Podcast appearances and mentions of michael bernstein

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

Michael Bernstein of Clean Prosperity joins Tom Heintzman, Vice Chair, Energy & Climate Finance, to discuss key provisions of the Canada-Alberta Memorandum of Understanding on industrial carbon pricing. They explore the agreement's strengths, weaknesses, and market impacts, and whether it ‘makes the grade' in advancing credible, long-term decarbonization for Alberta's oil and gas sector. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Alberta Premier Danielle Smith has a plan: Hold a referendum in October to decide whether to push for a legally binding referendum on independence in the future. Confused? Host Catherine Cullen has it covered. Pro-separation lawyer Keith Wilson and federalist champion Thomas Lukaszuk will share their thoughts on Smith's decision. And the godfather of Prairie populism, former Reform Party leader Preston Manning, weighs in on the vote.Plus, environmental groups are concerned about Canada's climate commitments after Carney signed an agreement with Alberta that could usher in a new oil pipeline. Is the Prime Minister simply being pragmatic or has he largely given up the fight against climate change? Current and former climate advisors Michael Bernstein and Simon Donner explain their opposing reactions to Carney's new deal.And, Conservative MP Michael Chong went to Taiwan this week… specifically because Chinese officials warned Canadian parliamentarians not to go. Chong talks to The House about his defiant trip, his hopes of shoring up peace in the region and whether he believes his travel could hurt Canada's growing economic ties to Beijing.This episode features the voices of:Keith Wilson, pro-separation lawyerThomas Lukaszuk, leader of the Forever Canadian petitionPreston Manning, former leader of the Reform Party of CanadaMichael Bernstein, CEO of Clean ProsperitySimon Donner, climate scientist and professor at the University of British ColumbiaMichael Chong, Conservative MP

It's been just over four months since Prime Minister Mark Carney and Alberta Premier Danielle Smith signed a memorandum of understanding giving Alberta special exemptions from federal environmental laws and setting the stage for a new oil pipeline to the west coast. But both sides are set to miss an early deadline on April 1st because of sticking points. Former oil executive Richard Massen and Clean Prosperity President and CEO Michael Bernstein discuss what's at stake if delays continue.

After 40 years in the apparel industry, Michael Bernstein had a realization: seeing the problem wasn't enough—he had to act. On this episode of Daily Influence, Michael shares how that conviction led him to launch Burnastic, a company transforming textile and plastic waste into a sustainable polymer designed to reduce global carbon footprints. In this inspiring conversation, we explore: • The moment purpose met possibility • How innovation can offset fast fashion's environmental impact • Why failure is part of leadership • The courage it takes to disrupt traditional industries • How pursuing passion prevents lifelong regret Michael reminds us that influence starts with responsibility—and that even “sleepy industries” can be transformed by bold vision. Connect with Michael and learn more at: https://bernastic.com/

On this episode of Managed Care Cast, I spoke with Dr. Michael Bernstein, an assistant professor in the department of diagnostic imaging at the Warren Alpert Medical School, and Brian Sheppard, a professor of law at Seton Hall Law who focuses on malpractice litigation, to discuss the broader implications of AI becoming more prevalent in health care; understanding its potential impact on legal outcomes and physician liability is essential for providers, risk managers, and policymakers alike.

Michael Bernstein has spent his entire career solving problems most people never see.As a college student given only $1,000 to last four years, Michael had no safety net and no days off. To survive, he built a sweater business from scratch, negotiating directly with his uncle, who owned one of the largest U.S. sweater mills. With no family discounts and a one-time credit, Michael would buy closeouts at full price and resell them on campus. He struck a deal with the Dean for premium selling space, expanded to 20 colleges across the East Coast, and became the mill's largest buyer—all while attending classes. By senior year, he had generated more than a million dollars in sales and graduated able to buy his first car in cash. That relentless resourcefulness carried into his career, where he rose to senior roles in a $2.5B apparel company and later invented the MRI-safe, metal-free plastic snap that transformed hospital gowns and has now been used in over 30 million gowns worldwide.Early on, he developed a small but revolutionary innovation in healthcare: an MRI-safe, metal-free plastic snap for hospital gowns. It seems like a minor detail — until you learn it's now been used in over 30 million gowns worldwide, changing how hospitals think about safety, laundering, and patient dignity. That experience taught him something essential: the right material, used in the right place, can disrupt an entire institution.Years later, while touring a sustainable brewery, Michael noticed something that didn't match the marketing. Beneath all the environmentally friendly messaging sat hundreds of virgin-wood pallets — the backbone of the operation and a major driver of deforestation. At that moment, the problem crystallized: sustainability messaging meant nothing if the infrastructure underneath it was still destroying forests.This sparked a new question:What if the materials we throw away could replace the materials we're overusing?That insight led Michael to apparel waste — a global problem he knew intimately from decades in the textile industry. Denim scraps, cotton remnants, and discarded clothing are burned or buried by the millions of tons each year. If textile waste could be re-engineered into a strong, injection-molded material, it could become the base of products that currently rely on wood or virgin plastic.This wasn't recycling.This was redesigning waste into something new.More Info: Bernastic.comSponsors: Become a Guest on Master Leadership Podcast: Book HereAgency Sponsorships: Book GuestsMaster Your Podcast Course: MasterYourSwagFree Coaching Session: Master Leadership 360 CoachingSupport this show http://supporter.acast.com/masterleadership. Hosted on Acast. See acast.com/privacy for more information.

Steven Guilbeault, Canada's former Environment Minister, says the plan for a new oil pipeline means its impossible for the country to meet its 2030 emissions reduction targets. Not only that, he fears that today's pipeline plan puts Canada's goal of a net-zero economy by 2050 in serious doubt. We kick things off with his one-on-one chat with Vassy Kapelos, as the present-day Liberal MP mulls over his long-term political future. On today's show: B.C. political correspondent Rob Shaw breaks down the resignation of B.C. Conservative leader John Rustad. Talk Science To Me with CTV Science and Technology specialist Dan Riskin: How social media makes teenagers 'measurably dumber'. The Daily Debrief Panel - featuring Brian Platt, Laura Stone, and Rob Benzie. Vassy speaks with Michael Bernstein, the CEO of climate thinktank Clean Prosperity, about the impact of the Alberta MOU on federal climate policy goals.

Now, if you run any kind of business, you know that finding the right talent can be one of your hardest problems. You can spend months recruiting, weeks interviewing, and then hope your new hire works out. But what if that whole model is becoming obsolete? What if, instead of hoarding talent, you could access exactly the expertise you need, exactly when you need it—and have a high-performing team assembled in minutes? Melissa Valentine and Michael Bernstein are Stanford professors who've spent years studying the future of work, and their new book, Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work, reveals how this radical shift is already happening. We'll hear from them in just a moment. Then in the second half we'll hear some Big ideas from the 2024 book Open Talent: Leveraging the Global Workforce to Solve Your Biggest Challenges by John Winsor and Jin Paik.

AI as a Lifeline for US Energy and Russian Economic Collapse Guest: Michael Bernstein Michael Bernstein describes how artificial intelligence, which is highly electricity intensive, is providing a "lifeline" to US shale energy, especially natural gas production in Texas, by creating substantial new demand for power infrastructure. This new supply is also fundamentally transforming the European energy landscape, where Russian dominance has been eliminated. Simultaneously, sanctions are forcing major Russian companies like Rosneft and Lukoil into "fire sales" of assets outside Russia at steep discounts.

Across industries, organizations are struggling to move as quickly as they need to on key priorities and new initiatives. The solution for many, says Stanford's Melissa Valentine, might be "flash teams" -- project groups that can be instantly, efficiently, and cost-effectively brought together and organized via online labor markets and AI and other digital tools to solve any problem. She explains why companies and leaders should embrace this new type of collaboration, how flash teams work in practice, and the pitfalls to look out for. Valentine is coauthor along with Michael Bernstein of the book Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work.

In this Mission Matters episode, Adam Torres interviews Michael Bernstein, Founder of Bernastic. Michael shares his journey as an inventor and entrepreneur, explaining how his patented material combines apparel waste with HDPE to create sustainable pallets. With applications across supply chains worldwide, Bernastic's innovation reduces waste, strengthens products, and helps companies meet carbon reduction goals. Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this Mission Matters episode, ⁠Adam Torres⁠ interviews ⁠Michael Bernstein⁠, Founder of Bernastic. Michael shares his journey as an inventor and entrepreneur, explaining how his patented material combines apparel waste with HDPE to create sustainable pallets. With applications across supply chains worldwide, Bernastic's innovation reduces waste, strengthens products, and helps companies meet carbon reduction goals. Follow Adam on Instagram at ⁠https://www.instagram.com/askadamtorres/⁠ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: ⁠https://missionmatters.lpages.co/podcastguest/⁠ Visit our website: ⁠https://missionmatters.com/⁠ More FREE content from Mission Matters here: ⁠https://linktr.ee/missionmattersmedia⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

What was meant to be a gathering to support Israeli hostages held by Hamas turned into a crime scene in Boulder, Colorado on Sunday when a man attacked Jewish attendees with Molotov cocktails and a makeshift flame thrower. This is just the latest in a series of heinous assaults on Jewish people in America. Joining the show to discuss is Michael Bernstein, chair of the board of the Tree of Life, a non-profit organization started by the community and congregation leaders in Pittsburgh after 11 Jewish worshippers were brutally gunned down in 2018.  Also on today's show: Col. Cedric Leighton, Military Analyst / U.S. Air Force (Ret.); Rebecca Winthrop, Director, Center for Universal Education, Brookings / Author, "The Disengaged Teen"; composer David Yazbek ("Dead Outlaw"); Michael Luo, Executive Editor, The New Yorker  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us a textIn this illuminating discussion, Michael Bernstein from the Atlanta Journal Constitution reveals practical applications of AI in strategic partnership development. Drawing from his experience at AJC, Bernstein explores how artificial intelligence enhances market analysis, streamlines partnership communications, and drives innovative engagement strategies. The conversation provides actionable insights into leveraging AI for partnership growth while maintaining a human-centric approach to relationship building.Detailed AnalysisThe discussion delves deep into the practical implementation of AI across various partnership development stages. Bernstein outlines several key applications, starting with market analysis and data analytics for identifying potential partnerships and understanding market shifts. He emphasizes AI's role in strategic planning, particularly in modeling expansion strategies and evaluating program feasibility.A compelling example shared involves AJC's partnership with Mercer University, where AI helped craft targeted marketing strategies to boost student engagement. The initiative resulted in innovative approaches, including an iPad giveaway campaign and strategic placement of ads in student newspapers, demonstrating AI's ability to enhance traditional partnership programs.The conversation also explores AI's utility in streamlining day-to-day operations, from automating communication processes to analyzing partnership performance metrics. Bernstein's practical approach focuses on using AI to "make life easier" rather than replacing human judgment, particularly in areas like presentation development and partnership model creation.One notable insight reveals how AI assisted in identifying new partnership channels, such as the HR channel for corporate-wide subscription programs. This led to a successful engagement with the Society of HR Management's Atlanta chapter, accessing over 8,000 HR executives.The discussion concludes with practical examples of AI-driven research for identifying potential partners, including real estate firms for subscription bundling programs, showcasing how AI can open new avenues for partnership growth and customer engagement.Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybookPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

Send us a textIn this compelling episode, Michael Bernstein of the Atlanta Journal Constitution shares his expertise on leveraging strategic partnerships to drive customer success. He reveals how implementing a B2B partnership channel led to remarkable growth, accounting for 25% of AJC's subscription acquisitions within just six months. Through practical examples like the Dancing Goats coffee shop collaboration, Bernstein demonstrates how partnerships can unlock access to previously untapped customer segments.Detailed AnalysisBernstein's approach to partnership development encompasses three crucial elements: economic modeling, operational infrastructure, and marketing strategy. His insights highlight the importance of thorough preparation before launching partnership initiatives.Key takeaways include:The necessity of executive buy-in and resource allocation for successful partnership programsCritical importance of operational capabilities specific to B2B partnershipsThe value of pilot programs in testing and refining partnership modelsThe role of seamless customer experience in maintaining successful partnershipsThe significance of cross-departmental alignment in partnership executionThe discussion emphasizes how partnerships can extend beyond traditional direct-to-consumer channels, particularly in the digital space. Bernstein's experience with the hospitality sector exemplifies how strategic partnerships can create win-win situations, benefiting both the media organization and its partners while enhancing customer value.The success metrics shared demonstrate the tangible impact of well-executed partnership strategies on business growth. Bernstein's implementation of digital access programs and app-centric promotions shows how traditional media companies can innovate through strategic partnerships.Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybookPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

Send us a textIn this compelling episode, Michael Bernstein, Senior Partner and Business Development Lead at Atlanta Journal Constitution, shares his expertise on creating successful strategic partnerships. He introduces the innovative concept of "win-win-win" scenarios, emphasizing how partnerships must benefit all three key stakeholders: the organization, partners, and end consumers. Through practical examples from AJC's digital subscription programs, Bernstein demonstrates how to structure partnerships that drive meaningful value across the entire ecosystem.Detailed AnalysisBernstein's approach to partnership development reveals sophisticated strategies for modern business relationships. His emphasis on thorough research and active listening sets the foundation for successful partnerships, demonstrating how preliminary understanding of partner priorities can shape more effective collaboration.The discussion showcases practical applications through AJC's innovative membership programs, where digital subscriptions are leveraged as value-adds for partner organizations. This strategy exemplifies how traditional media organizations can adapt to create mutually beneficial relationships in the digital age.Key insights include:The importance of preliminary research using both AI and existing relationship networksHow to structure tiered value propositions that incentivize increased engagementTechniques for frictionless customer onboarding in partnership programsMethods for balancing multiple stakeholder interests in partnership agreementsThe critical role of clear communication and documentation in partnership successThe conversation provides valuable lessons for business development professionals looking to create sustainable partnership programs that drive value across all stakeholders. Bernstein's emphasis on customization and clear objective setting offers a practical framework for partnership development in today's complex business environment.Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybookPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

In the latest episode of the Insight In-House Legal podcast, Ben White chats with Michael Bernstein, Senior Legal Counsel at Trustpilot.Michael shares how his entrepreneurial upbringing and creative background shaped his approach to in-house legal roles, emphasising problem-solving, adaptability, and building strong professional relationships.He also discusses the transformative role of AI in legal and offers advice for young lawyers: “Your job as an in-house lawyer is to be the brakes on the car—knowing when to say yes and when to say no.”Don't miss this episode packed with insights!

Recorded on-stage at Øredev 2024, Fredrik talks to Laura Herman about creativity, creation, and AI. Among other things, we discuss: How the perspectives of different groups differ, and Laura talks about the many factors which inform how people feel about generative AI. Generative AI as curation. How and where in our work processes we want AI assistance. Dataset curation and specialized models, and how they can be important and interesting going forward. What happens if we have to be very picky about what we train models on? How are people working with sustainability for generative models? Laura’s own research into AI and creativity, and how other inventions have affected creativity and art. Finally, we discuss curation, and the possibilities of alternate curation platforms for finding things you like. Many thanks to Øredev for inviting Kodsnack again, they paid for the trip and the editing time of these keynote recordings, but have no say about the content of these or any other episodes. Thank you Cloudnet for sponsoring our VPS! Comments, questions or tips? We a re @kodsnack, @tobiashieta, @oferlund and @bjoreman on Twitter, have a page on Facebook and can be emailed at info@kodsnack.se if you want to write longer. We read everything we receive. If you enjoy Kodsnack we would love a review in iTunes! You can also support the podcast by buying us a coffee (or two!) through Ko-fi. Links Øredev All the presentation videos from Øredev 2024 Laura Creation as curation - Laura’s keynote The handmade effect Jake Elwes Support us on Ko-fi! The inclusive AI lab Mubi Michael Bernstein at Stanford Titles Many question marks An ethically sound decision A human touched this Craving for the human touch Let me build a model That’s five PhD:s In this emotional turmoil

The nocebo effect demonstrates how the mind can cause illness through negative expectations, as highlighted by a famous incident in a U.S. textile factory in the 1960s. Workers believed a bug was causing dizziness, nausea, and other symptoms, yet no physical cause was found. This mysterious outbreak underscores the potent influence of beliefs on health, a phenomenon that's becoming increasingly relevant in understanding modern psychosomatic conditions like the controversial Havana Syndrome. In this episode, Michael H. Bernstein, an expert on placebo and nocebo effects, explains how psychological factors can result in perceived physical harm. As co-author of The Nocebo Effect: When Words Make You Sick, Bernstein shares insights into the intersection of psychology, medicine, and public health. His research focuses on reducing opioid dependence by leveraging the placebo effect, while also exploring the ethical concerns surrounding nocebo-related side effects. Michael Bernstein, Ph.D., is an experimental psychologist and an Assistant Professor in The Department of Diagnostic Imaging at Brown University's Warren Alpert Medical School. His work is focused on harnessing the placebo effect to reduce opioid use among pain patients. He is Director of the Medical Expectations Lab at Brown. He is the co-author of the new book The Nocebo Effect: When Words Make You Sick, with Charlotte Blease, Cosima Locher, and Walter Brown. Shermer and Bernstein discuss: the placebo and nocebo effects, brain imaging, and the ethics of using these phenomena in medicine. Bernstein discusses the biology and psychology behind these effects, touching on notable cases such as Voodoo deaths and Havana Syndrome. Other subjects include psychogenic illnesses, patient-clinician interactions, alternative medicine, and how expectations can amplify or mitigate pain, anxiety, and depression. The conversation also delves into anticipatory nausea, psychotherapy, and the impact of cognitive behavioral therapy (CBT).

In this episode (part 1 of 2), Dr. Diane Reidy-Lagunes speaks with a team of MSK experts to demystify three breast cancer treatment options: surgery, radiation, and reconstruction. Breast surgeon Dr. Tracy-Ann Moo explains the difference between a lumpectomy and a mastectomy, and why one isn't always better than the other. Radiation oncologist Dr. Michael Bernstein discusses the role of radiation therapy in the treatment of breast cancer. Plastic and reconstructive surgeon Dr. Michelle Coriddi describes various breast reconstruction and preservation techniques, which can help patients maintain a positive body image after treatment. Subscribe to Cancer Straight Talk to be notified when Part 2 airs, covering chemotherapy, hormone therapy and survivorship for people with breast cancer. Episode Highlights:1:27 - Surgery7:36 - Radiation15:10 - Reconstruction See omnystudio.com/listener for privacy information.

We've already had a few glimpses at the next federal budget, thanks to a flurry of announcements this week and last. The finance minister joins The House to talk about those pledges and what more to expect.Then, it's clear that Pierre Poilievre hates the carbon tax. But if the Conservatives gain power, what will they do instead? Two experts sit down to discuss.And — 75 years after the founding of NATO, is the alliance ready for a second Trump term? The House speaks to representatives from some of Canada's most important allies.Plus — the CBC's expert foreign interference inquiry watcher walks us through what exactly happened in the hearing room this week.This episode features the voices of:Deputy Prime Minister and Finance Minister Chrystia FreelandMichael Bernstein, executive director, Clean ProsperityNicholas Rivers, associate professor, University of OttawaU.S. Ambassador David CohenU.K. High Commissioner Susannah GoshkoThe CBC's Janyce McGregor

You've heard of the Placebo Effect, which is the tendency to experience improvement in disease or complication because we expect it to get better. But do you know about the Nocebo effect? Turns out we also experience negative side effects and health problems more often when we expect them. This psychological dynamic has implications for counseling, medical care, parenting, and more. Michael Bernstein is a researcher and co-editor of "Nocebo Effect: When Words Make You Sick." He joins the show to tell us more about this important part of our lives, of which we are often unaware. Goto www.dwighthurst.com to participate with the show and support mental health advocacy.

Michael Bernstein, Clean Prosperity and Emilia Belliveau, Environmental Defence; Shachi Kurl, Angus Reid Institute and Christian Bourque, Leger; The Front Bench with: Sabrina Grover, Melanie Paradis, Gurratan Singh and Rachel Aiello.

Michael Bernstein, executive director of Clean Prosperity, joins the Vassy Kapelos Show to discuss Canada's global commitments and what he'd like to see happen at COP28. On today's show: Pascale St-Onge, Minister of Canadian Heritage, on the federal government's news deal with Google Talk Science to Me with Dan Riskin The Daily Debrief Panel with Robert Benzie, Queen's Park Bureau Chief for the Toronto Star, Marieke Walsh, senior political reporter with The Globe and Mail, Laura Stone, Queen's Park reporter with The Globe and Mail Cory Renner, Associate Director of Economic Forecasting with the Conference Board of Canada, on new GDP numbers and threat of a recession

Michael Bernstein, Executive Director of Clean Prosperity, and Advisory Board Chair of Carbon Removal Canada joined the podcast in November 2023 for a conversation about carbon policy and management. We talk about the importance of good modeling of net zero pathways, carbon markets and the need for carbon contracts for difference, and carbon management, both carbon capture and direct air capture. We also talk about the politics of climate policy and the need for support across the political spectrum for climate action. We end the conversation with Michael's recommendation for an addition to the Flux Capacitor Book Club.Links:Clean Prosperity: https://cleanprosperity.ca/Carbon Removal Canada: https://carbonremoval.ca/Michael Bernstein at Clean Prosperity: https://cleanprosperity.ca/team/michael-bernstein/Michael Bernstein on LinkedIn: https://www.linkedin.com/in/michael-bernstein-47b61b7/Primer on Carbon Contracts for Difference (CCFD): https://climateinstitute.ca/what-are-contracts-for-difference/

Michael Bernstein of Clean Prosperity joins CIBC Capital Markets' Tom Heintzman to discuss the current challenges posed by carbon price uncertainty, the opportunities for a Carbon Contracts for Difference (CCFD) regime in Canada, and what this means for large emitters.

In this episode we speak to Dr Michael Bernstein who is an Experimental Psychologist and Assistant Professor in the Department of Diagnostic imaging at Warren Alpert Medical School, Brown University, USA. Along with team at the Brown Radiology Human Factors Lab, he conducted a fascinating study that reveals how radiologists have a tendency to follow AI prompts, even when they are incorrect. We discuss the implications of this for patient care as clinical AI adoption increases, and what can be done to mitigate this phenomenon. Check out the full study here: https://link.springer.com/article/10.1007/s00330-023-09747-1

President Biden's Inflation Reduction Act may be good news for the clean energy sector, but what does it mean for Canada? That's a more complex question. On one hand, some aspects of it are likely to benefit Canadian businesses. But much of the funding included in the legislation is reserved for American producers and could make it difficult for Canada to build its own domestic clean energy sector. If clean energy is going to be a big part of developed economies in the future, that's a problem. On this episode, Michael Bernstein, the Executive Director of Clean Prosperity, joins us to break down what's in the Inflation Reduction Act, what it means for Canada, and why he thinks Canada needs to develop its own clean industrial strategy that's tailored for our strengths. ----- Links: More episodes of Free Lunch by The Peak: https://readthepeak.com/shows/free-lunch Follow Taylor on Twitter: @taylorscollon Follow Sarah on Twitter: @sarahbartnicka Subscribe to The Peak's daily business newsletter: https://readthepeak.com/b/the-peak/subscribe

Markham interviews Michael Bernstein, head of Clean Prosperity, and a co-author of “Creating a Canadian Advantage Policies to help Canada compete for low-carbon investment.”Link:  https://cleanprosperity.ca/new-data-shows-what-canada-can-do-to-compete-for-low-carbon-investment/

In this episode Pooja Shah, founder and CEO at Tephra Labs, talks about how decentralized human networks can address large-scale problems. Governments and corporations have failed to move the needle against many of humanity's most important challenges and opportunities. Pooja believes that decentralized human networks have the right meta-coordinating structures to do so. These networks can captivate participants from everywhere and grow exponentially, and furthermore, don't rely on a centralized direction-setting as governments and corporations do. We dive deep into the primitive form of human coordination all the way to the present on the internet and what the expectations are for the future. Additionally we converse about why hyper-scale decentralized networks will grow by engaging people as independent workers, rather than employees, about Tephra Labs and it's first product Radius - a decentralized network that connects independent workers and teams to the best projects in web3 - and how Pooja is thinking about coordinating capital, talent and ideas together in Radius' protocol. If you're interested in joining Tephra Labs or Radius, check out their open roles at https://tephra.com/careers. SUPPORT US ON PATREON: https://www.patreon.com/rhyslindmark JOIN OUR DISCORD: https://discord.gg/PDAPkhNxrC Topics: Welcome Pooja Shah to The Rhys Show!: (00:00:00) Goal for listeners: (00:02:18) Where did Pooja's curiosity for human coordination come from: (00:02:38) Core primitives of how we humans coordinate to do the things we do: (00:05:41) Pre web3: How people coordinate in the internet & what does internet enable us to do: (00:10:53) Web3: What is Pooja doing with Radius and Tephra Labs?: (00:16:43) Different levels of coordination scaling from 1 to a million: (00:24:39) Pooja's thoughts on an independent work economy: (00:29:58) About Radius & Radius vs. other work platforms: (00:32:54) Other platforms at team level instead of personal level: (00:38:29) Future: Building a protocol for Radius in terms of ideas, capital and talent: (00:41:57) Underrated & overrated questions about job boards, reputation and the “meme” of human coordination: (00:48:46) Wrap-up: (00:52:12) Mentioned resources: “Flash Teams” Talk by Melissa Valentine & Michael Bernstein: https://www.youtube.com/watch?v=dUqrQxs8SCE Connect with Pooja Shah: Twitter: https://twitter.com/pooja_eth Radius: https://www.radius.space/about Tephra Labs: https://www.tephra.com/ Linkedin: https://www.linkedin.com/in/pooja01

Host James Molesworth takes us on a deep dive into the Robert Mondavi legacy, with exclusive interviews with Wine Spectator editor and publisher Marvin R. Shanken, Continuum Estate co-founder and Robert's son Tim Mondavi, international winemaking star and Mondavi winery alum Paul Hobbs, Mount Veeder winery founders and former Mondavi winery tour guides Arlene and Michael Bernstein, and Robert Hanson, President of Constellation's Wine & Spirits division, which now owns the Mondavi winery and its esteemed To Kalon Vineyard. Plus, we page Dr. Vinny, and much more!Thirsty for more? Check out:• Wine Spectator's Nov. 30, 2022, issue• Latest News and Headlines• Ask Dr. Vinny• WS website members: More on James' Sneak Peek PickA podcast from Wine SpectatorMarvin R. Shanken, Editor and PublisherHost: James MolesworthDirector: Rob TaylorGuests: Marvin R. Shanken, Tim Mondavi, Paul Hobbs, Arlene and Michael Bernstein, Robert Hanson, and MaryAnn Worobiec as Dr. VinnyAssistant producer, Napa: Elizabeth Redmayne-Titley

After hearing people from both sides of the debate, Deb Hutton asks you: Should the handgun freeze exemption include a wider range of sport shooters?  On today's show:  A conversation with Heidi Rathjen, a member of the gun-control group PolySeSouvient and witness to the 1989 Polytechnique massacre, and Wes Winkel, president of the Canadian Sporting Arms and Ammunition Association. Katie Weatherston, a Canadian retired ice hockey player and Olympic gold medalist, on how much Hockey Canada paid her to help with post-concussion medical expenses versus their use of funds to pay out sexual assault claims. Graham Richardson, chief anchor for CTV Ottawa News at Six, provides an Emergencies Act inquiry update. The War Room political panel with Bob Richardson, Tim Powers and Tom Mulcair. David Campbell, the head of the B.C. River Forecast Centre, on a months-long drought on B.C.'s Sunshine Coast. Michael Bernstein, executive director of Clean Prosperity, on Prime Minister Trudeau saying he will guarantee that Canada will meet its emissions targets.

Neste episódio recebemos Michael Bernstein, cofounder e CTO da Clicksign, empresa pioneira no desenvolvimento de tecnologia para assinatura eletrônica. Fundada em 2011, a startup tem fomentado a digitalização de processos oferecendo aos clientes a possibilidade de assinar documentos de onde quer que estejam em menos de um minuto. É uma proposta que une eficiência, segurança e sustentabilidade. Durante a conversa comandada pelos sócios da KPMG no Brasil, Diogo Garcia e Carolina de Oliveira, Michael abordou o desafio de romper com uma cultura que, muitas vezes, mostra resistências ao digital. O executivo também destacou as mais recentes inovações da empresa, como o recurso do Aceite via WhatsApp e a autenticação via Pix.   Ouça a conversa!   Ouça a série completa:  https://spoti.fi/30De1gs   Conheça mais sobre a KPMG no Brasil e nos acompanhe nas redes sociais:   LinkedIn: https://www.linkedin.com/company/kpmg-brasil Instagram: https://www.instagram.com/kpmgbrasil Twitter: https://twitter.com/KPMGBRASIL Facebook: https://www.facebook.com/KPMGBrasil YouTube: https://www.youtube.com/KPMGBR    

The Forum is back after a quick Summer Break with the awesome story of Michael Bernstein aka Bernie's journey! From almost 400 pounds to taking the stage at transformation bodybuilding shows, Bernie's adventures have taken him a lot of places. Our discussion not only gets into the tools he used to change his life but how he handles the things that don't magically just change and you still need to work on every day. His story is one of persistence and consistency that I think will inspire everyone who listens! Connect with Bernie on IG at @bernie1b17! https://www.instagram.com/bernie1b17/  Fat Guy Forum PATREON is here! Click this link to see how YOU can support this show and help Gormy keep bringing you the stories of these amazing men: https://www.patreon.com/Gormygoesketo  Interested in working with Gormy on Accountability & Goal Setting, Ketogenic Nutrition or more? Get more info at https://www.theketoroad.com/coach-mike You can also find Gormy on IG at @gormygoesketo, Twitter at @gormygoesketo and you can email the show at TheFatGuyForum@gmail.com! You can support us and save money yourself by using code GORMY to get 15% your order at Redmond Real Salt(https://shop.redmond.life/?afmc=GORMY) NEW! Code GORMYGOESKETO will save on your purchase of all Kettle & Fire products at https://glnk.io/v153/gormygoesketo These are great products I use every day!! Check them out! Don't forget to give us a rating and review on iTunes or whatever platform you use!! Thank you and be sure to amaze yourself today!

James Cham is a co-founder and partner at Bloomberg Beta, an early-stage venture firm that invests in machine learning and the future of work, the intersection between business and technology. James explains how his approach to investing in AI has developed over the last decade, which signals of success he looks for in the ever-adapting world of venture startups (tip: look for the "gradient of admiration"), and why it's so important to demystify ML for executives and decision-makers. Lukas and James also discuss how new technologies create new business models, and what the ethical considerations of a world where machine learning is accepted to be possibly fallible would be like. Show notes (transcript and links): http://wandb.me/gd-james-cham --- ⏳ Timestamps: 0:00 Intro 0:46 How investment in AI has changed and developed 7:08 Creating the first MI landscape infographics 10:30 The impact of ML on organizations and management 17:40 Demystifying ML for executives 21:40 Why signals of successful startups change over time 27:07 ML and the emergence of new business models 37:58 New technology vs new consumer goods 39:50 What James considers when investing 44:19 Ethical considerations of accepting that ML models are fallible 50:30 Reflecting on past investment decisions 52:56 Thoughts on consciousness and Theseus' paradox 59:08 Why it's important to increase general ML literacy 1:03:09 Outro 1:03:30 Bonus: How James' faith informs his thoughts on ML --- Connect with James:

No Agenda Episode 1448 - "French Rats" "French Rats" Executive Producers: Sir Onymous of Dogpatch and Lower Slobbovia Sir DomNasty of Long Beach Ca Anne Rondepierre-Riczu Sir Chris Wilson Paul Zimmerman Sir David, Fresh Prince of Bellaire Keith Larson Jonathan Bazata Eric Knol Mark Goll Sir Buddy of Cajun Country Kathryn Sutton Sir Jim Watts, Baron of Whistler, and Metropolitan Garibaldi Sir Matthew Zachary Stockstill Marcos Minguela John Delk Associate Executive Producers: Sir Kris, Baron Knight of the Vortex Ring State Bob Rathmell Viscountess Dame Jennifer Kaytlyn Williams Michael Bernstein Rhonda & Rachel Moraca Sir Salahauser of the 321 Become a member of the 1449 Club, support the show here Boost us with with Podcasting 2.0 Certified apps: Podfriend - Breez - Sphinx - Podstation - Curiocaster - Fountain Title Changes Dame Jennifer -> Viscountess Jim Blanchard, Sir Kull of the Left Turn at Albuquerque -> Sir Kull of the Front Range, Baron of the Northwest Greater Denver area Sir Kris -> Sir Kris, Baron Knight of the Vortex Ring State Knights & Dames Dominic Adame -> Sir DomNasty of Long Beach CA Buddy Arceneaux -> Sir Buddy of Cajun Country Matt Litke -> Sir Matthew Skye Kilbury -> Sir Skizzle420 I love blunts Aaron Lambert -> Sir Aaron Lambert Troy Watson -> Sir Watson Knight of The Mushroom People of Nova Scotia Art By: Nessworks End of Show Mixes: Dees Laughs - Secret Agent Paull Engineering, Stream Management & Wizardry Mark van Dijk - Systems Master Ryan Bemrose - Program Director Back Office Aric Mackey Chapters: Dreb Scott Clip Custodian: Neal Jones NEW: and soon on Netflix: Animated No Agenda No Agenda Social Registration Sign Up for the newsletter No Agenda Peerage ShowNotes Archive of links and Assets (clips etc) 1448.noagendanotes.com New: Directory Archive of Shownotes (includes all audio and video assets used) archive.noagendanotes.com RSS Podcast Feed Full Summaries in PDF No Agenda Lite in opus format NoAgendaTorrents.com has an RSS feed or show torrents Last Modified 05/05/2022 16:44:39This page created with the FreedomController Last Modified 05/05/2022 16:44:39 by Freedom Controller  

Jerry DeMarco, Environment Minister; Julie Dabrusin, Liberal MP; Kyle Seeback, Conservative MP; Laurel Collins, NDP MP; Bob Rae, Canada's Ambassador to the United Nations; Glen McGregor, CTV News; Bob Fife, the Globe and Mail; Fatima Syed, The Narwhal; and Michael Bernstein, Clean Prosperity.

The executive director of Clean Prosperity gives us his view of the strengths and weaknesses of the Liberal government's new plan to slash greenhouse gas emissions.

Evan Solomon and Michael Bernstein, executive director of Clean Prosperity, discuss the federal government's new emissions-reduction plan to reach its new greenhouse-gas targets by 2030. On today's show:  Filomena Tassi, Canada's minister of public services and procurement, on Canada's final negotiations to purchase F-35 fighter jets. Claire Brownell, a cryptocurrency reporter at The Logic, on MP Pierre Poilievre wanting to make Canada the 'blockchain capital of the world'. We play parts of Evan's conversations with Ontario Education Minister Stephen Lecce and Karina Gould, Canada's Minister of Families, Children and Social Development, on the $10-a-day child care deal. Dan Snow, an award-winning historian, broadcaster and author, on discovering Ernest Shackleton's lost ship Endurance. Michael Bernstein, the executive director of Clean Prosperity, on Canada's new climate plan.

In this episode of Policy Speaking, our host and PPF's President and CEO Edward Greenspon chats with the co-chairs of the Energy Future Forum's Carbon Management Work Group, Janet Annesley (Chief Sustainability Officer at Kiwetinohk Energy) and Michael Bernstein (executive director of Clean Prosperity). They discuss small and large pathways to net-zero as Canada tries to reach our 2030 and 2050 targets through carbon capture and storage as well as other carbon management technologies. Annesley and Bernstein also examine the role different industries can play, from the oil and gas sector to agriculture, risk management strategies in decarbonization efforts and the importance of investing in the commercialization and domestic production of new technologies. Finally, they look to examples from other nations and how Canada can collaborate to manage carbon in the economy. During Today in Policy, Katie and Edward discuss the Convoy's presence in Ottawa over the past few weeks and the protests and blockades across the nation. They also reflect on understanding the rise in populism, the motivations behind the actions of protestors and how to rebuild cohesion across Canada. This episode included a #PPFProud shout out to the YWCA for launching Canada's first National Emergency Survivor's Support Fund which will be used to help women and gender diverse people experiencing intimate partner and domestic abuse escape and rebuild their lives.

In this episode of Policy Speaking, our host and PPF's President and CEO Edward Greenspon chats with the co-chairs of the Energy Future Forum's Carbon Management Work Group, Janet Annesley (Chief Sustainability Officer at Kiwetinohk Energy) and Michael Bernstein (executive director of Clean Prosperity). They discuss small and large pathways to net-zero as Canada tries to reach our 2030 and 2050 targets through carbon capture and storage as well as other carbon management technologies. Annesley and Bernstein also examine the role different industries can play, from the oil and gas sector to agriculture, risk management strategies in decarbonization efforts and the importance of investing in the commercialization and domestic production of new technologies. Finally, they look to examples from other nations and how Canada can collaborate to manage carbon in the economy. During Today in Policy, Katie and Edward discuss the Convoy's presence in Ottawa over the past few weeks and the protests and blockades across the nation. They also reflect on understanding the rise in populism, the motivations behind the actions of protestors and how to rebuild cohesion across Canada. This episode included a #PPFProud shout out to the YWCA for launching Canada's first National Emergency Survivor's Support Fund which will be used to help women and gender diverse people experiencing intimate partner and domestic abuse escape and rebuild their lives.

No Agenda Episode 1422 - "Honk Honk" "Honk Honk" Executive Producers: Sir Sort It Out Dame Jennifer Chap Williams Chris Keller Associate Executive Producers: onno priester Scott K Jeremy Cartwright Jenifer Rein John Wynn Barron Finch Black Knight Sir Kelly Spongberg and Dame Andrea Aaron Weisgerber Lili -dame of the happy hummers Sir Whisker Biscuit Michael Bernstein Anonymous Darius Gandhi Chuck and Inger Moe Become a member of the 1423 Club, support the show here Boost us with with Podcasting 2.0 Certified apps: Podfriend - Breez - Sphinx - Podstation - Curiocaster - Fountain Art By: Nessworks End of Show Mixes: Jesse Coy Nelson - Adelaide SA Engineering, Stream Management & Wizardry Mark van Dijk - Systems Master Ryan Bemrose - Program Director Back Office Aric Mackey Chapters: Dreb Scott Clip Custodian: Neal Jones NEW: and soon on Netflix: Animated No Agenda No Agenda Social Registration Sign Up for the newsletter No Agenda Peerage ShowNotes Archive of links and Assets (clips etc) 1422.noagendanotes.com New: Directory Archive of Shownotes (includes all audio and video assets used) archive.noagendanotes.com RSS Podcast Feed Full Summaries in PDF No Agenda Lite in opus format NoAgendaTorrents.com has an RSS feed or show torrents Last Modified 02/03/2022 14:33:48This page created with the FreedomController Last Modified 02/03/2022 14:33:48 by Freedom Controller  

Tamara Cherry, filling in for Evan Solomon, speaks with Canadian veteran Trevor Green and reflects on the 100th anniversary of the Remembrance Day poppy with Canadian War Museum historian Tim Cook.  On today's show:  A conversation with Canadian veteran Trevor Greene and his wife Debbie Greene. Dr. Ken Coates, a leading Canadian expert in public policy at the University of Saskatchewan, on Premier Scott Moe calling Saskatchewan a ‘nation within Canada.' Matthew Soules, architect and urbanist, on ‘iceberg homes' being a cause of environmental concern. Michael Bernstein, the executive director of Clean Prosperity, on the latest developments at COP26 as the summit begins to wrap up.  Tim Cook, Canadian War Museum historian, on the 100th anniversary of the Remembrance Day poppy. Then we ask you: what is the most ridiculous reason you've called in sick from work?

Evan Solomon speaks with Susan Loggans, the lawyer representing Kyle Beach, who came forward as "John Doe" in the Chicago Blackhawks sexual assault scandal.  On today's show: We play Evan's full interview with Canada's Environment and Climate Change Minister Steven Guilbeault on the upcoming UN Climate Change Conference. Michael Bernstein, the executive director of Clean Prosperity, on his organization releasing a new report which discusses how likely it is the Liberals will meet its 2030 target. Susan Loggans, the lawyer representing Kyle Beach, who came forward as "John Doe 1" in the Chicago Blackhawks sexual assault scandal.  Wab Kinew, Leader of the Manitoba New Democratic Party on his new book and Pope Francis announcing he will visit Canada for indigenous reconciliation.

Mary Ng, International Trade Minister; Michael Chong, Conservative MP; Heather McPherson, NDP MP; Bhinder Sajan, CTV News; Joe Savikataaq, Nunavut Premier; Joyce Napier, CTV News; Robert Benzie, the Toronto Star; Michael Bernstein, Clean Prosperity; and Perrin Beatty, Canadian Chamber of Commerce.

Evan Solomon speaks with Ontario Liberal Leader Steven Del Duca on his party announcing they will explore a 4-day workweek if elected in 2022. Then, we ask you: Are you for or against 4-day work weeks?  On today's show:  We play Evan's full interview with Canada's Minister of Public Safety, Bill Blair, on PCR testing at the U.S-Canada border.  Mikael Cardinal with Quebec-based company Unither Bioelectronique on being behind the world's first delivery of lungs by drone. Steven Del Duca, Ontario Liberal Leader, on exploring 4-day work weeks.  Luc Houle, Toronto resident and designer who has created shoes that will grow into apple trees. Monte McNaughton, Ontario Labour Minister, on new temp agency laws that will end working conditions he describes as akin to ‘modern-day slavery'. Michael Bernstein, the executive director of Clean Prosperity, on the WHO urging all nations to deliver a rapid and just transition away from fossil fuels.  

Evan Solomon discusses the end of the war in Afghanistan and what this means for the people left behind.  On today's show:  We play Evan's full interview with retired General Rick Hillier. Nova Scotia Supreme Court Justice Mona Lynch explains how she is helping to evacuate Afghan women judges.  Michael Bernstein, the executive director of Clean Prosperity, compares the federal parties' climate platforms.  'The War Room' panel with political strategists Zain Velji and Tasha Kheiriddin, and former NDP leader Tom Mulcair. Clive Thompson, WIRED contributing editor, discusses China's video game ban. 

Evan Solomon discusses the U.N's new report on climate change and whether or not it will impact voters in the upcoming federal election.  On today's show:  Tom Cardoso, investigative reporter with The Globe and Mail, discusses his new report on the calls for reparations from the Catholic Church for residential schools. Steve Paikin, anchor of "The Agenda with Steve Paikin” on TVO, remembers former Ontario Premier Bill Davis after his death.   Nik Nanos, chief data scientist at Nanos Research, discusses a new survey on boycotting the Beijing Olympics.  Michael Bernstein, executive director of Clean Prosperity, breaks down the U.N.'s new report on climate change. 

Tim Uppal, Conservative MP; Jonathan Wilkinson, Environment Minister; Joyce Napier, CTV News; John Ivison, The National Post; and Michael Bernstein, Clean Prosperity.

Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat and interpretability, and the future of AI in healthcare.Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.comAbout the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Michael Bernstein - The Big Six of Stable Personal Finance Michael Bernstein launched Bernstein Financial Services, Inc. in 1987, with an emphasis in tax accounting. This tax consulting and accounting firm prepares approximately 2,000 individual tax returns and more than 250 business returns (LLCs, corporations, fiduciary returns, and estate tax returns) each year. Mike is known as “Your Working Out Accountant” and you can follow him on YouTube. He is passionate about both fiscal and physical fitness and I will ask him if there is a parallel. He is the author of 'The Ultimate Guide to Planning Your Personal Finances' which can be found on Amazon or on his website. Michael is here to share: Based on his 30 years of experience, he says there’s a “big six of stable personal finance.” What are the six?He will share tips for each of the six elementsPlanning your personal financesWhat is the one thing we should do each year? Find Michael Bernstein on the web: Website | LinkedIn | YouTube | Facebook | FREE book'The Ultimate Guide to Planning Your Personal Finances'

Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat and interpretability, and the future of AI in healthcare.About the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat, and interpretability, and the future of AI in healthcare.About the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Host: Interim President Michael Bernstein About Dr. Fiorella: Dr. David J. Fiorella, Director of the Stony Brook Cerebrovascular Center, Co-Director of the Stony Brook Cerebrovascular and Comprehensive Stroke Center, and Professor of Neurosurgery and Radiology joined the Department of Neurosurgery in 2009. Dr. Fiorella is considered a pioneer in the field of neuro-interventional therapies, advancing new devices and techniques for the treatment of Cerebrovascular disease. Dr. Fiorella spearheaded the acquisition of 2 Mobile Stroke units for Stony Brook University Hospital, the first program in Suffolk County. He is the Principle Investigator or Co-PI on numerous national trials evaluating new devices and techniques for the treatment of aneurysms, acute stroke and intracerebral hemorrhage. He is a senior member of the Society for Neuro-interventional Surgery (SNIS) and senior associate editor of the Journal of Neurointerventional Surgery. Dr. Fiorella has been named amongst the best interventional radiologists/endovascular surgeons in Castle Connolly's Top Doctors for several years in a row. About the Episode: Stony Brook Medicine Mobile Stroke Unit founder, Dr. David Fiorella, is considered a pioneer in the field of neuro-interventional therapies -- advancing new devices and techniques for the treatment of Cerebrovascular disease. He took that pioneering inspiration one step further in 2019 when he and a team of Stony Brook clinicians and colleagues launched the first two Mobile Stroke Units on Long Island. These Mobile Stroke Units enable stroke patients to be triaged and treated in the field, wherever the patient is located. Clinicians can administer IV TPa, a medication that minimizes brain injury, at any remote location and then immediately transport the patient to the closest appropriate care facility, where physicians can initiate further care. In this episode of “Beyond the Expected,” Michael Bernstein talks to Dr. Fiorella about his trajectory as a vascular brain surgeon and what inspired him to pursue the complex initiative of starting a Mobile Stroke Unit program. You'll also hear heartwarming stories of patient survival, and learn what this groundbreaking program has meant for Long Island stroke care since it launched in April 2019. Credits: Thanks to Dr. David Fiorella Guest Host: Michael Bernstein Executive Producer: Nicholas Scibetta Producer: Lauren Sheprow Art Director: Karen Leibowitz Assistant Producer: Emily Cappiello Assistant Producer: Joan Behan-Duncan Social Media: Meryl Altuch, Casey Borchick Podcast photography and YouTube Technician: Dennis Murray Podcast Director: Jan Diskin-Zimmerman Engineer/Technical Director: Phil Altiere Production Manager: Tony Fabrizio Camera/Lighting Director: Jim Oderwald Camera: Brian DiLeo Camera: Greg Klose Original score: “Mutti Bug” provided by Professor Tom Manuel Special thanks to the Stony Brook University School of Journalism for use of its podcast studio.

Today on the show the talent broadcasted from home where they opened up the phone lines to see if they were ride or die with their partner after Idris Elba's wife stayed by her husbands side once discovered he was tested positive for the Corona Virus. Also, Charlamagne gave "Donkey of the Day" to Mayor Pam Triola, city manager Michael Bernstein and all the councilman of Lakeworth Florida because they were abusing their power over the community, which led us to open up the phone lines to see who has been struggling with still paying bills during this pandemic. Learn more about your ad-choices at https://news.iheart.com/podcast-advertisers

Funded by a grant from the New York Community Trust Equity Fund, “We Are Still Here! Be Counted in 2020! Indigenous Suffolk 2020 Census Project” was created for the purpose of coordinating and maximizing local efforts to ensure that Suffolk County's Native American community is not undercounted. In this episode of “Beyond the Expected,” Michael Bernstein talks to Stony Brook University School of Social Welfare's Dr. Carolyn Peabody and her student Meesha Johnson about a groundbreaking project they are spearheading on to ensure all indigenous Long Islanders are counted.

Beyond the Expected host, Michael Bernstein, sits down with Sharon Nachman MD, Chief of the Division of Pediatric Infectious Diseases at Stony Brook Children's Hospital and Associate Dean for Research in the Renaissance School of Medicine at Stony Brook University. Dr. Nachman is an international leader in the area of pediatric infectious diseases and the treatment of children with AIDS, flu and measles. She has been the principal investigator of more than 30 clinical trials of promising medicines for patients treated at Stony Brook University Hospital. These include international trials in areas such as new vaccines, Lyme disease, and AIDS. She also directs the Maternal Child HIV/AIDS Program. In this episode, Michael talks to Sharon about the international health crisis brought on by the 2019 Novel Coronavirus, the impact of the new New York State vaccine law on Long Island's school districts and discusses vaccines and immunizations and if vaccines are truly safe; what children and their families should know and how can they prepare; and what are some complications/symptoms that can occur if children are not vaccinated. Production Credits Thanks to episode two guest, Dr. Sharon Nachman, Chief of the Division of Pediatric Infectious Diseases at Stony Brook Children's Hospital and Professor of Pediatrics at the Stony Brook University Renaissance School of Medicine Host: Michael A. Bernstein, Interim President Executive Producer: Nicholas Scibetta Producer: Lauren Sheprow Art Director: Karen Leibowitz Assistant Producer: Joan Behan-Duncan Director: Jan Diskin-Zimmerman Engineer/Technical Director: Phil Altiere Production Manager: Tony Fabrizio Camera/Lighting Director: Jim Oderwald Chief Editor: Frank D'Aurio Editor/Camera: Brian DiLeo Camera: Greg Klose Original score: “Mutti Bug” provided by Professor Tom Manuel Special thanks to the Stony Brook University School of Journalism for use of its podcast studio

In this episode of Beyond the Expected, Michael Bernstein is talking with Stony Brook University Physics Professor, Abhay Deshpande, who, in addition to his role as the Director of the Center for Frontiers in Nuclear Science at Stony Brook University, has a joint appointment at Brookhaven National Laboratory, where he is Director of EIC Science. Professor Deshpande works in experimental nuclear physics, and his current research includes various exploratory and precision studies in QCD using polarized proton-proton, proton-nucleus and nucleus-nucleus beams of the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory on Long Island. He also does research with high intensity polarized electron beams of the recently upgraded Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Laboratory in Newport News, Virginia. He was one of the original proposers of and has been involved deeply in the development of the science and promotion of the Electron Ion Collider, a $1.6 billion-dollar Department of Energy development project -- the first of its kind in the world -- that was awarded in January 2020 to Brookhaven National Lab. He's been focused on this area of science for more than 20 years, and now, with the certainty that this new facility will come to fruition, he will actually see his dream come to fruition. We can be assured that scientists around the world will have an opportunity to explore new frontiers that have never been explored before using the BNL's Electron Ion Collider. Production Credits Thanks to Abhay Deshpande, Professor of Physics and Astronomy at Stony Brook University, Director, EIC Science at Brookhaven National Lab and Director of the Center for Frontiers in Nuclear Science at Stony Brook University. Host: Michael A. Bernstein, Interim President Executive Producer: Nicholas Scibetta Producer: Lauren Sheprow Art Director: Karen Leibowitz Assistant Producer: Joan Behan-Duncan Director: Jan Diskin-Zimmerman Engineer/Technical Director: Phil Altiere Production Manager: Tony Fabrizio Camera/Lighting Director: Jim Oderwald Chief Editor: Frank D'Aurio Editor/Camera: Brian DiLeo Camera: Greg Klose Original score: “Mutti Bug” provided by Professor Tom Manuel Special thanks to the Stony Brook University School of Journalism for use of its podcast studio.

Jazz Artist-In-Residence, Thomas Manuel, may have been at a crossroads the year he completed his D.M.A. at Stony Brook in 2016; if not for an unexpected intervention by the Ward Melville Heritage Organization in Stony Brook. A Newsday article published in May of 2014 entitled, “Tom Manuel has a collection. Now all he needs is a museum,” brought them together, which led to Tom emerging as founding president of The Jazz Loft in Stony Brook Village that same year. It was serendipitous: Ward Melville Heritage Organization's Gloria Rocchio, saw the story and immediately called him to offer him space to start a museum and Jazz entertainment venue in an empty firehouse building in historic Stony Brook Village. The rest, as they say, is history. In this podcast episode, Beyond the Expected host, Michael Bernstein, will explore Tom's trajectory as an endowed Jazz Artist in Residence at Stony Brook University, his work with young jazz musicians, and his contributions to this popular community hot spot. He may even convince Tom to smoke a jazz jam while he's at it. Production Credits Thanks to episode one guest, Stony Brook University Professor Thomas A. Manuel, D.M.A. ('16), President & Founder of The Jazz Loft, Inc., Endowed Jazz Artist in Residence at Stony Brook University and Director of the Stony Brook University Young Artists Jazz Program Host: Michael A. Bernstein, Interim President Executive Producer: Nicholas Scibetta Producer: Lauren Sheprow Art Director: Karen Leibowitz Assistant Producer: Joan Behan-DuncanPodcast Director: Jan Diskin-Zimmerman Engineer/Technical Director: Phil Altiere Production Manager: Tony Fabrizio Camera/Lighting Director: Jim Oderwald Chief Editor: Frank D'Aurio Editor/Camera: Brian DiLeo Camera: Greg Klose Original score: “Mutti Bug” provided by Professor Tom Manuel Special thanks to the Stony Brook University School of Journalism for use of its podcast studio

On the September 24 London Live podcast: The generational gap in how younger and older people in Ontario view the carbon tax with the executive director of Clean Prosperity, Michael Bernstein. Part 2 of Global's "Failure to Launch Kids" series: Canadian school counsellors are stretched thin — and it's our students that suffer with Global's Meghan Collie. Trick-or-Treating for UNICEF is back with UNICEF chief program officer Rowena Pinto Chief.

A former federal finance minister says Canada will benefit economically from climate change and its time to stop stoking fear to our children with a climate crisis.  Is he correct?  If not, what are the costs?  And as we head into an election, we'll rate the environmental policies of the liberals, conservatives, ndp and greens. Guests:  Joe Oliver, Former Federal Finance Minister; Kai Chan, University of British Columbia Environment Professor; Michael Bernstein, Canadians for Clean Prosperity Executive Director

Capacity awareness of our teams is something that to think about when we are in pain – we have too much or too little time within the team. What we do to anticipate the changing needs of our business includes the brain power and skills of the people working with you. Jess Dewell hosts panelists Mario Pena, Sales Manager and Michael Bernstein, Lead Account Executive, to discuss the changing capacity of our team.

Books expand perspective. What you read influences how you look at daily situations as well as unexpected problems, an enhances the ability and desire to be more inquisitive. Books and podcasts offer concepts that you can practically apply – just read (and listen)! Jess hosts panelists Michelle McGee, Creator of EAE program and Property Accountant, and Michael Bernstein, Lead Account Executive for T-Mobile, to discuss business book must-reads.

Eric Weiss, Scott Boyles, and Michael Bernstein discuss Alternative Investments. This includes what they are, why they should be included in portfolios, and how to invest in them.

The Future of Everything with Russ Altman: "Michael Bernstein: Welcome to the future of crowdsourcing" On The Future of Everything radio show, a computer scientist explores the rise of automation, crowdsourcing communities and the ethical implications of the gig economy. Originally aired on SiriusXM on March 24, 2018. Recorded at Stanford Video.

While billions scroll their merry ways through Facebook and Twitter each day, behind the scenes are legions of reviewers scanning photos and video to prevent graphic content from making the newsfeeds of unsuspecting users. Elsewhere, the faceless armies of the gig economy are making movies, building homes, driving Uber and working piecemeal to caption innumerable images for people too busy to do it for themselves. Welcome to the future of crowdsourcing. While the collective actions of those on the frontlines of crowdsourcing save millions of others from drudgery and from psychological trauma, the ascension of automation is raising questions that human society has never had to deal with before. These are the “wicked problems” — questions in which success cannot be determined with certainty or where multiple, mutually exclusive goals must be delicately balanced to create an optimal outcome. These are questions that Stanford's Michael Bernstein, an assistant professor of computer science and an expert on Human-Computer Interaction (HCI), grapples with on a daily basis. What is the optimal organizational structure for such crowdsourcing communities? What are the ethical implications of the gig economy? And, who are the right people to answer these questions? On The Future of Everything radio show, host Russ Altman and Bernstein discuss those question and explore what our increasingly automated future will look like.

Michael Bernstein, Founder MEB Finance Interviewed on Capital Club Radio Michael Bernstein, Chief Executive Officer MEB Finance Solutions LLC Michael E. Bernstein Mr. Bernstein founded CreditMax, LLC in 2001 and built it into one of the 25 largest debt buyers in the ARM industry by 2005 when the company generated over $60mm in revenue. In 2009, during the height of the macro-economic crisis, Mr. Bernstein made a strategic decision to exit the debt purchasing business and initiate a finance subsidiary, CMAX Finance, LLC to provide capital to underbanked, small to medium sized, debt buyers. In 2010, as part of his growth initiative for the finance business, Mr. Bernstein sold part of CreditMax to outside investors to provide additional capital for infrastructure build-out and to support a larger credit facility. Between 2009 and 2016, Mr. Bernstein originated over $350mm of loans and had his highest production year in 2016, originating over $100mm in loan volume. Mr. Bernstein sold his remaining interest in CMAX in May 2017 and subsequently formed his own company, MEB Finance Solutions LLC, which entered into an exclusive strategic alliance with Flock Specialty Finance, LLC. Mr. Bernstein's efforts will be focused on growing Flock's origination volume. In the first 3 months of their alliance, Flock funded over $13mm, $9mm, and $8mm, their largest 3 month funding total since Flock's inception in 2009. Prior to founding CreditMax, Mr. Bernstein acquired several mortgage servicing companies and portfolios in the mid to late 1990's which he then aggregated and sold to one of the largest mortgage originators and servicers in the country. Mr. Bernstein holds a Bachelor of Science degree in finance from Georgetown University. Linkedin: https://www.linkedin.com/in/michael-bernstein-23877a9 Capital Club Radio Hosted by: Michael Flock Sponsored by: Flock Specialty Finance Providing a forum for leaders in the middle market segment which has typically been undeserved by traditional banking. Listeners gain valuable business insights and perspectives to deal with market uncertainty. Topics include: key success factors, both personal and professional, dealing with adversity, outlook for the industry and your business.   For more info about Michael Flock and Flock Specialty Finance visit: www.FlockFinance.com To nominate or submit a guest request visit: www.CapitalClubRadioShow.com To view more photos from this show visit: www.ProBusinessPictures.com ‹ › × × Previous Next jQuery(function() { // Set blueimp gallery options jQuery.extend(blueimp.Gallery.prototype.options, { useBootstrapModal: false, hidePageScrollbars: false }); });

Craig and Rimas are joined by Brian Doll and Michael Bernstein to explain why two engineers decided to start Reify, a B2B focused marketing consultancy, after being inspired by the business side of selling software.

Craig and Rimas are joined by Brian Doll and Michael Bernstein to explain why two engineers decided to start Reify, a B2B focused marketing consultancy, after being inspired by the business side of selling software. The post Ep. #11, Reify: An Engineering Driven Marketing Consultancy appeared first on Heavybit.

Imagine a collective brain shaped by human insights and powered by technology - that's crowdsourcing. Michael Bernstein, computer scientist at Stanford University, explores how to harness crowdsourcing to tackle daunting challenges. In this episode of Stanford Innovation Lab, Tina Seelig meets with Michael to discuss examples of successful crowdsourcing, tools to gather collective insights, and the evolving relationship between humans and machines.

"Dr. Michael J. Bernstein is an Associate Professor of Psychology at Penn State Abington. He earned his PhD in Social Psychology at Miami University in 2010. His research focuses on the impact that groups have on the way people think, feel, and behave. His primary areas of research include face memory, consequences of social rejection, and intergroup relations including stereotyping, prejudice, and discrimination. His work also extends into consulting realms with regard to consumer behavior .

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Investigative journalist Bob Woodward and former White House aide Alex Butterfield join Michael Bernstein for a conversation about Butterfield’s decision to reveal the existence of tape recordings that eventually led to Richard Nixon’s resignation from the presidency. Series: "Helen Edison Lecture Series" [Public Affairs] [Humanities] [Show ID: 30187]

Would you like to learn the science behind deciphering non-verbal cues and performing in high stress situations? Listen in as Award Winning Professor Michael Bernstein discusses how you can improve your communication skills with non-verbal cues. Michael is an Associate Professor of Psychology at Penn State Abington. He received a National Science Foundation grant, and won several Penn State accolades, such as the Faculty Senate Scholar award, Faculty Senate Outstanding Teaching award and the Public Scholar award. Michael is also published and cited widely in psychology journals, consults for Procter & Gamble, among other cooperate and non-profit organizations, and is the co-director and co-founder of ACCESS, which connects businesses and communities with faculty and student researchers. Find out more about Michael Bernstein and his organization ACCESS.   Heroic Public Speaking http://heroicpublicspeaking.com Send in your questions questions@michaelport.com Give us a review and help others find this show better! http://stealtheshow.com/podcast/reviews    

The latest ABI podcast features ABI Deputy Executive Director Amy Quackenboss talking with Michael Bernstein of Arnold & Porter LLP (Washington, D.C.) and Prof. George Kuney of the University of Tennessee College of Law (Knoxville, Tenn.) about their book, Bankruptcy in Practice, Fifth Edition. Bernstein and Kuney discuss how the book was revised to incorporate recent case law and changes to the Bankruptcy Code, bridges the divide between classroom theory, courtroom procedure and conference room negotiation.

For those paying attention, what's happening in Greece is playing out like an ancient tragedy on a macro economic scale. Banks shut down for weeks and the international community looked on as the Greek people voted against accepting new austerity measures in order to avoid a default on the nation's debt to other members of the European Union. While the problems have been percolating for the better part of a decade, the fact that it hasn't been in the news until recently means many people have questions about what's happening. In the hopes of answering some of those questions, New Wave sat down with Michael Bernstein, who is not only the provost but also the John Cristie Barr professor of history and and a professor of economics.

Michael Bernstein of Code Climate explains how to monitor your code's quality with static analysis. He tells us how you can maintain or improve quality over time, and what you can do to fix poor code.

Michael Bernstein is the co-owner of Savour This Kitchen, and Savour This Sauce. A California fresh catering company dedicated to using wholesome flavors and natural ingredients. After receiving rave reviews for the unique sauces in their dishes, brother and sister entrepreneurial super team, Michael + Marlene, decided to sell these sauces as stand alone products. Putting in at least 80 hours a week, Michael knows he is involved in something special and is following his passion! The post BUFF 055 – Michael Bernstein | Co-owner Savour This Kitchen | Business Buff Entrepreneurs appeared first on Business Buff Entrepreneurs.

A conversation with Michael Bernsten (@mrb_bk) from Code Climate.

A conversation with Michael Bernsten (@mrb_bk) from Code Climate.

Vali Nasr, author of "The Shia Revival," and UC San Diego sociologist Gershon Shafir explore the tensions between the Shiites and Sunnis and how the longstanding rivalries have affected the Middle East in this discussion led by historian Michael Bernstein, the dean of UCSD's Division of Arts and Humanities. Series: "Body Politic, The" [Public Affairs] [Show ID: 12120]

Vali Nasr, author of "The Shia Revival," and UC San Diego sociologist Gershon Shafir explore the tensions between the Shiites and Sunnis and how the longstanding rivalries have affected the Middle East in this discussion led by historian Michael Bernstein, the dean of UCSD's Division of Arts and Humanities. Series: "Body Politic, The" [Public Affairs] [Show ID: 12120]

Vali Nasr, author of "The Shia Revival," and UC San Diego sociologist Gershon Shafir explore the tensions between the Shiites and Sunnis and how the longstanding rivalries have affected the Middle East in this discussion led by historian Michael Bernstein, the dean of UCSD's Division of Arts and Humanities. Series: "Body Politic, The" [Public Affairs] [Show ID: 12120]