Podcast appearances and mentions of Andrew White

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. 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.

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.

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.

Spain is enduring its worst flooding disaster in decades, with at least 95 people dead and dozens more missing. We get the latest on the situation with John Marsham, Professor of Atmospheric Science at the University of Leeds and also Andrew White, of Valencia GAA.

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.

What a great conversation we have lined up for you today!  Our guest, Andrew White, is the President of Comptus, a New Hampshire manufacturer and seller of commercial environmental sensors and controls. You will learn what they do and how they serve many important markets. Andrew earned his Bachelor of Arts in Government from Harvard University, and an MBA from Plymouth State University. When asked to be a guest on our podcast, he was happy to have a chance to share his success stories with other exporters or those new to exporting. That, of course, is what Export Stories is all about. Comptus is a fascinating business, and I hope you enjoy this episode. After you've listened, we would love to hear your thoughts and comments, which you can post at https://www.exportstoriespodcast.com/ or on our Facebook or LinkedIn pages.

Een verhaal van Rinke Verkerk In een van de langst lopende conflicten in de moderne geschiedenis is er één man die het vertrouwen geniet van twee gezworen vijanden. Die door zowel de Palestijnse president als Israëlische premiers gevraagd wordt een crisis te bezweren. Maak kennis met de Britse priester en vredesonderhandelaar Andrew White. Wat kunnen wij, gewone burgers drieduizend kilometer verderop, van hem leren? Meer dan je misschien denkt. Opname: Julius van IJperen Montage en mixage: Tom Ruijg Lees hier het stuk: https://decorrespondent.nl/15561/maak-kennis-met-de-man-die-door-de-israeli-s-en-de-palestijnen-wordt-gebeld-als-het-crisis-is/f4803f54-d0ec-02f7-151f-4cc5688971e0

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.

Emma and Will sing about the periodic table! Wait what the... *checks notes*... who wrote these notes... *double checks notes*... this can't be right... *throws out notes*. Emma and Will talk about the May 2024 MMMM puzzle? Yes that sounds much more likely. Spoilers for "Gold-Plated Record" ahead!Links of interest:Muller Monthly Music Meta: https://pmxwords.com/"Gold-Plated Record" by Will Pfadenhauer, Pete Muller, Mack Meller, and Andrew White: https://pmxwords.com/2024-puzzle-5-gold-plated-record/Pete Muller's solution (and cover of the meta answer!) to "Gold-Plated Record": https://pmxwords.com/2024-puzzle-5-gold-plated-record-solution/"Muller Monthly Music Meta, May MMXXIV" on Crossword Fiend: https://crosswordfiend.com/2024/05/12/muller-monthly-music-meta-may-mmxxiv/#more-160108"Gold-Plated Record" discussion on XWord Muggles Forum: https://xword-muggles.com/viewtopic.php?t=2882MMMM on the Washington Post's website: https://www.washingtonpost.com/crossword-puzzles/monthly-music-meta/Other element-al puzzles:"Table Setting" by Evan Birnholz: in the CCCC puzzle pack, information below on how to get that!"Addition Reactions" by Mikey G: https://crosshare.org/crosswords/R6ftkA9uIUyk7hMlhfn3/addition-reactions"AP Chemistry" by Pete Muller & Milo Beckman (WSJ Contest 7/12/2019), Crossword Fiend write-up: https://crosswordfiend.com/2019/07/14/wsj-contest-friday-july-12-2019/"It's Elementary" by Emma Oxford (Universal 8/13/2020), Crossword Fiend write-Up: https://crosswordfiend.com/2020/08/12/thursday-august-13-2020/#unUntitled by Jack Murtagh (NYT 12/10/2020), Crossword Fiend write-up: https://crosswordfiend.com/2020/12/09/thursday-december-10-2020/#ny "A-to-Z Crosswords" Kickstarter from Peter Gordon (live through June 2, 2024):  https://www.kickstarter.com/projects/petergordonpuzzler/a-to-z-crosswords-2024Contest Crosswords Combating Cancer (CCCC): crosswordsforcancer.com TO GET ACCESS TO THE CCCC PUZZLE PACK:1. Make a donation (suggested amount $13) to a cancer-related charity.2. Email a copy of your receipt to crosswordsforcancer@gmail.com.3. Receive puzzle pack!4 (optional but encouraged). Tell other people to do steps 1-3.Is anybody reading this? Please confirm.-------------------------------------------Want to get in touch with us? We would love to hear from you! You can reach Emma at damefoxwords@gmail.com, and you can reach Will at pandorasblockswmc@gmail.com. We may read your letter on a future episode! Podcast hosting by Buzzsprout Music by FASSounds from Pixabay

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

Hello CoCreators! This week on the podcast we're talking about the importance of personal transformation and why it needs to happen before organizational transformation can happen. This is something La'Kita has been talking about for years and we heard over and over from participants the Culture Crash Course just how impactful understanding this was, so we're sharing it with you!Mentioned in today's epsiode:Emotional Agility: Get Unstuck, Embrace Change, and Thrive in Work and Life by Susan DavidConnect: Building Exceptional Relationships with Family, Friends, and Colleagues by David Bradford Ph.D. and Carole Robin Ph.D. 6 Key Levers of a Successful Organizational Transformation by Andrew White, Michael Wheelock, Adam Canwell, and Michael SmetsResources:Our next session of the Culture Crash Course is currently open for enrollment with the course starting on April 29th and running through May 10th. For more information, please visit cocreatework.com/crashcourse. Enroll today!Additionally, we will be launching our CoCreate Work Leadership Book Club on May 1st. Registration is now open!Rolling admission is now open for The Culture Certification from CoCreate Work. The Culture Certification is an 18-week live, virtual intensive that empowers human-centered leaders with actionable strategies to shape the future of work in any organization.Are you ready to learn the step by step process for building great culture?! You can learn more here!Our Shared Purpose, Mission, and Principles Audit is a great opportunity for you to make sure you have the compass you need to provide purpose-driven direction, create an inclusive culture, engage team members, and make purpose-aligned decisions.At CoCreate Work, we believe in asking great questions. Click here to receive our guide to 40 Powerful Questions to accelerate your growth.Check out our hiring course, Hiring the Right Team for Your Business, and be sure to subscribe to our email list so you'll know when it's available for purchase.We would love to connect with you!CoCreate Work on InstagramLa'Kita on InstagramChloe on InstagramVisit our Podcast PageQuestions you would like us to answer on the podcast? email us at podcast@cocreatework.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

Alison Mitchell, Jim Maxwell and Charu Sharma discuss the retirement of New Zealand's Neil Wagner after his former teammate Ross Taylor said his retirement was 'forced'. Plus the team try and decipher why Australia and New Zealand play so few Test matches against each other. In the 78 years of Test match cricket between the two nations, Australia has played New Zealand just 60 times.We are joined by former Ireland player Andrew White who earned 232 caps for his country and now men's national selector. He tells us about their first ever Test match victory, his hopes that this could ensure more Test matches are played and if a two-tier World Test Championship is needed.We reflect on the first leg of the Women's Premier League in Bangalore and assess how the tournament is being perceived in its second season.Photo: Ireland players and support staff celebrate securing the nation's first victory in Test cricket. The Ireland team beat Afghanistan in a one off Test match in Abu Dhabi. (Credit: Afghanistan Cricket Board)

Andrew White is the CEO of Transcend.Space, which works with CEOs, senior leaders and their executive teams to successfully seize and transcend the greatest opportunities and challenges that they face through coaching interventions and leadership retreats. Andrew's research is focused on what it means to lead successfully in today's world, given the risks and opportunities that leaders face. — Join us for Real Leaders UNITE 2024: ⁠⁠https://real-leaders.com/unite/⁠ Apply for the The 2025 Real Leaders Impact Awards: ⁠⁠https://real-leaders.com/impact-awards-application/⁠