Podcasts about model building

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Roman Chernin is Co-Founder and Chief Business Officer of Nebius, one of the fastest-growing AI infrastructure companies in the world. Today, Nebius operates some of the largest AI compute clusters globally and serves leading AI labs, enterprises, and developers. Today, Nebius has a market cap of $57BN.  AGENDA:  00:00 — Why AI Infrastructure Is Not a Bubble 05:00 — The Real Impact of Open Source on OpenAI & Anthropic 11:00 — Jevons Paradox: Why Cheaper AI Creates More Demand 13:00 — The Four Layers of AI Infrastructure Explained 19:00 — If Nebius Had 10x More Capacity Tomorrow 26:00 — The Shift from Training to Inference and Agents 31:00 — How Token Factory Cuts AI Costs by 70% 44:00 — Sovereign AI, Europe, and the Future of Model Building 49:00 — Competing Against Hyperscalers with 10x More Capital 59:00 — The Biggest Threat to Nebius Isn't Competition—It's Consolidation    

"Don't overvalue the top procurement spot, and don't undervalue the tools and the budget required. It's an ecosystem." - Stephen Rauf, Global Head of Indirect Procurement, Zoetis Delivering more value with fewer resources is an easy thing to want, but tough to execute consistently over time… unless procurement rethinks their entire approach, from operating model and team structure to technology and stakeholder influence. In this episode, Philip Ideson speaks with Stephen Rauf, Global Head of Indirect Procurement at Zoetis, the world's leading animal health company. Stephen unpacks how he has steered a lean, high-impact team through transformation, why "build vs. buy" is a weekly question, and what it takes to create true business partnership – while surfacing next-gen use cases for AI.  In this episode, Stephen shares his point of view on: -Building nimble procurement teams that can punch above their weight -Moving from managing spend to shaping demand with stakeholders -Using tech – and especially AI – to enable rather than overwhelm -Deciding when to build internal strength vs. partnering for expertise -Measuring a broader spectrum of procurement value, not just cost savings   Links: Stephen Rauf on LinkedIn: https://www.linkedin.com/in/stephenrauf/  Subscribe to the AOP Newsletter: https://resources.artofprocurement.com/art-of-procurement-podcast-subscribe  Subscribe to Art of Procurement on YouTube: https://www.youtube.com/@ArtofProcurement   

In this episode of the Sports Tech AllStars Podcast, we present Evan Kirkham, CEO and Co-Founder of Outlier.The conversation explores how Outlier is building a data-driven sports betting platform that charges bettors directly rather than taking affiliate fees from sportsbooks, why that single decision shapes everything about the product, and where the $11 million Series A company is heading next - including a data acquisition and a new live betting product.TakeawaysOutlier charges bettors a subscription fee instead of taking affiliate money from sportsbooks - a deliberate choice that keeps the product fully aligned with the userTaking affiliate fees from sportsbooks creates an incentive to funnel users rather than serve them - Outlier refuses to make that trade-offThe platform is essentially a Bloomberg Terminal for sports bettors, taking users from insight to analysis to bet execution in one seamless flow65% to 70% of Outlier's traffic is NBA, with prop bets driving 95 percent of overall usageThe US skipped the betting shop era entirely - everyone came to digital sports betting at the same time, which is why adoption has been so fastThe financialization of sports betting is the defining US trend - bettors will increasingly move in and out of positions like traders, with limit orders and automated executionThe market is bifurcating between serious data-driven bettors and casual fans who just want entertainment - most products are still trying to serve both at onceUK bettors have ingrained behaviors tied to legacy sportsbooks - breaking those habits is the core challenge of international expansionOutlier is acquiring a data technology provider and launching a net new live in-game betting productTo learn more, visit: https://www.outlier.betGet in touch with Evan Kirkham at: linkedin.com/in/evan-kirkham-581b1220 Hosted by ⁠Rohn Malhotra⁠ from ⁠SportsTechX⁠ - Leading source of Investment and Innovation insights in sports. As promised, here's your small surprise:Unlock your 30-day growth plan (worth €49) on the SportsTechX Intelligence Hub for free!Simply verify your company details and you get access to 1,500+ investors, programmes, initiatives and events in the sportstech ecosystem.Here's how to get set up and if you'd like a walkthrough of the platform, feel free to book a call here.More from SportsTechX:Explore the SportsTechX Intelligence Hub, an interactive database of over 8,000 sports tech companies, 8,000+ deals, 1,000+ investors, programs and events - HEREDownload the latest Global Sports Tech Ecosystem Report - HERESign Up for the Sports Tech Weekly Newsletter for more news, features & insights on Sports Tech - HERE Stay Connected and follow for more:LinkedInYouTubeSpotifyApple PodcastChapters00:00 Introduction02:59 What Outlier Is and Why It Calls Itself a Bloomberg Terminal for Sports03:28 Is Outlier for Casual Bettors or Serious Ones?04:08 The Match.com Model - Building a Suite of Data Products05:14 The Origin Story05:51 Game Lines vs Prop Bets08:40 Why Outlier Charges Subscribers Instead of Taking Sportsbook Affiliate Fees10:13 Subscription as the Core Business Model 12:10 Why Affiliate Models Serve the Sportsbook, Not the Bettor13:18 100,000 Monthly Active Users and the NBA Dominance13:48 Expanding Into Soccer and International Markets 16:00 Why the US Adopted Sports Betting So Fast Compared to Europe17:57 The Learned Behavior Problem in UK Betting Markets19:47 The Stale UK Market and the Opportunity for a Fresh Product21:13 The Financialization of Sports Betting23:20 The Bifurcation of Sports Betting: Serious vs Fun24:11 Prediction Markets, Cultural Betting and Responsible Gambling in Europe25:03 Betting as a Shared Entertainment Experience26:38 Why Sportsbooks Are Missing the Fun Segment Entirely27:12 What Is Next: Series A, CMO Hire, Data Acquisition and Live Betting Product29:18 Favourite Sporting Moment

More revenue per patient. It's one of the most talked-about goals in private practice right now. But in this episode of Power Hour, Dr. Patricia Poma makes something very clear: You do not get there by chasing every new machine, trend, or specialty that shows up on the market. You get there by building intentionally. That is exactly why the shift toward a specialty-driven practice is starting to get serious attention. Dr. Patricia Poma has built a model that achieves over $1,000 in revenue per patient. By building one specialty at a time until it becomes a "beautiful beast," she has created a practice that is sustainable on its own. Not a boardroom theory. Not just a retail idea.  A solution built over two decades in a real private practice. In this week's Power Hour, Dr. Patricia Poma-Nowinski, Owner of Birmingham Vision Care and KOL for Johnson & Johnson Health and Wellness Solutions Inc., breaks down how she built a high-profitability model focused on non-doctor-driven revenue and medical-heavy specialties.  

Time to pop the hood and get a look at our wiring! What has brain science to do with emotional resilience? God has designed our brains to run on the fuel of joy, and joy is what is crucial to resilience. So, understanding how God designed our brains to operate helps us understand what we should develop in order to be as resilient as possible. With that in mind, in this On the Trail episode, we discuss Building Bounce Chapter Three: A Basic Brain Model, where we unpack the Joy Elevator and the Narrative Engine to get context and clarity as we press deeper into developing emotional capacity. Thank you for joining us – father-daughter duo Marcus Warner and Stephanie Warner – on the trail to a deeper walk with God! 

What's up everyone, just checking in and letting you know what I've been up to lately. In this episode, I talk about making YouTube my main content hub, current events in the hobby, and what's been going on behind the scenes. Thanks for tuning in and being part of the journey.Free US Shipping on orders $50 and over — Limited Time! Use code: FREESHIP50https://scaleriders.com/https://www.youtube.com/@scaleriders#scalemodel #modelcar #hobby

In this episode, Rory speaks with Mark Martukovich, Executive Director and Managing Partner of Business Advisory and Accounting Partners powered by Harness, about how CPA firms can shift from compliance-driven practices to high-value advisory services. Mark shares how he evolved his own firm over 12 years using the Practice Forward model, moving from a highly transactional tax practice to an advisory-centric operation with over 200 advisory engagements. He explains why the first conversation with any business owner should be about their exit, how to build a structured and scalable execution model from prospect screening to delivery, and why value-based pricing matters in an AI-driven world. They explore how Mark uses AI to run prospect tax strategy screeners against a curated database of strategies, why advisory is not additional work but a repackaging of knowledge firms already have, and how building structure around advisory conversations can strengthen both client outcomes and firm positioning. Want to know how to build a duplicatable advisory model that deepens client relationships and elevates your practice? Curious how AI and structured execution can move your firm from reactive to proactive? Find out the answers to these questions and more in this practical conversation with Mark Martukovich."Rory Henry is a registered investment adviser representative of Arrowroot Family Office. This podcast is published independently through Advis-ROR®, an outside business activity, and does not represent the views of Arrowroot Family Office.  It is intended for informational and educational purposes only and does not constitute investment advice. Mark Martukovich is not a client of Arrowroot nor was there any compensation for his inclusion in this podcast."

Our guest today is Thierry Laugel, Managing Partner of Kurma & Chairman of Argobio. With a PharmD, PhD in pharmacology, and an INSEAD MBA, Thierry has spent more than two decades bridging cutting-edge science and commercial success—first in pharma R&D, then as co-founder of Kurma Partners, and now leading Argobio's unique venture-builder model.Since raising €50 million in 2021, Argobio has co-founded and accelerated several companies from top European academic labs. Three of them—Enodia, Laigo Bio, and Elkedonia—have already closed seed rounds totaling more than €43 million, advancing novel platforms in targeted protein degradation, precision membrane protein degraders, and non-hallucinogenic neuroplasticity enhancers for depression. Thierry shares how Argobio reduces execution risk, embeds operational expertise, and turns promising science into investable companies that can compete worldwide. 04:02 Blending pharmacology expertise with business07:50 Vision behind starting Kurma Partners12:53 Launching Argobio to address gaps in European biotech 17:40 What makes the Argobio operational venture builder model unique25:02 Criteria for selecting academic scientific breakthroughs27:34 Changing dynamics of commercializing European research35:30 Europe vs US biotech investment climates37:47 Role of venture studios in Europe's biotech futureInterested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletterTo dive deeper into the topic: M Ventures: pharma CVC and biotech innovation in 2026Inside Flagship Pioneering's strategy: How this VC turns ideas into biotech giantsVenture capital co-creation: The next big thing in biotech investment?

Dushyanth shares his journey into AI, the challenges of building complex pipelines, and how to integrate responsible and ethical practices into machine learning workflows.Key Highlights:Scaling AI Systems: How to design and deploy pipelines that handle real-time inference, multimodal data, and production-level demands.Model Interpretability & Explainability: Strategies for making complex models understandable and accountable.Optimizing AI for Real-World Impact: Balancing performance, robustness, and human oversight in AI systems.Responsible AI Practices: Embedding ethics, fairness, and transparency in machine learning workflows.

Text me Your email for my Booking LinkIn this Season 6 kickoff episode, Michael sits down with Aurielle Vendemmia, founder of True Moon Yoga & Fitness, for a powerful conversation about what it really takes to build a sustainable, thriving Yoga Studio community.Aurielle shares her 20-year journey from opening studios out of survival, to running five locations, to intentionally scaling back to one boutique Yoga Studio built around nervous system regulation, clear leadership, and deep community connection.This episode is for Yoga Studio owners who want to create a business that supports both their students and their own life — while building a loyal, committed community that actually sticks around.In This Episode, You'll Learn• How Aurielle opened her first Yoga Studio without a business plan• Why running multiple studios led to burnout — and how she rebuilt with intention• How a serious accident reshaped her approach to movement and leadership• Why nervous system regulation matters for long-term community trust• How consistent class structure creates safety for students and teachers• What sustainable staffing and delegation really look like• How to use data instead of emotion to design a Yoga Studio schedule• Marketing strategies that attract the right students into your community• Why not being “for everyone” builds a stronger Yoga StudioQuote from the Episode“Burnout is not a badge of honor. If your business only works when you're exhausted, it's not actually working — it's extracting from you.”About Aurielle VendemmiaAurielle Vendemmia is the founder of True Moon Yoga & Fitness, a boutique heated Yoga Studio that blends yoga, strength training, Pilates-inspired movement, and nervous-system-aware teaching.After opening her first studio in 2007 and eventually running five locations, she intentionally returned to one Yoga Studio so she could create a space rooted in consistency, boundaries, and long-term community sustainability for both students and teachers.Connect with AurielleWebsite: https://www.truemoonyoga.com Instagram: @emilyauriellevendemmiaResources for Yoga Studio Owners• Free Yoga Studio Business Plan• 2026 Yoga Studio Planning Calendar• On-Demand Webinar Recordings https://www.yogabizchamp.comFree Strategy Call with Michael https://www.yogabizchamp.com/bookBook a complimentary 45 minute strategy session with the sales arms with my link https://www.thesalesarms.com/yogabizchamp Yoga Biz Champ listeners get 50% off the first 3 months of Offering Tree with my exclusive link www.offeringtree.com/yogabizchamppodcast Book a call with Mitch McGinley from the Boutique Fitness Brokers with my link. BOOK WITH MITCH HERE FREE RESOURCES AND BOOK A CHAT LINKhttps://yogabizchamp.link/podlink

In this episode, Brian sits down with Garrett from Neufit to talk about the 2026 Hybrid Practice Model—how physical therapy owners can balance insurance and cash-based services for stronger, more predictable revenue. Learn how the Neubie helps clinics boost retention, attract cash-paying patients, and create new income streams that fuel long-term growth.  

Filip Hrůza speaks with Troy Mix, Associate Director of the University of Delaware's Institute for Public Administration (IPA), about the thirteen-year partnership between their institutions, the adaptation of the "Delaware Model" of public service education (https://www.udel.edu/academics/colleges/biden-school/) to the Czech context, and the creation of "MuniLab" to engage students with practice. They also discuss the unique characteristics of Brno and the South Moravian region and the vital role universities play as a bridge between academic research and public administration practice. Filip Hrůza is the Director of the Institute for Public Administration and a member of the Department of Public Economics at the Faculty of Economics and Administration at Masaryk University in Brno, Czech Republic (https://www.econ.muni.cz/en). He focuses on connecting academic theory with public sector practice through initiatives like organizing conferences on inter-municipal cooperation and MuniLab, which engages students with public administration practitioners. He has worked for over a decade to adapt the University of Delaware's Legislative Fellows program and other public service models to strengthen local governance and public administration education in the Czech Republic. This episode was recorded on November 24, 2025, for First State Insights, a podcast presented by the Institute for Public Administration (IPA). For more First State Insights episodes, visit https://soundcloud.com/first-state-insights or search for "First State Insights" wherever you listen to podcasts. IPA is a research and public service center within the University of Delaware's Biden School of Public Policy & Administration. For more on IPA, visit https://www.bidenschool.udel.edu/ipa. Opening and closing music: "I Dunno" by Grapes, used under Creative Commons 3.0 License.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Teaching students to write out their own models is crucial.Developing a sports analytics portfolio is essential for aspiring analysts.Modeling expectations in sports analytics can be misleading.Tracking data can significantly improve player performance models.Ron encourages students to engage in active learning through projects.The importance of understanding the dependency structure in data is vital.Ron aims to integrate more diverse sports analytics topics into his teaching.Chapters:03:51 The Journey into Sports Analytics15:20 The Evolution of Bayesian Statistics in Sports26:01 Innovations in NFL WAR Modeling39:23 Causal Modeling in Sports Analytics46:29 Defining Replacement Levels in Sports48:26 The Going Deep Framework and Big Data in Football52:47 Modeling Expectations in Football Data55:40 Teaching Statistical Concepts in Sports Analytics01:01:54 The Importance of Model Building in Education01:04:46 Statistical Thinking in Sports Analytics01:10:55 Innovative Research in Player Movement01:15:47 Exploring Data Needs in American Football01:18:43 Building a Sports Analytics PortfolioThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M,...

Master Google Cloud's most advanced AI certification with this definitive 2025 study guide. From TensorFlow and data pipelines to ML ops, model deployment, and ethical AI—this book delivers the knowledge, tools, and confidence to help you ace the Professional Machine Learning Engineer Exam. Backed by real-world examples, mock exams, and hands-on insights.

In this episode of the Effective Challenge Podcast, I share a glimpse of recent adventures – including a moment where I found myself a quarter of a mile above the ground (full disclosure: I took the lift!). The main focus of this episode is an introduction to my PRIME model. This marks the first in a six-part series where I take a deep dive into the model I use personally and with clients to strengthen foundations and build resilience. The PRIME model consists of five key elements: Physical Rest Intake Mind Energy Each element plays a vital role in supporting your wellbeing and helping you perform at your best. I'd love to hear your thoughts, questions, or observations. Feel free to reach out to me at Damian@effectivechallenge.com.

Podcast Episode 199

David Morton is a technologist with extensive experience across various sectors, including retail, finance, consulting, energy, and commodities trading. David has successfully contributed to companies of all sizes, from small startups to large enterprises with up to 60,000 employees. Renowned for his ability to simplify complex concepts and solutions, he believes in using the most effective tools to address challenges efficiently and elegantly.   Topics of Discussion: [2:41] David Morton's background and early Career. [5:30] What is a data scientist? [7:35] Data Science vs. Software Engineering. [12:08] Hypothesis Testing and Model Building. [12:49] David explains the concept of a model in data science, using the metaphor of how a grandmother thinks about someone. [13:04] How models are mathematical representations of the real world, used for prediction and analysis. [15:06] Data science models vs. a GPT model. [18:08] The importance of using the right tool for the job. [26:10] The operational side of data science and the role of machine learning. [35:56] Practical examples of Data Science applications.   Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Programming with Palermo — New Video Podcast! Email us at programming@palermo.net. Clear Measure, Inc. (Sponsor) .NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon! Jeffrey Palermo's Twitter — Follow to stay informed about future events! David Morton LinkedIn David Morton GitHub   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Francois Chollet, a prominent AI expert and creator of ARC-AGI, discusses intelligence, consciousness, and artificial intelligence. Chollet explains that real intelligence isn't about memorizing information or having lots of knowledge - it's about being able to handle new situations effectively. This is why he believes current large language models (LLMs) have "near-zero intelligence" despite their impressive abilities. They're more like sophisticated memory and pattern-matching systems than truly intelligent beings. *** MLST IS SPONSORED BY TUFA AI LABS! The current winners of the ARC challenge, MindsAI are part of Tufa AI Labs. They are hiring ML engineers. Are you interested?! Please goto https://tufalabs.ai/ *** He introduced his "Kaleidoscope Hypothesis," which suggests that while the world seems infinitely complex, it's actually made up of simpler patterns that repeat and combine in different ways. True intelligence, he argues, involves identifying these basic patterns and using them to understand new situations. Chollet also talked about consciousness, suggesting it develops gradually in children rather than appearing all at once. He believes consciousness exists in degrees - animals have it to some extent, and even human consciousness varies with age and circumstances (like being more conscious when learning something new versus doing routine tasks). On AI safety, Chollet takes a notably different stance from many in Silicon Valley. He views AGI development as a scientific challenge rather than a religious quest, and doesn't share the apocalyptic concerns of some AI researchers. He argues that intelligence itself isn't dangerous - it's just a tool for turning information into useful models. What matters is how we choose to use it. ARC-AGI Prize: https://arcprize.org/ Francois Chollet: https://x.com/fchollet Shownotes: https://www.dropbox.com/scl/fi/j2068j3hlj8br96pfa7bi/CHOLLET_FINAL.pdf?rlkey=xkbr7tbnrjdl66m246w26uc8k&st=0a4ec4na&dl=0 TOC: 1. Intelligence and Model Building [00:00:00] 1.1 Intelligence Definition and ARC Benchmark [00:05:40] 1.2 LLMs as Program Memorization Systems [00:09:36] 1.3 Kaleidoscope Hypothesis and Abstract Building Blocks [00:13:39] 1.4 Deep Learning Limitations and System 2 Reasoning [00:29:38] 1.5 Intelligence vs. Skill in LLMs and Model Building 2. ARC Benchmark and Program Synthesis [00:37:36] 2.1 Intelligence Definition and LLM Limitations [00:41:33] 2.2 Meta-Learning System Architecture [00:56:21] 2.3 Program Search and Occam's Razor [00:59:42] 2.4 Developer-Aware Generalization [01:06:49] 2.5 Task Generation and Benchmark Design 3. Cognitive Systems and Program Generation [01:14:38] 3.1 System 1/2 Thinking Fundamentals [01:22:17] 3.2 Program Synthesis and Combinatorial Challenges [01:31:18] 3.3 Test-Time Fine-Tuning Strategies [01:36:10] 3.4 Evaluation and Leakage Problems [01:43:22] 3.5 ARC Implementation Approaches 4. Intelligence and Language Systems [01:50:06] 4.1 Intelligence as Tool vs Agent [01:53:53] 4.2 Cultural Knowledge Integration [01:58:42] 4.3 Language and Abstraction Generation [02:02:41] 4.4 Embodiment in Cognitive Systems [02:09:02] 4.5 Language as Cognitive Operating System 5. Consciousness and AI Safety [02:14:05] 5.1 Consciousness and Intelligence Relationship [02:20:25] 5.2 Development of Machine Consciousness [02:28:40] 5.3 Consciousness Prerequisites and Indicators [02:36:36] 5.4 AGI Safety Considerations [02:40:29] 5.5 AI Regulation Framework

This episode of To the Edge & Beyond is Part 2 of the Edge Neural Technology series, where host Michelle Dawn Mooney is joined by Intel AI experts Zach Meicler-Garcia, Sanjana Kamath, and Sanjay Addicam to explore the groundbreaking advancements in Intel's Edge Neural Technology. This episode delves into the inception, functionality, and far-reaching impact of Neural Code technology, a revolutionary approach to AI model building and training that is reshaping industries like healthcare and education.Zach Meicler-Garcia begins by tracing the origins of Neural Code technology, which draws inspiration from Dr. Sheila Nirenberg's pioneering research at Weill Cornell Medicine. "The neural code mimics the human retina's behavior, extracting key features from a scene and converting them into a format that AI can process efficiently," Garcia explains. This technology reduces reliance on large datasets by focusing on motion and essential features, making it an innovative solution for AI model creation with minimal data inputs.Sanjana Kamath discusses the practical applications and benefits of Neural Code technology, emphasizing its ability to enhance AI explainability. "The Neural Code enables the creation of shallower Convolutional Neural Networks (CNNs), which preserve privacy and remove bias, making them ideal for data-sensitive environments," she highlights. Kamath also underscores how Intel's no-code graphical interfaces and edge training capabilities make advanced AI accessible to users across various sectors, without the need for extensive coding expertise.Sanjay Addicam expands on the technology's potential, particularly in addressing challenges like hallucinations caused by generative AI video algorithms. "Even with limited data, Neural Code ensures accurate AI outputs and supports rapid model building," Addicam explains, pointing to the future of qualitative benchmarking as a game-changer in the AI space.Intel's Edge Neural Technology stands as a major leap forward in AI, offering a blend of accuracy, privacy, and seamless deployment. This revolutionary approach is poised to redefine AI applications across industries, transforming how we interact with technology.Discover more about their cutting-edge technology:Zach Meicler-GarciaSanjana KamathSanjay AddicamSubscribe to the "To the Edge & Beyond" podcast on Apple Podcasts and Spotify to engage with more thought leaders from the Intel and Edge Network group.

This episode of To the Edge & Beyond is Part 2 of the Edge Neural Technology series, where host Michelle Dawn Mooney is joined by Intel AI experts Zach Meicler-Garcia, Sanjana Kamath, and Sanjay Addicam to explore the groundbreaking advancements in Intel's Edge Neural Technology. This episode delves into the inception, functionality, and far-reaching impact of Neural Code technology, a revolutionary approach to AI model building and training that is reshaping industries like healthcare and education.Zach Meicler-Garcia begins by tracing the origins of Neural Code technology, which draws inspiration from Dr. Sheila Nirenberg's pioneering research at Weill Cornell Medicine. "The neural code mimics the human retina's behavior, extracting key features from a scene and converting them into a format that AI can process efficiently," Garcia explains. This technology reduces reliance on large datasets by focusing on motion and essential features, making it an innovative solution for AI model creation with minimal data inputs.Sanjana Kamath discusses the practical applications and benefits of Neural Code technology, emphasizing its ability to enhance AI explainability. "The Neural Code enables the creation of shallower Convolutional Neural Networks (CNNs), which preserve privacy and remove bias, making them ideal for data-sensitive environments," she highlights. Kamath also underscores how Intel's no-code graphical interfaces and edge training capabilities make advanced AI accessible to users across various sectors, without the need for extensive coding expertise.Sanjay Addicam expands on the technology's potential, particularly in addressing challenges like hallucinations caused by generative AI video algorithms. "Even with limited data, Neural Code ensures accurate AI outputs and supports rapid model building," Addicam explains, pointing to the future of qualitative benchmarking as a game-changer in the AI space.Intel's Edge Neural Technology stands as a major leap forward in AI, offering a blend of accuracy, privacy, and seamless deployment. This revolutionary approach is poised to redefine AI applications across industries, transforming how we interact with technology.Discover more about their cutting-edge technology:Zach Meicler-GarciaSanjana KamathSanjay AddicamSubscribe to the "To the Edge & Beyond" podcast on Apple Podcasts and Spotify to engage with more thought leaders from the Intel and Edge Network group.

In this episode of the Marketing Boost Solutions Podcast,  I had an insightful discussion with Alison Katschkowsky, a veteran swimmer and fitness expert who transformed her traditional fitness business into a successful hybrid model.

Send us a textLet's talk about change management. As educators, we know that change is a constant. But how can we effectively navigate it and ensure buy-in from our teams? That's where the ADKAR model comes in. It's a framework that helps organizations build momentum and sustain change. ADKAR can help you to drive successful change in your school. Join us as we talk through the AKCAR framework and provide information on how you can create a culture of continuous improvement and drive authentic change in your school. Remember, change is a journey, not a destination. Empower your team to embrace change!The Research:The ADKAR Model (from the creators - Prosci)BEGINNING OF SCHOOL YEAR SPECIAL[IN PERSON PD]  Breaking Through Resistance and Building Buy-InCONTACT US to qualify for the special and to get on our mailing list.[[FREE]] Sibme Coaching AppLet's Stay Connected!Website | Instagram | Twitter | Linkedin | Facebook | Contact Us

Shallow welcomes Dr. Patrick Davidson as they discuss the role of academia in the fitness industry. They reflect on the cycles and trends in the industry and the need for patience and discernment.  Dr. Davidson shares his thoughts on specialization versus generalization and the need for a holistic approach. He also discusses the importance of establishing a worldview and how it informs his coaching and teaching.   https://www.drpatdavidson.net/ https://www.instagram.com/dr.patdavidson/?hl=en   Join the Pre-Script® Level 1 Opt-In list now. Learn more at https://www.pre-script.com/psl1   We've got a new sponsor!   Marek Health is a health optimization company that offers advanced blood testing, health coaching, and expert medical oversight. Our services can help you enhance your lifestyle, nutrition, and supplementation to medical treatment and care. https://marekhealth.com/rxd Code RXD   Don't miss the release of our newest educational community - The Pre-Script ® Collective! Join the community today at www.pre-script.com.   For other strength training, health, and injury prevention resources, check out our website, YouTube channel, and Instagram.   For more episodes, subscribe and tune in to our podcast. Also, make sure to sign up to our mailing list at www.pre-script.com to get the first updates on new programming releases.   You can also follow Dr. Jordan Shallow and Dr. Jordan Jiunta on Instagram!   Dr. Jordan Shallow: https://www.instagram.com/the_muscle_doc/ Dr. Jordan Jiunta: https://www.instagram.com/redwiteandjordan/   Role of Academia in the Fitness Industry (00:03:07) Influence of Science and Well-Produced Content (00:07:23) Specialization vs Generalization in the Fitness Industry (00:11:02) Importance of Establishing a Worldview (00:17:27) Communication and Error in Driving Progress (00:27:07) Acting on Ideas and Spite (00:31:44) Pushing Boundaries in Training (00:36:12) Reconciling the Experiential Brain with the Theoretical Brain (00:40:43) Embracing Physical Discomfort and Physiological Changes (00:42:48) Using the Partner Fit App for Measuring Training Intensity (00:45:31)

In this episode of Construction Disruption, we sit down again with the renowned architect David Applebaum, famously known as 'architect to the stars'. With 40 years of experience shaping the skylines and living spaces in Los Angeles and beyond, David shares insights into the evolution of architecture, the impact of economic shifts, and his approach to creating spaces that resonate on a personal level.The conversation also explores the challenges posed by corporate flippers and the permitting process in Los Angeles, the architectural integrity of design over profit, and advice for aspiring architects. Alongside architecture, the episode features Applebaum's personal stories, including encounters with Frank Sinatra and Ray Charles, offering a glimpse into his life and career. Timestamps02:43 David's Journey into Architecture09:35 Navigating the Challenges of Modern Architecture24:01 The Art of Model Building in Architecture34:11 Client Stories and the Human Side of Architecture43:25 Unveiling the Mystery: The Secret Recording Studio43:45 Designing for Quincy Jones: A Piano Requirement45:39 The Architect's Dream: From Self-Storage to Sacred Spaces50:34 Crafting Unique Experiences: The Starbucks Design Challenge58:34 Personalizing Spaces: The Essence of Residential Design01:01:20 Navigating the Digital Age: The Challenge of Online Presence01:03:23 Advice for Aspiring Architects: Passion Over ProfitConnect with David OnlineWebsite: https://www.davidapplebaum.com/Email: david@davidapplebaum.comInstagram: https://www.instagram.com/davidapplebaum_architect/Facebook: https://www.facebook.com/DavidApplebaumArchitectFor more Construction Disruption, listen on Apple Podcasts or YouTubeConnect with us on Facebook, Instagram, or LinkedInThis episode was produced by Isaiah Industries, Inc.This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrpChartable - https://chartable.com/privacy

Peter Goff is back to give us an update as to what he has been up to in his life of scale aeromodelling. An accomplished scale model builder, Peter is renowned for his very detailed RC plane builds. He joins us to give us an update on some projects he is working on and a general life update in aeromodelling. Always good to talk to Peter and discuss his amazing models.

In this episode, we dissect the implications of OpenAI's fine-tuning capability for ChatGPT, outlining how it enhances customization and accuracy for users. Get on the AI Box Waitlist: ⁠⁠⁠⁠https://AIBox.ai/⁠⁠⁠⁠AI Facebook Community: ⁠⁠https://www.facebook.com/groups/739308654562189⁠⁠Podcast Studio AZ: ⁠⁠https://podcaststudio.com/⁠⁠Podcast Studio Network: ⁠⁠https://podcaststudio.com/network/⁠

In this episode, we delve into the impact of OpenAI's new fine-tuning capability on personalization and accuracy in ChatGPT, exploring its potential implications for the AI landscape. Get on the AI Box Waitlist: ⁠⁠⁠⁠https://AIBox.ai/⁠⁠⁠⁠AI Facebook Community: ⁠⁠https://www.facebook.com/groups/739308654562189⁠⁠Podcast Studio AZ: ⁠⁠https://podcaststudio.com/⁠⁠Podcast Studio Network: ⁠⁠https://podcaststudio.com/network/⁠

This episode of the Generation AI podcast delves into the evolution and application of predictive AI within higher education, focusing on enrollment predictions and marketing. Hosts Ardis Kadiu and Dr. JC Bonilla explore machine learning's roots, its distinction from generative AI, and its critical role in modeling prospective student behaviors. They discuss the transition from demographic to behavioral data for more accurate predictions, the importance of model tuning and validation, and the future of AI in personalizing student engagement through autonomous agents. The conversation highlights the blend of art and science in feature selection and the significance of adopting models that are understood and trusted by users.Introduction to Predictive AIHosts Ardis Kadiu and Dr. JC Bonilla delve into predictive AI's history and its application in higher education, focusing on enrollment and marketing.They discuss predictive AI's evolution from advanced analytics and machine learning (ML) to its current state.Machine Learning BasicsExplanation of machine learning as pattern recognition and its importance in predictive AI.The transition from demographic to behavioral data for improved predictions.Model Building and ValidationThe process of model building, including feature selection, training, and validation.The importance of model tuning and validation for accurate predictions.Behavioral Data in Predictive ModelsShift towards using behavioral data for more nuanced and accurate predictions.How behavioral data surpasses demographic data in predicting student behaviors and interests.Feature Engineering and SelectionThe art and science of selecting the right features for predictive models.Discussion on the significance of domain knowledge in feature selection.Model Adoption and InterpretationChallenges in model adoption and the importance of model interpretability for end-users.How understanding and trust in the model's predictions are crucial for successful implementation.Future of Predictive AI in Higher EducationInsights into the future of predictive AI, focusing on personalized student engagement and autonomous agents.The potential of large language models and AI agents in transforming higher education marketing and enrollment strategies.Remember to follow us on your favorite podcast platform to not miss any episodes of "Generation AI." Reach out with any questions or topics you'd like us to cover in future episodes. We thrive on your feedback and are here to offer insights that resonate with you. Thank you for joining us on this AI journey! - - - -Connect With Our Co-Hosts:Ardis Kadiuhttps://www.linkedin.com/in/ardis/https://twitter.com/ardisDr. JC Bonillahttps://www.linkedin.com/in/jcbonilla/https://twitter.com/jbonillxAbout The Enrollify Podcast Network:Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Some of our favorites include The EduData Podcast and Visionary Voices: The College President's Playbook.Enrollify is made possible by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com. Connect with Us at the Engage Summit:Exciting news — Ardis will be at the 2024 Engage Summit in Raleigh, NC, on June 25 and 26, and would love to meet you there! Sessions will focus on cutting-edge AI applications that are reshaping student outreach, enhancing staff productivity, and offering deep insights into ROI. Use the discount code Enrollify50 at checkout, and you can register for just $99! This early bird pricing lasts until March 31. Learn more and register at engage.element451.com — we can't wait to see you there!

We're back baby! After both of us being under the weather, Eden and I are back to discuss our adventures with model kits. That is, of course, after we catch up on a full month's worth of media consumption that, given our recent illnesses, was quite a lot. So, join us as we catch up, and then dive into Gundam model building and our experiences with it.

Join me as I dissect Datasaur's recent $7.9M funding round, exploring their approach to automating AI model generation from labeled data and its industry impact. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

Discover how Google's Vertex AI democratizes AI model creation, empowering everyone to craft potent AI models without complexity or expertise. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

Todd talks with Trekfest's top tinkerer about TOS, TNG, and other television tales today, taking time to try trivia, totaling in...eh, I can't keep that up. Here's my interview with Luke Grove!   Star Trek Prop Enthusiasts https://www.facebook.com/groups/468336335426

Tune in to this episode as we delve into Datasaur's groundbreaking achievement of securing $7.9 million in funding. Explore how Datasaur is reshaping the AI landscape by enabling automatic AI model construction from labeled data, simplifying the development process. Learn more about the implications of this innovation for AI developers and the future of machine learning. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

In this episode, we explore the groundbreaking achievement of Datasaur, which secured a substantial $7.9 million in funding to revolutionize AI model creation. Join us as we delve into the world of automating AI model development from labeled data and the potential this holds for streamlining machine learning projects. Discover how Datasaur's innovative approach is set to transform the way we build AI models and accelerate progress in the field. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

In this episode, we delve into Google's groundbreaking announcement of Vertex AI, a revolutionary platform that opens the doors for anyone to create potent AI models. Discover how Vertex AI democratizes AI development, potentially reshaping industries and innovation. Join us as we explore the implications of this game-changing technology. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Season 2: Forensic Interviewer Growth Continuum Episode 2: Unveiling the NCAC Pathways Model: Building deeper understanding of forensic interviewer training Forensic interviewers have a responsibility to stay up to date with best practices to guarantee that they are providing the best forensic interview possible. They can do this by participating in relevant and ongoing training. This spans from their first core training to advanced, specialized topics and everything in-between.  In this episode, we will explore the National Children's Advocacy Center new PATHWAYS approach to developing forensic interviewers, answering; What is PATHWAYS, how will it shift mindset in the field about training, and share some key takeaways for supervisors of forensic interviewers.   Episode Transcript Show Notes: In today's insightful episode, we're joined by Christina Rouse and a distinguished team of trainers from the National Children's Advocacy Center (NCAC) - Andra Chamberlin, Kim Madden, and Linda Cordisco Steele. The NCAC has an esteemed history, training over 200,000 child abuse professionals since 1985, and they are now developing a revolutionary approach to forensic interviewer training.  Recognizing a need to bridge the gap between basic and advanced interviewing skills, they're introducing a new training model called “Pathways.”  This model aims to improve integration of basic skills, preparing interviewers for more complex, topic-specific situations, and caters to different learning styles and levels of experience.   In their discussion, Andra, Kim, and Linda underline three critical skills for effective interviewing: the skillful use of a continuum of questions, providing social support, and exercising critical thinking.  These skills are key in eliciting narrative responses from children, supporting their unique needs, and guiding the direction of the interview.  But Pathways doesn't stop there; it also tackles challenges faced by both new and experienced interviewers, emphasizing skill development, case-specific training, and collaboration with multidisciplinary teams.  Andra  previews their upcoming ‘Beyond the Basics' curriculum, Kim explores the vital role of collaboration within investigative teams, and Linda stresses the importance of supportive supervision for the professional growth and wellbeing of interviewers. Stay tuned for an illuminating conversation about the future of this vital profession. Hit the subscribe button now!      Host:  Christina Rouse  Guests:  Andra Chamberlin, MA  Kim Madden, MEd, LCMHC  Linda Cordisco Steele, MEd, LPC    Quote:  “It is just not possible to “re-wire” our brains and change long-standing habits through one-week of instruction and a short episode of practice.”-Linda Cordisco Steele Blog    Links:  Contact Host:  Christina Rouse  Connect with the NCAC Trainers:  Andra Chamberlin, MA  Kim Madden, MEd, LCMHC  Linda Cordisco Steele, MEd, LPC    The SRCAC Exchange  SRCAC Facebook  SRCAC LinkedIn  National Children's Advocacy Center (NCAC)  Child Forensic Interview Training:  A Bibliography  Michael Lamb research forensically appropriate social support  “Difficulties translating research on forensic interview practices to practitioners: Finding water, leading horses, but can we get them to drink?”  “The Talent Code; Talent isn't born; it's grown” by Daniel Coyle  What is NCAC's Pathways Model?  What's up with the changes in Advanced Forensic Interview Training?  National Children's Advocacy Center Trainings:  Forensic Interviewing of Children  Beyond Basic Forensic Interviewing Training Courses for 2023:  Pathways for developing effective forensic interviewing skills  Sign up for emails regarding NCAC trainings  Handbook on Questioning Children:  A Linguistic Perspective    Follow SRCAC on Facebook and LinkedIn for more learning content! 

The saying goes that if you're not paying for the product, then you are the product. And every time you interact with the digital world, there's a good chance your data is going to be harvested for some alternative use.In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data.Guest BioDr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research.Talking PointsThe rights and responsibilities of consumers and organisations, when it comes to personal data.How organisations currently collect consumer data and what they are using that data for.The use of “harvested” data in AI tools, such as ChatGPT and Stable Diffusion.What data scientists should be aware of when sourcing data for their work.How to source data ethically.LinksConnect with Kate on LinkedInFollow Kate on TwitterCHOICE – Consumers and DataConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Genevieve Hayes Consulting Episode 21: Responsible Data Sourcing for AI Model Building The saying goes that if you're not paying for the product, then you are the product. And every time you interact with the digital world, there's a good chance your data is going to be harvested for some alternative use.In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data. Guest Bio Dr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research. Talking Points The rights and responsibilities of consumers and organisations, when it comes to personal data.How organisations currently collect consumer data and what they are using that data for.The use of “harvested” data in AI tools, such as ChatGPT and Stable Diffusion.What data scientists should be aware of when sourcing data for their work.How to source data ethically. Links Connect with Kate on LinkedInFollow Kate on TwitterCHOICE – Consumers and Data Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 21: Responsible Data Sourcing for AI Model Building first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

This episode is brought to you by Caldera Lab. Save 20% with code RUBEN on your new favorite skincare line with a 60-day guarantee. This is an Operation Podcast production, connect with us @operationpodcast. Strong Coffee CEO, Adam Von Rothfelder, is here to share his story of strength, resilience, and success – from growing up around abuse to creating a company that's stealing customers from Starbucks. Adam joins Ruben to share insights on how to keep moving forward when life gets hard, the unseen challenges of getting a product into grocery stores, and how to start a successful company from the ground up. How does Adam live through love? He puts his best forward, for himself and others, for no reason other than that's why we're here. ----- Key Highlights Adam gets vulnerable about the abuse he observed as a child and why he left his hometown of Milwaukee, WI The secrets behind Strong Coffee's success + Advice for aspiring entrepreneurs Adam details his experience on the NBC reality TV series, Strong, and addresses the backlash he got after it aired How do you move forward in business after a massive failure/loss? Proving to yourself that you are strong – How to do it and why it's necessary for success  ----- About The Guest: Adam Von Rothfelder is the CEO of Strong Coffee Company, a father of two girls, and former mixed martial artist, pro fighter, and trainer. Follow Adam Von Rothfelder @vonrothfelder ----- Love Notes from Adam Von Rothfelder: To get through life, you have to be willing to accept the fact that pain is part it. If your “why is weak, find a new one. Corrective actions require creative solutions. It's who you have around you that makes you who you are, so choose strong ones. ----- Follow Ruben on Instagram Watch and subscribe to Live Through Love on YouTube

Bible Reading: Psalms 25:8; 32:8-11After supper one evening, Caden helped clear the table and then covered it with old newspapers. "I'm going to work on my new model car," he said. He grinned as he took it out of the box. "Just look at all those pieces! And these details are awesome!"Caden began working, but it wasn't long before he went looking for his father. "Can you help me with my model car, Dad?" he asked.Dad looked up from his computer. "Sure, but can you manage it by yourself until I finish what I'm working on?""I'll try," Caden replied. But when Dad came to help him a little later, almost nothing had been done."What's the problem, buddy?" asked Dad.Caden sighed. "I don't understand how everything is supposed to fit together."Dad took the instruction sheet. "Okay," he said after glancing at it, "let's figure it out together." They got to work and soon were busy reading instructions, finding pieces, and fitting them to one another.After a while, Caden's mom reminded him that he needed to do his homework. "Okay, and I need to learn a verse for Bible club tomorrow too," said Caden. He sighed. "I like Bible club, but it takes up a lot of time." He turned to his dad. "Thanks for helping. I never would have gotten this far without you. I didn't understand what the sheet was saying until you walked me through it."Dad nodded. "Then trying to put this model together without my help would be a little like trying to live the Christian life without help from other Christians," he said. "That's something to remember as you learn your verse. Your Bible club teacher and our pastor and Sunday school teachers all help us understand the truth of the Bible. They help us understand the gospel--the good news that Jesus died and rose again to save us and give us eternal life--and how we should live as God's children. We need to remember to thank the Lord for them."Caden thought about it. "I guess you're right," he said. "There are lots of things in the Bible that I don't understand by myself." He grinned. "And you and Mom help too." -Dean KelleyHow About You?Do you thank God for those who help you understand His Word, the Bible? He wants you to learn His ways. One method of doing that is to read the Bible for yourself. But God also provides godly men and women who can help you understand passages that are difficult for you. Listen to them, learn from them, and thank God for them. Let them help you discover what God is telling you.Today's Key Verse:Teach me Your way, O Lord. (NKJV) (Psalm 27:11)Today's Key Thought:Thank God for Christian teachers

THANK GOD FOR CHRISTIAN TEACHERSKEY VERSE: TEACH ME YOUR WAY, O LORD. PSALM 27:11 

What makes us human?  Over the last several decades, the once-vast island of human exceptionalism has lost significant ground to wave upon wave of research revealing cognition, emotion, problem-solving, and tool-use in other organisms. But there remains a clear sense that humans stand apart — evidenced by our unique capacity to overrun the planet and remake it in our image. What is unique about the human mind, and how might we engage this question rigorously through the lens of neuroscience? How are our gifts of simulation and imagination different from those of other animals? And what, if anything, can we know of the “curiosity” of even larger systems in which we're embedded — the social superorganisms, ecosystems, technospheres within which we exist like neurons in the brain?Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I'm your host, Michael Garfield, and every other week we'll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.This week we conclude a two-part conversation with SFI External Professor John Krakauer, Professor of Neurology and Director of the Center for the Study of Motor Learning and Brain Repair at Johns Hopkins. In this episode, we talk about the nature of curiosity and learning, and whether the difference between the cognitive capacities and inner lifeworld of humans and other animals constitutes a matter of degree or one of kind…Be sure to check out our extensive show notes with links to all our references at complexity.simplecast.com  . If you value our research and communication efforts, please subscribe, rate and review us at Apple Podcasts or Spotify, and consider making a donation — or finding other ways to engage with us — at santafe.edu/engage. Please also note that we are now accepting applications for an open postdoc fellowship, next summer's undergraduate research program, and the next cohort of Complexity Explorer's course in the digital humanities. We welcome your submissions!Lastly, for more from John Krakauer, check out our new six-minute time-lapse of notes from the 2022 InterPlanetary Festival panel discussions on intelligence and the limits to human performance in space…Thank you for listening!Join our Facebook discussion group to meet like minds and talk about each episode.Podcast theme music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedInReferenced in this episode:Prospective Learning: Back to the Futureby The Future Learning Collective (Joshua Vogelstein, et al.)The Learning Salon: Toward a new participatory scienceby Ida Momennejad, John Krakauer, Claire Sun, Eva Yezerets, Kanaka Rajan, Joshua Vogelstein, Brad WybleArtificial Intelligence Hits the Barrier of Meaningby Melanie Mitchell at The New York TimesEconomic Possibilities for our Grandchildrenby John Maynard KeynesThe Intelligent Life of the City Raccoonby Jude Isabella at Nautilus MagazineThe maintenance of vocal learning by gene-culture interaction: the cultural trap hypothesisby R. F. Lachlan and P. J. B. SlaterMindscape Podcast 87 - Karl Friston on Brains, Predictions, and Free Energyby Sean CarrollThe Apportionment of Human Diversityby Richard LewontinFrom Extraterrestrials to Animal Minds: Six Myths of Evolutionby Simon Conway MorrisI Am a Strange Loopby Douglas HoftstadterCoarse-graining as a downward causation mechanismby Jessica FlackDaniel DennettSusan BlackmoreRelated Episodes:Complexity 9 - Mirta Galesic on Social Learning & Decision-makingComplexity 12 - Matthew Jackson on Social & Economic NetworksComplexity 21 - Melanie Mitchell on Artificial Intelligence: What We Still Don't KnowComplexity 31 - Embracing Complexity for Systemic Interventions with David Krakauer (Transmission Series Ep. 5)Complexity 52 - Mark Moffett on Canopy Biology & The Human SwarmComplexity 55 - James Evans on Social Computing and Diversity by DesignComplexity 87 - Sara Walker on The Physics of Life and Planet-Scale IntelligenceComplexity 90 - Caleb Scharf on The Ascent of Information: Life in The Human DataomeComplexity 95 - John Krakauer Part 1: Taking Multiple Perspectives on The Brain

We covered a VERY wide range of topics this week, starting with some Halloween fun, updates on what Brett & Todd have been working on, and some plaster tips from Todd! Vote for Brett's Cocoa Puffs Pumpkin here: https://verberdentalgroup.com/poll/pumpkin-contest-2022/   

It's been a long road to episode 200 but we're here! And because we talk about everything on GWK, we thought we'd finally delve into our deepest and darkest niches that we love. Warhammer, Pratchett, Model Building, Primitive Technology, Veronica Mars, and Twin Peaks?!?! Who picked what? And what do they love about it? -- Geeks with Kids is the bi-weekly geeky podcast where we take a look at movies, TV, video games, music, and anything geeky and break it down... all the while trying to make each other laugh. Subscribe to the channel, our podcast, and our Twitch stream. Our liveshow streams every other Monday and every Wednesday at https://www.twitch.tv/geekswithkids. We'll also try and sneak on at various times of the week as well. The Geeks are: Erik - http://twitter.com/erik_c Markus - https://www.instagram.com/markus_fx/ Brent - http://twitch.tv/thatssorabin Steve - http://twitter.com/indoskream Hawk - http://twitter.com/thehawk999 David - https://www.twitch.tv/arcrevenant #Niche #Pratchett #ModelMaking #warhammer #twinpeaks #veronicamars #primitivetechnology #React #comics #Analysis #React #Podcast #GeeksWithKids #Geek #Nerd #Culture #VideoGames #Movies #TV #Music #PodernFamily #PodNation --- Send in a voice message: https://anchor.fm/geekswithkids/message

Guest: Roy SorensonInstagram: @royrsorensonShow Notes:-NNL West 2022-Model Building-3d PrintingScale Riders Hobby Store: https://scaleriders.com/

Check out this episode's sponsor, the Cascadia Creation Conference Just over a year after Let's Talk Creation Launched, join Todd and Paul as they review past episodes and show how they fit into the creation model. Look to the future as they discuss possible future episodes to make sure the creation model is explained as fully as possible. Episode 8: Where is My Missing Link Episode 19: What's In a Name?: Looking at Other Names for Creationists (feat. Dr. Stephen Lloyd) Episode 5 CLIP: Where Did Cain Get His Wife? Episode 3: Model Building vs Evolution Bashing For questions or comments email podcast@coresci.org To find show notes and learn more about the podcast visit our website: coresci.org/podcast

Welcome to The ModelGeeks Podcast.  We are very happy and privileged to have Sean King from Primed Model Works YouTube Channel and TJ Haller from the Plastic Posse Podcast join us as guest hosts for EP29.  In this episode we do a little recap of the Old Dominion Open (ODO) held by IMPS Richmond.  In our main topic we scratch the surface of scale modeling benefits as it relates to both stress and mental health.     Of course we'll also give a run-down of what's new out there in kits, aftermarket, upcoming shows, and other news around the hobby. We would like to thank all the listeners for the support you have given the show over the past year now. We hope to see you out and about as we hit some of the shows, If you can't make it to the shows then you can still interact with us through social media, Facebook, Instagram, and email.We also want to thank each of our sponsors for their support. We are very lucky to have their support. When you have the time, pay a visit to their web sites, and have a look at their fine products. Sponsors:Detail and ScaleFurball Aero-DesignTamiya USASprueBrothersAlso, if you're a real Model Geek you'll check out the following links!IPMS USA Events PageIPMS Nationals 2022Butch O'Hare Modeling ClubThe Interesting Modeling CompanyIf you would like to support the Geeks please take a moment to head over to our new The ModelGeeks Patreon.  There you will be able to donate and help support us in our endeavor. We have three separate tiers listed and those tiers will offer you everything from podcast shout-outs and recognition as an official ModelGeeks Sponsor, to Exclusive Group membership where you will have access to tips, techniques, and private live streams and Q&A's. We are very fortunate to be able to join the scale modeling podcast community and are in the company of several other really GREAT podcasts. Hopefully, someday we'll earn our wings and be able to keep up with those guys!  Please check them all out at Scale Model Podcasts.Blogs:The Kit BoxSprue Pie with FretsIf you aren't interested in Patreon and would still like to donate, then please follow the link.  Support the Geeks! Remember, this is not a requirement, and we will continue to produce the best possible content for you the listener. That said, any donation amount is greatly appreciated and will help us with production costs.  Patreon Supporters:Sprue Brothers Models, Geoff Martin of Furball Aero Design, Scale Colors, Bullseye Model Aviation, Emilio Cuesta, Mike Talley, Dan Knofel, Stanton Fodness, Brent Leidig, Tim Cavileer, Paul Burdette, Cole Jacobsen, Craig Colledge, David Waples, Robert Lara, Ethan Idenmill, mfdyer, Connor Healey, Ray Boorman, Manuel Smith, Robert Morales, Len Steward, John Allen, Rick ReinertSupport the show (https://paypal.me/modelgeekspodcast?locale.x=en_US)

Have you ever wondered what approach Todd and Paul take to their research? Well in this episode of Let's Talk Creation they dive into just that! This week it's all about model building; what is a model, why should you care, and how can you become a model builder? Take some time out of your day and listen to the science podcast that is just for you! For show notes go to https://www.coresci.org/podcast or if you have any questions email them to podcast@coresci.org Check Us Out On Social Media: Facebook: https://www.facebook.com/LetsTalkCreation Twitter: https://twitter.com/TalkCreation Instagram: https://www.instagram.com/letstalkcreation Our Sponsors: Core Academy of Science: https://coresci.org/ Biblical Creation Trust: https://www.biblicalcreationtrust.org/