# AI for Science: Material Discovery Strategy

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-02-25

## Transcript

I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right?
Which is you have digital processing units and then you have physics processing units.
So it's basically nature doing computations for you.
It's the fastest computer known, possible even.
But in a way, it is a computation, and that's the way you want to see it.
So I want to you can do computations in a data center and then you can ask nature to do some computations.
Your interface with nature is a bit more complicated.
But then these things will have to seamlessly work together to get to a you know a new material that you're interested in.
Yeah, it's a pleasure to have Max Walling as a guest today.
Max has done so much over his career that I've been so excited about.
If you're in the deep learning community, you probably know Max for um his work on variational autocoders, which has literally stood the test of time or officially stood the test of prime.
If you are a scientist, you probably know him for his um like pioneering work on graph neural networks on equivariance.
And if you're a material science, you probably know him about his new uh startup, Caspei.
Max has a long history doing lots of cool problems.
Um you started in quantum gravity, which is I think very different than all of these other things you worked on.
The first question for AI engineers and for scientists, what is the thread in how you think about problems?
What is the thread and the type of things which excite you?
And how do you um decide what is the next big thing you want to work on?
So it has actually evolved a lot.
Um in my young days, let's prove.
I would just follow what I would find like super interesting.
I have kind of this sensor, I think many people have, but maybe not really uh sort of used very much, which is like you get this feeling about getting about very excited about some problem, right?
Like it could be, you know, what's inside of a black hole, or what's you know, at the boundary of the universe, or you know, what are what is quantum mechanics actually all about.
And so I've followed that basically throughout my career, but I have to say that as you get older, this changes a little bit, in a sense that there's a new dimension coming to it, and this is impact.
Working in two-dimensional quantum gravity, you pretty much guaranteed there's gonna be no impact in what you do, relative, you know, maybe a few papers, but not in the in this world, uh at this energy scale.
As I get closer to retirement, which is fortunately still uh you know 10 years away or so, I do want to kind of make a positive impact in the world, and um I got pretty worried about climate change.
Um I think we and um I think we should, you know, and uh politics seems to have a hard time solving it, especially these days, and so I thought better work on it from the technology side, and that's why we started Casp AI.
But there's also a lot of really interesting science problems in you know material science.
Um, and so I it's kind of combining both the impact you can make with it as well as the interesting science.
So it's sort of these two dimensions, like working on things which you feel is like, oh, there's something very deep going on here, and on the other hand, trying to build tools that can actually make a real impact in the world.
So the the thread that when I look back, look at the different things that you worked on, some of them seeming pre-connected, like the physics to equivariance and yeah, and uh graph neural networks, maybe.
And that that seems to be somewhat related to cusp.
Do you have a thread through there?
Yeah.
I think physics is the thread.
So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity.
So there's really the frontier.
But there's also a lot of mathematical tools that you can use, right?
In for instance, in particle physics, but also in general relativity, sort of symmetry space play an enormously important role.
And this goes all the way to gauge symmetries as well.
And so applying these kinds of symmetries to uh machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem.
I did this with Taco Cohen, and you know, Tacohan was the main driver behind this, went all the way from just simple like rotational symmetries all the way to gauge symmetries on spheres and stuff like that.
So, and uh Maurice Weiler, who's also here when he was a PhD student with me, you know, he wrote an entire book which I can really recommend about the role of symmetries in uh in AI and machine learning.
So I find it's a very deep and interesting problem.
So, more recently, so I've taken a sort of different path, which is the relationship between diffusion models and and a field called stochastic thermodynamics.
This is basically the thermodynamics, which is a theory of equilibrium, so but then formulated for out of equilibrium systems.
And it turns out that the mathematics that we use for diffusion models, but even all for reinforcement learning, for Schrdinger bridges, for MCMC sampling, all has the same mathematics as this theory, this physical theory of non-equilibrium systems.
And that got me very excited.
And actually, uh when I taught a course in um Meusenberg, uh it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences, Ames, and I turned that into a book.
So I two years later, the book is finished, I've sent it to the publisher, and this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics.
So it's always some kind of, I don't know.
I find physics very deep.
I also think a lot about quantum mechanics, and it's it's it's a completely weird theory that actually nobody really understands.
And there's a very interesting story, which is maybe good to tell, to connect sort of my my PhD back to where I'm now.
So I did my PSD with a noble laureate Gerard de Thoft.
He's just the most brilliant man I've ever met.
He was never wrong about anything as long as I've seen him.
And now he says quantum mechanics is wrong, and he has a new theory of quantum mechanics.
Nobody understands what he's saying, even though what he's writing down is not mathematically very complex.
But he's trying to address this understandability, let's say, of quantum mechanics head-on.
I find it very courageous.
And I'm completely fascinated by it.
So I'm also trying to think about okay, can I actually understand quantum mechanics in a more mundane way?
Sort of, you know, without all the weird uh multiverses and collapses and stuff like that.
So the physics is always been the threat, and I'm trying to apply the physics to the machine learning uh to build better algorithms.
You are still very involved in understanding and understanding physics and the worlds, yeah, even beyond just like applications to machine learning or uh introducing no formal formalisms.
That's really cool.
Yes, I would say I'm I'm not I'm not contributing much to physics, but I'm contributing to the interface between physics and science, and that's called AI for science or science or AI.
It's kind of a super it's actually a new discipline that's emerging.
Yeah, yeah.
Um and it's not just emergence, it's exploding, I would say.
That's the better term, because I know you go from investments into uh like in the hundreds of millions now in the billions.
So there's now actually a startup by Jeff Bezos that you know is at 6.2 billion C rounds, right?
It's like insane.
I guess it's the largest you know startup ever, I think, right?
And that's in this field, AF for science, right?
It tells you something that we are creating a new bubble here, yeah.
Right.
So why do you think it is?
What has changed that has like motivated people to start working on AI for science type problems?
So there's two two reasons actually.
The one one is that people have been applying sort of the tool, the new tools from AI to the sciences, which is quite natural.
And there's of course, I think there's two big examples.
Both of these have been actually very successful.
Both also had something to do with symmetries, which is also cool.
And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right?
Or you know, multimedia applications into something that could actually make a very positive impact in society, like health, drug development, materials for the energy transition, carbon capture.
These are all really cool, you know, impactful applications.
Besides that, the science and the kind of the is also very interesting.
Sort of the I would say the um the fact that these sort of these two fields are coming together, and that we're now at the point that we can actually model these things effectively and move the needle on some of these uh sort of science uh sort of uh uh methodologies is also a very unique moment, I would say.
And people recognize that okay, now some we're at the cusp of something new, right?
Which is also whether as we're also what the company is called after.
We're at the we're at the cusp of something new, and of course, that always creates a lot of energy.
It's like, okay, there's something, it's like sort of virgin field, right?
It's like nobody's green field, nobody's been there, you know.
Um, I can rush in and I can sort of start harvesting there, right?
And uh, and I think that's also what's causing a lot of uh sort of uh enthusiasm in the fields.
If you're an AI engineer, if the people that listen to this podcast, what we and you maybe don't have a strong science background, how does but are excited?
Most I would say most AI practitioners, be engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry, college, maybe even graduate school that they have been working or are starting out.
How does how does somebody who is not a scientist on a day-to-day basis, how do they get involved?
Well, they can read my book once it's out.
But um, this is basically saying that there is more we should create curricula that are on this interface.
So I'm not sure there is possibly already at some universities actual courses you can take, maybe online courses you can take.
These workshops where we are now are actually very good as well, and uh, we should probably have more tutorials before the workshop starts.
Actually, we've I've kind of proposed this at some point.
It's like maybe first have an hour of a tutorial so that people can get new into the field.
But yeah, there's a lot out there.
Um, most of it is, of course, inaccessible, but I would say um we will create much more books and other content that is more accessible, including this podcast, I would say, right?
So yeah, um, I think you know it it will come, and you know, these days you can watch videos and things.
There's a huge amount of content you can you can go and uh and and see.
So maybe uh a follow-up to that.
How do people learn and get involved?
But but why should they get involved?
I mean, that we have a lot of people who are over audience will be interested in AI engineering, but they may be looking for bigger impacts in the world.
Yeah.
What opportunities does AI for science provide, though, to make an impact to you know change the world?
That working in this, the world of pure bits would not.
So so my view is that um underlying almost everything is a material.
So we're we're focusing a lot on LLMs now, yeah, yeah, which is kind of the software layer.
But I would say if you think very hard, underlying everything is a material.
So I was saying, you know, there's the LLM underlying the LM is the GPU on which it runs, and then in order to make that GPU, uh, you have to put materials down on a wafer and sort of shine on it with uh sort of uh EOV light in order to etch kind of the structures in.
But that's now an actual material problem because more or less we've reached the limits of you know scaling things down, and now we are trying to improve further by new materials.
So that's the fundamental materials problem.
We need to get through the energy transition fast if we don't want to kind of mess up this world.
And so there is, for instance, batteries.
That's a complete materials problem, right?
There's fuel cells, um, there is solar panels, so that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light.
Where now we're at, I don't know, maybe 22 or something, right?
So these are huge changes all by material innovation.
And um, and yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you're doing is a material problem.
And so I think it's just you know very nice to work on this very, very foundation.
And also because I think this is maybe also something that's happening now, is we we can start to search through this material space.
This is this this has never been the case, right?
It's like scientists, but the the normal way of working is you read papers and then you come up with an hypothesis, you do an experiment and you learn, etc.
So that's a very slow process.
Now we can treat this as this as a search engine.
Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they're in the universe, but all of them, right?
And and we can make this kind of fully automated.
That's a hope, right?
We can just type, it becomes a tool where you type what you want, and something starts spinning, and some experiments get going.
Right.
And then you know, it's outcome a list of materials, and then you look at it and say maybe not, and then you refine your query a little bit.
Yeah.
And you kind of do research with this search engine, where a huge amount of computation is and and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this.
I find there's a very, very promising view of how we can sort of come, you know, build a much better sort of materials layer um underneath almost everything.
And also a more sustainable materials or plastics are polluting the planet.
If you can come up with a plastic that kind of destroys itself, you know, after I don't know, a few weeks, right?
And actually becomes a fertilizer, these are these are things that are not impossible at all.
These things can be done, right?
And we should do it.
Can you tell us what a little bit just generally about Cas B I?
And then we I have ton of questions.
Yeah.
So Caspii started uh about 20 months ago, and it was because um I was worried about I'm still worried about climate change.
And so I realized that in order to get you know to to stay within two degrees, let's say, we would not only have to reduce our emissions to zero by two thousand fifty, but then you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that's about half the rate that we now emit it.
And that is a unsolved problem.
But and if we don't solve it, two degrees is not gonna happen, right?
It's gonna be much more.
And I don't think people quite understand how bad that can be.
Uh uh, like four degrees, like very bad.
So um, so this technology needs to be developed.
And so this was uh uh my and my co-founder, Chad Atward's um motivation to start this startup.
And also because you know we saw the technology was ready, which is also very good.
So if you're you know the time is right to do it.
And uh, yeah, so we we now in in the meanwhile, we've grown to about 40 people.
We've kind of collected 130 million investment uh in the into the company, which is for a European company is quite a lot.
I would say it's interesting that right after that, you know, other uh startups got even more.
So that's kind of tells you how fast this this is growing.
But yeah, we are we are now at the so we we've built the platform, of course, but uh it's it's for a series of material classes and it needs to be constantly expanded to new material classes, and it can be more automated because you know, we know putting LLMs in, and so the whole thing gets more and more automated, and now we're moving to sort of high throughput experimentation, so connecting the actual platform, which is computational, to the experiments, so that you can get also get fast feedback from experiments.
And I I kind of think of experiments as something you do at the end, although that's what we've been doing so far.
I want to think of it as what I would call a uh sort of a physics processing unit, like a PPU, right?
Which is you have digital processing units and then you have physics processing units.
So it's basically nature doing computations for you.
It's the fastest computer known, possible, even uh it's a bit hard to program because you have to do all these experiments, it's also quite quite bulky, it's like a very large sort of thing you have to do.
But in a way, it is a computation, and that's the way you want to see it.
So I want to you can do computations in a data center, and then you can ask nature to do some computations, right?
Your interface with nature is a bit more complicated.
But then these things will have to seamlessly work together to get to a you know a new material that you're interested in.
And that's that's the vision we have.
We don't say super intelligence because I don't quite know what it means.
And I don't want to oversell it, but I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.
That's actually uh brings up a question I wanted to ask you.
First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?
Yeah, actually, it's been surprisingly it's not rocket science, I would say it's not rocket science in the sense of the design.
And basically the design that you know I wrote down at the very beginning, it's still more or less the design, although you add you add things like I I wasn't thinking very much about multi-scale models, and I was coming on our radar that actually multi-scale is very important.
And the beginning I wasn't thinking very much about self-driving labs, but now I think you know, we that's we are now at the stage we should be adding that.
And so there is sort of bits and details that we're adding, but more or less it's what you see in the slide decks here as well, which is there's a generative component that you have to train to generate candidates, and then there is a digital twin, multi-scale, multi-fidelity digital twin, uh, which you walk through the steps of the ladder, you know, that they do the cheap things first, you weed out everything that's obviously unuseful, and then you go to more and more expensive things later.
Um, and so you narrow things down to a small number, those go into an experiment, you know, do the experiment, get feedback, etc.
Now, things that also have been more recently added is uh sort of more agentic uh sort of parts.
You know, we have agents that search the literature and come up with you know, actually the chemical literature and come up with you know chemical suggestions or for doing experiments.
We have agents which uh sort of autonomously orchestrate all of the computations and the experiments that need to be done, you know, they're in various stages of maturity and they can be continuously improved, I would say.
And so that's basically I don't think that part is rocket science, but you know, the the design of that thing is not like surprising, but is it's surprising hard to actually build it, right?
So that's that's the thing that is yeah, where the moat is in the data that you can get your hands on, and the and actually building the platform.
And and I would say there's two people in particular I want to call out, which is Felix Hunker, who is actually you know building the scientific part of the platform, and Alessandro de Maria, who is building the sort of the scale kind of the ML ops part of the platform.
Yeah, and so and and recently uh we also added um sort of Aaron Walsh to our team, who is uh a very accomplished scientist uh from Imperial College.
We're very happy about that.
He's gonna be our chief science officer.
And uh we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important.
In principle, it's so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with uh sort of the domain experts, which are the companies typically.
So we always we only start to invest in the direction if we find a good uh industrial partner to go on that journey with us.
Makes a lot of sense.
Over the evolution of the platform, did you find that you that human intervention, human um I guess you could start out with a pure you could you can imagine two directions.
One, you start out making everything purely automatic, um automated, agentic, so on.
And then later on you like find that you need to have more human input and feedback different steps, or maybe did you start out with having human feedback lots of steps and then like kind of uh figure out ways to remove, you know, that's it.
It's the second one.
So you build tools.
So you so it's much more modular than you think, but it's like we need these tools for this epic, we need these tools.
So you build all these tools, and then you go through a workflow, actually in the beginning, just manually.
So you put them, okay.
Now first this tool, then run this tool, then run this, what's the reason so you put them in in a workflow, and then you figure out, oh, actually, you know, this this porous material that we we're trying to make actually collapses if you shake it a bit.
Okay, then you add a new tool that says test for you know stability, right?
Yeah, and so there's more and more tools, and then you build the agent, which could be a Bayesian optimizer or it could be an actual LLM, you know, maybe trained to be a good chemist, that will then start to use all these tools in the right way in the right order.
Yeah.
Right.
But in the beginning, it's like you as a chemist are putting the workflow together, yeah.
And then you think about okay, how am I going to automate this, right?
One very easy question you can ask yourself is you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation, has to go to somebody who knows DFT.
And so could you start to automate that away, which is like, okay, make it so user-friendly so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it's a good outcome, etc.
So you start to automate smaller, small pieces and more bigger pieces, etc.
And then the end the whole thing is automated.
So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and uh less so trying to create uh an automated process.
I think it's this is sort of the same what you're saying, because yes, we want to automate, yeah, but we don't see something very soon where the chemists and the domain expert is out of the loop.
Yeah, but it but it's a retreat, right?
It's like, okay, so first you needed an expert to tell you precisely how to set the parameters of D DFT calculation.
Yeah, okay, maybe we can take that out.
We can maybe automate it, right?
And so increasingly more of these things are going to be removed.
Yeah.
Um, in the end, the vision is it will be a search engine where you where somebody, a chemist will type things and will get list candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right?
And so the vision of a completely dark lab where you can close the door and you and you just say just you know, find something interesting and then it will cut it well, it will just figure out what's interesting and we'll figure out, you know, it's like, oh, I found this new material too, blah, blah, blah, blah, right?
That's not the vision I have, at least not for you know, a long time.
So for me, it's really empowering the domain experts that are sitting in the companies and in the universities to be much faster in the in in developing their materials.
And I should say it's also good to be a little humble at times, because it is very complicated, you know, to bring the to make it and to bring it into the real world.
And there are people that are doing this for their entire lives.
Yeah.
Right.
And it's like, I wonder if they scratch their head and say, well, you know, how are how how are you going to completely automate that away, like in in in the next five years?
I don't think that's gonna happen at all.
Um yeah.
So so to me it's a increasingly powerful tool in the hands of the chemists.
I have a question.
You've talked before about getting people interested based on having, you know, sort of a big referendum materials just incremental change.
I'm curious what you think about the platform you have now in our sort of stepping towards and how are you chasing the big change or is this like incremental or is there they're not mutually exclusive obviously but yeah what do you think about that?
We follow a mixed strategy.
So we are definitely going after a big material.
Again we do this with a partner I'm not going to disclose precisely what it is but we have our own kind of long term goal.
You could call a lighthouse or you know uh um sort of moonshot or whatever but um it is going to be a you know really impactful material that we want to develop as a proof point that it can be done and that's that it will make it into the into the real world and that AI was essential in actually making it happen.
At the same time we also are quite happy to work with companies that have more modest goals.
Like I would say one is a very deep partnership where you go on a journey with a company and that's a long term commitment together.
And the other one is like somebody says I need I need a force field can you help me train this force field and then maybe analyze this particular problem for me and I'll pay you a bunch of money for for that and then maybe after that we will see.
And that's fine too right but we prefer you know the deep partnerships where we can really change something for the good.
Yeah and do you feel like from a platform standpoint you're ready for that or what are the things that and and again not asking you to disclose proprietary secret sauce but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs I got what I find interesting about this field is that every time you build something it's actually immediately useful right and so unlike uh quantum computing which or nuclear fusion so you work for I don't know 20 30 40 years and nothing nothing nothing nothing and then it has to happen.
Yeah right and when it happens it's huge so it's uh it's quite different here because every time you introduce you so you go to a customer and you say so what do you need right so we work uh let's say on uh on a problem like uh water filtration we want to remove PFAS from water yeah right so we do this with a company Camera so they are deep partner yeah for us right so we're on a journey together I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it's us who will you know help them with AI training I have and new methods.
And in that kind of this interface, this interactions, something beautiful will happen.
And that that will have to happen first before this field will really take off, I think.
And so, in the sense that it's not a bubble, let's put it that way.
So that's people see that's the actual real what's happening.
So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say.
And I would I would hope we will have this new sort of breakthrough material before you know everything is completely automated because that will take a while.
And also it is very vertical specific.
So it's like completely automating something for problem A, you know, you can probably achieve it, but then you'll sort of have to start over again for problem B because you know your experimental setup looks very different.
You know, the machines that you characterize your materials look very different.
Even the models in your platform will have to be retrained and fine-tuned to the new class.
So every time you know you you have a lot of learnings to transfer, but also, you know, the problems are actually different.
Yeah, yeah.
And so, yeah, so I I would want that breakthrough material before it's completely automated, which I think is kind of a long-term vision.
And I would say every time you move to something new, you'll have to start retraining, and humans will have to come in again and sort of, okay.
So, what does this problem look like?
And now sort of, you know, point the to the machine again, you know, in the new direction, and and then uh and then use it again.
For the non-scientists among us, me included a bit of a scientist.
There's a lot of terminology.
You mentioned DFT, uh you meant equivariance we've talked about.
Can you sort of explain in you know, engineering terms or the level of sophistication and engineering will have what is equivariance?
So equivariance is the infusion of symmetry in neural networks.
So if I build a neural network, let's say that needs to recognize this bottle, right?
And then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle, well, actually the input that represents a rotated bottle is actually a rotated bottle.
It just doesn't understand that.
Where if you build equivariance in, basically, once you've trained it in one orientation, it will understand it in any other orientation.
So that means you need a lot less data to train these models.
And these are constraints on the weights of the model.
So the so basically you have to constrain the weights such that it understand it.
And you can build it in, you can hard code it in.
And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations, like in graph neural network, there are permutations.
And then physics, of course, there's many more of these groups.
To play devil's advocate, why not just use data augmentation by you know your bottle is in all the different orientations?
As an option, it's just not exact.
It's like uh why would you go through the work of doing all that where you would really need an infinite number of augmentations to get it completely right?
Um, where you can also hard code it in.
Now, I I have to say, sometimes actually data augmentation works even better than hard coding the equivariance in.
And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated.
And so it's harder to find good minima.
So there is also in complicated interplay, I think, between the optimization process and and these constraints you put on your network.
And so, yeah, you'll you'll hear kind of contradicting claims in this field.
Like some people and for certain applications it's it works just better than not doing it.
And sometimes you hear other people say if you have a lot of data and you can do data mutation, then actually it's easier to optimize them and it actually works better than putting the aggregates in it.
So you think there's kind of a bitter lesson for um mathematically founded uh models and strategies for doing deep learning?
Yeah ultimately it's a trade-up between data and uh and inductive bias.
Yes.
So if you're in if your inductive bias is not perfectly correct, you have to be careful because you you you put a ceiling to what you can do.
But if you know you know the symmetry is there, it's hard to imagine there's there isn't a way to actually leverage it.
But yeah, so there is a bitter lesson.
One of the bitter lessons is you should always make sure your architecture scale unless you have a tiny data set in which case it doesn't matter.
But if you you know the the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well I think.
Can you talk a little bit about your upcoming book and tell the listeners like what's exciting about it?
Yeah, I they should read it.
So this book is about so it's called uh generative AI and stochastic thermodynamics.
It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just you know moving around and uh you know relaxing to their ground state or that you can control to have certain, you know, be in a certain state.
The mathematics of these two is actually identical.
And so that's fascinating.
And in fact, what's interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for uh machine learning a long time ago.
And there's also Carl Friston's work on free energy principle and active inference.
But now we've related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems like fluctuation theorems, which are which we don't typically talk about, but we can learn a lot from.
And I think it's just it can sort of now start to cross-fertilize.
When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that's out there developed by these very smart physicists and say, okay, what can we take from here that will make our algorithms better?
At the same time, we can use our models to now help the scientists, you know, do better science, right?
And so it becomes a beautiful cross-fertilization between these two fields.
Yeah, it the book is rather technical, I would say, and it takes all sorts of things that have been done in stochastic thermodynamics and all sorts of models that have been done in in the machine learning literature, and it basically equates them to each other.
And I think hopefully that sense of unification will be revealing to people.
Wait, and when is it out?
Well, it depends on the publisher now, but uh I I hope in April I'm gonna give a keynote at iClear.
And I would be very nice if I have this book in my hand, but you know, it's hard to control uh these kind of timelines.
Yeah, I'm looking forward to it.
Thank you very much.