Podcast appearances and mentions of Andrew White

Editor's note: In our first BioHub pod with Priscilla and Mark they discussed their acquisition of EvoScale, led by Alex Rives, who is now Head of Science at BioHub. With ESM-1 they trained language models on millions of protein sequences drawn from across life, with a simple “next token” objective: predict the amino acids that have been randomly masked out, based on the context of the rest of the sequence. But they soon found that these models also learned biological structure and function, including properties the model had never been explicitly shown AND that this ability scales predictably with compute, leading to ESM2 and ESM3.Today, Alex announced ESMFold 2, an open scientific engine to power prediction, design, and discovery across protein biology.Building on Cryo-EM data (discussed in the CZI pod), ESMFold2 reports state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics, and evidence that inference time scaling is also working across five targets in cancer and immunology.In a nod to that other famous AI x protein folding project, they are also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures, which you can play around with on their website. We are honored to work with them for this huge release!One of the refrains we've heard on the Science pod has been that protein folding, materials design, cellular biology, etc. are very different problems from Language Modeling. They definitely are. Yet Alex Rives and the ESM team at BioHub just released a preprint and model, demonstrating that vanilla BERT-like transformer models trained on sufficiently large and diverse data sets can beat specialized models like AlphaFold3 on some of the hardest protein-related problems. Andrew White had a great segment in our first LS-Science episode that explained how mind blowing AlphaFold2 was when it was released in 2020: it suddenly solved problems on a GPU on your desktop that DESRes had built custom-ASIC supercomputer clusters to solve. John Jumper and Demmis Hassabis received the Nobel Prize in Chemistry for this work.AlphaFold2 took advantage of an very clever observation: if multiple species co-evolve pairs of mutations, this implies that the mutations correspond to parts of the protein that are close in 3d space. This is usually shorthanded as MSAs (multi-sequence alignments), and is the key insight which makes AlphaFold2 so effective.Like other inductive biases, however, it hurts generalization.Scale-pilled before it was coolIf you take a look at the timeline for scaling laws for LLMs and release of structure prediction models, the ESM team notably doubled down on their MSAs-be-damned approach after AlphaFold2 released. This obviously requires a great deal of belief in the scale hypothesis.Why the conviction?ESM developed at a time when many of the scaling laws and the “Bitter Lesson” were proving increasingly correct. AlphaFold2's wild success must have been both exciting and bitterly disappointing. But using MSAs mean that the model is is dependent on training data that contains MSAs in order to be accurate in a given domain. For things like antibodies that don't have MSAs to train on, AlphaFold tends to do poorly.ESM takes a different approach: learn the relationship between different proteins by unsupervised training on as much diversity as you can find (sound familiar?) and then correlate that back to structures know from the Protein Data Bank (PDB) and other sources. In other words, a World Model.World Model for proteins“World Model” is a hype term that I define like this:Use unsupervised training to learn abstract patterns from the data:* The abstraction should be semantic - novel constructions represent things that obey the rules of the real world* The abstraction should be compositional - recombining different patterns leads to novel and often valid constructions* The abstraction should support generalization - it predicts things in the real world it wasn't trained on Once you have a world model, you can attach “heads” to it for downstream tasks: predict properties of a protein, decompose its functional features, or search the representation for proteins that meet design criteria. The two big models BioHub just released under MIT license map directly onto this:* World model → ESMC (a model trained on 2.8 billion sequences)* Structure-prediction head → ESMFold2One of the interesting ways the world model can “predict things” is to generate proteins sequences and then measure the predicted properties, such as binding affinity, in the lab. Alex talks in the episode about validating some of the harder molecules they predicted in the wet-lab. Very cool!Another way is to use mech-interp techniques such as Sparse Auto Encoders (SAEs) to extract semantic features from your model, and then find novel features that predict unknown biology. I won't spoil this part for you: it was one of the highlights of the episode for me!A cell is a computerWe have all heard that genes are like computer programs, but usually the analogy fizzles after that. Of course genes are transcribed into RNA and RNA is translated into proteins, so genes are programs for building proteins, but that carries the analogy only to “binary digits are programs.” Here's a better analogy: you can think of the cell nucleus as a storage device / storage controller, the ribosome as a JIT-compiler and runtime, and the semantic features that we learn from our world model via SAEs as functions, proteins as processes that interact together in workflows (signalling pathways) to produce behaviors and outputs (phenotypes). Like functions, the SAE features have a hierarchical composition from local, secondary and tertiary structures (mimicing protein structure), but also motifs that are conceptual, such as membrane integrations, disordered regions and disulfide bonds. As we learn to compose these features we into novel protein designs, we move further towards programmable biology. Alex goes into much more detail about this in the episode, as well as:* Principles for new data collection* BioHub's vision* Modeling the cellEnjoy!Full Video podcastplease like and subscribe!* X: https://x.com/alexrives* LinkedIn: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

For over 50 years, Saturday Night Live has been one of the most Important shows for rock music in the history of the nascent television medium. While, arguably, the most famous moment in the show's history (and Tony's favorite?) is RHCP's multi-key 1992 version of “Under The Bridge”, there have been others. (Musical Guest The Ting Tings ripping up a photo of Colonel Sanders comes to mind.) Fortunately, just in time for NBC's never more relevant, May sweeps season finale of SNL, they pulled in the big guns: RHCP! Oh s**t, Frusciante and Kiedis (LLC) are busy? Maybe (checks notes) Chad Smith is free? Might as well bring in noted Chad Smith lookalike Will Ferrel (“Elf”, Capitol One) to teach Chad how to host the show! Everyone knows Chad Smith's best friend is Paul McCartney. So Chad called Sir Paul to see if he'd learn the bass part for the AI generated RHCP songs “Funky California Space Cadet” and “Santa Monica Ding-Dong”, as onetime Macca drummer Chris Whitten beat Flea's ass in Hamburg. Where you ask? Why, under the bridge. When asked for comment, Flea replied, I don't ever wanna feel, like when Chris Whitten kicked my ass.” Later that week, as longtime Macca drummer Abe Laboriel JR was (Alonzo) mourning young mister Christopher's legal predicament and unable to travel, Chad Smith had an idea. He could play in Paul's band! On SNL! The very talented singer songwriter Ingrid Michaelson can even sing back-up, and perhaps even “Cook Of The House”! And Paul could completely ignore any lame Beatles music during the live, 90 minute broadcast, because nobody wants to hear that junk! (#Motorcars #Handlebars) Are you still with us? Da f is wrong with you? You already know Tony & T.J. will talk Macca's historic SNL performance and also hit you up with such mind benders as:

Sports presenter David Alorka, sports broadcaster and boxing commentator Steve Bunce, and comedians Chloe Petts and Andrew White join Rick Edwards for an hour of sporting punditry, humour and entertainment. Points are awarded for informed comment, wit and passion, but taken away for nonsense and answers lacking in conviction.In the final round, the top two points scorers go head-to-head in 'Defend the Indefensible' where they must both defend a statement however ludicrous or distasteful for twenty seconds. There can only be one winner!Listen to the podcast on BBC Sounds

In this special episode we are joined by Andrew White – who shares his learning of how to be “client-focused” from more than 20 years as a partner at a City law firm – with many years of experience spent delivering client advisory projects, and also client-facing learning programmes.  Andrew discusses what are the “perennial truths” of client service, things which have not changed, and also the “new”: what is becoming even more important for client in 2026 (and beyond), which their advisers need to understand?  Practical tips and ideas on different aspects of being “client-focused” are given on a range of topics – including how one needs to think very carefully about communication styles and strategies.The episode also covers the special importance of the “human touch” in personalising one's service – to show one has truly listened to a client.  Andrew then discusses a number of practical strategies for developing key commercial and client-facing skills, ranging from critical thinking to story-telling.  As a final take-away, Andrew also offers three key areas for lawyers to keep in mind: “VAR” – the important link between client-service and one's “Values”, managing new “AI” tools as part of client service, and the importance of “Reflection”.   Actions and resources for listeners: ·       Read this blog on “the Human Touch” – is there anything you would add to Andrew's list in the article of what are the critical elements? ·       Can you be too “client-focused” as a lawyer?  Read this article and consider 2 points on each side of the argument!  

We've been on a bit of a mini World Models series over the last quarter: from introducing the topic with Yi Tay, to exploring Marble with World Labs' Fei-Fei Li and Justin Johnson, to previewing World Models learned from massive gaming datasets with General Intuition's Pim de Witte (who has now written down their approach to World Models with Not Boring), to discussing the Cosmos World Model with with Andrew White of Edison Scientific on our new Science pod, to writing up our own theses on Adversarial World Models. Meanwhile Nvidia, Waymo and Tesla have published their own approaches, Google has released Genie 3, and Yann LeCun has raised $1B for AMI and published LeWorldModel.Today's guests have a radically different approach to World Modeling to every player we just mentioned — while Genie 3 is impressive, its many flaws demonstrate the issues with their approach - terrain clipping, noninteractivity (single player, no physics/no objects other than the player move), and maximum of 60 second immersion. Moonlake AI (inspired by the Dreamworks logo) is the diametric opposite - immediately multiplayer, incredibly interactive, indefinite lifetime, capable of MANY different kinds of world models by simulating environments, predicting outcomes, and planning over long horizons. This is enabled by bootstrapping from game engines and training custom agents: In Towards Efficient World Models, Chris Manning and Ian Goodfellow join Fan-Yun in explaining why their approach to efficiency with structure and casuality instead of just blind scaling is sorely needed:SOTA models still show physical or spatial understanding glitches, such as solid objects floating in mid-air or moving “inside” other solid objects.If the goal is to plan for the next action, how often is a high-resolution pixel view necessary for modeling the world? Our bet is that there is a disproportionately large share of economically valuable tasks where such detail is not required. After all, humans with a wide variety of sensory limitations have little difficulty doing almost everything in the world. Furthermore, for a large number of purposes, describing a scene or a situation in a few words of language (“the car's tires squealed as it cornered sharply”) is sufficient for understanding and planning.Experiments also show that humans only partially process visual input in a top-down, task-directed way, often making use of abstracted object-level modeling. In almost all cases, partial representations combined with semantic understanding are sufficient.…If the goal is to facilitate the understanding of causality in multimodal environments, then the world model—whether it is used in the virtual world or the physical world—must prioritize properties such as spatial and physical state consistency maintained over long time periods, and an ability to evolve the world that accurately reflects the consequences of actions. That's what Moonlake is building.Game engines are the right starting point abstraction to efficiently extract causal relationships, and building the interfaces and community (including their new $30,000 Creator Cup) to kickstart the flywheel of actions-to-observations.We were fortunate enough to attend their sessions at GDC 2026 (the Mecca of Game Devs), and were impressed by the huge variety and flexibility of the worlds people were building with Moonlake's tools already! Live videos on the pod.Full Video Pod on YouTube!Timestamps00:00 Benchmarking Gets Hard00:47 Meet Moonlake Founders01:26 Why Build World Models03:12 Structure Not Just Scale05:37 Defining Action Conditioned Worlds07:32 Abstraction Versus Bitter Lesson14:39 Language Versus JEPA Debate20:27 Reasoning Traces And Rendering Layer37:00 Gameplay Over Graphics38:02 Fiction Rules And World Tweaks39:15 Code Engines Beat Learned Priors41:10 Diffusion Scaling Limits43:23 Symbolic Versus Diffusion Boundary46:14 Platform Vision Beyond Games50:24 Spatial Audio And Multimodal Latents54:23 NLP Roots Hiring And Moon Lake NameTranscript[00:00:00] Cold Open[00:00:00] Chris Manning: Think this whole space is extremely difficult as things are emerging now. And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks.[00:00:20] But these days so much of what people are wanting to do is nothing like that, right? You're wanting to get some recommendations about which backpack would be best for you for your trip in Europe next month. It's not so easy to come up with a benchmark, and it's the same problem with these world models.[00:00:41] Meet the Founders[00:00:41] swyx: Okay. We're back in the studio with Moon Lake's, two leads. I, I guess there's other founders as well, but, sun and Chris Manning. Welcome to the studio.[00:00:54] Fan-yun Sun: Thanks. Thanks, Chris. Thanks for having us.[00:00:56] swyx: You've got, you guys have, come burst onto the scene with a really refreshing [00:01:00] new take of mold models.[00:01:01] I would just want to, I guess ask how you, the two of you came together. Chris, you're a legend in NLP and just AI in, in, in general. You're, you're his grad student, I guess[00:01:10] Fan-yun Sun: Actually my co-founder.[00:01:11] swyx: Oh, yeah.[00:01:12] Fan-yun Sun: I should give a lot of credit to my co-founder, Sharon. Yeah. She was, she was actually working with Professor Fe Androgyn and then she ended up working with, Ron and Chris Manning here.[00:01:22] And then, so I got connected through to Chris initially, actually through my co-founder,[00:01:26] What is Moon Lake?[00:01:26] swyx: what is Moon Lake? What, what is, actually, I'm also very curious about the name, but like why going into world models?[00:01:33] Fan-yun Sun: So I was working a lot. With actually Nvidia research during my PhD years on essentially generating interactive worlds to train reinforcement learning agents or embody EA agents.[00:01:44] And then there's two observations. One in academia and one in industry. An industry like folks at Nvidia are actually paying a lot of dollars to purchase these types of interactive worlds, whether it's for the sake of evaluation or training the robots, or policies or models. And [00:02:00] then, in academia, same thing is happening.[00:02:02] And more specifically, when I was actually working with Nvidia on the synthetic data foundation model training project, we were actually generating a lot of these synthetic data and showing that, hey, you can actually, these synthetic data are actually as useful as real world data when it comes to multimodal pre-training.[00:02:16] But then, like I said, there's a lot of dollars being paid out to like external vendors or, or like. Other folks to manually curate these types of data. It was very clear to us that, okay, on our way to, let's call it embody general intelligence models need to learn the consequences behind their actions, which means that they need interactive data and the demand for those types of data are growing exponentially.[00:02:38] But everybody's sort of thinking about it from a pure, say, video generation perspective or something else. But we feel like the true actually opportunity is actually building reasoning models that can do these things, like how humans do these things today. So that's a little bit on the genesis of Moon Lake, and I think the reason I got into world models was partly.[00:02:59] A philosophical [00:03:00] take of the on the world where I like, believe the simulation theory and stuff like that. But on the other, on the other hand, it's really just like, oh, like there's an opportunity there that I feel like nobody's doing it the way I think should be done.[00:03:10] Structure, Not Scale: The Vision[00:03:10] Chris Manning: I can say a little bit about that.[00:03:12] Yeah. So of the overall goal is the pursuit of artificial intelligence and most of my career has been doing that in the language space and that's been just extremely productive. As we all know, the story of the last few years, I don't have to tell about how much we've achieved with large language models, but, uh.[00:03:31] Although they have been extremely effective for ramping language and general intelligence, it's clearly not the whole world. There's this multimodal world of vision, sound, taste that you'd like to be dealing with more than just, language. And then the question is how to do it. And despite, a huge investment in the computer vision space, right, as the research field computer [00:04:00] vision has been for decades, far, far larger than the language space, actually.[00:04:05] I think it's fair. Say that, vision, understanding sort of stalled out, right? You got to object recognition and then progress just wasn't being made right? If you look at any of these, vision language models, it's the language that's doing 90% of the work and the vision barely works. And so there's really an interesting research question as to why that is and at heart, the ideas behind Moon Lake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.[00:04:50] swyx: I think one of your blog posts, you put it as structure, not scale. Is that, a general thesis?[00:04:57] Chris Manning: Yeah. Well, scale is good too.[00:04:58] swyx: Yeah. Scale is good. Too[00:04:59] lot,[00:04:59] Chris Manning: [00:05:00] lots of data is good as well and scale, but nevertheless, you want the structure Yeah. To be able to much more efficiently learn.[00:05:07] swyx: Yeah. The other thing I really liked also is you put out an example of what your kind of reasoning traces look like.[00:05:12] Right. Which you would distill is the word that comes to mind. I don't even think that's a good, good description, but it would involve, for example, geometry, physics, affordances, symbolic logic, perceptual mappings, and what, what have you. But like that, that is the kind of example that involves, let's call it spatial reasoning, role model reasoning as as compared to normal LM reasoning.[00:05:35] Yeah.[00:05:36] Defining World Models vs Video Generation[00:05:36] Vibhu: But also like taking it a step back. So how do you guys define world models? A lot of people see okay, you can do diffusion, you can do video generation. But, you guys put out quite a few blog posts. You put out a essay recently, we can even pull it up about efficient world models. You have a pretty like structural definition here, but for the general audience that don't super follow the space, right.[00:05:55] What's, what's the difference in what we see from like a video generation model to [00:06:00] a world gen A simulator? How do you kind of paint that last[00:06:02] Chris Manning: year? Yeah, so I think this is actually a little bit subtle because, people look at these amazing generative AI video models, SAWA VO three, one of these things, and they think Genie, they think, oh, this is amazing.[00:06:17] This is we've solved understanding the world because you can produce these generative AI videos, but. The reality is that although the visuals do look fantastic, those visuals actually are accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence.[00:06:49] So I mean, a term we sometimes use is that you need action condition, world models. That you only actually have a world model if you can predict, [00:07:00] given some action is taken, what is going to change in the world because of it. And in particular, that becomes hard over longer time scales. So if you're simply, trying to.[00:07:12] Predict the next video frame. That's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually much more of an abstracted semantic model of the world.[00:07:32] The Bitter Lesson & Data Abstraction[00:07:32] swyx: Yeah, the question comes where you want to have more structure than is available in just predicting the next token.[00:07:41] And typically, well, let's, let's call it the experience of the last five years has been that is just washed away by scale, right? So what is the right middle ground here that, you don't ignore the bitter lesson, but also you. Can be more efficient than what we're doing today.[00:07:57] Chris Manning: One possibility [00:08:00] is, look, if we just collect masses and masses and masses and masses of video data, this problem will be solved.[00:08:11] Under certain assumptions that could be true, but there are sort of multiple avenues in which it could not be true. The first is what's really essential is understanding the, the consequences of actions producing an action conditioned world model. And if you are simply, collecting observational video data, which is the easy stuff to collect, when you're sort of mining online videos, you don't actually.[00:08:41] Know the actions that are being taken to see how the video is changing. And so if you are never collecting directly actions and you are having to try and infer them from what happened in the observed video, that's not impossible. But it's very [00:09:00] hard and it's not really established that you can get that to work at any scale yet.[00:09:05] And so there's a lot of premium on collecting action condition video data, which is part of why there's been a lot of interest in using simulation so that you can be collecting data where you do know the actions, which isn't quite limited supply, but there's also in the limit of as much data as you could possibly have.[00:09:28] Maybe the problem is eventually solvable, but. Even though we collect huge amounts of text data is always at a great level of abstraction, right? Language is a human designed, abstracted representation where there's meaning in each token and it's representing and abstraction of the world, right?[00:09:51] As soon as you are describing someone as a professor, and as soon as you are saying that they're condescending, right? These are very [00:10:00] abstracted descriptions of the world. It's not at what you're observing as pixel level, and to get to that kind of degree of abstraction, starting from pixels is orders and magnitude of extra data and processing.[00:10:14] And so, although, we absolutely want to exploit, get as much data as possible, use the bitter lesson. Nevertheless, if there are ways in which you can work with five orders of magnitude less data than people working purely from pixels, you're gonna be able to make a lot more progress, a lot more quickly.[00:10:34] And that's the bet here. And so you could just say that's only wanting to be able to, do it more efficiently, do it more quickly, do it more cheaply. But I think it's actually more than that, I think. One should be making the analogy to how human beings work at one level. You know? Yes, we have these high [00:11:00] resolution eyes and we can look and see a scene like a video, but all of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed.[00:11:13] Right. That you are doing fairly fine ated processing of exactly what you're focusing on. But as soon as it's away from that of yeah, there's another guy over there that you've sort of only processing top down this very abstracted semantic description of the world around you. And so, that's what human beings are doing.[00:11:33] They're working with semantic abstractions and so. I think it is just the right representation. ‘cause we also have other goals we want to be able to do, real time worlds. So that means there's a limit to how much processing you can do and we want to do long-term planning and consistency. And again, that favors abstraction.[00:11:55] I mean, I guess there was actually a recent. Blog posts that [00:12:00] came out from our Friends of physical intelligence and, they were sort of heading in the same direction they were saying Oh, to the pay[00:12:06] swyx: pay model.[00:12:07] Chris Manning: Yeah. Yeah. To maintain a long term memory of what's happening in the world. So we can, do longer term we actually storing text of what is, been happening in the world.[00:12:19] Right. It is not such a successful strategy of trying to keep it all at a pixel level.[00:12:24] Vibhu: And yeah, I mean, you can see it in video models like that Temporal consistency. We're at a scale of train on, all the video data we have. We have it for maybe 30 seconds, a few minutes. That's not the same as a game state played for half an hour.[00:12:37] Right. I thought you guys break it down pretty well. You have a, you have a blog post about. Building multimodal worlds with an agent. I dunno if you guys wanna talk about this. This is one of the things I read, I[00:12:48] swyx: thought, yeah, it's the thing I talked about with the reasoning chain. Yeah.[00:12:51] Vibhu: So there's like different phases to this.[00:12:53] It seems like it's more of an agent, a scaffold, very different approach than just, type in a prompt and you, you don't have the same consistency. [00:13:00] It also, like, for people that are listening, I, I would highly recommend reading it. It breaks down the problem in a different light, right?[00:13:06] So like, what do you need to consider when you're talking about video, like world game models, right? How would, what do you need to consider? What are the factors? What are the elements? What's the state? So I don't know if you guys have stuff to talk about for this one.[00:13:19] Fan-yun Sun: Yeah. Actually, I wanted to add on a little bit Yeah.[00:13:22] On our previous point, which is just like, change topics so quickly. I, I do feel like sometimes people confuse like, oh, like we're taking an an, an method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believed is a bitter lesson. But then I feel like the question that we always discuss is like, what is the right abstraction level today?[00:13:42] The analogy I like to make is like, let's just say we can encode and decode. Represent all of images, videos, audio and bytes. Then the most bitter lesson approached is to train a next byte prediction model as opposed to the next token prediction model where it's just like, okay, it's natively multimodal, can just, but it's like, yeah, like [00:14:00] to, to Chris's point, it's like the scale and computing you need to achieve that.[00:14:03] So that's why we always come back to like, okay, what is the most efficient way to do it? And reasoning models to the point of this blog post is a showcase of like, Hey, we're actually just like reasoning about the world and reasoning about. The aspects of the world that CAGR that matter for me to learn what I want to learn from this role model.[00:14:21] swyx: Yeah, it's like you're improving the en encoder of whatever you're, trying to model. And like a better representation would just represent the important things in less space. Yeah. Which would just be more efficient.[00:14:33] Fan-yun Sun: Yeah.[00:14:34] swyx: So yeah, I, I, I fully agree that it is not, antagonistic to, bitter lesson.[00:14:38] I do wanna wanna mention one more thing. Is there any philosophical differences with the JPA stuff that, Yun is working on? I gotta go there. You, you, you, you're, you're imagining like some latent abstraction. I'm like, okay, fine. Let's, let's talk about it, right? Like it's an elephant in the room.[00:14:52] Chris Manning: Yeah.[00:14:53] JEPA & Philosophical Differences with LeCun[00:14:53] Chris Manning: There are philosophical differences. Jan Lacoon is a dear friend of mine, but. [00:15:00] He has never appreciated the power of language in particular, or symbolic representations in general. Yarn is a very visual thinker. He always wants to claim that he thinks visually and there are no words, symbols, or math in his head.[00:15:21] Maybe that's true of yarn. It's certainly not the way I think. Um. But at any rate, the world according to yarn is the basic stuff of the, the world and of intelligence is visual and language is just. This low bit rate communication mechanism between humans and it doesn't have much other utility and it's far inferior to the high bit rate video, that comes into your eyes.[00:15:53] And I think he's fundamentally missing a number of important things [00:16:00] there. Think of this evolutionary argument looking at animals, right? That the closest analogies, the things with chimps, right? So chimpanzees, have fairly similar brains to human beings. They have great vision systems, they have great memory systems.[00:16:18] They've got, better memory than we do of short term memories. They can plan, they can build primitive tools that, humans. Massively ahead in what we understand about the world, what we can plan, what we can build. And essentially what took off for us was that humans managed to develop language and that gave a symbolic knowledge, representation, and reasoning level, which just, okay if this sort of vaulting of what could be done with the intelligence in brains.[00:16:59] So the [00:17:00] philosopher Dan de refers to language as a cognitive tool and argues that, humans unique among the creatures in the world have managed to build their own cognitive tools and language is the famous first example. But other things like, mathematics and programming languages are also cognitive tools.[00:17:21] They give you an ability to. Think in abstractions, in extended causal reasoning chains. And that allows you to do much more. And we use that for spatial representation and intelligence and planning and gameplay as well. So we believe, and this is, underlying the specific technologies that Moon Lake is making, that symbolic representations are powerful.[00:17:50] And you want to use that in your understanding of the visual world when you want a causal understanding, when you want to maintain long-term [00:18:00] consistency and prediction. And as I understand it, that's just not in ya Koon's worldview. So I think that's the fundamental philosophical difference. Then there's the specific model.[00:18:11] He's been advancing jpa, that's a reasonable. Research bed is a direction as to, to head for building out a model of the visual world. To my mind, it's sort of one reasonable research bed. It's not really established. It's the best one that everyone should be following,[00:18:32] swyx: at least developed at scale, at Meta.[00:18:34] But it's not just vision, right? Like, I mean, JPA is a, just joint admitting prediction can be applied to anything really. And people have done it. The argument is that there is a latent representation or that is probably more. Suited to the task, then why not let machines do it for us instead of predefining it at all?[00:18:50] And isn't something like a JPA shaped thing the right answer? And if not, why not?[00:18:55] Chris Manning: So I think there's a part of jpa that's right, which is [00:19:00] you do want to have a joint. Embedding that gives you a consistent model of the world. And Jan's argument is you can never get that from auto aggressive language models ‘cause they're sort of left to right churning out one token at a time.[00:19:22] I guess this is where we're the research arguments of the field, I'm not actually convinced that's right. ‘cause although the token production is this auto aggressive, process that's heading, left to right, I guess don't have to be left to right. But anyway, in sequence of tokens we could have right to left Arabic.[00:19:40] But although that's true, all of the weights of the model that are internal to the transformer, they are a joint model of the model's understanding of the world. And so I think you can think of the weights of the model as a form of. Joint representation, [00:20:00] and therefore it is plausible to think that could be the basis of a world model, which avoids, ya's objections.[00:20:10] swyx: I think I follow, and obviously that would touch on what Moon Lake eventually ends up doing as well. Right. Like, which it's hard to tell because you put out the end results, but we don't know the inputs that go into it. So it's, it's, that's something that we have to figure out over time.[00:20:25] Vibhu: Yeah. I mean, I guess this kind of breaks down some of the outputs. Do you wanna walk us through it?[00:20:31] Reasoning Traces & Interactive Worlds[00:20:31] Fan-yun Sun: Yeah. So this, this really just walks us through the reasoning traces of like, okay. So that just say, if we wanna build a world in this context, it's really just a game demo that, that shows the, the variety of interactions that this world model can build.[00:20:45] And yeah, it's really just a reasoning traces of like, okay it prompted to create a bowling game. Like how did it achieve what you saw? That level of causality, interaction and consistency, right? So yeah, this is almost just like a, an example of [00:21:00] like a reasoning traces. Very[00:21:01] swyx: detailed.[00:21:01] Fan-yun Sun: Yeah.[00:21:01] Vibhu: Very, very detailed.[00:21:02] You gotta you don't even realize it, right? Like when a video is generated, what happens when a ball strikes a pin, right? So first, like you, there's audio in that, like audio triggers happens, score increments, the world changes. Like pins have to start dropping. There's a timer that goes on. It's just like very similar to how now we're used to reasoning for language models.[00:21:20] There's a whole state of what happens. So geometry, physics, all this stuff. And then yeah, there's kind of that single prompt. So asset, ation all this stuff. It's like a, it's a nice view to see what's going on.[00:21:32] swyx: I think Sun is also too polite to point out that, both like Google's genie, demos as well as world Labs is marble, do not have interactive worlds.[00:21:41] Fan-yun Sun: That's the benefit of having a reasoning model, right? Like, because you can, you can say, oh, like maybe in this particular context, I want to learn how to bowl. And then you can say, okay, then what is it important when it comes to learning how to bowl? Okay, maybe it's like I need to understand the, the basic of like, physics and I want to throw it over [00:22:00] them.[00:22:00] I wanna know that when I, when it resets it's a new game. So I know that yeah, basically, you know to pick up the ball, you know that ball's gonna cause the pins to fall down. You know that what's important to this particular bowling game is to score and you know that the score corresponds to the number of pins that fell down.[00:22:19] So it's just like, if it's a model that sort of knows what it. Looks like, knows what a bowling game looks like, but doesn't actually allows you to practice over and over again and to understand that, oh, like what it takes to actually get a high score. Then it sort of doesn't actually allow you to learn what you set out to learn within the world model.[00:22:38] And I think this is really just one example of showing like the advantages of the approach that we're taking over most the, let's call it the zeitgeist, is today, when people talk about clinical role models,[00:22:51] Chris Manning: right? So it sort of seems like the question to ask when there's a world model is.[00:22:58] Can I not [00:23:00] only just wander around the world and look at the beautiful graphics, can I interact with the objects in the world and see the right consequences of actions?[00:23:11] Vibhu: And you also understand what the consequences would be if you do something right. So it's not just like, okay, there's one thing if I pick it up, something will happen.[00:23:19] But, there's 50 options and I know I can expect, I can infer what would happen if I do any of them. Right. So very different when you can actually see it play around with it.[00:23:28] swyx: There,[00:23:28] Beyond Unity: Cognitive Tools for World Building[00:23:31] swyx: there's two cheeky elements of that. I mean, the, the, the I guess, less ambitious one is, let's really establish for listeners, why is this fundamentally different than writing Unity code, right?[00:23:40] Like just creating a model to translate a prompt into Unity code[00:23:44] Fan-yun Sun: so there is an underlying physics engine. Yeah. In that sense, there's some overlapping things to Unity, but the way we think about it is like physics engine. Tools or code are cognitive tools like borrowing Chris's term, right? Like tools [00:24:00] that the model can employ as means to an end.[00:24:04] So today maybe you say, okay, in this particular context we care about physics, we care about the long-term causality consequences. Then yes, we deploy it, employ physics engine, and then maybe tomorrow we say, okay, we're we're training that. Just say drones where we only care about really fluid dynamics and the visual aspect of the world.[00:24:25] Then, then yeah, maybe we don't actually, the model actually doesn't have to use a physics engine. Or maybe it employs other types of representation or physics engine to achieve the task. So yes, writing code for Unity is sort of similar to a tool that our A model can employ, but our goal is for a model to take a representation conditioned reasoning.[00:24:46] Approach or process.[00:24:47] swyx: Yeah,[00:24:47] Fan-yun Sun: internally.[00:24:48] swyx: Yeah. Using these things as just like general two calls. Right. Which I think is very interesting. The other more ambitious one is, some kind of recursive element where it becomes multiplayer, right? Like here, there's a single player element, you're not [00:25:00] modeling any other people involved.[00:25:01] And that is a whole other thing.[00:25:04] Fan-yun Sun: But in fact, we can really do multiplayers. Oh yeah, okay. I haven't seen any double situations. So just actually just like prompt our, our model to say, Hey, like configure to multiplayer. Then it'll do like this. You'll be able to configure multiplayer[00:25:16] swyx: great[00:25:17] Fan-yun Sun: persistency database for you.[00:25:18] Easy. Yeah.[00:25:19] Vibhu: So what, what are like some of the current limitations in where we're at? So there's one approach of like, okay, scale up video predictors. Obviously there's data issues. With approaches like this, is it data constraints? What are like the next steps? Is it real time? Like, so there's one side of, write an agent to write Unity code, but okay, I want to be streaming a game real time.[00:25:38] I want to have characters being also like agent, but where, where do we kinda see this scaling up? Right?[00:25:44] Fan-yun Sun: Yeah, there's definitely a data constraint. Like the more data, the, the better. This reasoning model can almost basically act as humans to like operate a variety of tools and softwares to build whatever's necessary.[00:25:57] And then there's a sort [00:26:00] of fidelity constraint, which we're actually solving with another model, which we can talk about later. But it's like, it's not as easy to get to photorealism with the approach that we're taking. But we think there are better solutions to that, which is we can dive into later.[00:26:14] Later.[00:26:15] Vibhu: The one one thing you note here is it's a diffusion model, right? So there's, there's a few approaches, diffusion caution, splatting, yeah, so Ry diffusion model, you guys wanna[00:26:25] Fan-yun Sun: Yeah.[00:26:25] Vibhu: Introduce,[00:26:26] Fan-yun Sun: yeah, totally.[00:26:26] Rie: Neural Rendering & Skins for Worlds[00:26:26] Fan-yun Sun: So within our world modeling framework, we think there are two models that we train, right?[00:26:31] Like, there's the multimodal reasoning model that we just talked about that essentially handles. Mainly the, the causality, the persistency and logic determinism of the world. And then RY is our bet on saying, okay, like while all those model, can take care of all these things that we just talked about, it's limitations compared to existing, say, video models, is that it doesn't have as high of a pixel [00:27:00] ality right off the gate, right?[00:27:02] And EE is to say, Hey, we can actually take whatever persistent representation that we generate with our multimodal reasoning model and learn to restyle it into photo photorealistic styles or arbitrary styles you want. So this model is almost to say, Hey, I'm going to respect the persistency and interactivity of the world that you created, but my only job is to make sure that its pixel distribution is close to what we want.[00:27:29] Vibhu: Yeah.[00:27:30] swyx: Great example right there. You kept the KL divergence.[00:27:33] Fan-yun Sun: Oh. Where,[00:27:34] swyx: no, no. I mean this, this is a, a classic like, how you don't stray too far from the source material as you, you kept the kl, which is Oh yeah. Kind of cool. Yeah.[00:27:43] Fan-yun Sun: Yeah.[00:27:44] swyx: I mean, and the[00:27:44] Chris Manning: difference is, and I mean sun was pointing at this, where sort of saying it's in one way a more difficult path, but a better path that, typically the diffusion models are producing the whole scene and it looks lovely, [00:28:00] but there isn't spatial understanding behind it, which is allowing for the real time graphics gameplay, the spatial intelligence, understanding the consequences of worlds where this is, taking a path where it is assuming an abstracted semantic model of the world's state.[00:28:20] And then the diffusion model is then being used on top of that to produce the high quality graphics.[00:28:27] swyx: Is there an intended practical, or business use for this, or is it like a, like a demonstration of capabilities?[00:28:34] Fan-yun Sun: We actually believe that this is gonna be the next paradigm of rendering. So it's gonna replace how ra raizer, it's gonna replace DLSS today because it not only has these pixel prior that's learned from the world such that you can literally play any game in photo realistic styles, which is a lot of people's desire when they do GTA, right?[00:28:51] Like,[00:28:51] Vibhu: all the mods, all the people adding perfect lighting and all this.[00:28:54] swyx: So[00:28:54] Fan-yun Sun: skins[00:28:55] swyx: for worlds, let's call it[00:28:56] Fan-yun Sun: skins, let's call it skin for worlds. I,[00:28:58] Vibhu: it's also like, you can call it skin, you can call it [00:29:00] customization. You can play it how you want, right?[00:29:01] Fan-yun Sun: Yeah, exactly. And I think another thing that we really pointed out specific specifically in this blog is the programmability of it, right?[00:29:09] So what this means is that this render historically render is always a derivative of the game state, right? You're saying, oh, here's the game state, I'm rendering out a frame. But here I'm saying actually this render can be part of the gameplay loop. I can say something along the lines of, if upon getting 10.[00:29:26] Apples, I'm gonna, my weapon of choice, my bullet's gonna turn into apples. And that's, that's possible because we can say, we can basically dynamically have certain game state trigger the, the preconditions to the render such that the rendering is now part of the game loop too. One thing is to just say, okay, it's, it's, it's the appearance.[00:29:47] But the second thing is also to say there's these novel interactions that are possible because this render now has actually priors of the world.[00:29:57] swyx: It is up to the artist to figure out what to do with it.[00:29:59] Fan-yun Sun: It [00:30:00] is up to the creators. Yes.[00:30:01] swyx: Yeah.[00:30:01] Fan-yun Sun: And I also think that's actually another big argument that we're making and the reason that we're picking, taking the bet we're baking is that a lot of the times, whether it's for embody AI gaming, like you want a layer where human can inject their intentions.[00:30:15] So, for example, let's just say in the context of gaming, it's obviously like my creative intent, but maybe in the context of embodied ai, it's like, oh, like I take this foundational policy and I want to actually fine tune it to deploy in my house. So you want to almost say, inject, have a layer where human can say, oh, here's the distribution of things I want to create to achieve my goal.[00:30:35] And I think 3D graphics as it as it is today, is basic, the layer for people to say, Hey, what do I care about in this world? And it allows, basically human intent to be expressed in these worlds much more explicitly and distributionally as opposed to just saying, Hey, I'm gonna generate like, arbitrary.[00:30:54] And it's like just prompts,[00:30:55] swyx: it's one of those things where like, I think you, you're going to build up a series of models, right? [00:31:00] This is just one of, this is probably like the highest utility or heaviest, frequency one, I don't dunno what to call this. Where like you Yeah. You can immediately drop this in on any game and you don't need anything else that.[00:31:10] That you guys do. But, I, I could see, I could see that I think the, the human intent is something that people are not even used to because we're so used to static worlds or, worlds that just don't react, or, I don't know. It's, it, you're kind of blowing my mind right now with like, I'm, I wonder if you've talked to people at GDC Hmm.[00:31:27] And what are they gonna do with it?[00:31:30] Fan-yun Sun: Yeah. Now the stance that we take on this front is like, we're not gonna be more creative than our users to ship[00:31:35] swyx: it out.[00:31:35] Fan-yun Sun: Yeah. But we wanna make sure that we're building things in a way that really allows them to express their intent.[00:31:41] swyx: The thing that you said about, here's the distribution that I want.[00:31:45] I think text may be too low of a bandwidth to. To really demonstrate, because I, I, there, I'm, I'm probably just gonna want to drop in a bunch of, reference assets and then you can figure it out from[00:31:58] Vibhu: there. But you probably wanna do a, a mixture of [00:32:00] both, right? Like you throw in a few images. I wanted this style.[00:32:02] Yeah. I want it to look like this. So it, it's, it's a mixture, right?[00:32:05] Chris Manning: I, I think it's a mixture. I mean, yeah, I mean there's clearly a visual component of this, and it's not that, everything can be text. ‘cause of course you want to give a visual look, but there's also a massive amount of giving the overall picture of the look of the world and the behavior of things that you can express in a few words of text.[00:32:32] And it be very time consuming and difficult to do via visual means. So I think, yeah, you want a combination of both.[00:32:40] Evaluating World Models[00:32:40] Vibhu: So one question I kind of have is, how do we go about evaluating world models? So like, there's many axes, right? One is like, okay. I have preferences. How well do we adhere to prompts? One is the simulation.[00:32:50] One is like do things, is there core logic that's broken? So coming from we know how to evaluate diffusion, there's fidelity, there's [00:33:00] stuff like that. But what are some of the challenges that most people probably aren't thinking about?[00:33:04] Fan-yun Sun: Yeah, I think this is like a great question and probably one of the hardest questions in role models because like, I think it always comes back to what are you building this role model for?[00:33:13] And depending on your end goal and purpose, the evaluation should defer. So in the context of games, then the most direct way of measuring is how much behind are people actually spending in this world that you create? And if your goal is to say, for example, in the context that we just talked about, like, hey, deploying, deploying action in body, a agent, then your, your end.[00:33:33] Metric is then, okay, after training in these worlds that you generate how robust it is to when you actually deploy to the target environment. But then, it's, it's hard to measure these end metrics. So today people have like these proxy metrics that I call that basically try to measure what we really care about, which is the end metrics, but then frankly it's different for every use case.[00:33:57] Yeah,[00:33:57] Vibhu: which seems like quite a challenge, right? Like in [00:34:00] in language models or video models. Image models, your benchmarks are proxies, right? People aren't actually asking instruction, following tool use questions. They're proxies of how well it will do downstream. But for this, so like, should teams, should companies have their own individual benchmarks outside of games?[00:34:16] If you think of stuff like, okay, video production, movies, stuff like that, that also want to use world models. Should, should they sort of internalize like. Their own proxy. Is this something you guys do? Where, where does that connect[00:34:28] Chris Manning: go? Yeah, I think this whole space is extremely difficult as things are emerging now.[00:34:35] And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks and could you answer the question based on these documents and the various other kinds of, do pieces of logical reasoning or math.[00:34:58] But again, these are sort of. [00:35:00] And there were sort of visual equivalents of things like object recognition, right? For these small component tasks. These days so much of what people are wanting to do also with language models is nothing like that, right? You're wanting to, have an interaction with the language model and get some recommendations about which backpack would be best for you for your trip in Europe next month.[00:35:25] And it's not the same kind of thing, right? And it's not so easy to come up with a benchmark as to does this large language model give you an effective interaction for guiding you in a good way for shopping, right? So, and it's the same problem with these world models. So if we take the game design case, well success is that a game designer can.[00:35:57] Produce what they are [00:36:00] imagining in a reasonable amount of time. And that's really the kind of macro task. That's a very hard thing to turn into a benchmark and I think a lot of this is actually going to turn into people walking, walking with their feet. Right? I mean, I guess that's what's happening, at the large language model level, right?[00:36:23] When people are choosing to use, GPT five or Gemini or clawed, individuals are trying out these different models and deciding, oh, I like the kind of answers that GT five gives me, or no, I feel like I get more accurate detail from Claude, right?[00:36:43] Vibhu: It's a lot of[00:36:43] Chris Manning: vitech, a lot of people just using it.[00:36:45] It's vibe checking. I realize that, but it's actually whether. People feel it's giving them utility in what they want. Right.[00:36:52] Vibhu: And the the interesting thing there is like a lot of people prefer the visual, right? This looks pretty, which is not the objective of what this is [00:37:00] for, right? It's if a, if a game designer is working on something, they care about the game engine, right?[00:37:04] The state, it's, it can look whatever. You can fix that up later. Or you can have a really good game state and you can quickly edit it to 20. 20 different versions, like Keep State,[00:37:14] Chris Manning: right?[00:37:14] Vibhu: So[00:37:14] Chris Manning: that's a really important distinction, for and for speaking to Moon Lake strength, right? So, yeah, great visuals are lovely to look at for a few seconds, but gains are really all about the concept, the game play.[00:37:33] And a lot of the time that doesn't actually even require great visuals. I mean, there are just lots of very successful games which have relatively primitive visuals, and there are other games where people have spent millions producing photo realistic, visuals, and the game sucks, right? So, keeping those two axes apart is really important in thinking about what's important in a [00:38:00] world model for different uses.[00:38:02] swyx: This conversation is reminding me of some game review and fiction discussions I've, had in my sort of non-AI related life. Some, for some people might know Brandon Sanderson, who's a very famous, fiction author, had, is is a big game reviewer. And he, he's a big fan of video games where you change one thing about a normal what you might assume about, about the world.[00:38:22] For example, Baba is you, I don't know if you might have come across that, where like the rules change as you play the game. And also like where, you can do things like reverse time selectively or like change gravity selectively. And I think this is also reminds, reminds me of other kinds of world models that are created by authors.[00:38:38] Where Ted Chang is, is my typical example where he'll take the world that, you know today, but change one thing about it and, but then create a consistent world based on that. Which is long-winded answer of me to, of. For me to say is it's it easy to create alternative roles that don't exist, but you change one thing and then let's, let's run a whole bunch of people through it to see if it works.[00:38:58] Chris Manning: My first dance will [00:39:00] be, that seems a lot easier and more conceivable to do using Techn technology like Moon Lakes than with some of the other world models out there, where the sun can actually make it happen. I'll let him give a second answer.[00:39:15] swyx: If I guess for you, you're constrained by the game engine tool, right?[00:39:18] Like at the end of the day, that's the, that's the thought, partner that you have. If I ask for something where like, if it never is allowed to reverse time or if gravity only ever works one way, then well that's it. But sometimes gravity might change,[00:39:33] Fan-yun Sun: but it's a lot easier to change with code as opposed to a model that is learned primarily on data of.[00:39:42] Real world and virtual worlds that are, I guess, like for example, junior, like there's actually trained on a lot of real world data and a lot of virtual gaming data, and it's hard to say maybe it's easier to say, okay, I wanna change the visuals in like the time period of, of the world. Like, you can't change gravity, for [00:40:00] example.[00:40:00] Vibhu: I feel like you can to light bounds, right? Everything comes down to like, code is a better way to execute it, but the models aren't that diverse and creative, right? You can say, okay, make gravity slower. It can do that, but it's limited to your representation of how you text it out, right? Like they're, they're only gonna do a few iterations, whereas programmatically, if there's a game engine under the hood, you can kind of go wild, right?[00:40:22] So one of the, I dunno, one of the limitations of most models is that they're very overtrained to one style. Right. And extracting diversity is pretty difficult. At least that's something we've seen.[00:40:35] Fan-yun Sun: I mean, are there examples you have in mind where you Existing models? Yeah. Like it would be easier to do that's not using code.[00:40:43] Certain types of creative intent or like transition state transitions,[00:40:47] swyx: Clipping, other models, other wo models are very good at clipping through things. Clipping my, my, my legs clipping through a rock because it's, it's just, it's just bad. [00:41:00] Like, you would have to struggle very hard with your stuff to actually make that happen.[00:41:04] Which I think is maybe a topic that you actually prepared on, Gian Splatting versus, the other stuff.[00:41:09] Vibhu: Yeah. Yeah. It's just for those not super familiar, right? There's a, there's gian splatting, there is diffusion. Like what works, what scales up. I feel like in February when Soro one came out the blog post was literally titled like,[00:41:21] swyx: you bring it up.[00:41:22] You never know.[00:41:23] Vibhu: World, world, video generation models are world simulators. It's super bitter lesson pilled. Yeah, emer, a lot of it is emergence, right? So, not to go through their blog post, basically their whole thing was as you scale up all this consistency, all this stuff just kind of solves, it's a very simple premise, right?[00:41:41] They just scaled up, diffusion, and from there, this is, this is Feb 2024, how much can we, it's already been two years, which is basically five years. How much more in AI time do we need to just scale up or, or do we hit a data cap? But I think we already talked about this a lot, right? Like this is back to the beginning discussion of what's [00:42:00] appropriate for the time.[00:42:01] And that seems like your approach, right?[00:42:03] Fan-yun Sun: Yeah. The point I'm trying to make is that they're very many, many different types of world simulators and like having a world simulator that can produce pixel coherency is very, very useful for games and, marketing and all these things, but it's not as useful as people think when it comes to causal reasoning.[00:42:25] When it comes to embodied ai. Yeah, like it this title is true. We're not saying that it's, it's like, not a great world simulator, but actually in the blog that we, we, we, we wrote, the bet is more so that there are gonna be disproportionately large share of value of real world tasks or, and virtual tasks where high resolution pixel fidelity is not needed.[00:42:47] Yes. Video models have their values.[00:42:50] swyx: Yeah. This is at the absolute limit of my physics understanding, but one example that comes to mind is basically having to solve like ba the equivalent of a three [00:43:00] body problem in a deterministic Well, where the video models, which is approximated good enough. Yeah.[00:43:08] Right. Like there's, there's some point at which your approach kind of runs into like the you now have to simulate the world. Please, thank you very much. And like you're trying to do that, but only to the extent that the game engine lets you and like game engines cannot do some things.[00:43:23] Fan-yun Sun: Yeah, no, I mean, I think the interesting or more technical question here actually is where do you draw the boundary between.[00:43:32] What's handled with, let's say, diffusion prior and what, when? What's handled with symbolic priors?[00:43:38] swyx: Yes.[00:43:38] Fan-yun Sun: Okay.[00:43:38] swyx: Okay.[00:43:39] Fan-yun Sun: Right. Let's go there. Because this, this boundary can actually be fluid. Like I think like maybe what you're trying to get at is like, okay, people are saying pixel prior, everything. But what we're saying is, okay, there's a boundary that we draw where this is where we think provides the most economical value for the domains and things that we care about today.[00:43:59] [00:44:00] And I actually do think, and it's something that we do internally all the time, which is like, okay, given new equations that we learn or new elements of the world and that we, we learn, or maybe some other knowledge that we acquire in the process of developing the models. Should we still be maintaining this line exactly as it is today?[00:44:22] Or should we move it a little bit left or a little bit right? Right. Like sometimes that we realize that, oh, like maybe customers or, or folks like want certain things that are better handled with preop pryor as opposed to, symbolic prior than,[00:44:34] swyx: yeah. Your, your skin thing is a, is a example moving it, right.[00:44:37] Yeah.[00:44:37] Or left. Yeah,[00:44:37] Fan-yun Sun: exactly.[00:44:38] swyx: I dunno what the, the left right is.[00:44:39] Fan-yun Sun: Yeah, yeah, yeah. No the, the model.[00:44:42] swyx: Yes.[00:44:42] Fan-yun Sun: Actually we have a few iterations of them. They're actually at slightly different[00:44:45] swyx: I know boundaries. You should, you should do that. That's a cool dimension to show.[00:44:49] Fan-yun Sun: Yeah.[00:44:50] swyx: Is quantum mechanics the diffusion prior of our world?[00:44:55] Right. It's like that's the boundary of classical mechanics versus quantum. Right? Like, that's it. At one [00:45:00] point God plays dice and the other point doesn't.[00:45:02] Fan-yun Sun: I dunno if Chris, you wanna say it, but I think, I think generally I feel like physics is better with symbol P priors.[00:45:08] Chris Manning: Even quantum physics.[00:45:09] Fan-yun Sun: Even quantum physics.[00:45:11] swyx: Yeah. This is starts against to, MLST territory is, is what I call it, where, he, he likes to get philosophical. We, we we're quite friendly.[00:45:18] Vibhu: I mean, we need to get, we need to get singularity. I heard some of that.[00:45:23] swyx: No, no, I think that is actually really helpful and man, I just want you to productize this like, as a product guy, I'm just like, oh, also[00:45:32] Vibhu: a gamer, I[00:45:33] swyx: wanna, it's like a researcher, like, it's cool.[00:45:35] Like this is a, the theoretical, like you have a very good, I don't know, like the way of thinking about these things, but I just wanna see you like, express it. I do think like your fundamentally things when, when you leave open new tools, like, okay, use, use human intent to incorporate it into how you render.[00:45:52] Artists are gonna have to take like two to three years to figure out what to do with this. And you just don't know.[00:45:57] Chris Manning: Right. But I think, this is, [00:46:00] gives a much more approachable and controllable world for the society, which is the beauty, the beauty of, NLP, that that will enable it to be adopted and used.[00:46:10] And we are very hopeful about that. Yeah,[00:46:13] Fan-yun Sun: yeah. Yeah. I mean, we are, we are very focused actually on commercialization in the sense that like we do, we do really believe in the data flywheel app approach. Yeah. Where, we put this in the hands of the creators and the users and then they will teach us when, what capability our model should improve.[00:46:27] And that's why we are, we are actually, like products and beta[00:46:31] swyx: Yeah. Focusing on gaming. What, what's like the adjacent thing to gaming[00:46:34] Fan-yun Sun: embody adjacent, basically. So maybe we can, we can I'll maybe start with where we see the platform in three years. Yeah. Which is like, okay. The users would tell us what they want to achieve.[00:46:45] The end goal could be, Hey, I just, I wanna make something to teach my kids the value of humility. Or it could be, Hey, I wanna fine tune my, drones to be really good at rescue situations. I could be vacuum robots. I want to like train [00:47:00] my manipulation or like vacuum robot to be very robust to my office, right?[00:47:04] But it's like, whatever it is, scenario robust to[00:47:06] swyx: my office[00:47:07] Fan-yun Sun: or like navigate very robustly in my office. But then it's like, whatever end goal that you want, our role model will say, okay, given what you want to achieve, let me generate a distribution of environments such that I can train and evaluate whatever it is you want.[00:47:24] Yeah. Right. Maybe for the purpose of games, it's just the end simulation and that's the end product for certain policies. It's like I can train it within these environments and then help you see where your policy is failing or not. Yeah. And then, so I think,[00:47:37] swyx: so in that case, much more of a training tool.[00:47:40] Than in other training[00:47:41] Vibhu: evaluation? Both. Right?[00:47:43] swyx: Sure. Same. Same thing.[00:47:43] Fan-yun Sun: Yeah, same thing. I think it's just this role model that allows people to train any policy that can act in any multimodal environments.[00:47:51] swyx: Would it be harder to reward hack? Is there an angle here where it is harder to reward hack? Like it's just, I'll just put it generally because I think that's a, that's obviously a key [00:48:00] problem that a lot of people face when in training agents in these environments, and I don't know, can you solve it?[00:48:07] Chris Manning: I think not necessarily. To the extent that there's a mis specified reward that. It seems like it could be hacked in a more symbolic world or in a more pixel based world. I dunno if Sun's got any thoughts, but I don't think that's really being solved.[00:48:26] swyx: The other thing that comes to mind is just you could just build a better sawa as a video generator model, right?[00:48:31] Because then you, you would move the diffusion, side a bit more further to the right. I think if I got the directionality correct. And that's it.[00:48:40] Vibhu: It's better on domains, right? Like on consistency over now, or for sure it exists versus something doesn't, right.[00:48:46] Chris Manning: So[00:48:46] swyx: yeah. Yeah. Is[00:48:49] Vibhu: is a question more like, like[00:48:51] swyx: I'm just riffing on like, how do you, what can you build, you know?[00:48:54] Oh, with the stuff that you have. I do think that the minor, the academic does go immediately to training [00:49:00] and in eval evaluation, but like art tends to take unusual directions. Like you might end up,[00:49:06] Chris Manning: okay. Yeah. But the question is, can you use this piece of software to develop compelling gameplay and. I don't think you can take SOAR and produce compelling gameplay, right?[00:49:19] If you want to have a world that you can wander around in a bit, you are good. But what are your abilities to have gameplay mechanics implemented the way you'd like them to be and to have things stay, with the long-term history of your gameplay that influences future actions. I think there's just nothing there for that.[00:49:39] swyx: Yeah, I do tend to agree. I, I'm just trying to sort of test the boundaries. I would also make the observation that as AAA games industry has developed the line between what is a movie and what is a game has blurred. And you, you, you do end up basically producing a two hour movie as part of your game.[00:49:57] Fan-yun Sun: No, honestly, there, there's so many actually [00:50:00] applications in adjacent markets that our world model can go into. Yeah. But yeah, it, it's sort of fun to riff, riff on. Although on the execution side, we we, we need to stay focused with like, okay, what are the capabilities we want to unlock over time?[00:50:11] And there's a roadmap for that. But yeah, if we're just riffing on sort of like the possibilities, I feel like, whether it's endless Yeah, it's like classic[00:50:18] swyx: and the embedding for a possibility and endless in my mind, it's very close. Yeah. I do wanna, focus on one, like weird choice. I, I don't know if it's weird.[00:50:28] Maybe I'm, I got something here. Audio, right? You could have just said no audio And audio in my mind has a lot of recursion, whereas in video you can just do recasting and that's much computationally much simpler. Audio just seems way harder. I don't know if you wanna just comment on just the special 3D audio.[00:50:46] Problem. Did you really have to do it? I guess you do to be immersive, but like a lot of people do treat it as like, well, you just stick a, a tt S model on top of[00:50:57] Vibhu: Well, there's a lot more to game audio than [00:51:00] just speech. Right. It's not just[00:51:01] swyx: tts. Yeah. Tts. S Fxt, GM Spatial in my mind Echoes[00:51:06] Chris Manning: Yeah.[00:51:06] swyx: And reflections.[00:51:07] And I, I don't even know what's, what else? I don't know what, what other problems in this space.[00:51:13] Fan-yun Sun: Yeah, I think this point like the, it's sort of a more, more pointing to the benefits of using an game engine as a tool that's available to the model, right? Because like part of the spatial audio is from the code that is underlying the simulation.[00:51:32] And while we do give our model access to other types of audio models as. Tools.[00:51:39] swyx: None of them would be spatial, I think.[00:51:41] Fan-yun Sun: But that's exactly sort of more 0.2. We're giving our model an abstraction or a suite of tools such that it's able to achieve that. And you can argue that sort of spatial is like a, like a emergence out of the, the tools that we and abstraction that we provide to the agents.[00:51:59] And I think that's the beauty of [00:52:00] this, this, this approach is like there's a lot of things kind of like how human's built technology and they're like Lego blocks that build on top of each other. And it's the same thing here. There's gonna be things that sort of just sort of emerges from being able to put these things together in like combinatorially interesting ways,[00:52:14] Chris Manning: right?[00:52:15] So this integrated audio model exploits the understanding and semantics of the Moon Lake world, right? And whereas in general for the Gen AI video models. There's no actual integration across to audio at all, right? That someone might stick some music or stick a soundscape or whatever else on top of their video.[00:52:44] So it's not a silent video, but they're in no way connected into a consistent world model. And there's nothing that's okay. An action is happening in the video. Therefore there should be a sound that's [00:53:00] coming from this part of the visual field.[00:53:03] swyx: Yeah.[00:53:03] Vibhu: Is that different than Sora too? Does it not have audio?[00:53:06] Not to say it's not like[00:53:08] swyx: amazing[00:53:08] Vibhu: isn't a spatial[00:53:09] swyx: audio.[00:53:09] Vibhu: It doesn't,[00:53:10] swyx: no. I've played around it with it enough. It just sounds like someone put an 11 laps voice on top of it and just tried to do the lip sync.[00:53:18] Vibhu: Oh, yeah. I've seen, okay. Generate a dog at the beach and reactions to big wave and move[00:53:23] swyx: around.[00:53:23] It's definitely like, so have the dog, have the dog move away from camera and see if the, the song goes down. It doesn't. ‘Cause they don't have facial audio.[00:53:32] Fan-yun Sun: We do want to basically like we, our moral model, like the one we're training is basically towards the goal of having a combined latent representation across all these different modalities.[00:53:42] Right? Such that it can like reason across these different modalities. So for example, if I close my eyes and like you play a video, you play a sound of like a car skidding away from me. I almost can like, visually extrapolate that trajectory in my mind. And I think that type of capability, we want our model to be able to reason, right?[00:53:59] And that's the reason that [00:54:00] we're sort of taking this multimodal reasoning approach. It's like we want this combine late in space that can[00:54:05] swyx: Yeah. Oh, you said late in space. We like that. Here we have to play the, the bell Every time that someone says late in space, no, you gotta train daredevil one. Where you, you, you, it's only audio, but you have to work out.[00:54:15] Where everything is.[00:54:19] Cool. I I think that that was, that was about it for our Moon Lake coverage. I do think that we have like a couple of, Chris Madden questions on, on IR and, just any, any other sort of attention topics or n NLP topics.[00:54:31] Vibhu: Okay.[00:54:31] swyx: Go ahead.[00:54:32] Chris Manning's Journey: From NLP to World Models[00:54:32] Vibhu: Well, no, I mean, yeah, it's just fun. We talked a bit about how you guys met, but you basically, you, you were like the godfather of NLP per se, right?[00:54:39] You spent the whole career from early embeddings, early early attention. You did 2015 attention for machine translation, everything. You, you had information retrieval, so RAG before rag, we just wanna shout that out and admire a lot of that. Right? So what prompted the switch over to world models?[00:54:56] How, how'd all that come about?[00:54:58] Chris Manning: To some answer it [00:55:00] is, the enthusiasms and creativity of students, but there's a bit of a history there, right? So, yeah. So clearly most of my career has been doing stuff with language and how I got into research was thinking, ah, this is just so amazing how humans can produce speech and understand each other in real time.[00:55:21] And somehow they managed to learn languages from their kids. How could this possibly happen? And so, yeah, starting off I was very focused on language, but as it sort of got into the 2000 and tens, I started, going, I'd been working on question answering, and then I started to get, interest in visual question answering.[00:55:42] And that was an area where it was very noticeable. That the visual understanding was bad. Right. These were the days when like, it sort of seemed like there's almost no visual [00:56:00] understanding. You were just getting answers that came from priors. So, if you asked how many people are sitting at the table, it'd always answer two regardless of how many, how many people you could see in the picture.[00:56:11] And so it seemed like, oh, these models actually aren't able to get semantic information outta

Send us a message or question! Episode available on general release on Wednesday 1st April. Episode SummaryIn the final episode of Series 4, Jane and James are joined by historian and former RAF intelligence officer Andrew White to explore a lesser-known aspect of the Second World War air war: the internment of Allied airmen in neutral countries.When aircraft came down in neutral territory, aircrew entered a complex legal and diplomatic grey area — neither prisoners of war nor free to return home. Drawing on Andrew's research and experience, this episode examines how internment worked in practice, how different countries interpreted their obligations, and what life was like for the men caught in between war and neutrality.We explore the legal framework governing internment, the countries involved, the lived experiences of interned airmen, and the moral and political tensions that shaped their treatment. The discussion also touches on escape attempts, repatriation, and the controversial question of whether some airmen may have sought internment deliberately.As Series 4 draws to a close, this episode reflects the podcast's wider aim — to go beyond operations and aircraft, and to uncover the human stories and complexities behind the bombing war.What We Cover What internment meant under international law during WWII  Which nations interned British and Allied airmen  Living conditions and day-to-day experiences of interned crews  How politics and neutrality influenced treatment  Changes in policy as the war progressed  Repatriation and the duration of internment  Escape attempts from neutral countries  The controversial idea of “choosing” internment About Our GuestAndrew White is a retired RAF intelligence officer (Wing Commander) who served from 1985 to 2011, including operational tours in Northern Ireland, Bosnia, and Iraq.He now works as a battlefield guide and military historian, and is the author of three published biographies of airmen from the First and Second World Wars.Series 4This episode marks the final instalment of Series 4 of Never Mind the Dambusters.Across the series, we've explored a wide range of topics, including: RAF Bomber Command operations and strategy  The Short Stirling and De Havilland Mosquito  The Peenemünde raids and V-weapons programme  The USAAF bombing campaign over Schweinfurt  Bomb disposal in Hamburg  Cold War bombers and evolving air strategy Thank you to all our guests — and to everyone who has listened, supported, and joined the conversation along the way. Support the showPlease subscribe to Never Mind The Dambusters wherever you get your podcasts. You can support the show, and help us produce great content,  by becoming a paid subscriber from just $3 a month here https://www.buzzsprout.com/2327200/support . Supporters get early access to episodes and invitations to livestreams.  Thank you for listening! You can reach out to us on social media at @RAF_BomberPod (X) or @NeverMindTheDambusters (Instagram)You can find out about James' research, articles, lectures and podcasts  here .You can read more about Jane's work on her website at https://www.justcuriousjane.com/, and listen to podcasts/media stuff here

Send us Fan MailInsanity After Dark: The Ultimate Horror Movie ShowdownComing off the heals of The Insanity After Dark: The Ultimate Sci-Fi Movie Showdown, Dave Goldfinch aka The Podfather from the On the Bench Podcast, Andrew White aka Whitey from the ModelGeeks Podcast and Rob Riv from the Modeling Insanity Podcast battle it out to talk about their favorite Horror movies of all time.  Lots of laughs, lots of insults, and lots of chaos ensue on this hilarious and inciteful special episode of the Modeling Insanity.  Sit back, have a few laughs and enjoy....Thanks goes to Sullivan King for the awesome rendition of the Exorcist Theme Music used at the end of the show.Opening and end music by Supernova by Arthur Vyncke https://soundcloud.com/arthurvostMusic promoted by http://www.free-stock-music.comJoin the Podcast on Facebook on The Modeling Insanity Podcast PageEmail the Insanity Crew at modelinginsanitypodcast@gmail.com for any comments or suggestions.

Send a textInsanity After Dark: The Ultimate Sci-Fi Movie ShowdownComing off the heals of The On the Bench: After Dark War Movie Debate, Dave Goldfinch aka The Podfather from the On the Bench Podcast, Andrew White aka Whitey from the ModelGeeks Podcast and Rob Riv from the Modeling Insanity Podcast battle it out to talk about their favorite Sci-Fi movies of all time.  Lots of laughs, lots of insults, and lots of chaos ensue on this hilarious and inciteful special episode of the Modeling Insanity.  Sit back, have a few laughs and enjoy....Thanks goes to Armstrong for the awesome rendition of the Terminator Theme Music used at the beginning and end of the show.  Opening and end music by Supernova by Arthur Vyncke https://soundcloud.com/arthurvostMusic promoted by http://www.free-stock-music.comJoin the Podcast on Facebook on The Modeling Insanity Podcast PageEmail the Insanity Crew at modelinginsanitypodcast@gmail.com for any comments or suggestions.

In this episode, Joe Williams speaks with Andrew White about how the digital economy is reshaping inequality, work, and the social contract. Drawing on the themes of his book Inequality in the Digital Economy: The Case for a Universal Basic Income (Palgrave Macmillan, 2024), our conversation explores why technological progress has not translated into shared prosperity, how structural features of digital markets concentrate power and wealth, and what this means for the future of work and social policy. We discuss universal basic income as part of a broader attempt to rethink how societies provide security and dignity in an era of automation, and consider what a more sustainable and humane economic model might look like in practice. Joe Williams website here - Censorship and Sacralisation of Politics in the Portuguese Press during the Spanish Civil War- "Year X of the National Revolution" — Salazarist Palingenetic Myth in the Diário da Manhã Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

In this episode, Joe Williams speaks with Andrew White about how the digital economy is reshaping inequality, work, and the social contract. Drawing on the themes of his book Inequality in the Digital Economy: The Case for a Universal Basic Income (Palgrave Macmillan, 2024), our conversation explores why technological progress has not translated into shared prosperity, how structural features of digital markets concentrate power and wealth, and what this means for the future of work and social policy. We discuss universal basic income as part of a broader attempt to rethink how societies provide security and dignity in an era of automation, and consider what a more sustainable and humane economic model might look like in practice. Joe Williams website here - Censorship and Sacralisation of Politics in the Portuguese Press during the Spanish Civil War- "Year X of the National Revolution" — Salazarist Palingenetic Myth in the Diário da Manhã Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

In this episode, Joe Williams speaks with Andrew White about how the digital economy is reshaping inequality, work, and the social contract. Drawing on the themes of his book Inequality in the Digital Economy: The Case for a Universal Basic Income (Palgrave Macmillan, 2024), our conversation explores why technological progress has not translated into shared prosperity, how structural features of digital markets concentrate power and wealth, and what this means for the future of work and social policy. We discuss universal basic income as part of a broader attempt to rethink how societies provide security and dignity in an era of automation, and consider what a more sustainable and humane economic model might look like in practice. Joe Williams website here - Censorship and Sacralisation of Politics in the Portuguese Press during the Spanish Civil War- "Year X of the National Revolution" — Salazarist Palingenetic Myth in the Diário da Manhã Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

In this episode, Joe Williams speaks with Andrew White about how the digital economy is reshaping inequality, work, and the social contract. Drawing on the themes of his book Inequality in the Digital Economy: The Case for a Universal Basic Income (Palgrave Macmillan, 2024), our conversation explores why technological progress has not translated into shared prosperity, how structural features of digital markets concentrate power and wealth, and what this means for the future of work and social policy. We discuss universal basic income as part of a broader attempt to rethink how societies provide security and dignity in an era of automation, and consider what a more sustainable and humane economic model might look like in practice. Joe Williams website here - Censorship and Sacralisation of Politics in the Portuguese Press during the Spanish Civil War- "Year X of the National Revolution" — Salazarist Palingenetic Myth in the Diário da Manhã Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

In this episode, Joe Williams speaks with Andrew White about how the digital economy is reshaping inequality, work, and the social contract. Drawing on the themes of his book Inequality in the Digital Economy: The Case for a Universal Basic Income (Palgrave Macmillan, 2024), our conversation explores why technological progress has not translated into shared prosperity, how structural features of digital markets concentrate power and wealth, and what this means for the future of work and social policy. We discuss universal basic income as part of a broader attempt to rethink how societies provide security and dignity in an era of automation, and consider what a more sustainable and humane economic model might look like in practice. Joe Williams website here - Censorship and Sacralisation of Politics in the Portuguese Press during the Spanish Civil War- "Year X of the National Revolution" — Salazarist Palingenetic Myth in the Diário da Manhã Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Editor's note: Welcome to our new AI for Science pod, with your new hosts RJ and Brandon! See the writeup on Latent.Space (https://Latent.Space) for more details on why we're launching 2 new pods this year. RJ Honicky is a co-founder and CTO at MiraOmics (https://miraomics.bio/), building AI models and services for single cell, spatial transcriptomics and pathology slide analysis. Brandon Anderson builds AI systems for RNA drug discovery at Atomic AI (https://atomic.ai). Anything said on this podcast is his personal take — not Atomic's.—From building molecular dynamics simulations at the University of Washington to red-teaming GPT-4 for chemistry applications and co-founding Future House (a focused research organization) and Edison Scientific (a venture-backed startup automating science at scale)—Andrew White has spent the last five years living through the full arc of AI's transformation of scientific discovery, from ChemCrow (the first Chemistry LLM agent) triggering White House briefings and three-letter agency meetings, to shipping Kosmos, an end-to-end autonomous research system that generates hypotheses, runs experiments, analyzes data, and updates its world model to accelerate the scientific method itself.* The ChemCrow story: GPT-4 + React + cloud lab automation, released March 2023, set off a storm of anxiety about AI-accelerated bioweapons/chemical weapons, led to a White House briefing (Jake Sullivan presented the paper to the president in a 30-minute block), and meetings with three-letter agencies asking “how does this change breakout time for nuclear weapons research?”* Why scientific taste is the frontier: RLHF on hypotheses didn't work (humans pay attention to tone, actionability, and specific facts, not “if this hypothesis is true/false, how does it change the world?”), so they shifted to end-to-end feedback loops where humans click/download discoveries and that signal rolls up to hypothesis quality* Cosmos: the full scientific agent with a world model (distilled memory system, like a Git repo for scientific knowledge) that iterates on hypotheses via literature search, data analysis, and experiment design—built by Ludo after weeks of failed attempts, the breakthrough was putting data analysis in the loop (literature alone didn't work)* Why molecular dynamics and DFT are overrated: “MD and DFT have consumed an enormous number of PhDs at the altar of beautiful simulation, but they don't model the world correctly—you simulate water at 330 Kelvin to get room temperature, you overfit to validation data with GGA/B3LYP functionals, and real catalysts (grain boundaries, dopants) are too complicated for DFT”* The AlphaFold vs. DE Shaw Research counterfactual: DE Shaw built custom silicon, taped out chips with MD algorithms burned in, ran MD at massive scale in a special room in Times Square, and David Shaw flew in by helicopter to present—Andrew thought protein folding would require special machines to fold one protein per day, then AlphaFold solved it in Google Colab on a desktop GPU* The E3 Zero reward hacking saga: trained a model to generate molecules with specific atom counts (verifiable reward), but it kept exploiting loopholes, then a Nature paper came out that year proving six-nitrogen compounds are possible under extreme conditions, then it started adding nitrogen gas (purchasable, doesn't participate in reactions), then acid-base chemistry to move one atom, and Andrew ended up “building a ridiculous catalog of purchasable compounds in a Bloom filter” to close the loopAndrew White* FutureHouse: http://futurehouse.org/* Edison Scientific: http://edisonscientific.com/* X: https://x.com/andrewwhite01* Cosmos paper: https://futurediscovery.org/cosmosFull Video EpisodeTimestamps00:00:00 Introduction: Andrew White on Automating Science with Future House and Edison Scientific00:02:22 The Academic to Startup Journey: Red Teaming GPT-4 and the ChemCrow Paper00:11:35 Future House Origins: The FRO Model and Mission to Automate Science00:12:32 Resigning Tenure: Why Leave Academia for AI Science00:15:54 What Does ‘Automating Science' Actually Mean?00:17:30 The Lab-in-the-Loop Bottleneck: Why Intelligence Isn't Enough00:18:39 Scientific Taste and Human Preferences: The 52% Agreement Problem00:20:05 Paper QA, Robin, and the Road to Cosmos00:21:57 World Models as Scientific Memory: The GitHub Analogy00:40:20 The Bitter Lesson for Biology: Why Molecular Dynamics and DFT Are Overrated00:43:22 AlphaFold's Shock: When First Principles Lost to Machine Learning00:46:25 Enumeration and Filtration: How AI Scientists Generate Hypotheses00:48:15 CBRN Safety and Dual-Use AI: Lessons from Red Teaming01:00:40 The Future of Chemistry is Language: Multimodal Debate01:08:15 Ether Zero: The Hilarious Reward Hacking Adventures01:10:12 Will Scientists Be Displaced? Jevons Paradox and Infinite Discovery01:13:46 Cosmos in Practice: Open Access and Enterprise Partnerships Get full access to Latent.Space at www.latent.space/subscribe

Stories we're following this morning at Progress Texas:Dr. Peter Hotez, Texas' top medical hero, recommends that parents ignore nonsensical new childhood vaccine recommendations coming from the Trump administration, and instead rely on the expertise of their kids' doctors: https://www.tpr.org/bioscience-medicine/2026-01-05/texas-expert-says-changes-to-childhood-vaccine-schedule-are-a-grave-mistakeRepublicans in both Dallas and Hays counties have abandoned plans to hand-count ballots in the upcoming March primary: https://www.nbcnews.com/politics/2026-election/republicans-two-texas-counties-ditch-plans-hand-count-ballots-rcna252388Gubernatorial candidate Andrew White has dropped out of the race, citing fundraising hurdles - he throws his support behind Democratic frontrunner Gina Hinojosa, who graciously accepts: https://www.texastribune.org/2026/01/05/andrew-white-drops-out-texas-governor-democratic-primary/Michelle Davis at Lone Star Left reminds us that, amidst the news of the move on Venezuela, Big Oil is not an actual economic necessity for Texas - it's rather a symptom of Republican corruption: https://www.lonestarleft.com/p/texas-oil-american-warsOn this fifth anniversary of the January 6th uprising, we must renew the events of that day in our minds - not as a memory of a past threat, but as a reminder of what's at stake in the midterm elections: https://www.thebulwark.com/p/january-6th-and-the-never-ending-coup-trump-election-venezuela-stephen-miller-greenland?utm_source=substack&utm_medium=email&utm_content=shareEarly voting in the March primary starts in mere weeks, on February 17 - the time to research your ballot is right now: https://apps.texastribune.org/features/2026/texas-march-2026-primary-ballot/?_bhlid=7d8eca3d2a16adc7c9b44185414443fa32be6d84See the full list of 2026 races and candidates, courtesy of Lone Star Left, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠HERE ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠HERE⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Check out our web store, including our newly-expanded Humans Against Greg Abbott collection: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://store.progresstexas.org/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Thanks for listening! Our monthly donors form the backbone of our funding, and if you're a regular, we'd like to invite you to join the team! Find our web store and other ways to support our important work at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://progresstexas.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Executive Manager of Rundle Mall, Andrew White, joined Jonathan and Casey. See omnystudio.com/listener for privacy information.

Chuck Todd breaks down why this year’s elections may be local—but their impact will be national. From Virginia’s bellwether governor’s race to key contests in New Jersey and New York City, these results will offer a preview of the political mood heading into the 2026 midterms. Chuck dives into Abigail Spanberger’s cautious campaign strategy, Winsome Earle-Sears’ grievance-fueled messaging, and why Virginia voters rarely reward extremes. Plus, a look at how third-party candidates could shake up the New Jersey race and why Zohran Mamdani’s performance in NYC will signal the direction of the progressive movement. Veteran Nevada journalist and author of the upcoming book “The Game Changer”, Jon Ralston joins to break down how the Silver State became America’s ultimate political bellwether — and what that means heading into 2026. They explore how the state’s service-based economy, booming Hispanic population, and explosion of non-affiliated voters have reshaped Nevada politics, plus how “No Tax on Tips” gave Trump an unexpected foothold. Ralston explains why Vegas’ tourism slump could upend the next governor’s race and how corporatization has changed the city’s character. They also dig into the state of local journalism — from the challenges of nonprofit reporting to competing against hedge fund-owned outlets — and reflect on the late Harry Reid’s political legacy. From power-hungry governors to the fight for Nevada’s “first-in-the-nation” status, this episode reveals why what happens in Vegas won’t be staying there in 2026. Finally, Chuck reveals his ToddCast Top 5 list of American political scions running in upcoming election and answers listeners’ questions in the “Ask Chuck” segment. Got injured in an accident? You could be one click away from a claim worth millions. Just visit https://www.forthepeople.com/TODDCAST to start your claim now with Morgan & Morgan without leaving your couch. Remember, it's free unless you win! Timeline: (Timestamps may vary based on advertisements) 00:00 Chuck Todd’s introduction 02:45 Chuck will be LIVE on Youtube & X on election night! 06:15 The 2025 elections are local, but will have national impact 07:00 Virginia is a fairly good bellwether state for national politics 07:45 Virginia is purple but is not MAGA 08:15 Party controlling White House almost always loses VA governor race 09:30 Spanberger has run a very cautious campaign 10:30 Winsome Earle-Sears has been throwing things at the wall 11:30 Virginia voters don’t reward grievance politics 13:00 Virginia hasn’t split ticket amongst big three races since 2005 15:00 Spanberger has kept Jay Jones at arms length 17:45 Virginia will give us preview of which way field is tilting for midterms 18:15 2018 class of Democrats has produced some high profile candidates 19:30 Mikie Sherill has run a more contested race than Spanberger 20:30 Ciaterreli outperformed polls in 2021, could happen again 22:30 Third party candidates could swing the NJ governor race 22:45 Mamdani will win in NYC, it’s a matter of whether he clears 50% 24:45 Mamdani needs a big margin in order to have a mandate 26:00 What the results will tell us about the 2026 midterm landscape 30:15 Jon Ralston joins the Chuck ToddCast 32:15 Adapting to the breakneck speed of the news cycle 34:15 Nevada has become the preeminent swing state in America 37:00 The service industry & growing hispanic population define Vegas 37:45 Nevada is a bellwether for the Democratic party 39:30 Nevada continuing to lobby for first in the nation status 41:00 Las Vegas natives are a rarity, Vegas is a destination 42:45 Trump was able to connect with NV voters via "No Tax on Tips" 43:45 NV voters felt Democratic party took them for granted 44:45 Nevada's governor has a lot of power 47:15 There's been an explosion of non affiliated voters in Nevada 48:45 Is either major party making a strong case to non affiliated voters? 50:15 How competitive will the Nevada governor's race be? 52:45 Does Joe Lombardo have ambition outside the state of Nevada? 53:45 Lombardo's strategy could look similar to Glenn Youngkin's 55:45 What's behind the drop in tourism to Vegas? 56:45 Canada, immigration policy and lack of value proposition hurting Vegas 57:45 Corporatization of casinos & high prices have driven away tourists 58:45 Tourism drop could greatly impact the governor's race 59:15 Any progress on diversifying the Nevada economy? 1:00:30 Making Vegas "Hollywood East" comes with huge challenges 1:02:00 Would energy be the best way to diversify the Vegas economy? 1:02:45 Warren Buffet has monopoly on NV utilities, preventing new investment 1:03:45 Nonprofit vs for profit journalism 1:05:15 Dealing with big moneyed interests as a nonprofit journalists 1:07:15 Local journalism in Nevada has mostly been hollowed out 1:08:00 Dealing with "donor fatigue" as a nonprofit journalist 1:09:30 Journalism skills translate well to uncomfortable fundraising asks 1:11:15 Challenges in the advertising space for journalism 1:13:15 Why have advertising dollars been harder to get for news orgs? 1:15:45 Hedge funds acquired newspapers for their real estate 1:17:45 Journalism has to be done in-person and in the field 1:18:30 What would Harry Reid's advice be for the Dem party of today? 1:20:00 Reid died early on into the process of Jon writing "The Game Changer" 1:21:00 Reid wouldn't be happy with what Chuck Schumer is doing 1:23:15 Reid and McConnell collectively delegitimized the judicial branch 1:25:15 How would Reid have handled confrontation with Trump? 1:29:30 How are you feeling about your Buffalo Bills? 1:35:00 Chuck's thoughts on interview with Jon Ralston 1:35:30 ToddCast Top 5 - Top 5 American Political Scions 1:37:15 It's been a bad run lately for run for political scions 1:38:00 #1 Maine governor race features 3 political scions 1:40:00 #2 Georgia governor race features 2 political scions 1:41:15 #3 Beau Bayh 1:42:30 #4 Jack Schlossberg 1:44:15 #5 Chip Keating 1:45:15 Honorable mention - Andrew White 1:46:15 Ask Chuck 1:46:30 What if we had public debates where only verified facts are allowed? 1:50:00 Would state level Democratic parties create a separate platform from DNC 1:53:30 How do you define "short term" and "long term" when describing politics? 1:57:00 Will markets dip in Trump's second year like it does historically? 2:02:45 Who are three modern political thinkers best suited to express our ideals? 2:06:30 How can Trump try to disrupt the election and how effective will he be? 2:10:00 Is it more likely that Kirk's shooter was part of Trump's community?See omnystudio.com/listener for privacy information.

Chuck Todd breaks down why this year’s elections may be local—but their impact will be national. From Virginia’s bellwether governor’s race to key contests in New Jersey and New York City, these results will offer a preview of the political mood heading into the 2026 midterms. Chuck dives into Abigail Spanberger’s cautious campaign strategy, Winsome Earle-Sears’ grievance-fueled messaging, and why Virginia voters rarely reward extremes. Plus, a look at how third-party candidates could shake up the New Jersey race and why Zohran Mamdani’s performance in NYC will signal the direction of the progressive movement. Finally, Chuck reveals his ToddCast Top 5 list of American political scions running in upcoming election and answers listeners’ questions in the “Ask Chuck” segment. Got injured in an accident? You could be one click away from a claim worth millions. Just visit https://www.forthepeople.com/TODDCAST to start your claim now with Morgan & Morgan without leaving your couch. Remember, it's free unless you win! Timeline: (Timestamps may vary based on advertisements) 00:00 Chuck Todd’s introduction 02:00 Chuck will be LIVE on Youtube & X on election night! 05:30 The 2025 elections are local, but will have national impact 06:15 Virginia is a fairly good bellwether state for national politics 07:00 Virginia is purple but is not MAGA 07:30 Party controlling White House almost always loses VA governor race 08:45 Spanberger has run a very cautious campaign 09:45 Winsome Earle-Sears has been throwing things at the wall 10:45 Virginia voters don't reward grievance politics 12:15 Virginia hasn't split ticket amongst big three races since 2005 14:15 Spanberger has kept Jay Jones at arms length 17:00 Virginia will give us preview of which way field is tilting for midterms 17:30 2018 class of Democrats has produced some high profile candidates 18:45 Mikie Sherill has run a more contested race than Spanberger 19:45 Ciaterreli outperformed polls in 2021, could happen again 21:45 Third party candidates could swing the NJ governor race 22:00 Mamdani will win in NYC, it's a matter of whether he clears 50% 24:00 Mamdani needs a big margin in order to have a mandate 25:15 What the results will tell us about the 2026 midterm landscape 29:00 ToddCast Top 5 - Top 5 American Political Scions 30:45 It's been a bad run lately for run for political scions 31:30 #1 Maine governor race features 3 political scions 33:30 #2 Georgia governor race features 2 political scions 34:45 #3 Beau Bayh 36:00 #4 Jack Schlossberg 37:45 #5 Chip Keating 38:45 Honorable mention - Andrew White 39:45 Ask Chuck 40:00 What if we had public debates where only verified facts are allowed? 43:30 Would state level Democratic parties create a separate platform from DNC 47:00 How do you define "short term" and "long term" when describing politics? 50:30 Will markets dip in Trump's second year like it does historically? 56:15 Who are three modern political thinkers best suited to express our ideals?See omnystudio.com/listener for privacy information.

Folks who have followed Texas politics for awhile will remember Houston businessman Andrew White, who put on a spirited run for governor in 2018, but was edged out in the primary that year. White is back for a second run, and this time, as he says, he's running as an "independent Democrat" - holding that progressive litmus tests and box-checking may be part of what's been holding the party back on statewide wins for three decades now.Learn more about Andrew White and his campaign at https://andrewwhite.com/.Thanks for listening! Learn more about Progress Texas and how you can support our ongoing work at https://progresstexas.org/.

Sports reporter Katie Smith, writer and football pundit Mina Rzouki, and comedians Andrew White and Danny Mcloughlin join Rick Edwards for an hour of sporting punditry, humour and entertainment. Points are awarded for informed comment, wit and passion, but taken away for nonsense and answers lacking in conviction.In the final round, the top two points scorers go head-to-head in 'Defend the Indefensible' where they must both defend a statement however ludicrous or distasteful for twenty seconds. There can only be one winner!Listen to the podcast on BBC Sounds

Get this full episode and all the Sunday Shows by becoming a member at Patreon.com/leftreckoningDavid and Matt discuss the ethical implications of comedians performing in Saudi Arabia, the political landscape in Texas with Andrew White's gubernatorial campaign, and Megyn Kelly stares into the TPUSA abyss and doesn't like the way it talks about "billionaire jews."

202 - Jon Stickley and Larry Keel In episode 202 of “Have Guitar Will Travel”, presented by Vintage Guitar Magazine, host James Patrick Regan speaks with bluegrass guitarists Jon Stickley and Larry Keel. The conversation starts with Jon Stickley who joins us from Asheville North Carolina and then Larry Keel joins after just a couple minutes. Jon talks about how he makes time for guitar being a young dad and he gives us a brief history of his musical background. Larry joins the conversation and tells us how the two met jamming in a kitchen. Larry tells us about living and growing up in Virginia in a musical family. The two discuss their collaboration that's become an EP and why it's not a full album and how the two are going to make time from their own personal bands to tour together. The two describe their influences: Tony Rice. The two talk about their musical education and the guitars they're playing: Larry an Andrew White guitar and Jon a Preston Thompson guitar and the compromise they make using onboard electronics and the effects they use in their signal chain. The two discuss the camaraderie of the bluegrass culture and the logistics of their tour. Finally the two describe their passions outside of music. To find out more about Jon Stickley you can go to his website: jonstickley.com and for more information about Larry Keel you can go to his website: larrykeel.com Please subscribe, like, comment, share and review this podcast! #JonStickley #LarryKeel #Bluegrassguitar #VintageGuitarMagazine #TonyRice #AndrewWhiteGuitars #JamesPatrickRegan #PrestonThompsonGuitars #theDeadlies #haveguitarwilltravelpodcast #HGWT Please like, comment, and share this podcast! Download Link

After Paul's first show back at TN Motorcycle Music Revival he came out to my neck of the woods to play an acoustic show at the Aztec Theatre and I met up with him and the rest of the Tejas Thunder Moto Club. Greg Giannukos, Andrew White, and Taylor Garrigan. After a long day in the saddle the fellas opened up about their first run in with Velardi, and Paul's recent battle with cancer. Due to some technical difficulties most of the conversation got scrapped so I met up with Paul before a recent show at the Kessler and talked about his career, his father and grandfather being his inspiration to ride, and how a Shovelhead helped solidify one of the his greatest albums, Room 41. Check out his tour schedule here and watch out for a Harley Davidson Limited Road Glide while out on the open road as he mows down the miles playing shows across the country. KickStart Danger Dan's Talk ShopMCshopTsLowbrow CustomsKnives Made By Nick Permalink

This week marks the 210th anniversary of the Battle of Waterloo, the epic battle that resulted in the defeat of Napoleon and the rewriting of European history. But recent research has revealed that one man who fought at the battle had a fascinating connection with Australia. Lieutenant Andrew White of the Royal Engineers had been born in the fledgling colony of NSW, the son of a convict. His journey from colonial child to gentleman officer serving on the staff of the Duke of Wellington is one of the most remarkable tales of early Australia. Join Mat as he tells the story of Andrew White, Australia's first returned serviceman and only Waterloo veteran.Presenter: Mat McLachlanProducer: Jess StebnickiJoin one of our battlefield tours and walk in the footsteps of the Anzacs! Visit https://battlefields.com.au/ for more information.Find out everything Mat is doing with books, tours and media at https://linktr.ee/matmclachlanFor more great history content, visit www.LivingHistoryTV.com, or subscribe to our YouTube channel at https://www.youtube.com/c/LivingHistoryTV Hosted on Acast. See acast.com/privacy for more information.

Guests include Nia Griffith MP; musician Peredur ap Gwynedd; Middle East historian Diana Darke and Middle East analyst Dr Laura James; US political watcher Spencer McKinney; author Cormac Moore. Paper reviews: Bethan Sayed and Andrew White.

Many of us have heard the expression “doing good is good for business.” In this episode, Simon Kingston sits down with former MTV International Chairman and CEO Bill Roedy about how he put this concept into practice on a truly global scale. Bill takes us on his journey of how he redefined broadcast television, launching the most channels in television history with more than 200 global channels and 20 brands, including MTV, Nickelodeon, Comedy Central, and numerous others. He discusses how and why he started MTV's Staying Alive Foundation, Suga, and other social responsibility initiatives to realize the ethos of “doing good is good for business.” And Bill shares his journey from West Point to MTV to GAVI and beyond. We'll also hear from Andrew White, a leadership advisor who specializes in executive assessment and development, who will discuss why curiosity and adaptability are essential leadership traits in today's business environment. Four things you'll learn from this episode: Why doing good is good for business and how to achieve it at scale How to navigate the various challenges when launching a media startup How to deal with uncertainty and risk to realize global growth How to adapt a business background to serve in global non-profits and NGOs If you enjoyed this episode, you might also like these Redefiners episodes:Talking Transformational Leadership with RRA's CEO Constantine Alexandrakis Leadership Lounge: Boardroom Bound: How to Navigate Your Journey from Executive to Board Director Action Creates Hope: A Conversation with IRC President and CEO David Miliband Leadership Lounge: How to develop your personal leadership brand The Business of Football with Los Angeles Rams COO Kevin Demoff Leadership Lounge: Advice on when—and how—to weigh in on social issues

In this episode, you'll discover:New Tax Advantage Plan allows business owners to use employee tax dollars to pay for chiropractic care for their employeesThis applies to chiropractic businesses as well + get paid to adjust your team ANDSave money on employment taxes = lowers employee tax burdenDr. Andrew White, CEO of Align & Co., explains how this worksEpisode Highlights01:25 – Learn how a new tax-advantaged wellness plan allows businesses to pay for employee chiropractic care using tax dollars.03:40 – Discover how software and data are transforming chiropractic corporate wellness programs into measurable ROI opportunities.05:31 – Understand how the AlignWell Certified Chiropractor program connects chiropractors to local businesses through vetted partnerships.07:42 – Hear how a 50-person company saved over $35,000 in taxes while providing two free adjustments per employee per month.08:49 – Learn how leveraging tax dollars for wellness care reduces employers' tax burdens while expanding employee benefits.10:18 – Understand how “near-site” plans now allow employees to receive adjustments in clinics rather than just on-site at work.13:29 – Discover how chiropractic business owners can use this plan to get paid to adjust their own staff and save thousands in taxes.15:03 – Hear a real-world example where one chiropractor added $14K in net income by offering the plan to their team.16:56 – Be inspired by Andrew's and his challenge to chiropractors: think bigger and lower barriers to better serve your communities.18:40 – Learn how to join the network or attend upcoming events, and how the program fits into your current marketing strategy.19:22 - Dr. Pete is joined by Success Partner, Drew Grinnell, of Cutting Edge Laser Technologies. They dive into how advanced laser therapy is transforming chiropractic care—delivering drug-free pain relief, boosting patient outcomes, and creating new self-pay revenue streams. With over a decade of experience, Drew breaks down how to integrate these tools into your practice and how robotics can enhance efficiency, consistency, and overall results. Resources MentionedFor more information about the Alignwell Certified Chiropractor Program, please visit: https://alignwellcertified.com For more information about Cutting Edge Laser Technologies please visit: https://celasers.comTo learn more about the REM CEO Program, please visit:  http://www.theremarkablepractice.com/rem-ceoSchedule a Brainstorming call with Dr. PeteFollow Dr Stephen on Instagram: https://qr.me-qr.com/l/riDHVjqt  Follow Dr Pete on Instagram: https://qr.me-qr.com/I1nC7Hgg  Prefer to watch? Catch the podcast on YouTube at: https://www.youtube.com/@TheRemarkablePractice1To listen to more episodes visit https://theremarkablepractice.com/podcast/ or follow on your favorite podcast app.

In this episode, you'll discover:The Rule of 72: How compressing the conversion process compounds long-term successTime Kills All Deals Stick rate: What it really means and why it's crucial to your businessYou can't manage what is not measuredThe definition of True Conversion: The people who start care, stay under careThe key conversion and retention metrics that matter most—and how to improve themEpisode Highlights00:53 – Learn how the three-stage framework—operationalize, professionalize, optimize—leads to productivity, durability, and profitability.03:33 – Discover how conversion is both emotional and urgent, and why delays can derail a patient's decision to begin care.06:36 – Understand the "Rule of 72," a time-based system that compresses patient onboarding to reduce attrition and increase conversions.09:28 – Learn the step-by-step breakdown of what should happen within each 72-hour window from initial contact to family referrals.12:14 – Explore the role of urgency and conviction in the report of findings and how they directly impact conversion outcomes.16:34 – Hear why training on systems like the Rule of 72 is essential for improving proficiency and clinic-wide consistency.19:34 – See how time gaps between steps (Day 1, Day 2, workshop) create “heat loss” in conversion, impacting volume and income.22:58 – Discover how each conversion step can be measured as a KPI to track and improve business performance.25:45 – Learn how to define and apply "stick rate" metrics to understand patient retention across care phases.29:42 – Understand how chiropractic is a lifestyle strategy and why retention reflects true impact and long-term care success.32:53 - Dr. Eric DiMartino welcomes Dr. Andrew White from Success Partner, Align & Co to discuss an innovative approach to corporate wellness. Inspired by personal experiences, Dr. White shares how his company integrates chiropractic care into businesses using tax advantages. Learn how chiropractors can secure corporate contracts and leverage tax incentives at no extra cost. With a vision for nationwide expansion, this model offers businesses an effective way to support employee health. Don't miss this game-changing insight into the future of chiropractic care. Resources MentionedDownload your copy of the Remarkable Standards here: www.theremarkablepractice.com/podcast-ep301-standardsTo learn more about the REM CEO Program, please visit:  http://www.theremarkablepractice.com/rem-ceoFor more information about Align & Co please visit: https://www.alignco.lifeSchedule a Brainstorming call with Dr. PeteFollow Dr Stephen on Instagram: https://qr.me-qr.com/l/riDHVjqt  Follow Dr Pete on Instagram: https://qr.me-qr.com/I1nC7Hgg  Prefer to watch? Catch the podcast on YouTube at: https://www.youtube.com/@TheRemarkablePractice1To listen to more episodes visit https://theremarkablepractice.com/podcast/  or follow on your favorite podcast app.

If you're feeling stuck in the grind of daily operations, it might be time for a reset. This episode is all about the “why” behind quarterly board meetings—how they act as a reset button to re-align your team, recenter your vision, and reignite momentum. Dr. Lona and Dr. Bobby  share how these meetings evolved from casual vision chats to a strategic system that powers growth, focus, and leadership. You'll learn how intentional time away, vision casting, and revisiting personal goals are key to building a culture of accountability and sustainable success.Key Highlights01:01 – Reflecting on Q1 performance and how better visibility and systems helped the team stay on track.03:03 – The evolution from weekly/monthly rhythms to powerful quarterly board meetings that drive alignment.04:49 – Why board meetings act as a reset button, reuniting and reenergizing the entire team toward a shared vision.06:37 – How early vision casting sessions on the beach evolved into full-fledged board meetings with intentional outcomes.08:04 – Lessons from early meetings: big-picture energy is great, but follow-through systems are essential for execution.09:36 – Board meetings as a leadership tool—creating clarity, focus, and accountability across all levels of the business.10:52 – Understanding team motivation by aligning business goals with individual visions and dreams.12:40 – Dr. Bobby's process of vision casting during onboarding—helping each team member connect to their personal "why."14:39 – How painting a vivid picture of a team member's goals increases long-term buy-in and loyalty.20:46 – The power of writing down goals and team objectives—how vision becomes action when it's clearly documented and shared.24:25 - Dr. Eric DiMartino welcomes Dr. Andrew White from Success Partner, Align & Co to discuss an innovative approach to corporate wellness. Inspired by personal experiences, Dr. White shares how his company integrates chiropractic care into businesses using tax advantages. Learn how chiropractors can secure corporate contracts and leverage tax incentives at no extra cost. With a vision for nationwide expansion, this model offers businesses an effective way to support employee health. Don't miss this game-changing insight into the future of chiropractic care. Resources MentionedFor more information about Align & Co please visit: https://www.alignco.life/To schedule a Strategy Session with Dr Lona: https://go.oncehub.com/DrLonaBuildPodcastTo schedule a Strategy Session with Dr Bobby: https://go.oncehub.com/DrBobbyBuildPodcastFollow Dr Bobby on Instagram: https://qr.me-qr.com/WOz1qy6E   Follow Dr Lona on Instagram: https://qr.me-qr.com/o2oFbovyLearn what it takes to be Remarkable!: https://theremarkablepractice.com/

Right before hoping on stage at PagerDuty on Tour London, Andrew White joins us to chat about their journey at Checkout.com to migrate from legacy operations, how AI helped this and how to balance cultural changes.

In this episode, you'll discover:Discover the clear difference between an opportunity and an appointment.Why actively creating margin in your life is critical for success.How the Hedgehog Concept becomes even more essential as you grow.Your role as a leader: Marshaling your team's four limited resources through all seasons of your career.Episode Highlights00:47 - Learn how to distinguish between opportunities and appointments, ensuring you focus on what aligns with your long-term purpose​.03:03 - Understand why success comes from alignment between vision, values, and behaviors to guide your decisions​.05:32 - Discover how saying no to distractions strengthens your ability to say yes to the right opportunities​.08:26 - Explore why decision-making requires discipline and how to prioritize wisely​.10:48 - Learn how resisting distractions helps you stay focused on high-return investments and opportunities​.12:08 - Understand the power of divine appointments and how aligning with purpose creates greater fulfillment​.15:15 - Gain insight into resource allocation—time, energy, focus, and money—and how they shift through business growth phases​.18:07 - Discover why clarity in priorities prevents decision fatigue and ensures efficiency in leadership​.19:56 - Learn why being disciplined with investments leads to better financial and business outcomes​.21:18 - Hear why focusing on what you can be the best at ensures long-term success and scalability​22:52 - Dr. Eric DiMartino welcomes Dr. Andrew White to discuss an innovative approach to corporate wellness. Inspired by personal experiences, Dr. White shares how his company integrates chiropractic care into businesses using tax advantages. Learn how chiropractors can secure corporate contracts and leverage tax incentives at no extra cost. With a vision for nationwide expansion, this model offers businesses an effective way to support employee health. Don't miss this game-changing insight into the future of chiropractic care. Resources MentionedTo learn more about the Remarkable CEO Program, please visit:  http://www.theremarkablepractice.com/rem-ceoFor more information about Align & Co please visit:  https://www.alignco.life/ Schedule a Brainstorming call with Dr. PeteFollow Dr Stephen on Instagram: https://qr.me-qr.com/l/riDHVjqt  Follow Dr Pete on Instagram: https://qr.me-qr.com/I1nC7Hgg  Prefer to watch? Catch the podcast on YouTube at: https://www.youtube.com/@TheRemarkablePractice1To listen to more episodes visit https://theremarkablepractice.com/podcast/ or follow on your favorite podcast app.

Discover how Andrew navigated a toxic work environment and learn about the lasting impact it had on his life and career. He shares insights on how our unique perceptions shape our realities and encourages listeners to view their experiences as part of an ongoing journey, rather than final destinations. Andrew opens up about a difficult year marked by bullying and emotional struggles, and how his vulnerability and emotional intelligence led him to a healthier, more supportive role. He also reflects on a life-changing health crisis that forced him to reassess his priorities, ultimately inspiring him to write a book and launch his podcast, "Leading Our Own Way." Tune in for practical advice on handling toxic work cultures, as Andrew urges you to trust your instincts and seek environments that foster your well-being.About our guest:Andrew White is the dynamic host of Leading Our Own Way and the author of How to Lead with Purpose. Passionate about personal growth, authentic leadership, and mental well-being, Andrew blends his deep insights on self-reflection with actionable strategies to inspire others. Through his podcast, he engages with thought leaders and experts to share transformative wisdom on navigating life's challenges, leading with purpose, and embracing one's true potential. With a focus on resilience, self-awareness, and meaningful connection, Andrew empowers individuals to carve their own path to success, fulfillment, and lasting impact. Follow Our Guest:Website: https://leadingourownway.com/Podcast: https://leadingourownway.com/podcastInstagram: https://www.instagram.com/awhite2345/TikTok: https://www.tiktok.com/@leadingourownwayFollow Us On:Instagram: https://www.instagram.com/thestevehodgson/https://www.instagram.com/sharewithsteve/Episode Highlights:02:39 - The Joy of New Beginnings06:28 - Advice for Those in Toxic Environments10:13 - The role of venting in processing emotions12:02 - Gratitude for the Journey15:35 - Creating Safe Spaces for Sharing

In this episode, we chat with Andrew White, an educator and mental health advocate, who shares his mission to inspire leaders to create positive, supportive work environments. Andrew talks about the importance of self-leadership, being present, and fostering connection, drawing on his background in teaching and coaching. He reflects on his journey from Manchester to Australia, where he faced challenges in the education system, including toxic workplace culture and the pressures of bureaucracy. We dive into how leaders can build trust by being vulnerable and authentic, and how this can make a big difference in employee well-being. Andrew also opens up about how his personal struggles with bullying inspired him to turn pain into purpose, focusing on mental health and leadership through his podcast and writing.About our guest:Andrew White is the dynamic host of Leading Our Own Way and the author of How to Lead with Purpose. Passionate about personal growth, authentic leadership, and mental well-being, Andrew blends his deep insights on self-reflection with actionable strategies to inspire others. Through his podcast, he engages with thought leaders and experts to share transformative wisdom on navigating life's challenges, leading with purpose, and embracing one's true potential. With a focus on resilience, self-awareness, and meaningful connection, Andrew empowers individuals to carve their own path to success, fulfillment, and lasting impact.Follow Our Guest:Website: https://leadingourownway.com/Podcast: https://leadingourownway.com/podcastInstagram: https://www.instagram.com/awhite2345/TikTok: https://www.tiktok.com/@leadingourownwayFollow Us On:Instagram: https://www.instagram.com/thestevehodgson/https://www.instagram.com/sharewithsteve/Episode Highlights:00:00 - Episode Trailer04:15 - How Sports Shaped Andrew's Teaching Path12:34 - Teaching Through Imperfection20:07 - The Changing Dynamics of Families and Society24:01 - The Impact of Nature on Kids in a Digital World27:29 - The Four C's Philosophy32:43 - Unseen pressures teachers face daily42:57 - Winning the National Teacher of the Year Award44:39 - Dealing with Tall Poppy Syndrome01:00:04 - Handling Toxic Work Environments01:07:10 - The Power of Individual Leadership01:09:17 - How do you lead your own way01:16:55 - Understanding Yourself Before Leading01:20:35 - Overcoming Self-Doubt and Imposter Syndrome01:34:32 - Finding Your Personal Self-Care Formula01:43:25 - Rewiring the Brain01:46:18 - The Three Key Elements of Happiness01:49:34 - The Role of Mentorship in Personal Growth

Find out more: http://alignwellcertified.com Dr. Andrew's email address: hello@alignco.life   Summary: In this episode of the ChiroCandy podcast, Dr. Andrew White shares his journey into chiropractic care and the innovative corporate wellness initiatives he has developed. He discusses the impact of personal experiences on his career path, the challenges faced in healthcare, and the importance of making chiropractic care accessible to more people through corporate wellness programs. Dr. White emphasizes the need for chiropractors to connect with businesses and leverage tax advantages to provide care for employees, ultimately aiming to improve overall health and wellness in the workplace.   Takeaways: Dr. White's journey into chiropractic was influenced by his mother's struggles with opiate addiction. Corporate wellness can eliminate barriers to accessing chiropractic care. Building relationships is key to effective patient care in chiropractic. Tax advantages can be leveraged to provide chiropractic care to employees. There is a growing demand for chiropractic services in corporate settings. Chiropractors can increase their patient base through corporate wellness programs. The adjustment serves as an entry point for deeper patient relationships. Employers are looking for ways to improve employee health and reduce costs. Chiropractors can work with businesses to create mutually beneficial partnerships. The opportunity for chiropractors to make a significant impact in their communities is immense.   Case Study #1: https://go.chirocandy.com/case-study Case Study #2: https://www.youtube.com/watch?v=po2nWAaKcho

Sometimes, life and work can feel like a never-ending loop—same routines, same challenges, no real progress. If 2024 felt like a year where things didn't quite click, you're not alone. Many of us struggle with finding the right balance between leading effectively, making meaningful changes, and just feeling genuinely happy. But it's time to change that.In this episode of the Happiness Squad Podcast, we're taking a fresh look at what it takes to thrive. As 2024 comes to a close, we're celebrating the standout episodes that resonated with listeners worldwide. This special compilation brings you three impactful conversations packed with insights to help you thrive in life and work.Things you will learn in this episode:• Driving Deep Organizational Change with Robert Quinn• Embracing Modern Leadership with Andrew White• Building Happy Habits for Long-Term Joy with Ashish KothariWe learned a lot this year about dealing with challenges and creating a better future. Join us as we revisit the year's most inspiring conversations on leadership, change, and happiness, and discover useful lessons for the coming year.Resources:✅• Robert E. Quinn∙ https://www.linkedin.com/in/robert-e-quinn-57b9a9100/ ∙ http://www.bob-quinn.com/ ∙ https://michiganross.umich.edu/faculty-research/faculty/robert-quinn ∙ Deep Change by Robert Quinn: https://michiganross.umich.edu/faculty-research/faculty/robert-quinn ∙ More books by Robert Quinn: https://www.amazon.com/stores/author/B001H6MQSK • Andrew White∙ https://www.linkedin.com/in/dr-andrew-white/?originalSubdomain=uk ∙ Transcend.Space on LinkedIn: https://www.linkedin.com/company/transcend-space/ ∙ Transcend.Space Website: https://transcend.space/who-we-are/ ∙ Andrew White's TED Talk: https://www.youtube.com/watch?v=p9fwC3tInlo ∙ Leadership 2050 Newsletter: https://transcend.space/leadership-2050/ • Ashish Kothari∙ https://www.facebook.com/ashish.kothari.397∙ https://www.instagram.com/myhappinesssquad/∙ https://www.linkedin.com/in/ashishkothari1/∙ https://www.linkedin.com/company/happiness-squad/ Books:✅• Hardwired for Happiness by Ashish Kothari:

listener comments? Feedback? Shoot us a text!In this episode, Dr. Andrew White joins us to talk about the real-world consequences of pseudo-archaeology, and how it is used to accomplish nationalist and frequently racist objectives. So join us as we take a bizarre journey through a world of neo-nazi Netflix fanboys, Atlantis-pushing occultists, and anti-Indigenous propaganda, and discuss the need for archaeologists to confront this bullshit head-on!About our guest:Andrew White is anthropological archaeologist (PhD 2012, University of Michigan) with interests in hunter-gatherers, lithic technology, human evolution, and complex systems theory.  He is particularly interested in combining archaeological methods and theory with ethnographic data and computational modeling to develop new ways to push the boundaries of our understanding of the social, cultural, and evolutionary aspects of the human past. He has spent some time confronting pseudo-archaeological claims about the human past and feels that other professional archaeologists should do the same.https://www.youtube.com/@andrewwhite33Your Host:Kurly Tlapoyawa is an  archaeologist, ethnohistorian, and filmmaker. His research covers Mesoamerica, the American Southwest, and the historical connections  between the two regions. He is the author of numerous books and has  presented lectures at the University of New  Mexico, Harvard University,  Yale University, San Diego State University, and numerous others. He is also a cultural consultant for Nickelodeon Animation Studios. His recent projects include LiDAR-assisted survey in the Maya hinterlands of southern Belize, the documentary short film "Guardians of the Purple Kingdom," and "The Casagrandes Movie" on Netflix. @kurlytlapoyawa Support the showFind us: https://www.facebook.com/TalesFromAztlantis Merch: https://chimalli.storenvy.com/ Book: The Four Disagreements: Letting Go of Magical Thinking (Amazon)

In this episode of The Cognitive Revolution, Nathan interviews Andrew White, Professor of Chemical Engineering at the University of Rochester and Head of Science at Future House. We explore groundbreaking AI systems for scientific discovery, including PaperQA and Aviary, and discuss how large language models are transforming research. Join us for an insightful conversation about the intersection of AI and scientific advancement with this pioneering researcher in his first-ever podcast appearance. Check out Future House: https://www.futurehouse.org Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Shopify: Shopify is the world's leading e-commerce platform, offering a market-leading checkout system and exclusive AI apps like Quikly. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/cognitive CHAPTERS: (00:00:00) Teaser (00:01:13) About the Episode (00:04:37) Andrew White's Journey (00:10:23) GPT-4 Red Team (00:15:33) GPT-4 & Chemistry (00:17:54) Sponsors: Oracle Cloud Infrastructure (OCI) | SelectQuote (00:20:19) Biology vs Physics (00:23:14) Conceptual Dark Matter (00:26:27) Future House Intro (00:30:42) Semi-Autonomous AI (00:35:39) Sponsors: Shopify (00:37:00) Lab Automation (00:39:46) In Silico Experiments (00:45:22) Cost of Experiments (00:51:30) Multi-Omic Models (00:54:54) Scale and Grokking (01:00:53) Future House Projects (01:10:42) Paper QA Insights (01:16:28) Generalizing to Other Domains (01:17:57) Using Figures Effectively (01:22:01) Need for Specialized Tools (01:24:23) Paper QA Cost & Latency (01:27:37) Aviary: Agents & Environments (01:31:42) Black Box Gradient Estimation (01:36:14) Open vs Closed Models (01:37:52) Improvement with Training (01:40:00) Runtime Choice & Q-Learning (01:43:43) Narrow vs General AI (01:48:22) Future Directions & Needs (01:53:22) Future House: What's Next? (01:55:32) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

"[This college] was not established to serve or to magnify Cornell University. It belongs to the people of the state. The farmers of the state have secured it. Their influence has placed it here... If there is any man standing on the land, unattached, uncontrolled, who feels that he has disadvantages and a problem, this College of Agriculture stands for that man." – Liberty Hyde BaileyIn 1868, as the nation still felt the aftershocks of the American Civil War, a small town in the rolling hills of upstate New York became the cradle of a groundbreaking vision. In Ithaca, on a modest farm, an institution was born - one that would go on to revolutionize agriculture and the fresh produce industry, leaving a lasting impact on the United States and the world.Who were Ezra Cornell and Andrew White, the visionaries behind this ambitious endeavor? How did their bold ideas and the Morrill Land-Grant Act transform a farm into a university with a mission to reshape agriculture?What role did Liberty Hyde Bailey play in establishing Cornell as a leader in agricultural innovation? How did the university's experiment stations and the Cornell Cooperative Extension spread cutting-edge techniques across the globe? What was the significance of the Cornell-Nanking project, and how did Cornell's plant breeding programs produce iconic crops like the Empire apple and Concord grape?Looking ahead, how will Cornell continue to drive the evolution of agriculture in the years to come?Join John, Patrick, and special guest Corey Ryan Earle of Cornell University as they explore the rich history of this esteemed institution and its extraordinary contributions to agriculture and fresh produce.---------------------------------------------Visit the Cornell College of Agriculture and Life Sciences (CALS): https://cals.cornell.edu/Apply for the Executive Leadership Development Program at Cornell, March 23-27, 2025: https://www.freshproduce.com/events/executive-leadership-development-program-at-cornell-university/In Sponsorship with Cornell University: Dyson Cornell SC Johnson College of BusinessJoin the History of Fresh Produce Club (https://app.theproduceindustrypodcast.com/access/) for ad-free listening, bonus episodes, book discounts and access to an exclusive chatroom community.Instagram, TikTok, Threads:@historyoffreshproduceEmail: historyoffreshproduce@gmail.com

In this episode we were joined not just by the family of Herbert Martin Massey, the Senior British Officer in Stalag Luft III, but also his official biographer, Andrew White. In the first of our biographical episodes we look at the life of Massey, but also discuss his role in The Great Escape and how there would have been no Great Escape without his sign off on the plan. For You The War Is Over is a podcast that looks at the real life stories of Prisoner-of-War escapes from the the Second World War. Hosted by Dave Robertson and Tony Hoskins, each episode looks at a new escape. If you would like to follow us on Twitter we can be found @FYTWIO we can also be found on Facebook https://www.facebook.com/FYTWIO/ or if you would prefer to send a more long form message we can also be reached via email at FYTWIOpodcast@gmail.com Hosted on Acast. See acast.com/privacy for more information.

In this episode Nurse Jessica and Nurse Erica interview Andrew White, creator of the 'Meanwhile In the Breakroom' Series on TikTok and Instagram, and a seasoned nurse and social media content creator. They discuss Andrew's journey from CNA to ICU nurse and the public perception of nurses leading to unrealistic expectations. The conversation touches on toxic management, the role of hospice, the implications of CPR, and nurse burnout. The speakers discuss extreme behaviors exhibited by some patients, the emotional toll of patient abuse on nurses, and the pervasive martyr mentality within the nursing profession. They also explore the transition to social media content creation as a coping mechanism for burnout and the challenges of navigating cancel culture. The conversation dives into the creative process behind Andrew White's series 'Meanwhile in the Break Room', character development, and storytelling in nursing.   Thank you to our sponsor, Stink Balm Odor Blocker! Please visit  https://www.stinkbalmodorblocker.com/  and use promo code UNCORKED20 for 20% off your purchase this holiday season!   Thank you to our Enema Award Sponsor, Happy Bum Co. Please visit https://happybumco.com/ and use promo code NURSESUNCORKED for 15% off your first bundle.   Interested in Sponsoring the Show?  Email with the subject NURSES UNCORKED SPONSOR to nursesuncorked@nursesuncorked.com    Help Us Keep This Podcast going and become an official Patron of Nurses Uncorked! Gain early access to episodes, patron only bonus episodes, giveaways and earn the title of becoming either a Wine Cork, Wine Bottle, Decanter, Grand Preserve, or even a Vineyard member for exclusive benefits! Benefits also include patron only Zoom parties, newsletters, shout-outs, and much more.   https://patron.podbean.com/nursesuncorkedpodcast   Chapters: 00:00 Introduction to Andrew White 03:07 Cocktail of the Week 07:50 The Role of CNAs in Nursing 11:43 New Nurses as Managers and CRNAs 18:45 Toxic Management in Nursing 22:12 Navigating Patient Ratios in ICU Nursing 26:16 Utilizing Hospice and Understanding CPR Implications 31:23 Problem of the Week 38:30 Addressing Nurse Burnout and Coping Strategies 41:14 The Emotional Toll of Patient Abuse 47:57 Transitioning into Social Media Content Creator 51:25 Navigating Cancel Culture 1:01:20 Creative Expression: Meanwhile in the Break Room 1:09:25 Enema of the Week Award   Follow Andrew White:   TikTok: @plantymurse https://www.tiktok.com/@plantymurse?lang=en    Instagram: @plantymurse https://www.instagram.com/plantymurse/    You Tube:  https://www.youtube.com/@plantymurse    Nurses Station the Series on YouTube: https://www.youtube.com/@nursesstationseries    Cocktail of the Week: Whisky Smash 1 oz. Simple Syrup Juice from 1/4  of a lemon 3-4 mint leaves Muddle ingredients together Add 2 oz. whisky Shake in shaker Serve over ice with lemon slice and optional mint leaves   New episodes of Nurses Uncorked every Tuesday (Monday for patrons!). Help us grow by giving our episodes a download, follow, like the episodes and a 5 ⭐️ star rating!   Please follow Nurses Uncorked at!  https://www.tiktok.com/@nurses.uncorked?_t=8drcDCUWGcN&_r=1 https://instagram.com/nursesuncorked?igshid=OGQ5ZDc2ODk2ZA== https://youtube.com/@NursesUncorkedL https://www.facebook.com/profile.php?id=100094678265742&mibextid=LQQJ4d You can listen to our podcast at: https://feed.podbean.com/thenurseericarn/feed. https://podcasts.apple.com/us/podcast/nurses-uncorked/id1698205714 https://spotify.link/8hkSKlKUaDb https://nursesuncorked.com    DISCLAIMER: This Podcast and all related content published or distributed by or on behalf of Nurse Erica, Nurse Jessica Sites or Nurses Uncorked Podcast is for informational purposes only and may include information that is general in nature and that is not specific to you. Any information or opinions expressed or contained herein are not intended to serve as legal advice, or replace medical advice, nor to diagnose, prescribe or treat any disease, condition, illness or injury, and you should consult the health care professional of your choice regarding all matters concerning your health, including before beginning any exercise, weight loss, or health care program.  If you have, or suspect you may have, a health-care emergency, please contact a qualified health care professional for treatment.   Any information or opinions provided by guest experts or hosts featured within website or on Nurses Uncorked Podcast are their own; not those of Nurse Jessica Sites, Nurse Erica or Nurses Uncorked Company. Accordingly, Nurse Erica, Nurse Jessica Sites and the Company cannot be responsible for any results or consequences or actions you may take based on such information or opinions. All content is the sole property of Nurses Uncorked, LLC. All copyrights are reserved and the exclusive property of Nurses Uncorked, LLC.

PJ talks to Andrew White from Valencia GAA club about the massive clean up job Hosted on Acast. See acast.com/privacy for more information.

Get ready for a side-splitting and nostalgic episode! Hosts Chris and Stu are joined by special guest, comedian Andrew White, as he counts down his Top 5 charity shops. From hidden gems to thrift store classics, Andrew takes us on a journey through his favourite spots to find unique bargains and quirky treasures. Expect plenty of laughs, fun stories, and some unexpected picks along the way!This episode was an absolute blast to record, and we hope you enjoy listening to it as much as we enjoyed making it!HUGE THANKS to No Need To Shout for curating this episodeSpecial Thanks to Our Sponsor:A huge shoutout to our fantastic sponsor, the Say What Podcast. Your support for them means everything to us, so be sure to check them out!Watch and Support Hardcore Listing!If you want to watch this episode and help keep Hardcore Listing going strong, head over to our Patreon page. By becoming a patron, you'll not only unlock exclusive content and behind-the-scenes footage, but you'll also get the chance to pick your very own Top 5 topics for future episodes!Stay Connected!Don't miss out on updates, extra content, and all things Hardcore Listing by following us on social media:Twitter: @hardcorelistingInstagram: @hardcorelistingThank you for your continued support—we couldn't do this without you! Hosted on Acast. See acast.com/privacy for more information.

This week's book guest is The Whale Tattoo by Jon Ransom.Sara and Cariad are joined by comedian, writer and actor Andrew White to discuss fish, fishing, dads, love, sex, queer writing and penises.Thank you for reading with us. We like reading with you!The Whale Tattoo by Jon Ransom is available to buy here or on Apple Books here.Andrew is on tour with his new stand-up show 'Young, Gay And A Third Thing' from 13 Oct - 20 Dec. For tickets and information head to standupandrew.com.You can find Andrew on Instagram @standupandrew and Twitter @StandUpAndrewSara's debut novel Weirdo is published by Faber & Faber and is available to buy here.Cariad's book You Are Not Alone is published by Bloomsbury and is available to buy here.Cariad's children's book The Christmas Wish-tastrophe is available to pre-order now.Follow Sara & Cariad's Weirdos Book Club on Instagram @saraandcariadsweirdosbookclub and Twitter @weirdosbookclub Recorded by Naomi Parnell and edited by Aniya Das for Plosive.Artwork by Welcome Studio. Hosted on Acast. See acast.com/privacy for more information.

Believe it or not, many of the "ancient aliens" conspiracy theories have racist roots. Dr. Andrew White explains.LINKS:Dr. White's YouTube channelFraudulent Archaeology Wall of ShameVIDEO of this conversationBecome a supporter of this podcast: https://www.spreaker.com/podcast/thethinkingatheist--3270347/support.

After Dark with Hosts Rob & Andrew – White liberals, stay out of Black lives. Stop dictating actions, defining racism, and acting as spokespersons. Your advice often causes division and harm. Diversity Equity Inclusion policies promote segregation and place unfair targets on Black people. Black people don't need your guidance; they understand their own experiences and challenges far better than you ever will.

This week on the Missouri Woods & Water Podcast we get the chance to sit down with Stephanie & Andrew White. The Whites are avid hunters and farmers that both grew up with a love for the outdoors. That love has continued into them being full time farmers and avid hunters to this day. One thing that has come out of the love for those things is Stephanie's desire to write books that gave more of a voice to those communities, especially as it relates to children getting excited about them. Stephanie's newest book titled "The Hunt" is an awesome read for kids and parents alike. In today's show, we talk with the WHites about their background, go off on a few tangents and rabbit holes, and then talk about Stepahnies book journey, and even get into the hate from.......well.......haters. Thanks to the Whites for taking the time to come out and record and thanks for listening!   Check out the MWW Website for shows, partner discounts, and more!!!   Subscribe To Our YouTube Channel!!! Weber Outfitters   Athlon Optics OnX: Use code MWW20 for 20% off  Camofire Black Ovis: Use code MWW10 for 10% off Huntworth Gear: Use code MWW15 for 15% off Alps Outdoorz: Use code 2024woodswater for 30% off  Zamberlan Boots Reveal Cameras by Tactacam Habitat Works Facebook Page: Mention us when you call and get 15% off any service 816-752-7390  habitatworksllc@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on the Missouri Woods & Water Podcast we get the chance to sit down with Stephanie & Andrew White.  The Whites are avid hunters and farmers that both grew up with a love for the outdoors.  That love has continued into them being full time farmers and avid hunters to this day.  One thing that has come out of the love for those things is Stephanie's desire to write books that gave more of a voice to those communities, especially as it relates to children getting excited about them.  Stephanie's newest book titled "The Hunt" is an awesome read for kids and parents alike.  In today's show, we talk with the WHites about their background, go off on a few tangents and rabbit holes, and then talk about Stepahnies book journey, and even get into the hate from.......well.......haters.  Thanks to the Whites for taking the time to come out and record and thanks for listening! Check out the MWW Website for shows, partner discounts, and more!!! Subscribe To Our YouTube Channel!!!Weber Outfitters  Athlon OpticsOnX:  Use code MWW20 for 20% off CamofireBlack Ovis:  Use code MWW10 for 10% offHuntworth Gear:  Use code MWW15 for 15% offAlps Outdoorz:  Use code 2024woodswater for 30% off Zamberlan BootsReveal Cameras by TactacamHabitat Works Facebook Page:  Mention us when you call and get 15% off any service816-752-7390 habitatworksllc@gmail.com

In this episode, you'll discover:Do you have a Personal Succession Plan? (Is your Estate Plan complete and current?) What would happen to your business if you were out of the picture?Does your spouse / family know what to do if you pass?Is there a written plan of action for your beneficiaries?Included: the Remarkable Succession Plan Guide (PDF) Do not procrastinate: Start this / complete this todayEpisode Highlights01:03 - The importance of succession planning for chiropractors and having a system in place. 03:11 - Having a succession plan in place, especially for chiropractors, to mitigate potential consequences.07:16 - Estate planning process, including a will, trusts, and a board of trustees to execute wishes and values.09:15 - Retirement planning for entrepreneurs, who are often risk-averse but need to prioritize their financial futures.12:01 - Clear planning and execution will protect your legacy and create a generational experience.17:33 - The importance of organization and documentation in succession planning, using the law of Dominion as a powerful reference.22:28 - Updating one's vision story and business succession planning annually.26:36 - Essential steps for a successful business transition.28:17 - The importance of being a ready leader and business in today's fast-paced world30:26 - Dr. Andrew White, the founder of Align & Co, is this week's Success Partner. Join Dr. Lona Cook and Dr. Andrew as they discuss the evolution of launching Align & Co and how it aims to revolutionize the delivery of chiropractic care. With a focus on bringing chiropractic care into the workplace, Dr. Andrew outlines how Align &  Co partners with businesses to provide on-site services, while highlighting the innovative approach to working with insurance companies. Explore the vision and passion driving the movement to make chiropractic care more accessible and impactful in the healthcare landscape. Resources MentionedDownload your copy of the Succession Planning Worksheet here: https://theremarkablepractice.com/podcast-ep247-succession   To learn more about the REM CEO Program, please visit:  http://www.theremarkablepractice.com/rem-ceoBuild your dream team with Chiro Match Makers. Learn more at https://chiromatchmakers.com/For more information about Align Co. please visit: https://www.alignco.life/trpSchedule a Brainstorming call with Dr. PeteDr. Stephen's LinkedInDr. Peter's LinkedInThe Remarkable CEO WebsiteDr. Stephen's Book – The Remarkable Practice: The Definitive Guide to Build a Thriving Chiropractic Business

In today's fast-moving world, leaders can't afford to stick to the old ways. Ignoring the ever-changing landscape around us means missing out on new opportunities and risking stagnation. It's essential for any leader to stay agile and resilient – that's what modern leadership is all about in these challenging times.In this episode of the HAPPINESS SQUAD Podcast, Ashish Kothari and Andrew White, CEO of Transcend.Space, dive deep into the concept of modern leadership.Andrew White is a visionary in modern leadership, dedicated to understanding and shaping the role of business leaders in addressing global challenges. At Said Business School, he directs the Oxford Advanced Management and Leadership Programme, annually guiding 80 global leaders towards new strategic directions. As the founder of Transcend.Space, Andrew works with purpose-driven leaders to foster positive change beyond profit. His insights extend to global leadership retreats, contributions to Harvard Business Review, Fast Company, a TEDx talk, a podcast, and his popular LinkedIn newsletter 'Leadership 2050' with over 5,000 subscribers.In the conversation, Ashish and Andrew address how leadership is transforming in response to the unique challenges and rapid changes of the modern world.Things you will learn from this episode:The Challenges of 21st Century LeadershipHuman-Centric Approach to LeadershipThe Importance of Active Listening SkillsIntegrating Technology in LeadershipThe role of Personal Development and Mindfulness in LeadershipTune in now to gain invaluable insights and tools to transform your leadership style and make a positive impact in today's dynamic world.Resources:Transcend.Space on LinkedIn: https://www.linkedin.com/company/transcend-space/ Transcend.Space Website: https://transcend.space/who-we-are/ Andrew White's TED Talk: https://www.youtube.com/watch?v=p9fwC3tInlo Leadership 2050 Newsletter: https://transcend.space/leadership-2050/ Books:Andrew White's Harvard Business Review article: https://hbr.org/2016/06/lessons-from-companies-that-put-purpose-ahead-of-short-term-profits Hardwired for Happiness: 9 Proven Practices to Overcome Stress and Live Your Best Life.https://www.amazon.com/Hardwired-Happiness-Proven-Practices-Overcome/dp/1544534655