. •[02:53] He talks about methods for identifying plant grains among archeological artifacts. •[04:49] Lordkipanidze explains the significance of finding bread wheat and goat grass grains together. •[06:45] He talks about how the findings change the origin story of bread wheat. •[08:28] He lists the caveats and limitations of the study. •[09:24] Conclusion. About Our Guest: David Lordkipanidze Director Georgia National Museum View related content here: https://www.pnas.org/doi/10.1073/pnas.2537697123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Es steht vor Deiner Haustür, kostet nichts und lädt Deinen Kopf schneller auf als jede Couch: ein Stück Grün. In dieser Folge erfährst Du, warum Dein Gehirn Natur regelrecht braucht – für mehr Fokus, ein niedrigeres Stresslevel und bessere Regeneration.Du erfährst, was fast 20.000 Menschen über die richtige Dosis verraten (Spoiler: rund 17 Minuten am Tag reichen), warum ein Spaziergang Dein Arbeitsgedächtnis messbar verbessert – selbst wenn Du überhaupt keine Lust drauf gehabt hättest. Und warum das Training draußen langfristig oft leichter fällt als drinnen. Plus: wie Du das Ganze in Deinen Alltag schmuggelst, ohne Dir Extra-Zeit dafür nehmen zu müssen.Versprochen: Es geht weder ums Bäume-Umarmen noch um sonstigen Esoterik-Kram. Nur Wissenschaft, die Du ab heute nutzen kannst. Denn Dranbleiben schlägt Perfektion – auch auf dem Weg zum nächsten Park.____________*WERBUNG: ➜ Infos zum Werbepartner dieser Folge und allen weiteren Werbepartnern findest Du hier.

A 1992 PNAS paper showed how birdsong upregulates the expression of an immediate early gene in bird forebrains. The work revealed to Ribeiro the importance of studying molecular responses in naturalistic contexts.

Une étude publiée en 2026 dans la revue scientifique PNAS indique un fait surprenant, les fortes chaleurs pourraient réduire le nombre de naissances de garçons.  En analysant plus de 5 millions de naissances en Afrique subsaharienne et en Inde, des chercheurs d'Oxford ont observé que ce phénomène est dû à la fois pour des raisons biologiques et sociales. Pourquoi la chaleur impacte-t-elle plus les garçons ? Quelles sont les raisons qui expliquent ce mécanisme biologique ? Doit-on s'inquiéter de ce phénomène en France ? Écoutez la suite de cet épisode de "Maintenant Vous Savez". Un podcast Bababam Originals écrit et réalisé par Ludivine Morales. À écouter ensuite : Pourquoi y a-t-il plus de naissances à certaines périodes que d'autres ? Congé de naissance : enfin une égalité entre les parents ? Qu'est-ce que le "tourisme de naissance" ? Retrouvez tous les épisodes de "Maintenant vous savez". Pour rester informé et recevoir le meilleur de “Maintenant vous savez” chaque semaine, abonnez-vous à notre newsletter. Suivez Bababam sur Instagram. Learn more about your ad choices. Visit megaphone.fm/adchoices

(A note before we get into it: this article is educational, not medical advice. Talk to your doctor or a registered dietitian before making major changes to your diet — especially if you have diabetes or pre-diabetes, take blood-sugar medication, are pregnant or nursing, or are managing any chronic condition. Nothing here is intended to diagnose, treat, cure, or prevent any disease.)Research shows well-built plant-based diets usually lower blood sugar. If yours moved the other way, one of six silent glucose traps is almost always the reason — and every one of them is fixable.TLDR* The research is actually on vegetarianism's side. Large reviews of clinical trials show vegetarian and vegan diets tend to lower A1C, not raise it.* So if your numbers went up, something in how the diet was built is the real cause — not the fact that it's plant-based.* Six usual suspects: too little protein, “naked” carbs with no brakes, shrinking muscle mass, too much fermentable fiber too fast, high stress/cortisol, and late-night eating.* Each one is a fixable habit, not a reason to abandon vegetarianism.Wait — Doesn't “Plant-Based” Mean Healthier Blood Sugar?Usually, yes. When researchers actually put vegetarian diets to the test in clinical trials, the pattern is consistent: people following them tend to see their A1C go down, not up. A meta-analysis pooling multiple randomized trials found vegetarian diets were linked to a meaningful drop in A1C, and a separate meta-analysis of nine trials found a similar reduction — enough that it would be considered clinically significant by the FDA's own bar for new diabetes drugs.So if you switched to vegetarian eating and your last lab draw came back worse, I want to be straight with you: that's not the expected outcome, and it's not “just what happens” when you cut out meat. Something specific is working against you. The good news is that it's almost always one of a handful of well-understood mechanisms — and once you know which one, it's a quick fix, not a diet overhaul.Think of it like this: a car engine doesn't run worse because you switched to a different brand of gas. It runs worse because something specific — a clogged filter, bad timing, low oil — is getting in the way. Same idea here. Let's go through the six most common “clogs.”THRIVE 120 - TriplePlayDoc is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.1. The Protein Drop You Didn't NoticeThis is the single most common one. When people cut out meat, they usually don't replace that protein gram-for-gram with plant protein — they replace it with more carbs, because carbs are what fill the plate: extra rice, extra bread, extra pasta.Here's why that matters for blood sugar specifically. Protein eaten alongside carbohydrate isn't just “extra food” — it changes how your body handles the carbs. A meta-analysis of controlled feeding trials found that adding protein to a carb-containing meal meaningfully lowers the post-meal glucose spike, largely by boosting the insulin response that clears sugar out of the bloodstream faster. Pull protein out of the meal and carbs hit your blood essentially unguarded.Think of protein as the brakes on a sugar rollercoaster. Take the brakes off, and the same carbs you were eating before now send your blood sugar higher and faster than they used to.2. Naked Carbs, No BrakesThis is the same problem from a different angle: oatmeal for breakfast, a rice bowl for lunch, a smoothie for a snack. All reasonable foods — but if none of them are paired with enough protein or fat, and you're not about to go move that sugar with a workout, it hits your bloodstream as a fast, unbuffered spike. Your body answers with a bigger insulin surge, and doing that meal after meal, day after day, is exactly the pattern linked to declining insulin sensitivity over time.3. The Muscle FactorThis one surprises people: your muscles are the biggest “storage tank” for the sugar you eat. After a meal, skeletal muscle soaks up roughly 70–80% of the glucose that comes out of your bloodstream, using it to refill glycogen stores. Researchers who study this consider muscle glucose uptake the single biggest lever on whole-body insulin sensitivity.So when protein intake drops and resistance training isn't part of the picture, muscle mass tends to shrink over time — and that storage tank gets smaller. Less tank space means the same meal now leaves more sugar circulating in your blood for longer, which shows up as a slow, creeping rise in A1C.Muscle is basically your body's biggest gas tank for sugar. Shrink the tank, and the same fill-up overflows.4. High Fiber, High StressFiber itself is not the villain here — in fact, the right kind of fiber is one of the best tools for blood sugar control. Viscous, soluble fibers (found in oats, beans, and psyllium) form a gel in your gut that physically slows down how fast sugar gets absorbed, and trials show this type of fiber measurably lowers both A1C and fasting blood sugar.The issue is a different kind of fiber problem: volume and speed. Vegetarian diets are often naturally high in fermentable fiber — beans, lentils, certain grains — and when your gut bacteria break that fiber down, the fermentation process produces gas as a byproduct. Ramp up fiber intake quickly, especially the fast-fermenting kinds, and you can outpace your gut's ability to comfortably process it, leading to bloating and discomfort. That physical stress on your body, especially combined with everyday life stress, triggers cortisol release — and cortisol has a direct, well-documented job of raising blood glucose by signaling your liver to make more sugar and by making your cells less responsive to insulin.Think of fiber fermentation like a construction project in your gut. The building itself (your microbiome) benefits — but a project moving too fast kicks up a lot of dust (gas and bloating) along the way.5. Timing and Circadian RhythmsThe lifestyle shift that often comes with going vegetarian — more grazing, more small meals, more snacking — can quietly push more of your eating into the evening. That timing matters more than most people realize. Multiple studies using identical meals given at different times of day have found that the exact same meal produces a bigger blood sugar spike at night than it does in the morning, because your insulin sensitivity and beta-cell function both naturally dip in the evening. One study even found a late meal after 8pm was independently linked to worse A1C.Your body runs on a work shift for carbs. The day shift (morning, early afternoon) handles them efficiently. The night shift is running on a skeleton crew — the same carb load left unprocessed for longer, working against you while you sleep.6. Cortisol: The Common ThreadYou'll notice cortisol keeps coming up — that's not a coincidence. Cortisol's actual job during stress is to make more glucose available fast, by triggering your liver to produce it and by directly blunting how well your cells respond to insulin. Whether the stressor is emotional (a hard week at work) or physical (a gut that's working overtime to ferment more fiber than it's used to, or a body that's lost muscle mass and is struggling to regulate blood sugar as efficiently), the hormonal response is the same: more sugar released, less efficiently cleared.Who's Most Likely to Notice This?Not everyone who goes vegetarian sees their A1C move. The people most likely to notice these effects are those who already have some combination of insulin resistance, chronic stress, poor sleep, low muscle mass, or a sensitive gut. For them, a vegetarian diet doesn't cause the problem — it tends to reveal a metabolic bottleneck that was already there, quietly, underneath a different diet.This isn't a case against vegetarian eating. The clinical research is genuinely favorable toward it. It's a case for building it correctly — with enough protein, the right kind and pace of fiber, resistance training to protect muscle, and carbs eaten earlier in the day, paired with something that slows them down.Six Bottlenecks at a GlanceThe Bottom LineThe question was never really “is vegetarianism good or bad for blood sugar?” — the research says it's generally good when it's built well. The real question is: what's your body's current metabolic bottleneck? Is it protein? Muscle? Fiber pacing? Timing? Stress? Most people are dealing with one or two of these more than the others, and once you know which, the fix is usually a small, specific adjustment — not throwing out the diet.The goal isn't just to tweak what's on your plate. It's to find the actual thing standing between you and the results you were expecting when you made this change in the first place.References* Vegetarian and Vegan Dietary Patterns to Treat Adult Type 2 Diabetes: A Systematic Review and Meta-Analysis of RCTs — ScienceDirect: https://www.sciencedirect.com/science/article/pii/S2161831324001285* Vegetarian diets and glycemic control in diabetes: a systematic review and meta-analysis — PubMed: https://pubmed.ncbi.nlm.nih.gov/25414824/* The Effect of Adding Protein to a Carbohydrate Meal on Postprandial Glucose and Insulin Responses: A Systematic Review and Meta-Analysis — ScienceDirect: https://www.sciencedirect.com/science/article/pii/S0022316624003924* The Role of Skeletal Muscle Glycogen Breakdown for Regulation of Insulin Sensitivity by Exercise — Frontiers in Physiology: https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2011.00112/full* Glucose Uptake by Skeletal Muscle within the Contexts of Type 2 Diabetes and Exercise — MDPI/Nutrients: https://www.mdpi.com/2072-6643/14/3/647* Effect of viscous soluble dietary fiber on glucose and lipid metabolism in patients with T2DM: systematic review and meta-analysis — PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500602/* Dietary fiber in irritable bowel syndrome (fermentation and gas production) — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC5548066/* Fibre, fermentation, FODMAPs and flatulence — Quadram Institute: https://quadram.ac.uk/blogs/fibre-fermentation-fodmaps-and-flatulence/* Physiology, Cortisol — StatPearls, NCBI Bookshelf: https://www.ncbi.nlm.nih.gov/books/NBK538239/* Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans — PNAS: https://www.pnas.org/doi/10.1073/pnas.1418955112* Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes — Diabetologia/PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC7002226/* Chronotype, Chrononutrition and Glucose Tolerance Among Prediabetic Individuals (late-dinner/A1C link): https://cdn.clinicaltrials.gov/large-docs/64/NCT05163964/Prot_SAP_ICF_004.pdfTHRIVE 120 - TriplePlayDoc is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit tripleplaydoc.substack.com/subscribe

We all want to eat healthier. The Paleo diet promises exactly that: eat like our ancestors, avoid modern foods, and improve your health. But does it really support your gut, heart and long-term health, or is the story more complicated? In this episode, Dr James Cole, a world-leading expert on prehistoric diets, joins Dr Federica Amati to explore what Paleo gets right and why some of the claims may be dangerously wrong. James explains what ancient evidence tells us about human diets, why the modern-day Paleo diet may go too far in its restrictions, and what that might mean for your heart disease risk and gut health. By the end of the episode, you'll have ideas on what principles to keep from the Paleo diet, and what rules to avoid. Could trying to eat as our ancestors did force you to cut foods your body actually needs? Before you give up grains, beans or dairy, it may be worth asking what ancient humans really ate.

I received a podcast review that basically said because a guest talked about chronic inflammation it was a "MAHA podcast." So I decided to go straight to one of the top inflammation researchers in the world and ask him directly: is chronic inflammation actually real, or is it pseudoscience?  My guest is Dr. David Furman, the Director of Stanford Medicine's 1000 Immunomes Project (the largest longitudinal study on aging and the immune system in the world) and an Associate Professor at the Buck Institute for Research on Aging, and his answer might surprise you. We play a game of Myth or Fact and bust some of the biggest inflammation claims all over the internet, from gluten and dairy to seed oils, and microplastics. We also get into how to actually test whether you're inflamed, the surprising link between chronic stress, depression, and your immune system, why your coffee habit might be doing you a favor, and exactly what to do about all of it if you have no money to spend.

GET HEIRLOOM SEEDS & NON GMO SURVIVAL FOOD HERE: https://heavensharvest.com/wam USE Code WAM to save 25% plus free shipping! USE Code WAM50 for 50% off on select items like the #10 cans & MRE packs! Pledge here! Just a dollar a month can help keep us alive! https://www.patreon.com/user?u=2652072&ty=h&u=2652072 EXCLUSIVE replays of hour plus long live shows are available here at $5 a month or more! BUY GOLD HERE: https://firstnationalbullion.com/schedule-consult/ Avoid CBDCs! GET 10% OFF ON SHILAJIT FROM DR. KAUFMAN WHEN YOU USE CODE WAM10 HERE: https://medauthentica.com/discount/WAM10?redirect=/products/authentica-shilajit%3Fsca_ref=10867124.wrNV3jkYSaMg9 HELP SUPPORT US AS WE DOCUMENT HISTORY HERE: https://gogetfunding.com/help-keep-wam-alive/# Josh Sigurdson reports on the brainwashing of the masses via social media, television and social constructs, utilizing algorithms and repetitive conversation. We are witnessing mass social engineering and absolutely every single person on earth who has access to electronics is being manipulated no matter how awake they think they are. While X is criticized for manipulating users with the algorithm which it certainly is (not just right, but also to the left and center), Facebook has faced severe scrutiny in the past over their mind control programs, similar to MK Ultra. Facebook manipulated user news feeds in order to create compliance to issues, cause depression or cause elation. In 2014, Facebook faced controversy after it was revealed in a PNAS article titled "Experimental evidence of massive-scale emotional contagion through social networks" that the social media network used data to plant stories and ideas on timelines to make people either sad or happy based on psychological surveys on certain demographics. This experiment led to depression among some and elation among others. Facebook claimed their data use policies allowed them to do this, though they actually changed it weeks after the experiment to include "research" in their data policy. In this video, we delve into X, Facebook, mainstream media, mainstream alternative media, MK Ultra, The Tavistock Institute, culture wars and the establishment's goals. The Limited Hangouts we are witnessing in culture and media today mimic many other the experiments we saw between the 1950s and 1970s. However, today it is far more severe and the implications are far more dire as the establishment approaches the reset utilizing the new narrative. They are creating a "hive mind." Researchers and authors like David Icke have been warning about this for decades and yet most continue to fall into paradigms without a second thought because the group think feels comfortable vs "being alone" in your mindsets. Most don't want solutions. They want whatever comes easiest. Stay tuned for more from WAM! GET YOUR WAV WATCH HERE: https://buy.wavwatch.com/WAM Use Code WAM to save $100 and purchase amazing healing frequency technology! Get Your SUPER-SUPPLIMENTS HERE: https://vni.life/wam Use Code WAM15 & Save 15%! Life changing formulas you can't find anywhere else! Get local, healthy, pasture raised meat delivered to your door here: https://wildpastures.com/promos/save-20-for-life/bonus15?oid=6&affid=321 USE THE LINK & get 20% off for life and $15 off your first box! DITCH YOUR DOCTOR! https://www.livelongerformula.com/wam Get a natural health practitioner and work with Christian Yordanov! Mention WAM and get a FREE masterclass! You will ALSO get a FREE metabolic function assessment! GET YOUR APRICOT SEEDS at the life-saving Richardson Nutritional Center HERE: https://rncstore.com/r?id=bg8qc1 Use code JOSH to save money! PayPal: ancientwonderstelevision@gmail.com FIND OUR CoinTree page here: https://cointr.ee/joshsigurdson PURCHASE MERECHANDISE HERE: https://world-alternative-media.creator-spring.com/ JOIN US on SubscribeStar here: https://www.subscribestar.com/world-alternative-media For subscriber only content! BITCOIN ADDRESS: 18d1WEnYYhBRgZVbeyLr6UfiJhrQygcgNU World Alternative Media 2026

+++ Argumente einer KI kamen bei Probanden authentischer rüber als die von Menschen +++ Warum sich Delfine an Fischerboote hängen +++ Rechtshänder, Linkshänderin - es hat viel mit Übung zu tun +++ Mücken haben gerade optimale Bedingungen +++**********Weiterführende Quellen zu dieser Folge:KI-Experiment mit politischen Diskussionen, Plos One, 01.07.2026Studie zu Delfinen und Fischerbooten, Frontiers, 03.07.2026Studie zu dominanten Händen, PNAS, 30.06.2026Der Mückenatlas unter anderem vom Leibniz-Zentrum für Agrarlandschaftsforschung**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok und Instagram .

In Part 2 of The Mechanics of Magick: Drumming, Trance, and the Brain, we follow rhythm from the sacred road into the war road and the modern machine. This episode examines war drums, military cadence, synchronized movement, crowd power, ritual physiology, propaganda, slogans, media framing, algorithmic repetition, moral-emotional contagion, and the illusory truth effect. The argument is not that rhythm is evil. The argument is that rhythm is morally flexible and powerful. It can heal, gather, strengthen, command, manipulate, or capture depending on the world built around it. The drum teaches us to hear the visible pulse first, so we can recognize the hidden drums of the modern world: the chant, the slogan, the feed, the notification loop, the soundtrack, the repeated frame, and the rhythm that trains attention before thought has time to speak.Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsRhythm, Marching, Synchrony, and the GroupBodyMcNeill, William H. Keeping Together in Time: Dance and Drill in Human History. Cambridge, MA: Harvard University Press, 1995.Use for dance, drill, marching, synchronized movement, and “muscular bonding.” This is one of the best historical anchors for the claim that moving together in time can help bind human groups through the body. It belongs in the war drum, marching, military cadence, procession, and crowd-power material.Wiltermuth, Scott S., and Chip Heath. “Synchrony and Cooperation.” Psychological Science 20, no. 1 (2009): 1–5.Use for the claim that synchronized action can increase cooperation. This supports the argument that marching, chanting, dancing, and acting in time can alter group attachment and behavior.Hove, Michael J., and Jane L. Risen. “It's All in the Timing: Interpersonal Synchrony Increases Affiliation.” Social Cognition 27, no. 6 (2009): 949–960.Use for interpersonal synchrony and affiliation. This supports the softer social-bonding side of rhythm: people who coordinate timing can feel more connected.Tarr, Bronwyn, Jacques Launay, and Robin I. M. Dunbar. “Music and Social Bonding: ‘Self-Other' Merging and Neurohormonal Mechanisms.” Frontiers in Psychology 5 (2014): 1096.Use for music, synchrony, bonding, self-other merging, endorphins, and why rhythmic group activity can feel socially powerful. This belongs in both crowd sections and the “operator as instrument” material.Reddish, Paul, Ronald Fischer, and Joseph Bulbulia. “Let's Dance Together: Synchrony, Shared Intentionality and Cooperation.” PLOS ONE 8, no. 8 (2013): e71182.Use as extra support for synchrony and cooperation. Good optional source if you want more than Wiltermuth and Heath.Ritual Physiology, Crowd Arousal, and Collective EffervescenceKonvalinka, Ivana, Dimitris Xygalatas, Joseph Bulbulia, Uri Schjødt, Else-Marie Jegindø, Sebastian Wallot, Guy Van Orden, and Andreas Roepstorff. “Synchronized Arousal Between Performers and Related Spectators in a Fire-Walking Ritual.” Proceedings of the National Academy of Sciences 108, no. 20 (2011): 8514–8519.Use for the strongest fire-walking physiology source. This is the study showing synchronized arousal between active ritual performers and related spectators. It supports the claim that intense ritual fields can show up in bodies, not only in symbols.Xygalatas, Dimitris, Ivana Konvalinka, Joseph Bulbulia, and Andreas Roepstorff. “Quantifying Collective Effervescence: Heart-Rate Dynamics at a Fire-Walking Ritual.” Communicative & Integrative Biology 4, no. 6 (2011): 735–738.Use as a shorter interpretive companion to the PNAS fire-walking study. Good for the phrase “collective effervescence” and for explaining shared heart-rate dynamics in accessible language.Xygalatas, Dimitris. Ritual: How Seemingly Senseless Acts Make Life Worth Living. New York: Little, Brown Spark, 2022.Use for ritual, pain, synchrony, group bonding, arousal, and embodied social meaning. This is useful when moving from older ritual containers into modern crowd and spectacle.Hobson, Nicholas M., Juliana Schroeder, Jane L. Risen, Dimitris Xygalatas, and Michael Inzlicht. “The Psychology of Rituals: An Integrative Review and Process-Based Framework.” Personality and Social Psychology Review 22, no. 3 (2018): 260–284.Use for ritual as emotion regulation, performance regulation, social bonding, and formalized action. This supports the “ritual does things, it does not merely represent things” argument.War, Crowds, Mass Movements, and Collective IdentityLe Bon, Gustave. The Crowd: A Study of the Popular Mind. 1895.Use carefully. Le Bon is historically important for crowd psychology, but outdated and often elitist. Good as a historical source on crowd fear and mass suggestion, but balance it with modern social psychology.Canetti, Elias. Crowds and Power. New York: Viking Press, 1962.Use for a literary-philosophical treatment of crowds, power, command, fear, and collective bodies. Good for atmosphere and conceptual framing, not as a modern experimental source.Hoffer, Eric. The True Believer: Thoughts on the Nature of Mass Movements. New York: Harper & Brothers, 1951.Use for mass movements, fanaticism, belonging, identity, resentment, sacrifice, and the psychology of ideological devotion. Use carefully; it is sharp and useful, but not a modern empirical study.Tajfel, Henri, and John C. Turner. “An Integrative Theory of Intergroup Conflict.” In The Social Psychology of Intergroup Relations, edited by William G. Austin and Stephen Worchel, 33–47. Monterey, CA: Brooks/Cole, 1979.Use for social identity theory, in-groups, out-groups, belonging, status, and group comparison. This supports the claim that slogans and group language do not only communicate ideas; they also mark identity.Tajfel, Henri. Human Groups and Social Categories: Studies in Social Psychology. Cambridge: Cambridge University Press, 1981.Use as a broader social-identity source. Good for group belonging, categorization, prejudice, and in-group/out-group dynamics.Propaganda, Symbols, Slogans, and Mass CommunicationLasswell, Harold D. Propaganda Technique in the World War. New York: Alfred A. Knopf, 1927.Use for propaganda as symbolic management, war messaging, enemy construction, morale, and the manipulation of attitudes through stories, reports, images, rumors, and other significant symbols. This is a core Part 2 source.Lasswell, Harold D. “The Theory of Political Propaganda.” The American Political Science Review 21, no. 3 (1927): 627–631.Use for the clean definition: propaganda as the management of collective attitudes through significant symbols. This is perfect for the slogan/crowd/media rhythm sections.Ellul, Jacques. Propaganda: The Formation of Men's Attitudes. Translated by Konrad Kellen and Jean Lerner. New York: Alfred A. Knopf, 1965.Use for propaganda as a modern social technique, not merely lies. Ellul belongs in the “modern machine” argument because he treats propaganda as environmental, repetitive, social, technological, and tied to belonging.Bernays, Edward. Propaganda. New York: Horace Liveright, 1928.Use for public relations, engineered consent, mass persuasion, and the management of public opinion. Good supporting source, especially if you want advertising and PR to sit beside political propaganda.Herman, Edward S., and Noam Chomsky. Manufacturing Consent: The Political Economy of the Mass Media. New York: Pantheon Books, 1988.Use if you want a media-system critique: institutional filtering, agenda power, elite framing, and consent formation. This is useful for Part 2 but has a more political-economy angle than the rhythm/trance argument.Media Framing, Agenda-Setting, and Repeated AttentionMcCombs, Maxwell E., and Donald L. Shaw. “The Agenda-Setting Function of Mass Media.” Public Opinion Quarterly 36, no. 2 (1972): 176–187.Use for agenda-setting: media may not tell people exactly what to think, but it can influence what people think about and how important issues feel. This fits the “repetition trains attention” argument.Entman, Robert M. “Framing: Toward Clarification of a Fractured Paradigm.” Journal of Communication 43, no. 4 (1993): 51–58.Use for framing: selecting certain aspects of reality and making them more salient. This supports the section on media frames as secular ritual structures that define problems, causes, moral judgments, and remedies.Gitlin, Todd. The Whole World Is Watching: Mass Media in the Making and Unmaking of the New Left. Berkeley: University of California Press, 1980.Use if you want a historical case study on media framing and protest movements. Good optional source for how movements get shaped, simplified, or distorted by media attention.Postman, Neil. Amusing Ourselves to Death: Public Discourse in the Age of Show Business. New York: Viking Penguin, 1985.Use as cultural/media theory support for entertainment, spectacle, image, and public discourse. Useful for the “modern machine” angle, but more essayistic than experimental.Repetition, Familiarity, and the Illusory Truth EffectHasher, Lynn, David Goldstein, and Thomas Toppino. “Frequency and the Conference of Referential Validity.” Journal of Verbal Learning and Verbal Behavior 16, no. 1 (1977): 107–112.Use for the classic repetition/familiarity basis of the illusory truth effect. This supports the claim that repeated statements can gain perceived validity because they become easier and more familiar to processAlso want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. 

Ein Handy-Diebstahl empört uns sofort, aber Milliardenbetrug geht in der Wahrnehmung oft unter. Dabei beeinflusst Wirtschaftskriminalität unseren Alltag stärker als viele denken. Was wäre, wenn der Staat bei Steuerhinterziehung und Betrug härter durchgreifen würde? Hast du selbst ein Szenario, das wir prüfen sollen oder Feedback? Schreib uns gerne an: malangenommen@ard.de Eure Hosts: Julia Nestlen und Matthis Dierkes Hörtipp: Organisierte Kriminalität in Europa (3 Teile), Das Wissen, SWR Kultur https://www.swr.de/swrkultur/wissen/organisierte-kriminalitaet-in-europa-die-kriminellen-das-wissen-2026-03-25-100.html Mehr zum Hören: INSIDE CumEx – Jagd auf die Steuer-Mafia (7 Teile), ARD Sounds https://www.ardsounds.de/sendung/inside-cumex-jagd-auf-die-steuer-mafia/urn:ard:show:fc02770b3b4ca5a1/ Alle Quellen findest du hier:  https://www.quarks.de/gesellschaft/wirtschaftskriminalitaet-steuerhinterziehung-mal-angenommen   Ein Auszug unserer wichtigsten Quellen:  Expertin: Anne Brorhilker, Geschäftsführerin bei Finanzwende, ehemalige Oberstaaatsanwältin der Staatsanwaltschaft Köln Buch: Was über Täterinnen und Täter im Bereich Wirtschaftskriminalität bekannt ist, The Oxford Handbook of White-Collar Crime https://www.researchgate.net/publication/321606412_Who_Commits_White-Collar_Crime_and_What_Do_We_Know_About_Them Studie: Reichere Menschen handeln in der Tendenz öfter unethisch, PNAS https://www.pnas.org/doi/10.1073/pnas.1118373109 Forschungsprojekt: Wie Angeklagte mit viel Geld es leichter vor Gericht haben, Universität Hannover https://www.jura.uni-hannover.de/beck/forschung/einzelansicht/projects/soziooekonomische-ungleichheit-im-strafverfahren Studie: Wie Whistleblower-Gesetze in New York Lee sich auf Steuerhinterziehung auswirken, Management Science https://pubsonline.informs.org/doi/10.1287/mnsc.2023.02999 Wie in Lettland Wirtschaftskriminalität aufgedeckt und strafverfolgt wird, OECD https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/12/interagency-coordination-in-economic-crime-investigations-in-latvia_18576442/79d844e6-en.pdf Welche Gesundheitsfolgen der Abgasbetrug im VW-Dieselskandal in den USA hatte, Environmental Research Letters https://iopscience.iop.org/article/10.1088/1748-9326/10/11/114005

Using AI to predict the weather Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, researchers discuss advances in AI-enabled weather forecasting. In this episode, we cover: •[00:00] Introduction •[01:12] Jeffrey Shrader explains what 48 expert forecasters had to say about how weather predictions might further improve through 2100, including the potential role of AI. •[03:43] Ignacio Lopez-Gomez explains how he used generative AI to downscale large-scale earth system models into finer-scale regional climate projections. •[05:35] Xiaofeng Li explains how he used a machine learning model to forecast whether tropical cyclones will rapidly intensify. •[07:40] Hui Su explains what nowcasting is and how her deep diffusion model works. •[09:52] Pedram Hassanzadeh explains what grey swans are and why they may be challenging for AI to predict. •[10:47] Qiang Sun explains how he and his colleagues tested the ability of AI to predict gray swans. •[12:28] Final thoughts and conclusion. About Our Guests: Jeffrey Shrader Associate Professor of International and Public Affairs Columbia University Ignacio Lopez-Gomez Research Scientist Google Xiaofeng Li Research Scientist Institute of Oceanology, Chinese Academy of Sciences Hui Su Chair Professor Hong Kong University of Science and Technology Pedram Hassanzadeh Associate Professor University of Chicago Qiang Sun Research Scientist University of Chicago View related content here: https://www.pnas.org/doi/full/10.1073/pnas.2523372123 https://www.pnas.org/doi/full/10.1073/pnas.2420288122 https://www.pnas.org/doi/full/10.1073/pnas.2415501122 https://www.pnas.org/doi/full/10.1073/pnas.2517520122 https://www.pnas.org/doi/full/10.1073/pnas.2420914122 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up the PNAS Highlights newsletter

About a year ago an interdisciplinary tam at Oregon State invited a collection of subject matter experts for workshop on floodplain sedimentation processes.  The workshop took up a very specific question but gathering this much expertise on floodplain landforms and processes generated a wide-ranging discussion of how floodplains work, how to restore them, and even what they are.  So when Desiree Tullos reached out and invited me I brought my podcast gear in just in case… And I just found the discussions so useful that I wanted to share it with the other practitioners that have gathered around this podcast project.  We have spent a lot of time talking about channel form, function, and process on this podcast, I couldn't pass up the chance to give some time to these other, underrated, river landforms. So I asked three of the participants:  Dr. Desiree Tullos, Dr. Janine Castro and Dr. Jonathan Czuba if they'd be willing to debrief the themes and take aways from the gathering…and I think did a fantastic job replicating a lot of the value I got out of being at this workshop in this interview, with almost no prep.Desiree Tullos is a professor of Biological and Ecological Engineering at Oregon State and was one of the point people responsible for convening and imagining this workshop. Her research emphasizes sustainable engineering and management of rivers by examining the intersections of hydraulics, infrastructure, ecology, and society, and heavily emphasizes engaging and mentoring undergraduate students in research with societal relevance. Janine Castro is co-founder and Technical Director of the River Restoration Program at Portland State University and is one of the five founding members of River Restoration Northwest.  She recently retired from Federal service, where she worked as a geomorphologist for 34 years.Jon Czuba spent most of his 20 years measuring, modeling, and analyzing sediment transport across the U.S.  as a Professor of Ecological Engineering in the Department of Biological Systems Engineering at Virginia Tech.  He recently received an early career research award from the Universities Council on Water Resources for his work including publications in Science, Nature, and PNAS.This is a link to a version of the talk I gave at this workshop on floodplain modeling and processes: https://youtu.be/keGQviqInR0This series was funded by the Regional Sediment Management (RSM) program.Mike Loretto edited the first three seasons and created the theme music.Tessa Hall is editing most of Season 4.Stanford Gibson (HEC Sediment Specialist) hosts.Video shorts and other bonus content are available at the podcast website:https://www.hec.usace.army.mil/confluence/rasdocs/rastraining/latest/the-rsm-river-mechanics-podcast...but most of the supplementary videos are available on the HEC Sediment YouTube channel:https://www.youtube.com/user/stanfordgibsonIf you have guest recommendations or feedback you can reach out to me on LinkedIn or ResearchGate or fill out this recommendation and feedback form: https://forms.gle/wWJLVSEYe7S8Cd248

Una momificación de hace 14.000 años que sigue practicándose hoy en una aldea de Indonesia. El ARN de una cría de mamut congelada 50.000 años, rescatado cuando era imposible. La cara real de los europeos de hace 10.000 años. Y una roca en Marte con manchas de leopardo que puede ser la señal de vida más importante de la historia. Cuatro cartas que el tiempo no pudo borrar. Esta noche, aprendemos a leerlas. Bienvenido a Ciencia Fascinante 1x03: Lo que el tiempo no pudo borrar. ▶ EN ESTE EPISODIO: El humo que venció a 14.000 años — El equipo de Hsiao-chun Hung (Universidad Nacional de Australia) publicó en PNAS en 2025 el hallazgo más impactante de la arqueología reciente: la momificación deliberada de cuerpos humanos data de hace más de 14.000 años en el sudeste asiático. Y la misma técnica —ahumar el cuerpo de los muertos durante semanas para conservarlos— sigue practicándose hoy en el pueblo Dani de Indonesia. Catorce mil años de un gesto humano idéntico. ¿Cómo sabe la ciencia que no fue accidental? La respuesta está dentro de los propios huesos. La carta que no debía sobrevivir — Yuka es una cría de mamut lanudo que murió en la estepa siberiana hace 50.000 años. Su cuerpo lleva décadas en un museo. Pero en 2025, un equipo de Estocolmo publicó en Cell algo que la biología consideraba materialmente imposible: el ARN —la molécula mensajera que activa los genes y que se destruye en horas en condiciones normales— seguía ahí. Cincuenta mil años después. Intacto. Y decía algo. ️ El rostro que olvidamos que teníamos — Guido Barbujani, genetista de la Universidad de Ferrara, reconstruyó en pantalla la cara de un europeo de hace 10.000 años a partir de su ADN. El resultado no era lo que casi nadie esperaba: piel muy oscura, ojos azul-verdosos. Y no era una excepción. Era como éramos casi todos. La cara de Europa antes de que Europa fuera lo que creemos que siempre fue. Las manchas de leopardo de Marte — El rover Perseverance se detuvo ante una roca en el cráter Jezero y encontró un patrón de manchas oscuras con halo claro, como la piel de un leopardo. El espectrómetro reveló vivianita y greigita: dos minerales que en la Tierra casi siempre los fabrica algo vivo. Un equipo de la NASA publicó en Nature en 2025 lo que ningún científico serio dice a la ligera: es la señal más clara de vida extraterrestre hallada hasta la fecha. Síguenos en Redes Twitter: https://twitter.com/radioelrespeto Instagram: https://www.instagram.com/radioelrespeto/ Facebook: https://www.facebook.com/radioelrespeto Redes Sociales del Equipo: | Pablo Fuente | https://www.instagram.com/pablofuente/ | Nacho Sevilla | https://twitter.com/nachorsevilla | Fernando Sierra | https://twitter.com/Peeweeyo1

In this episode of the SpearFactor Podcast, I talk with Dr. Ray Hilborn, a fisheries scientist at the University of Washington, about the state of fisheries worldwide and the real-world data behind marine conservation. Ray has spent decades studying fish populations, fishing fleets, and management systems across the globe. He walks me through what his research shows about which fisheries are healthy, which are in trouble, and what separates the two. Many of the assumptions people hold about overfishing don't match the data, and Ray explains where the gap comes from. We also get into marine protected areas. MPAs are often presented as the default tool for ocean conservation, but Ray argues the picture is more complicated. We talk about where MPAs help, where they fall short, what they cost in terms of food production and displaced fishing effort, and why catch limits, gear rules, and stock assessments often do more for fish populations than closing off areas of the ocean. Ray explains that the best way to manage fisheries is not through MPAs, but through active fisheries management and enforcement — science-based catch limits, gear restrictions, stock assessments, and monitoring. He points to his own research showing that where fisheries are managed and enforced, stocks are at target levels or rebuilding, and where management is weak, stocks decline. Papers referenced in the episode: Hilborn, R. et al. (2020). Effective fisheries management instrumental in improving fish stock status. PNAS 117(4): 2218–2224. Hilborn, R. (2016). Policy: Marine biodiversity needs more than protection. Nature 535: 224–226. Hilborn, R. (2013). Environmental cost of conservation victories. PNAS 110(23): 9187. Hilborn, R. & Kaiser, M.J. (2021). Critique of Sala et al., Protecting the global ocean for biodiversity, food and climate, Nature (which was subsequently corrected). Hilborn, R. (2021). Increasing fisheries harvest with MPAs: Leaving South and Southeast Asia behind. PNAS — reply on Cabral et al. Topics covered: The state of global fisheries based on actual stock data Common myths about overfishing and where they come from How fisheries are managed in the U.S. and abroad Marine protected areas: where they work and where they don't Trade-offs between MPAs, food supply, and displaced fishing effort Why active fisheries management and enforcement outperform area closures Sources: Effective fisheries management instrumental in improving fish stock status — PNAS Policy: Marine biodiversity needs more than protection — Nature Environmental cost of conservation victories — PNAS Critique of Sala et al. 2021 — Sustainable Fisheries UW Increasing fisheries harvest with MPAs: Leaving South and Southeast Asia behind — PNAS Learn more about your ad choices. Visit megaphone.fm/adchoices

Cryopreservation of brain tissue structures Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Alexander German explains how to cryopreserve brain tissue through vitrification. In this episode, we cover: •[00:00] Introduction. •[01:14] Physician-scientist Alexander German introduces the problems with traditional cryopreservation methods. •[02:22] German explains why we cryopreserve tissue. •[03:23] He tells about vitrification and why it's different than traditional cryopreservation. •[04:13] German explains why osmotic stress is a concern in cryopreservation. •[04:51] He talks about how the protocol minimizes damage to brain structures and tissues. •[06:26] He recounts the tests they performed to evaluate the preservation and function of the vitrified tissues. •[07:03] German describes the usefulness of vitrification. •[08:48] He lists the caveats and limitations of the study. •[10:03] Conclusion. About Our Guest: Alexander German  Resident Universität Erlangen-Nürnberg View related content here: https://www.pnas.org/doi/full/10.1073/pnas.2516848123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

The Immune team talks about two papers on the effect of sleep and environment on seasonal allergies.  Hosts: Vincent Racaniello, Cindy Leifer, and Brianne Barker Subscribe (free): Apple Podcasts, RSS, email Become a patron of Immune! Links for this episode MicrobeTV Discord Server Sleep is needed for condition dependent allergic responses (PNAS, 2020) Original paper on allergy to artificial rose (Am. J. Med. Sci. 181, 45–56 (1886)) One of multiple personalities allergic (Am J Clin Hypn, 1983) Environmentally driven immune imprinting protects against allergy (Nature, 2026) News and Views on immune imprinting paper (Nature, 2026) Immune 90 discussion of "dirty mice" Time stamps by Jolene Ramsey. Thanks! Music by Tatami. Immune logo image by Blausen Medical Send your immunology questions and comments to immune@microbe.tv Information on this podcast should not be construed as medical advice.

Matthew O. Jackson is perhaps the world's most renowned scholar of the economics of networks; as a 2005-06 CASBS fellow, he wrote most of his still-influential book Social and Economic Networks. In this wide-ranging conversation with 2025-26 CASBS fellow Rajiv Sethi, Jackson discusses his foundational work on strategic modeling of networks, empirical applications on the role of economic connectedness in influencing people's life trajectories in the U.S., related multi-disciplinary and cross-national work he is undertaking at the Santa Fe Institute, and recent cutting-edge work using large language models to gain insights into human motivations and behaviors. Matthew O. Jackson: Stanford faculty page | Personal website | CASBS page | Wikipedia page | Google Scholar page | National Academy of Sciences bio | Stanford profile | SFI page | NBER working papers | Jackson CV | Rajiv Sethi: Barnard faculty page | Columbia page | CASBS page | Google Scholar page | SFI page | Rajiv's Substack newsletter, Imperfect Information |  Matt Jackson works referenced in this episode: Matthew Jackson and Asher Wolinsky, "A Strategic Model of Social and Economic Networks," Journal of Economic Theory (1996) Matthew Jackson and Alison Watts, "The Evolution of Social and Economic Networks," Journal of Economic Theory (2002) Raj Chetty, Matthew Jackson, et al., "Social Capital I: Measurement and Associations with Economic Mobiliity," Nature (2022) Raj Chetty, Matthew Jackson, et al., "Social Capital II: Determinants of Economic Connectedness," Nature (2022) Chetty, Jackson, et al., Opportunity Insights Social Capital Atlas (website)Dynamics of Wealth Inequality project (Santa Fe Institute) Matthew Jackson, Social and Economic Networks, Princeton University Press (2008) Matthew Jackson, The Human Network, Penguin Random House (2020) Mei, Yuan, and Jackson, "A Turing Test of Whether AI Chatbots are Behaviorally Similar to Humans," PNAS (2024) Xie, Mei, Yuan, and Jackson, "Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors," PNAS (2025) --- Rajiv Sethi's latest op-ed is "Polymarket Anonymity Must End," Financial Times (May 7, 2026) Subscribe to Rajiv's Substack newsletter, Imperfect Information   Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford UniversityExplore CASBS: website | Bluesky | X | YouTube |LinkedIn | podcast |latest newsletter | signup | outreach​Human CenteredProducer: Mike Gaetani | Audio engineer & co-producer: Joe Monzel |

The lost forests of Doggerland Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Robin Allaby explores the paleoecology of the submerged area known as Doggerland. In this episode, we cover: •[00:00] Introduction. •[01:10] Evolutionary biologist Robin Allaby introduces the location of Doggerland and history of its exploration. •[02:58] He introduces sedimentary DNA, including how it can be used to reconstruct paleoecology. •[03:43] Allaby describes the methods of the study. •[05:18] He introduces the primary findings about the plant and animal life of Doggerland. •[06:14] He describes the surprising find of Pterocarya in Doggerland.  •[08:07] Allaby discusses the habitability of Doggerland for Mesolithic societies. •[09:38] He lists the caveats and limitations of the study. •[10:13] Conclusion. About Our Guest: Robin Allaby  Professor University of Warwick View related content here: https://www.pnas.org/doi/full/10.1073/pnas.2508402123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Midlife-Crisis: Ein 50 jähriger Mann, der jetzt eine Harley kauft. Ist das nur ein Klischee oder ist da was dran? Gibt es echte Krisen in der Mitte des Lebens? Franca und Christian nehmen eines der bekannteste Konzepte der Alltagspsychologie auseinander. Das Gehirn beginnt sich ab Mitte 40 messbar, aber leise umzubauen. In dieser Folge geht es um den Unterschied zwischen kulturellem Skript und echter Biologie, über das, was Frauen in der Perimenopause wirklich erleben, und darüber, was die Forschung als das eigentliche Merkmal gesunden Alterns beschreibt. Quellen: Blanchflower DG, Bryson A, Xu X (2025): The declining mental health of the young and the global disappearance of the unhappiness hump shape in age. DOI: 10.1371/journal.pone.0327858 Schmidt S (2020): Midlife Crisis: The Feminist Origins of a Chauvinist Cliché. University of Chicago Press. DOI: 10.7208/9780226686998 Mujica-Parodi LR et al. (2025): Brain aging shows nonlinear transitions, suggesting a midlife ‘critical window' for metabolic intervention. PNAS. DOI: 10.1073/pnas.2416433122 Bromberger JT et al. (2011): Major depression during and after the menopausal transition: SWAN. Psychological Medicine. PMID: 21306662 Francas SISU- Kurssi gibt es zwischen dem 17.Mai und dem 2. Juni günstiger: www.sisu-online.de Francas neues Buch: Die innere Oma — ab 4. September 2026, jetzt vorbestellbar: https://shop.autorenwelt.de/products/die-innere-oma-von-franca-cerutti Alle Tourdaten und Tickets: https://www.190a.de/psychologie-to-go/ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/psychologietogo Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Is the DMT laser experiment proof we are in a simulation? Strangers see the same alien glyphs, the code of reality. Neuroscience explains it. Almost...Is the "Code of Reality" a genuine window into a computed simulation, or merely an artifact of our brain's architecture?I deep dive into the DMT laser experiment, a $15 protocol that allegedly reveals hidden source code of our universe. I investigate why strangers independently report the same bizarre characters and geometric structures, and whether this "shared reality" unveils the Matrix or is simply a trick of our visual cortex.Topics (among others):The Code of Reality: How Danny Goler's repeatable experiment uses a 650nm red laser and DMT to induce perceptions of a high-definition hyper-structure of code.The 40,000-Year Trail: Why Paleolithic cave art, UFO glyphs, and Soviet Kozyrev Mirror experiments all feature similar geometric codes/glyphs long before the modern simulation hypothesis interpretations.Neuroscience vs. Simulation: How the Bressloff-Cowan V1 Model provides a mathematical model for why our natural brain wiring produces similar "form constants" with hallucinogens.Consider Supporting + Receive Bonus Content⁠

Depuis toujours, il naît légèrement plus de garçons que de filles chez les êtres humains. En moyenne, pour 100 filles, environ 105 garçons viennent au monde. Cette différence compense le fait que les garçons sont biologiquement un peu plus fragiles durant l'enfance.Mais aujourd'hui, certains chercheurs observent un phénomène troublant : dans plusieurs régions du monde, cette proportion semble diminuer.Et une étude publiée dans la revue scientifique PNAS suggère que le réchauffement climatique pourrait jouer un rôle inattendu dans cette évolution.Les chercheurs de l'Université d'Oxford ont analysé plus de cinq millions de naissances sur plusieurs décennies. Leur objectif : comprendre comment les températures influencent le sexe des bébés à la naissance.Le résultat est frappant.Lorsque les températures dépassent environ 20 °C pendant des périodes prolongées, la proportion de garçons diminue significativement.Autrement dit : plus il fait chaud, moins il naît de garçons.Mais pourquoi ?La clé se trouve probablement dans la fragilité biologique des fœtus masculins.Dès les premières semaines de grossesse, les embryons mâles semblent plus vulnérables aux stress environnementaux : pollution, malnutrition, catastrophes naturelles… et désormais chaleur extrême. Les scientifiques pensent que le stress thermique pourrait augmenter les risques de fausses couches spontanées touchant davantage les fœtus masculins.Car porter un enfant représente déjà un immense effort physiologique pour l'organisme maternel. Or la chaleur ajoute un stress supplémentaire : déshydratation, inflammation, perturbation hormonale, augmentation du cortisol — l'hormone du stress.Et les embryons masculins résisteraient moins bien à ces conditions difficiles.Ce phénomène avait déjà été observé après certains événements extrêmes. Après des canicules, des famines ou des catastrophes naturelles, plusieurs pays avaient enregistré temporairement moins de naissances masculines.Mais l'étude d'Oxford est l'une des plus vastes jamais réalisées sur le sujet, et elle renforce l'idée que le climat pourrait influencer directement la composition démographique humaine.Attention toutefois : il ne s'agit pas d'une disparition massive des garçons. Le phénomène reste modéré. Mais à l'échelle de populations entières et sur plusieurs décennies, ces variations deviennent statistiquement très importantes.Les chercheurs soulignent aussi qu'il pourrait exister d'autres facteurs liés au réchauffement climatique : pollution atmosphérique accrue, perturbateurs endocriniens ou modification des conditions de vie.Cette découverte rappelle surtout une chose fascinante : le changement climatique n'affecte pas seulement les glaciers, les océans ou les forêts.Il pourrait aussi agir silencieusement sur la biologie humaine elle-même.Jusqu'à influencer, peut-être, le sexe des enfants qui naîtront demain. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejects1. Patel, A. K., et al. *Physiology, Sleep Stages*. StatPearls / NCBI Bookshelf, 2024.2. Jensen, O., & Mazaheri, A. “Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition.” *Frontiers in Human Neuroscience*, 2010.3. Cavanagh, J. F., & Shackman, A. J. “Frontal Midline Theta Reflects Anxiety and Cognitive Control: Meta-Analytic Evidence.” *Journal of Physiology-Paris*, 2015.4. Axmacher, N., et al. “Cross-Frequency Coupling Supports Multi-Item Working Memory in the Human Hippocampus.” *PNAS*, 2010.5. Lacaux, C., et al. “Sleep Onset Is a Creative Sweet Spot.” *Science Advances*, 2021.6. Horowitz, A. H., et al. “Targeted Dream Incubation at Sleep Onset Increases Post-Sleep Creative Performance.” *Scientific Reports*, 2023.7. Caporro, M., et al. “Functional MRI of Sleep Spindles and K-Complexes.” *Clinical Neurophysiology*, 2012.8. Ng, T., et al. “Bayesian Meta-Analysis Reveals the Mechanistic Role of Slow Oscillation-Spindle Coupling in Sleep-Dependent Memory Consolidation.” *eLife*, 2025.9. Datta, K., et al. “Electrophysiological Evidence of Local Sleep During Yoga Nidra Practice in Young Male Volunteers.” *Frontiers in Neurology*, 2022.10. Jensen, M. P., et al. “Brain Oscillations, Hypnosis, and Hypnotizability.” *American Journal of Clinical Hypnosis*, 2015.11. Huels, E. R., et al. “Neural Correlates of the Shamanic State of Consciousness.” *Frontiers in Human Neuroscience*, 2021.12. Ingendoh, R. M., et al. “Binaural Beats to Entrain the Brain? A Systematic Review...” *PLOS ONE*, 2023.13. Páez, A., et al. “Sleep Spindles and Slow Oscillations Predict Cognition and Biomarkers of Neurodegeneration in Mild to Moderate Alzheimer's Disease.” *Alzheimer's & Dementia*, 2025.14. Askitopoulou, H. “Sleep and Dreams: From Myth to Medicine in Ancient Greece.” *Journal of Anesthesia History*, 2015.15. Pavli, A. “Asclepieia in Ancient Greece: Pilgrimage and Healing.” *Journal of Integrative Medicine and Research*, 2024.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. Now let me introduce the rest of the panel and guests.

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.Papers and works referenced in the conversationBooksThe Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.htmlSocial and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.htmlPart I — The scaling of human networks"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf"Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571"Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114Part II — The scaling of AI agents"Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016"AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323"A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

Generative AI and scientific journals Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Yi Bu explores how generative AI has changed academic publishing. In this episode, we cover: •[00:00] Introduction. •[00:50] Computational social scientist Yi Bu tells about the policies academic journals have introduced to address generative AI. •[02:17] Bu describes the dataset he analyzed and his findings regarding journals' policies. •[04:07] He answers the question: Did journal policies have any effect on AI usage? •[05:39] Bu talks about how the rate of AI disclosure compares with estimates of probable AI use. •[06:53] He explains the takeaway for journal editors and the scientific community at large. •[07:27] He lists the caveats and limitations of the study. •[10:11] Conclusion. About Our Guest: Yi Bu Assistant Professor Peking University View related content here: https://www.pnas.org/doi/abs/10.1073/pnas.2526734123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Crystal investigates an anomaly in Google search data that predicted neural interface technology disclosures by two years.What We Cover:• Georgia Tech's peer-reviewed hair follicle sensor research (published PNAS, April 2025)• Federal contract analysis: microneedle manufacturing scale-up 2020-2025• Google Trends investigation: “moving hair” search clustering with vestigial body part queries• Geographic analysis: Aarau, Switzerland and the Interneuron consortium• Supply chain documentation: 3M, Vaxxas, Vaxess government contracts• Patent landscape: neural interface applications of microneedle technology• Charles Lieber connection: injectable mesh electronics and the i-BRAIN timelineKey Sources:        •       Proceedings of the National Academy of Sciences        •       US Patent Database        •       Federal procurement records (USAspending.gov)        •       Google Trends data analysis        •       Peer-reviewed neurotechnology literature#neurotechnology #supplychainanalysis #biomedicalengineering #searchtrends #microneedleresearch #georgatech #swissresearch #patentanalysis #governmentcontracts #datatechnology Reach out to share your story:moremorgellons.com

Something's not right in the woods, at least if you're a white-tailed deer. In this episode, the guys dig into chronic wasting disease (CWD), a strange illness reshaping deer populations in many areas of the Lower 48 (and Scandinavia!). It's not caused by a virus or a bacteria, but it is related to mad cow disease. They break down what it is, how it spreads, what's happening inside infected animals, and why it's so dang hard to contain. The deer are not alright… and there's a reason.This episode was recorded on April 23, 2026 at Walton Woods Park in Amherst, NY (a suburb of Buffalo). Episode Notes and Links· Are there different CWD strains in a single animal? Chronic wasting disease isn't a single, uniform pathogen. It's more like a shifting swarm. Infected deer can carry multiple prion “strains” at once, meaning different misfolded shapes of the same protein that behave in slightly different ways. They could spread through the body differently, build up in different tissues, and cause disease at different rates. Lab experiments show this most clearly: when CWD prions are passed through model systems, what looks like one strain can split into multiple distinct variants, or reveal that a mixed population was there all along (e.g., Angers et al. 2010 PNAS; Béringue et al. 2012 Journal of Virology; Li et al. 2010 Journal of Virology). In actual deer, the picture is harder to pin down, but studies comparing prions from different tissues and individuals show real strain diversity and suggest that more than one strain can exist within a single animal (e.g., Angers et al. 2009 Journal of Virology; Moore et al. 2016 Emerging Infectious Diseases). The takeaway is that CWD behaves less like a single disease agent and more like a moving target: a cloud of protein shapes, some dominant, some hidden in the background, that can shift over time, giving the disease more chances to adapt, persist, and potentially jump into new hosts.· Does repeated exposure to CWD reduce incubation time in deer? Repeated exposure to CWD prions does likely shortens incubation time, mainly because prion diseases are strongly dose-dependent. Higher cumulative exposure, whether from a single large dose or many smaller ones over time, can both increase the chance of infection and accelerate disease progression. Experimental studies in deer and elk show that animals exposed to higher or repeated doses tend to develop symptoms faster than those exposed once at low levels. In the wild, this likely plays out through repeated contact with contaminated environments like soil, plants, and carcass sites. That said, factors like genetics and prion strain can still influence how quickly the disease develops in any given animal.· Is CWD the only prion disease that affects wildlife? CWD is the only prion disease currently thriving as a self-sustaining epidemic in wild populations. The others mostly sit at the edges and are livestock diseases that occasionally spill into wildlife or appear in captive/wild interface cases. For example, scrapie occasionally “leaks” into the wild (it has been found in bighorn sheep), but it doesn't take over. It flickers at the edges of livestock systems. Nothing like the landscape-level, self-sustaining spread we see with CWD. That's what makes CWD so concerning: it's not just present in wildlife, it seems to be built for it.· Steve talked about the possibility of vampire bats and wild hogs spreading CWD. What's the story? There's currently no evidence that vampire bats are spreading CWD, but the wild hog story has gotten more interesting recently. Blood-feeding bats like the Common Vampire Bat (Desmodus rotundus) are often mentioned because prions can occur in blood at low levels, but there are no peer-reviewed studies showing bat-mediated transmission, nor any field patterns linking bats to CWD spread. So the bat idea remains speculative. Wild hogs (Sus scrofa), on the other hand, have moved beyond pure theory. A recent peer-reviewed study (e.g., Soto et al. 2025 Emerging Infectious Diseases) detected low levels of CWD prion activity in free-ranging pigs in endemic areas, suggesting they can pick up and carry prions after scavenging infected carcasses. Combine this with earlier work showing prions can survive digestion and still remain infectious (e.g., Nichols et al. 2009 PLoS ONE), it all points to hogs as plausible mechanical vectors: in other words, organisms that can move infectious material without necessarily developing the disease themselves. The takeaway: vampire bats are still a biologically interesting but unsupported idea, while wild hogs are emerging as potential “messy middlemen,” capable of redistributing prions across the landscape, even if they're not a primary engine of CWD transmission, which is still driven by deer-to-deer contact and long-lived environmental contamination.· Why doesn't NYS do more free testing?New York doesn't offer broad, free testing for every deer. Not because it's ignoring CWD, but because it uses a more targeted, strategic approach. There are a few key constraints on broad, free testing:Cost & logistics: Each test isn't just a swab. It involves lab processing (often PCR or amplification assays), trained staff, and sample handling. Scaling that to hundreds of thousands of deer is a major lift.Low prevalence (right now): When disease prevalence is near zero, mass testing tends to return very few positives, so agencies prioritize early detection in hotspots instead.Management strategy: Agencies often invest more in prevention (carcass transport rules, feeding bans, education) than broad surveillance.Hunter participation: “Free for all” testing can overwhelm systems unless tightly managed, and many states have learned that targeted programs get better data per dollar.So NYS is focusing its efforts on where they see it mattering most: high-risk areas, roadkills, sick/dead deer, and zones near known outbreaks—because testing every hunter-harvested deer statewide would be extremely expensive for relatively low yield in a state with no established CWD population.More info on NY's response, as well as what's happening nationally:The NYS Department of Environmental Conservation's page on CWD (including information on how you can help, scroll down to “Members of the Public”)CWD in Captive Deer: DEC's Response in 2024Chronic Wasting Disease Detection and Management: What Has Worked and What Has Not? A report by the CWD Alliance, a nonprofit organization focused on education, coordination, and outreach around chronic wasting disease. It was created to bring together a mix of stakeholders: state wildlife agencies, federal partners, scientists, and hunting/conservation groups to help share reliable information and improve how CWD is managed across North America. Sponsors and Ways to Support UsThank you to Always Wandering Art (Website and Etsy Shop) for providing the artwork for many of our episodes.Support us on Patreon.Works Cited Bian, J., et al. (2022). Transmission of cervid prions to humanized mice demonstrates the zoonotic potential of chronic wasting disease. Acta Neuropathologica Communications, 10, 149.Edmunds, D. R., Kauffman, M. J., Schumaker, B. A., Lindzey, F. G., Cook, W. E., Kreeger, T. J., Grogan, R. G., & Cornish, T. E. (2016). Chronic wasting disease drives population decline of white‑tailed deer. Ecology, 97(3), 620–632.Henderson, D. M., Denkers, N. D., Hoover, C. E., Garbino, N., Mathiason, C. K., & Hoover, E. A. (2015). Longitudinal Detection of Prion Shedding in Saliva and Urine by Chronic Wasting Disease-Infected Deer by Real-Time Quaking-Induced Conversion. Journal of virology, 89(18), 9338–9347. https://doi.org/10.1128/JVI.01118-15Küry, S., et al. (2023). The zoonotic potential of chronic wasting disease—A review. Pathogens, 12(3), 342.Miller, M. W., et al. (2024). U.S. Geological Survey science strategy to address chronic wasting disease. U.S. Geological Survey Circular 1546.Monello, R. J., Powers, J. G., Hobbs, N. T., Spraker, T. R., O'Rourke, K. I., & Wild, M. A. (2014). Endemic chronic wasting disease causes mule deer population decline in Colorado. PLOS ONE, 9(10), e110353.Pirisinu, L., et al. (2024). Zoonotic potential of chronic wasting disease after adaptation in sheep. Emerging Infectious Diseases, 30(12).Sandberg, M. K., et al. (2022). Humanized transgenic mice are resistant to chronic wasting disease prions from reindeer and moose. Journal of Infectious Diseases, 226(5), 933–942.Saunders, S. E., Bartelt‑Hunt, S. L., & Bartz, J. C. (2012). Occurrence, transmission, and zoonotic potential of chronic wasting disease. Emerging Infectious Diseases, 18(3), 369–376.Visit thefieldguidespodcast.com for full episode notes, links, and works cited.

In 1969, Harvard pathologist Kilmer McCully discovered elevated homocysteine causes arterial damage and heart disease. He was forced out of Harvard. The Framingham Heart Study confirmed him. Then the AHA buried it anyway.CHAPTERS:00:00 - McCully discovery and suppression01:20 - Part 1: What homocysteine does to arteries (6 mechanisms)03:25 - Part 2: The evidence they ignored03:40 - Every 5 umol/L: 20-30% higher CAD risk, 60% stroke risk04:50 - Alzheimers risk +48% (meta-analysis, 7,474 subjects)06:10 - Part 3: Why it was abandoned (VISP, NORVIT, HOPE-2)07:50 - Cochrane: B vitamins reduced stroke 10%08:20 - CSPPT: folic acid reduced stroke 21% to 73%09:10 - Part 4: Your brain on homocysteine09:25 - VITACOG: brain atrophy 30% slower, 53% in high-Hcy group10:05 - PNAS 2013: 7x less hippocampal atrophy11:00 - Part 5: What to do11:10 - Test fasting homocysteine: target under 8-10 umol11:40 - Methylfolate, methylcobalamin, P5PREFERENCES:Homocysteine CVD Risk 2025: PMC12564181VITACOG Trial (PLoS ONE, 2010): PMC2935890VITACOG 2025 Metabolomics: PubMed 40684250Homocysteine Alzheimers Risk: PMC12280720Cochrane B Vitamins Stroke: CochraneCSPPT Folic Acid: ACCJAMA 2010 Meta-Analysis: JAMAHOST: Dr. Robert Lufkin MD | robertlufkinmd.com⭐ Enjoying the show? Please leave a 5-star review on Apple Podcasts — it takes 30 seconds and helps more people discover the science of health and longevity. Thank you!New episodes every Tuesday & Thursday. Subscribe so you don't miss one.Continue this conversation on Substack: https://robertlufkinmd.substack.comLies I Taught In Medical School — Free sample chapter: https://www.robertlufkinmd.com/lies/Web: https://www.robertlufkinmd.comYouTube: https://www.youtube.com/robertlufkinmdX: https://x.com/robertlufkinmdInstagram: https://www.instagram.com/robertlufkinmd/TikTok: https://www.tiktok.com/@robertlufkinLinkedIn: https://www.linkedin.com/in/robertlufkinmd/

George Stark from the Cleveland Clinic reflects on his early training in biochemistry and how several sabbatical leaves sparked pivotal changes in his research trajectory, eventually drawing him to interferon signaling where he helped establish the core principles of the JAK–STAT pathway and uncovered interesting roles for interferon signaling in cancer. Host: Cindy Leifer Guest: George Stark Subscribe (free): Apple Podcasts, RSS, email Become a patron of Immune! Links for this episode MicrobeTV Discord Server Stark lab Development of the Northern blotting technique (PNAS, 1977) Development of Western blotting for proteins (PNAS, 1979) Discovery of Tyk2 as a critical kinase in interferon response (Cell, 1992) Topical PALA treatment for non-melanoma skin cancer (Exp Dermatol, 2023) Time stamps by Jolene Ramsey. Thanks! Music by Tatami. Logo image by Blausen Medical Send your immunology questions and comments to immune@microbe.tv Information on this podcast should not be construed as medical advice.

Hey Tinnitus Friends & Family, A new study from Oregon Health & Science University found a direct brain circuit linking serotonin to tinnitus symptoms. If you're taking antidepressants and have tinnitus, you've probably seen the headlines—and maybe felt some panic. Here's the truth: this is good science, not a reason to stop your medication. In this video, I break down what the research actually found, why mouse studies can't tell the whole story, and what this means if you're currently taking SSRIs. I also share my personal experience—I take SSRIs myself, and they haven't worsened my tinnitus. **Key Takeaways:** ✅ The study found a serotonin → auditory circuit that can create tinnitus-like behavior in mice ✅ This validates what some people report, but doesn't mean SSRIs "cause" tinnitus ✅ SSRIs can be life-changing for depression and anxiety—the benefits often far outweigh risks ✅ Never stop medication without talking to your doctor ✅ Habituation works regardless of whether you're on medication **Timestamps:** 0:00 Introduction: Who I Am (and Who I'm Not) 1:15 Why This Research Matters 2:20 What Are SSRIs? 3:40 The Study Explained: Serotonin → Auditory Circuit 5:10 How the Research Was Done (Optogenetics) 6:30 What Dr. Trussell Said About Future Treatments 7:45 My Take: What This Means for YOU 9:20 My Personal Experience with SSRIs 10:15 Bottom Line: Talk to Your Doctor 11:00 You Don't Have to Do This Alone **Resources Mentioned:**

Genomics of the Golden Horde Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Ayken Askapuli explains genomic insights into the ancestors and descendants of the Golden Horde. In this episode, we cover: •[00:00] Introduction. •[00:56] Population geneticist Ayken Askapuli introduces the Golden Horde. •[02:01] He describes the individuals in the mausoleums whose DNA the team sampled.  •[04:11] Askapuli explains findings about the modern populations the Golden Horde individuals were related to. •[05:08] He then explains findings about the Y chromosome characteristics of the Golden Horde individuals. •[06:14] Askapuli talks about what the results say about the ancestry of the Golden Horde. •[06:48] He describes how the results aid understanding of population genetics in central Eurasia. •[08:10] He lists the caveats and limitations of the study. •[09:53] Conclusion. About Our Guest: Ayken Askapuli PhD candidate University of Wisconsin-Madison View related content here: https://www.pnas.org/doi/abs/10.1073/pnas.2531003123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

What can the DNA of Neanderthals, woolly mammoths, and ancient proteins tell us about the future of medicine? In this episode, Professor César de la Fuente sits down to discuss his fascinating research goal: using the power of machines to accelerate discoveries in biology and medicine… This conversation explores: The growing global health threat of antimicrobial resistance (AMR). Why ancient DNA and extinct organisms may hold clues for next-generation antibiotics. The role that AI plays in uncovering the genetic data of extinct organisms. What the future of machine biology could mean for human health. Prof. de la Fuente is Presidential Associate Professor at the University of Pennsylvania, where he leads the Machine Biology Group. He is one of the youngest tenured professors in the history of Penn Medicine. He completed postdoctoral research at MIT and earned his PhD from the University of British Columbia. He is widely recognized for pioneering the first computer-designed antibiotic shown to be effective in animal models, which is an achievement that helped launch the emerging field of AI-driven antibiotic discovery. His lab has since identified more than one million potential antimicrobial compounds through computational biology. In addition, Prof. de la Fuente has delivered over 350 invited lectures worldwide, co-authored an influential book on machine learning for drug discovery, secured multiple patents, and published more than 180 peer-reviewed papers in leading journals, including Cell, Science, Nature Communications, PNAS, and Advanced Materials. You can follow Prof. de la Fuente's latest discoveries and research here!

On le sait intuitivement : certaines relations nous épuisent. Mais ce que la science révèle aujourd'hui va beaucoup plus loin. Certaines personnes de notre entourage pourraient littéralement accélérer notre vieillissement biologique.Une étude publiée le 22 janvier 2026 dans la prestigieuse revue PNAS, menée par des sociologues et spécialistes du vieillissement issus de plusieurs universités américaines, apporte des résultats frappants. Les chercheurs se sont intéressés à ce qu'ils appellent les “hasslers” : des individus qui génèrent du stress, des conflits ou rendent la vie plus difficile au quotidien.Leur conclusion est claire : ces relations négatives ne sont pas seulement désagréables, elles agissent comme de véritables accélérateurs du vieillissement.Pour le démontrer, les chercheurs ont analysé plus de 2 000 adultes, en combinant questionnaires sociaux et analyses biologiques à partir d'échantillons de salive. Grâce à des outils très avancés, ils ont mesuré l'âge biologique des participants, c'est-à-dire l'état réel de leurs cellules, indépendamment de leur âge chronologique.Et les résultats sont impressionnants.Chaque personne “toxique” supplémentaire dans l'entourage est associée à une augmentation d'environ 1,5 % du rythme de vieillissement. Concrètement, cela correspond à environ neuf mois de vieillissement biologique en plus.Pourquoi un tel effet ?Parce que ces relations agissent comme des sources de stress chronique. Or, le stress prolongé entraîne une cascade de réactions dans l'organisme : augmentation du cortisol, inflammation persistante, affaiblissement du système immunitaire. À long terme, ces mécanismes accélèrent l'usure du corps.Autrement dit, ces interactions négatives “passent sous la peau”. Elles modifient réellement notre fonctionnement biologique.L'étude montre aussi que ces relations ne sont pas rares. Près de 30 % des individus déclarent avoir au moins une personne de ce type dans leur entourage.Fait intéressant, toutes les relations négatives n'ont pas le même impact. Les tensions avec la famille ou certaines connaissances semblent plus délétères que celles avec un conjoint, probablement parce qu'elles sont plus difficiles à réguler ou à éviter.Ce que cette recherche met en lumière, c'est une idée essentielle : notre santé ne dépend pas uniquement de ce que nous mangeons ou de notre activité physique. Elle dépend aussi, profondément, de la qualité de nos relations.Au fond, bien s'entourer n'est pas seulement une question de bien-être émotionnel. C'est aussi, très concrètement, une question de longévité. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

How does something living emerge from something that isn't?  In this episode, Lee Cronin pushes the question back even further: before cells, before DNA, before biology as we usually think of it, what kind of process could make matter start organizing itself into something alive? He and Michael Shermer get into assembly theory, RNA, autocatalysis, and the deeper puzzle of whether causation and selection may already be at work long before the first organism appears. The conversation also branches into consciousness, free will, and the possibility that life may be widespread in the universe, even if it looks nothing like life on Earth. Lee Cronin is Regius Professor of Chemistry at the University of Glasgow, Scotland, where he leads one of the world's largest multidisciplinary chemistry research groups. He has raised more than $35 million in grant funding, with current research income of $15 million, and has authored more than 350 peer-reviewed papers, including recent work published in Nature, Science, and PNAS. He and his team are trying to make artificial life forms, find alien life, explore the digitization of chemistry, understand how information can be encoded into chemicals and construct chemical computers.

How much carbon dioxide concrete can absorb Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Hessam Azarijafari explains the extent to which concrete can absorb carbon dioxide over its lifecycle. In this episode, we cover: •[00:00] Introduction. •[00:56] Construction engineer Hessam Azarijafari introduces us to the recipe for concrete. •[01:41] He explains how concrete absorbs carbon dioxide throughout its lifespan, and why this absorption is important. •[02:52] Azarijafari talks about the background of the study. •[03:45] He describes the model built for the study and the data source. •[05:59] Azarijafari tells the study's results, including variation in absorption across sectors and between the US and Mexico. •[07:41] He compares this analysis with previous estimates of concrete carbon absorption. •[08:25] He explains the takeaways from this study for policymakers and the concrete industry. •[09:36] He lists the caveats and limitations of the study. •[10:19] Conclusion. About Our Guest: Hessam Azarijafari Research Scientist Massachusetts Institute of Technology View related content here: https://www.pnas.org/doi/abs/10.1073/pnas.2515116122 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Determining how well extinct animals could smell Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Quentin Martinez describes a reconstruction of olfactory capabilities in extinct mammals. In this episode, we cover: •[00:00] Introduction •[01:14] Evolutionary biologist Quentin Martinez tell why we want to reconstruct olfaction in extinct animals. •[02:35] He introduces the olfactory bulb endocast, or space within the skull that contained the olfactory bulb, and explains why it's important in evaluating olfaction in extinct animals. •[04:24] Martinez talks about studying the genomics of chemoreceptor genes, in addition to the bony structure of the olfactory bulb endocast. •[05:23] He tells about the results of the study. •[07:46] Martinez lists possible insights from reconstructing extinct animals' olfaction. •[08:53] He lists the caveats and limitations of the study. •[10:16] Conclusion. About Our Guest: Quentin Martinez Postdoctoral researcher Natural History Museum, Stuttgart, Germany View related content here: https://www.pnas.org/doi/10.1073/pnas.2510575122 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Join Ellen & Dalton Mason, creator and bird parent behind Apollo and Frens, for a look into life alongside the world-famous African grey parrot. You may have seen their videos on social media showing off Apollo's incredibly impressive vocabulary, answering questions and even speaking in full sentences, which have earned the bird a spot in the Guinness Book of World Records in 2025 - on top of all the pistachios, of course. We discuss animal cognition and what makes parrots such great models for animal intelligence, the brain soup machine, bird-proofing a home, parrots video calling each other, and so much more. Works Cited: "Birds have primate-like numbers of neurons in the forebrain" - Seweryn Olkowicz et al., PNAS, June 2016 "Birds of a Feather Video-Flock Together: Design and Evaluation of an Agency-Based Parrot-to-Parrot Video-Calling System for Interspecies Ethical Enrichment" - Rebecca Kleinberger et al., Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, April 2023 African Grey Parrot flight calls: Peter Boesman, XC719450. Accessible at www.xeno-canto.org/719450 Links: Learn more about Apollo and Frens on their website: https://apolloandfrens.com/ Follow Apollo and Frens on YouTube, Instagram, and TikTok! For more information about us & our podcast, head over to our website! Follow Just the Zoo of Us on BlueSky, Facebook, Instagram & Discord! Follow Ellen on Instagram or BlueSky!  

Extinction Rebellion's co-founder Clare Farrell and conservation scientist Dr Charlie Gardner team up once more to discuss issues and stories they feel are not getting enough airtime. They want to make sure that the latest news in science and important reports that are relevant to the climate and ecological crisis are flagged and explained in ways that are easy to understand.EPISODE 31: Biodiversity: suppressed report, finance gap, grassroot's protection, and Hannah SpencerIn this episode Clare and Charlie examine a censored UK intelligence assessment on biodiversity breakdown, the economic system driving ecosystem destruction, and the role of people power in protecting the planet. They also discuss what a surprising Green Party by-election victory might mean for climate politics.REFERENCESITV coverage of suppressed biodiversity national security reporthttps://www.youtube.com/watch?v=59DZiPdsOc8IPBES Business and Biodiversity Assessmenthttps://www.ipbes.net/bba-report/media-releaseRole of social movements in conserving nature New paper in PNAS based on Environmental Justice Atlashttps://www.pnas.org/doi/epdf/10.1073/pnas.2513327123NB the views in this show are Clare and Charlie's own and do not necessarily reflect the official position of Extinction Rebellion.---------------------Please, share, comment, subscribe, like, mobilise, and donate! https://chuffed.org/xr/ukExtinction Rebellion UK: https://extinctionrebellion.uk/

What does it truly take to change what we do — for yourself and at scale, sustainably in the real world?In this episode, Angela sits down with Hengchen Dai, Associate Professor at UCLA Anderson and co-director of the UCLA Nudge Unit, to explore the science behind why people do what they do, and what it takes to shift it.Hengchen brings rigorous academic research published in Nature and other notable publications, and field experiments run inside hospitals, university health systems, and national pharmacy chains with millions of participants. What emerges is a practical toolkit for anyone trying to create lasting change — whether you're a leader, a clinician, a policymaker, or just someone trying to get better at your own habits.Key Takeaways  -  Behaviour change is not one-size-fits-all — someone who skipped their flu shot this year (but got it last year) has a completely different barrier (forgetting, procrastination) than someone who has never been vaccinated (belief, scepticism). Targeting interventions based on past behaviour dramatically improves results. 

I raw meat the danger that some assume, or the superfood version of meat that others claim?  Let's look at the facts.   Some references: Micromorphology and geochemistry show controlled burning 30m inside Wonderwerk Cave, giving strong evidence that early Homo was using fire at least 1 million years ago. Berna F, Goldberg P, Horwitz LK, Brink JS, Holt S, Bamford M, Chazan M. 2012. Microstratigraphic evidence of in situ fire in the Acheulean strata of Wonderwerk Cave, Northern Cape province, South Africa. PNAS 109(20):E1215–E1220. PMID: 22474385. https://pubmed.ncbi.nlm.nih.gov/22474385/ Enamel crystal structure of carp teeth indicates low‑temperature, repeated heating, consistent with deliberate "oven‑like" cooking of fish by hominins ~780,000 years ago Zohar I, Biton R, Goren‑Inbar N, et al. 2022. Evidence for the cooking of fish 780,000 years ago at Gesher Benot Ya'aqov, Israel. Nature Ecology & Evolution 6:1797–1806. PMID: 36357607. https://www.nature.com/articles/s41559-022-01910-z   Join my NEW 90-day Carnivore Challenge group on Mighty Networks below! https://dr-chaffee-s-90-day-carnivore-challenge.mn.co/landing/ If you liked this and want to learn more go to my new website www.DrAnthonyChaffee.com

A new PNAS study found that the people who chronically stress you out don't just ruin your mood — they accelerate your biological aging. AJ and Johnny break down the research showing that each “Hassler” in your close network is linked to faster cellular aging, measurable at the DNA level. The real threat to your health isn't isolation — it's tolerating the wrong people. If you've optimized your fitness, discipline, and productivity but ignored your social environment, this episode explains why that might be costing you years. Chapters 00:00 – The DNA study on social stress02:00 – What a “Hassler” actually costs you04:00 – Why loneliness isn't the real problem06:30 – Family stress hits the hardest09:00 – Depth vs. breadth in relationships11:30 – Three practical moves to clean up your network Stop being over looked and unlock your X-Factor today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠unlockyourxfactor.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Check out Johnny on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Social_Intell⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tiktok @social_intel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  The very qualities that make you exceptional in your field are working against you socially.  Visit the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠artofcharm.com/intel ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠for a social intelligence assessment and discover exactly what's holding you back. Download Stuff for free today by going to trystuff.app or by searching for “Stuff” in the App Store. You can get 50% off your first year of Extra Stuff by using code CHARM at checkout. Don't let financial opportunity slip through the cracks. Use code CHARM at monarch.com in your browser for HALF OFF your first year. Indulge in affordable luxury with Quince. Upgrade your wardrobe today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠quince.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for free shipping and hassle-free returns. Ready to turn your business idea into reality? Sign up for your $1/month trial at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠shopify.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Need to hire top talent—fast? Claim your $75 Sponsored Job Credit now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Indeed.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. This year, skip breaking a sweat AND breaking the bank. Get your summer savings and shop premium wireless plans at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mintmobile.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Save more than fifty percent on term life insurance at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SELECTQUOTE.COM/CHARM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TODAY to get started  Curious about your influence level?  Get your Influence Index Score today! Take this 60-second quiz to find out how your influence stacks up against top performers at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠theartofcharm.com/influence⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check in with AJ and Johnny! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AJ on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Johnny on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AJ on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Johnny on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on TikTok⁠/ social stress, biological aging, epigenetic clock, toxic relationships, social health, stress and health, difficult family dynamics, social network audit, relationship boundaries, emotional stress, DNA aging, multiplex relationships, social environment, personal development Learn more about your ad choices. Visit megaphone.fm/adchoices

Why do some conversations feel effortless — while others fall flat even when you said nothing wrong? AJ and Johnny break down the neuroscience of “clicking,” including a 2022 PNAS study showing that connection often comes down to milliseconds. The secret isn't better stories or smarter answers — it's timing. Shorter response gaps signal attunement, alignment, and shared rhythm. Longer gaps quietly erode chemistry. If you've ever felt “off” despite saying the right things, this episode explains why — and how timing becomes social body language. 00:00 – The gap you can feel01:00 – Why chemistry isn't personality02:00 – The PNAS timing study03:15 – Same words, different timing04:15 – Why rhythm equals connection Stop being over looked and unlock your X-Factor today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠unlockyourxfactor.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Check out Johnny on ⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠@Social_Intell⁠⁠⁠⁠⁠⁠⁠⁠⁠ or on ⁠⁠⁠⁠⁠⁠⁠⁠⁠Tiktok @social_intel⁠⁠⁠⁠⁠⁠⁠⁠⁠  The very qualities that make you exceptional in your field are working against you socially.  Visit the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠artofcharm.com/intel ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠for a social intelligence assessment and discover exactly what's holding you back. Download Stuff for free today by going to trystuff.app or by searching for “Stuff” in the App Store. You can get 50% off your first year of Extra Stuff by using code CHARM at checkout. Don't let financial opportunity slip through the cracks. Use code CHARM at monarch.com in your browser for HALF OFF your first year. Indulge in affordable luxury with Quince. Upgrade your wardrobe today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠quince.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for free shipping and hassle-free returns. Ready to turn your business idea into reality? Sign up for your $1/month trial at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠shopify.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Need to hire top talent—fast? Claim your $75 Sponsored Job Credit now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Indeed.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. This year, skip breaking a sweat AND breaking the bank. Get your summer savings and shop premium wireless plans at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mintmobile.com/charm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Save more than fifty percent on term life insurance at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SELECTQUOTE.COM/CHARM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TODAY to get started  Curious about your influence level?  Get your Influence Index Score today! Take this 60-second quiz to find out how your influence stacks up against top performers at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠theartofcharm.com/influence⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check in with AJ and Johnny! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AJ on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Johnny on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AJ on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Johnny on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Art of Charm on TikTok⁠/ conversation timing, social connection, chemistry, attunement, communication skills, response time, neuroscience of connection, social rhythm, conversational flow, charisma, social intelligence, listening skills, human connection, conversational dynamics Learn more about your ad choices. Visit megaphone.fm/adchoices

We try and keep things light amidst the rising tide against ICE and the Trump administration (and as Bryan recovers from a procedure). Erin covers a study by PNAS showing how sexism physically changes and deteriorates the brain. Bryan discusses a current case of two gay Iranian asylum seekers on the verge of being deported back to where they fled persecution. GoFundMe Links: Support the Family of Parady La who died in ICE Custody Support the Family of Luis Beltrán Cruz who died in ICE custody Help Famiies Affected By ICE in Minneapolis Haven Watch MN If you have other resources you'd like us to highlight please send us a DM!See omnystudio.com/listener for privacy information.