#404 – Lee Cronin: Controversial Nature Paper on Evolution of Life and Universe

The following is a conversation with Lee Cronin, his third time in this podcast. He is a chemist from University of Glasgow, who is one of the most fascinating, brilliant and fun to talk to scientists of ever had the pleasure of getting to know. He is one of the most fascinating and fun to talk to scientists of ever had the pleasure of getting to know.

He is one of the most fascinating and fun to talk to scientists of ever had the pleasure of getting to know. He is one of the most fascinating and fun to talk to scientists of ever had the pleasure of getting to know. He is one of the most fascinating and fun to talk to science of ever had the pleasure of getting to know. He is one of the most fascinating and fun to talk to.

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Thanks. It created, I think it's fair to say a lot of controversy, but also a lot of interesting discussion. So maybe I can try to summarize assembly theory and you tell me if I'm wrong. Sorry for it. So assembly theory says that if we look at any object in the universe, any object that we can quantify how complex it is by trying to find the number of steps it took to create it. And also we can determine if it was built by a process akin to evolution by looking at how many copies

of the object there are. Yeah, that's spot on. Yeah, spot on. I was not expecting that. Okay. So let's go through definitions. So there's a central equation I'd love to talk about, but definition wise, what is an object? Yeah, an object. So if I'm going to try to be as meticulous as possible, objects need to be finite and they need to be decomposable into subunits. All human made artifacts are objects. Is a planet an object? Probably yes, if you scale out. So an object is finite and

countable and decomposable, I suppose mathematically. But yeah, I still wake up some days and go to think to myself, what is an object? Because it's a non-trivial question. Persists over time, I'm quoting from the paper here, an object that's finite is distinguishable. I'm sure that's a weird adjective distinguishable. We've had so many people help offering to rewrite the paper after it came out. You wouldn't believe it's so funny.

Persists over time and is breakable such that the set of constraints to construct it from elementary building blocks is quantifiable such that the set of constraints to construct it from elementary building blocks is quantifiable. The history is in the objects. It's kind of cool, right? So, okay, so what defines the object is its history or memory, whichever is the sexier word. I'm happy with both depending on the day. Okay, so the set of steps that took to create the object,

so there's a sense in which every object in the universe has a history. Yeah. And that is part of the thing that is used to describe its complexity, how complicated it is. Okay, what is an assembly index? So the assembly index, if you take the object to part and be super lazy about it or minimal, say what it, because it's like you've got a really short term memory.

So what you do is you lay all the parts on the path and you find the minimum number of steps you take on the path to add the parts together to reproduce the object. And that minimum number is the assembly index. It's a minimum bound. And it was always my intuition, the minimum bound in assembly theory was really important. And that only worked out why a few weeks ago, which is kind of funny, because I was just like, no, this is sacrosanct. I don't

know why it will come to me one day. And then when I was pushed by a bunch of mathematicians, we came up with the correct physical explanation, which I can get to, but it's the minimum. And it's really important as the minimum. And the reason I knew the minimum was right is because we could measure it. So almost before this paper came out, with published papers, explain how you

can measure the assembly index of molecules. Okay, so that's not so trivial to figure out. So when you look at an object, we can say molecule, we can say object more generally to figure out the minimum number of steps to take to create that object. That doesn't seem like a trivial thing to do. So with molecules, it is not trivial, but it is possible because what you can do, and because I'm a chemist, so I'm kind of like, I see the lens of the world for just chemistry.

I break the molecule part and break bonds. And if you break up, if you take a molecule and you break it all apart, you have a bunch of atoms. And then you say, okay, I'm going to then form bond, take the atoms and form bonds and go up the chain of events to make the molecule. And that's what made me realize, take a toy example, literally toy example, take a Lego object, which is broken up of Lego blocks. So you could do exactly the same thing. In this case, the Lego blocks are

naturally the smallest, they're the atoms in the actual composite Lego architecture. But then if you maybe take a couple of blocks and put them together in a certain way, maybe they're they're offset in some way, that offset is on the memory. You can use that offset again with only a penalty of one and you can then make a square triangle and keep going. And you remember those motifs

on the chain. So you can then leap from the the start with all the Lego blocks or atoms, just laid out in front of you and say, right, I'll take you, you connect and do the least amount of work. So it's really like the smallest steps you can take on the graph to make the object. And so for molecules, it came relatively intuitively. And then we started to apply to language. We've even

started to apply to mathematical theorems. But I'm so aware out of my depth, but it looks like you can take minimum set of axioms and then start to build up kind of mathematical architectures in the same way. And then the shortest path to get there is something interesting that I don't yet understand. So what's the computational complexity of theory out the shortest path in

with molecules, with language, with mathematical theorems? It seems that once you have the fully constructed Lego castle or whatever your favorite Lego world is figuring out how to get there from the basic building blocks. Isn't like a, is that an empty heart problem? It's a hard problem. But actually, if you look at it, so the best way to look at it for this take a molecule. So if the molecule has 13 bonds, first of all, take 13 copies of the molecule and just cut all the bonds,

so take 12 bonds, and then you just put them in order. And then that's how it works. So and you keep looking for symmetry or copies. So you can then shorten it as you go down. And that becomes combinatorially quite hard. For some natural product molecules, it comes very hard. It's not impossible, but we're looking at the bounds on that at the moment. But as the object gets bigger, it becomes really hard. But that's the bad news, but the good news is there are shortcuts.

And we might even be able to physically measure the complexity without computationally calculating it, which is kind of insane. Where would you do that? Well, in the case of molecule, so if you shine light on the molecule, let's take an infrared, the molecule has each of the bonds absorbs the infrared differently in what we call the fingerprint region. And so it's a bit

like because it's quantized as well, you have all these discrete kind of absorbances. And my intuition after we realized we could cut molecules up in mass spec, that was the first go at this. We did it with using infrared and the infrared gave us an even better correlation assembly index, and we used another technique as well. In addition to infrared called NMR, nuclear magnetic resonance, which tells you about the number of different magnetic environments in the molecule. And that also

worked out. So we have three techniques, which each of them independently gives us the same or tending towards the same assembly index from molecule that we can calculate mathematically. Okay, so these are all methods of mass spectrometry, mass spec, you scan a molecule, it gives you data in the form of a mass spectrum. And you're saying that the data correlates to the assembly index. Yeah. So how generalizable is that shortcut? First of all, to chemistry. It's not going to

be on that because that seems like a nice hack. And you're extremely knowledgeable about various aspects of chemistry. So you can say, okay, it kind of correlates. But you know, the whole idea behind a assembly theory paper and perhaps why it's so controversial is that it reaches bigger. It reaches for the bigger general theory of objects in the universe. Yeah, I'd say so. I'd agree. So I've started assembly theory of emoticons with my lab, believe

or not, so take emojis, pixelate them, and work out the assembly index of emoji. And then work out how many emojis you can make on the path of emoji. So there's the ubre emoji from which all other emojis emerge. And then you can, so you can then take a photograph and by looking at the shortest path on or by reproducing the pixels to make the image you want, you can measure that. So then you start to be able to take spatial data. Now there's some problems there. What is then the definition of

the object? How many pixels? How do you break it down? And so we're just learning all this right now. So how do you compute these? How would you begin to compute the assembly index of a graphical, like a set of pixels or a 2D plane that form a thing? So you would, first of all, determine the resolution. So then how how much what is your xy and what the number on the x and y plane? And then look at the surface area. And then you take all your emojis and make sure they're all looked

at the same resolution. Yes. And then we were basically then do the exactly the same thing we would do for cutting the bonds. You'd cut bits out of the emoji and look at them. You'd have a bag of pixels. So you would then add those pixels together to make the overall emoji. But like, first of all, not every pixels. I mean, this is at the core sort of machine learning and computer vision. Not every pixel is that important. And there's like macro features. There's

micro features and all that kind of stuff. Exactly. Like, you know, the eyes appear in a lot of them. The smile appears in a lot of them. So in the same way in chemistry, we assume the bond is fundamental. What we do in there here is we assume the resolution at the scale that we should do it is fundamental. And we're just working that out and that you're right, that will change, right? Because as you take your lens out a bit, it will change dramatically. But it's just

a new way of looking at not just compression. What we do right now in computer science and data. There's one big kind of kind of misunderstanding as assembly theory is telling you about how compressed the object is. That's not right. It's a how much information is required on a chain of events. Because the nice thing is if in when you do compression and computer science, we're wandering a bit here, but it's kind of worth wandering, I think. And you you assume you have

instantaneous access to all the information in the memory. Yeah. In assembly theory, you say, no, you don't get access to that memory until you've done the work. And then you don't access that memory. You can have access, but not to the next one. And this is how in assembly theory, we talk about the four universes, the assembly universe, the assembly possible and the assembly contingent, and then the assembly observed. And they're all all scales in this combinatorial universe.

Yeah. Can you explain each one of them? Yeah. So the assembly universe is like anything goes. Just it's just combinatorial kind of explosion and everything. That's the biggest one. That's the biggest one's massive. Assembly universe, assembly possible, assembly contingent, assembly observed. And on the y-axis is assembly steps in time. Yeah. And you know, in the x-axis, as the thing expands through time, more and more unique objects appear. So yeah. So assembly

universe, everything goes. Yeah. Assembly possible, laws of physics come in. In this case in chemistry bonds. In assembly, so that means- Those are actually constraints, I guess. Yes. And they're the only constraints. They're the constraints of the base. So the way to look at it, you've got all your atoms, their conties, you can just bung them together. So then you can become a kind of, so in the way in computer science speak, I suppose the assembly universe is just like no laws of

physics. Things can fly through mountains beyond the speed of light. And in the assembly possible, you have to apply the laws of physics. But you can get access to all the motifs instantaneously with no effort. That means you could make anything. Then the assembly contingent says no, you can't have access to the highly assembled object in the future until you've done the work in the past on the causal chain. And that's really the really interesting shift where you go from assembly

to assembly possible to assembly contingent. That is really the key thing in assembly theory that says you cannot just have instantaneous access to all those memories. You have to have done the work somehow. The universe has to somehow built a system that allows you to select that path rather than other paths. And then the final thing, the assembly observed is basically you're saying, oh, these are the things we actually see. We can go backwards now and understand

that they have been created by this this causal process. Wait a minute. So when you say the universe has to construct the system that does the work, is that like the environment that that allows for like selection? Yeah. Yeah. That's the thing that does the selection. You could think about in terms of a von Neumann constructor, first of selection of ribosome, Tesla, Ascent Plant, assembling Teslas. You know, the difference between the assembly universe in Tesla land and the

the Cessna factory is, everyone says, no, Teslas are just easy. They just spring out. You know how I'll make them all the Cessna factory. You have to put things in sequence and out comes a Tesla. Did you talk about the factory? Yes. This is this is really nice. Super important point is that when I talk about the universe having a memory or there's some magic, it's not that it's that tells you that there must be a process encoded somewhere in physical reality, be it a cell,

a Tesla factory, or something else that is making that object. I'm not saying there's some kind of woo-woo memory in the universe, you know, morphic resonance or something. I'm saying that there is an actual causal process that is being directed constrained in some way. So it's not kind of just making everything. Yeah, but Lee, what's the factory they made the factory? So what is the, so first of all, you assume the laws of physics is just sprung to existence at the

beginning. Those are constraints, but what makes the factory the environment that doesn't selection? This is the question, well, it's the first interesting question that I want to answer. Out of four, I think the factory emerges in the environment, the interplay between the environment and the objects that are being built. And here, let me, I'll have a go at explaining to you the shortest path. So why is the shortest path important? Imagine you've got, I'm going to have to go

chemistry for a moment and abstract it. So imagine you've got an environment, a given environment that you have a budget of atoms, you're just flinging together. And the objective of those atoms that being flung together and say molecule A have to make, they decompose. So molecules decompose over time. So the molecules in this environment, in this magic environment have to not die,

but they do die. There's a, there's a, they have a half life. So the only way the molecules can get through that environment out the other side that's to pretend the environment is a box, you can go in and out without dying. And there's a, there's just an infinite supply of atoms coming or a well, a large supply. The molecule gets built, but the molecule that is able to template itself being built and survives in the environment will, will basically reign supreme.

Now, let's say that that molecule takes 10 steps. Now, and it, and it's using finite set of atoms, right? Or, now let's say another molecule, smart-ass molecule, we'll call it comes in. And can survive in that environment and can copy itself, but it only needs five steps. The molecule that only needs five steps, because it's continued, both molecules have been destroyed, but they're creating themselves faster, they can be destroyed. You can see that the shortest path reign supreme.

So the shortest path tells us something super interesting about the minimal amount of information required to propagate that motif in time and space. And it's just like a kind of, it seems to be like some kind of conservation law. So one of the intuitions you have is the propagation of motifs in time will be done by the things that can construct themselves in the shortest path. So like, you're going to assume that most of the objects in the universe

are built in the shortest, in the most efficient way. So big loop I just took there. Yeah, no, yes and no, because there are other things. So in the limit, yes, because you want to tell the difference between things that have required a factory to build them and just random processes. But you can find instances where the shortest path isn't taken for an individual object, an individual function. And people go, ah, that means the shortest path isn't right. And then I

say, well, I don't know. I think it's right still because so of course, because there are other driving forces. It's not just one molecule. Now when you start to now you start to consider two objects, you have a joint assembly space. And it's not that now it's a compromise between not just making a and b in the shortest path. You want to make a and b in the shortest path, which might mean that a is slightly longer, you have compromise. So when you stay slightly more nesting in the

construction, when you take a given object, that can look longer. But that's because the overall function is the object is still trying to be efficient. Yeah. And this is still very hand-wavy. And maybe I have no legs to stand on. But we think we're getting somewhere with that. And there's probably some parallelization. Yeah, right. So this is all, this is not sequential. The building is, I guess, when you're talking about complex objects, you don't have to work sequentially.

You can work in parallel. You can get your friends together. And they can. Yeah. And the thing we're working on right now is how to understand these parallel processes. Now there's a new thing we've introduced called assembly depth. And assembly depth can be lower than the assembly index for a molecule when they're cooperating together because exactly this parallel processing is going on. And my team have been working this out in the last few weeks because we're looking at what

compromises does nature need to make when it's making molecules in the cell? And I wonder if, you know, I may be like, well, I'm always leaping out of my compass. But in economics, I'm just wondering if you could apply this in economic process. It seems like capitalism is very good at finding shortest path, you know, every time. And there are ludicrous things that happen because

actually the cost function has been minimized. And so I keep seeing parallels everywhere where they're complex nested systems where if you give it enough time and you introduce a bit of heterogeneity, the system readjusts and finds a new shortest path. But the shortest path isn't fixed on just one molecule now. It's in the actual existence of the object over time. And that object could be a city. It could be a cell. It could be a factory. But I think we're going way beyond

molecules and my competence to probably should go back to molecules. But hey, before we get too far, let's talk about the assembly equation. Okay, how should we do this? Now, let me just even read that part of the paper. We define assembly as the total amount of selection necessary to produce an ensemble of observed objects quantified using equation one. The equation basically has a on one side, which is the assembly

of the ensemble. And then a sum from one to n, where n is the total number of unique objects. And then there is a few variables in there that include the assembly index, the copy number, which we'll talk about. That's an interesting. I don't remember you talking about that. That's an interesting addition and a thing of powerful one has to do with what that you can create pretty complex objects randomly. And in order to know that they're not

random, that there's a factory involved, you need to see a bunch of them. That's the intuition there. It's an interesting intuition. And then some normalization. What else is it? And then minus one, just to make sure that I'm more than one object, one object could be a one off and random. And then you have more than one identical object. That's interesting. When there's one, there's two over thing. Two other thing is super important, especially if the

index assembly index is high. So we could say several questions here. Why don't we talk about selection? What is this term selection? What is this term evolution that we're referring to? Which aspect of Darwinian evolution that we're referring to? That's interesting here. So, yeah, so this is probably what you know, the paper. We should talk about the paper second, the paper did, what it did is it kind of annoyed. We didn't know it. I mean, it got intention. And obviously angry

people, the angry people were annoyed. There's angry people in the world. That's good. So what happened is the evolutionary biologists got angry. We were not expecting that because we thought evolutionary biologists would be cool. I knew that some, not many, computational, complexity people will get angry because I've kind of been poking them and maybe I deserved it. But I was trying to poke them in a productive way. And then the physicist kind of got grumpy because the initial

conditions tell everything. The pre-biotic chemist got slightly grumpy because there's not enough chemistry in there. Then finally, when the creationist said it wasn't creationist enough, I was like, no, I've done my job. The physics, what you see in the physics, they say because you're basically saying that physics is not enough to tell the story of how biology emerges. I think so, kind of. And then they said a few physics is the beginning and the end of the story.

Yeah. So what happened is a reason why people put the phone down on the call of the paper, and if you view the reading of the paper like a phone call, they got to the abstract. Yep. And in the abstract, it's for sentences, pretty. So first two sentences caused everybody. Scientists have grappled with reconciling biological evolution with the immutable laws of the universe defined by physics. True. Right. There's nothing wrong with that statement. Totally true.

Yeah. These laws underpin life's origin. Evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Wow. First of all, we should say the title of the paper. This is paper was accepted and published in Nature. The title is assembly theory explains and quantifies selection and evolution, very humble title. And the the entirety of the paper, I think, presents interesting ideas, but reaches high. I am not.

I would do it all again. This paper was actually on the pre-print server for over a year. You regret nothing. Yeah. I think, yeah. I don't regret anything. You and Frank Sinatra did it your way. What I love about being a scientist is kind of sometimes, I'm, because I'm a bit dim. I'm like, and I don't understand what people tell me. I want to get to

the point. This paper says, hey, laws of physics are really cool. The universe is great. But they don't really, it's not intuitive that you just run the standard model and get life out. I think most physicists might go, yeah, there's, you know, it's not just, we can't just go back and say that's what happened, because physics can't explain the origin of life yet. There's a, doesn't mean it won't or can't, okay? Just to be clear, sorry intelligent designers, we are

going to get there. Second point, we say that evolution works, but we don't know how evolution got going. So biological evolution and biological selection. So for me, this seems like a simple continuum. So when I mentioned selection and evolution in the title, I think, and in the abstract, we should have maybe prefaced that and said non-biological selection and non-biological

evolution. And then that might have made it even more crystal clear, but I didn't think that biology, evolutionary biology should be so bold to claim ownership of selection and evolution. And secondly, a lot of evolutionary biologists seem to dismiss the origin of life questions, just say it's obvious. And that causes a real problem scientifically, because when two different, when the physicists are like, we own the universe, the universe is good, we explain all of it,

look at us. And the biologists say we can explain biology. And the poor chemistry in the middle go, but hang on. And this paper kind of says, hey, there is an interesting disconnect between physics and biology. And that's the point in which memories get made in chemistry through bonds. And hey, let's look at this closely if we can quantify it. So, yeah, I mean, I never expected the paper to kind of get that much interest. And still, I mean, it's only been published just

over a month ago now. So just the link on the selection. What is the broader sense of what selection means? Yeah, that's really good for selection selection. So I think for selection, you need, so this is where for me, the concept of an object is something that can persist in time and not die, but basically can be broken up. So if I was going to kind of bolster the definition of an object.

So, so if something can form and persist for a long period of time, under an existing environment that could destroy other, and I'm going to use anthropomorphic terms, I apologize, that weaker objects, or less robust, then the environment could have selected that. So good chemistry examples, if you took some carbon and you made a chain of carbon atoms, whereas if you took some, I don't know, some carbon nitrogen and oxygen and made chains from those, you'd start to get

different reactions and rearrangements. So a chain of carbon atoms might be more resistant to falling apart under acidic or basic conditions versus another set of molecules. So survives in that environment. So the acid pond, the molecule, the molecule, the resistant molecule can get through, and then that molecule goes into another environment. So that environment now maybe being an acid pond, it's a basic pond, or maybe it's an oxidizing pond. And so if you've got carbon, and it goes

in oxidizing pond, maybe the carbon starts to oxidize and break apart. So you go through all these kind of obstacle courses, if you like, given by reality. So selection is the ability happens when an object survives in an environment for some time. But, and this is the thing that's super subtle, the object has to be continually being destroyed and made by process. So it's not just about the process, the object now is about the process and time that makes it, because a rock could just

stand on the mountain side for four billion years and nothing happened to it. And that's not necessarily really advanced selection. So for selection to get really interesting, you need to have a turn over in time. You need to be continually creating objects, producing them, what we call

discovery time. So there's a discovery time for an object. When that object is discovered, if it's say a molecule, that can then act on itself, or the chain of events that caused itself to bolster its formation, then you go from discovery time to production time, and suddenly you have more of it in the universe. So it could be a self-replicating molecule. And the interaction of the molecule in the environment in the warm little pond or in the sea or wherever in the bubble

could then start to build a proto-factory, the environment. So really, to answer your question, what the factory is, the factory is the environment, but it's not very autonomous, it's not very redundant. There's lots of things that could go wrong. So once you get high enough up the, the hierarchy of networks of interactions, something needs to happen, that needs to be compressed into a small volume of made resistant robusts. Because in biology, selection and evolution is robust,

that you have error correction built in. You have really, you know, that there's good ways of basically making sure propagation goes on. So really, the difference between inorganic abiotic selection, evolution, and evolution and stuff in biology is robustness. The ability to kind of propagate over the ability survive in lots of different environments, whereas our poor little inorganic, so molecule, whatever, just dies in lots of different environments.

So there's something super special that happens from the inorganic molecule in the environment, kills it to where you've got evolution and cells can survive everywhere. How special is that? How do you know those kinds of evolution factors are everywhere in the universe? I don't, and I'm excited because I think selection isn't special at all. I think what is special is the history of the environments on Earth that gave rise to the first cell that now has taken

all those environments and is now more autonomous. And I would like to think that, you know, this paper could be very wrong. But I don't think it's very wrong. It meets certainly wrong, but it's less wrong than some other ideas, I don't know. Right? And if this allows inspires us to go and look for selection in the universe, because we now have an equation where we can say we can

look for selection going on and say, oh, that's interesting. We seem to have a process that's given it because giving us high copy number objects are also a highly complex, but that doesn't look like life as we know it. And we use that. So there's a hydrophermal vent. All there's a process going on this molecular networks because the assembly equation is not only meant to identify at the higher end advanced selection, what you get, I record in biology, you super advanced selection.

And even, I mean, you could use the assembly equation to look for technology and go for a bid we could talk about consciousness and abstraction, but let's keep it primitive, molecules and biology. So I think the real power of the assembly equation is to say how much selection is going on in this space. And there's this is a really simple for experiment I could do. So you have a little petri dish and on that petri dish you put some simple food. So the assembly

index of all the sugars and everything is quite low. So then and you put a single cell of E. coli cell. And then you say, I'm going to measure the assembly in this amount of assembly in the box. So it's quite low, but the rate of change of assembly, DADT will go VUM sigmoidal as it eats all the food and the number of coli cells will replicate, because they take all the food, they copy themselves, the assembly index of all the molecules goes

up, up, up, and up until the food is exhausted in the box. So now the now the E. coli's stop, I mean, DAI is probably a strong way, they stop respiring because all the food is gone, but suddenly the amount of assembly in the box has gone up gigantically because of that one E. coli factory has just eaten fruit, milled lots of other E. coli factory has run out of food and stopped. And so that looking at that. So in the initial box, although the amount of assembly was really small,

it was able to replicate and use all the food and go up. And that's what we're trying to do in the lab actually is kind of make those kind of experiments and see if we can spot the emergence of molecular networks that are producing complexity as we feed in raw materials and we feed a challenge and environment, you know, we try and kill the molecules. And really that's the main kind of idea for the entire paper. Yeah, and see if you can measure the changes in the assembly index throughout

the whole system. Yeah. Okay, what about if I show up to a new planet, we'll go to Mars or some other planet from a different solar system. And how do we use a somebody index there to discover alien life? In very simply actually, if we let's say we'll go to Mars with a mass spectrometer with a sufficiently high resolution. So what you have to be able to do. So good thing about mass spec is that you can select the molecule from the mass. And then if it's high enough resolution, you can

be more and more sure that you're just seeing identical copies. You can count them. And then you fragment them and you count the number of fragments and look at the molecular weight and the higher the

molecular weight and the higher the number of fragments, the higher the assembly index. So if you go to Mars and you take a mass spec or high enough resolution and you can find molecules, and I'll give a guide on Earth, if you could find molecules say greater than 350 molecular weight with more than 15 fragments, you have found artifacts that can only be produced at least on Earth by life.

Now you would say, oh, maybe the geological process, I would argue very vehemently that that is not the case, but we can say, look, if you don't like the cut off on Earth, go up higher, 30, 100, right? Because there's going to be a point where you can find a molecule with so many different parts. The chances of you getting a molecule that has 100 different parts and finding a million identical copies, you know, that's just impossible that could never happen in an infinite set of

universes. Can you just linger on this copy number thing? A million different copies. What do you mean by copies and why is the number of copies important? Yeah, that was so interesting and I always understood the copy number was really important, but I never explained it properly for ages. I kept having this, it goes back to this, if I give you, I don't know, a really complicated molecule and I say it's complicated, you could say, hey,

that's really complicated, but is it just really random? So I realized that ultimate randomness and ultimate complexity are indistinguishable. Until you can see a structure in the randomness, so you can see copies. So copies implies structure. Yeah. The factory. There's a deeper, profound thing in there. Because if you just have a random process, you're going to get a lot of complex, beautiful, sophisticated things.

What makes them complex in the way we think life is complex or yeah, something like a factory that's operating under a selection process, there should be copies. Is there like some eluseness about copies? What does it mean for two objects to be equal?

It's all to do with the telescope or the microscope you're using. So at the maximum resolution, so in the nice thing about chemists, they have this concept of the molecule and they're all familiar with the molecule and molecules you can hold on your hand and lots of them, identical copies. A molecule is actually a super important thing in chemistry to say, look, you can have a mole of a molecule, an avogadro's number of molecules and they're identical. What does that mean?

That means that the molecular composition, the bonding and so on, the configuration is all indistinguishable. You can hold them together. You can overlay them. So the way of doing it is if I say, here's a bag of 10 identical molecules, that's pretty of their identical. You pick one out of the out of the bag and you basically observe it using some technique and then you put it, you take it away and then you take another one out. If you observe it using technique, you can see no differences

there identical. It's really interesting to get right because if you take say two molecules, molecules can be in different vibrational or rotational states, they're moving all the time. So with this respect, identical molecules have identical bonding. In this case, we don't even talk about chirality because we don't have a chirality detector. So two identical molecules in one conception assembly theory basically considers both hands as being the same. But of course, they're

not. They're different. As soon as you have a chiral to distinguish it, detect the left and the right hand, they become different. So it's to do with the detection system that you have and the resolution. So I wonder if there's an art and science to the which detection system is used when you show up to a new planet. Yeah. Yeah. So like you're talking about chemistry a lot today.

We have kind of standardized detection systems, right? Of how to compare molecules. So when you start to talk about emojis and language and mathematical theorems and I don't know, more sophisticated things at a different scale, a smaller scale than molecules, a larger scale than molecules, like word detection. If we look at the difference in you and me flexibly, are we the same? Are we different? Sure. I mean, of course, we're different close up.

But if you zoom out a little bit, it will morphologically look the same. Yeah. No, no, you know, it's like characteristics, hair lengths, stuff like that. Well, also like the species and yeah, yeah, yeah. And and also there's a sense why we're both from Earth. Yeah, I agree. I mean, this is the power of assembly theory in that regard that you if you so if everything, so the way to look at it, if you have a box of objects, if they're all if they're all indistinguishable,

then using your technique, what you then do is you then look at the assembly index. Now, if the assembly index of them is really low, right, and they're all indistinguishable, then you're then it's telling you that you have to go to another resolution. So that would be, you know, it's kind of a sliding scale. It's kind of nice. So those two kind of are attention with each other. Yeah, the number of copies on the assembly index. Yeah.

Yeah. That's really really interesting. So, okay. So you show up to your planet, you'll be doing what? I would do mass back. I would bring it on a sample of what like first of all, like how big of a scoop do you take? Did you take a scoop? Like what? Like, uh, so we're looking for primitive life. I would I would look, yeah. So if we're just going to Mars or Titan or in Celerda or somewhere, so the number of ways are doing it. So you could take a large scoop or

you could go for the atmosphere and detect stuff. So you could make a life, a life meter, right? So one of Sarah's colleagues at ASU Paul Davis keeps calling it a life meter, a life meter. Which is quite a nice idea because you think about it. If you've got a living system that's producing these highly complex molecules and they drift away and they're in a highly kind of demanding environment, they could be burnt, right? So they could just be falling apart. So you want to sniff a little bit

of complexity and say warmer, warmer warmer, oh, we found life. We found the alien. We found we found the alien Elon Musk smoking a joint in the bottom of the cave on Mars or Elon himself. Whatever, right? So, okay, we found it. So what you can do is the mass spectrometer, you could just look for things in the gas phase or you're going to surface drill down because you want to find molecules that are where you've either got to find the source living system because

the problem with just looking for complexity is it gets burnt away. So in a harsh environment on say on the Mars surface of Mars, there's a very low probability that you're going to find really complex molecules because of all the radiation and so on. If you drill down a little bit, you could drill down a bit into into soil that's billions of years old. Then I would put in some solvent, water, alcohol or something or take a scoop, put it in, make it volatile, put it into

the mass spectrometer and just trying to detect high complexity, high abundant molecules. And if you get them, hey, presto, you can have evidence of life. Wouldn't that then be great if you could say, okay, we've found evidence of life. Now we want to keep the life meter, keep searching for more and more complexity until you actually find living cells. You can get those new living cells and then and then you can bring them back to earth or you could try and sequence them. You could see that

they have different DNA and proteins. Go on the gradient of the life meter. How would you build a life meter? Let's say we're together, starting a new company launching a life meter. Mass spectrometer would be the first way of doing it. Just take it. No, no, but that's that's that's one of the major components of it. But I'm talking about like, I would, if it's a device, we got it and branding logo, we got to talk about that later. But what's the input? What's the

like, how do you get to the meter output? So I would take a life, so my life meter, our life meter, there you go. Thank you. Yeah, you're welcome. I would have both infrared and mass spectrometer, so it would have two ports so it could shine the light. And so what it would do is you would have a vacuum chamber and you would have an

electrostatic analyzer and you'd have a monochromator to producing infrared. You'd add the sum, so you'd take a scoop of the sample, put it in the life meter, it would then add a solvent or heat up the sample, so some volatiles come off. The volatiles would then be put into the into the mass spectrometer into electrostatic trap and you'd weigh the molecules and fragment them.

Alternatively, you'd shine infrared light on them, you'd count number of bands, but you'd have to, in that case, do some separation because you want to separate in and so in mass spec, it's really nice and convenient because you can separate electrostatically, but you need to have that. Can you do it in real time? Yeah, pretty much, pretty much. Yeah, so let's go all the way back. So this, okay, we're really going to get this work off. The Lexus life meat, Lexus leaves.

No, absolutely. It's a good, good, good ring to it. All right, so you have a, you have a vacuum chamber, you have a little nose, the nose would have some, a packing material. So you would take your sample, add it onto the nose, add a solvent or a gas, it would then be sucked up the nose and that would be separated using chrome, what we call chromatography.

And then as each band comes off the nose, we would then do mass spec and infrared. And in the, in the case of infrared, count the number of bands, in the case of mass spec, count the number fragments and weigh it. And then the further up in molecular weight range for the mass spec and the number of bands, you go up and up and up from the, you know, dead, interesting, interesting, over the threshold. Oh my gosh, earth life. And then right up to the batshit crazy, this is

definitely, you know, alien intelligence that's made this life, right? You could almost go all

the way there, same in the infrared. And it's pretty simple. The thing that it's really problematical is that for many years, decades, what people have done, and I can't blame them, is there's rather that they've been obsessing about small biomarkers on that we find on earth, amino acids, like single amino acids or evidence of small molecules and these things and looking for those run, looking for complexity, that will the beautiful, beautiful thing about this is you can look for complexity

without earth chemistry bias or earth biology bias. So assembly theory is just a way of saying, hey, complexity and abundance is evidence of selection. That's how our universal life meter will work. Complexity in abundance is evidence of selection. Okay. So let's apply our life meter to earth. So what, you know, if we were just to apply assembly index measurements to earth, what what what kind of stuff are going to be get, are going to get what's impressive

so some of the complexity on earth. So we did this a few years ago in the when I was trying to convince NASA and colleagues that this technique could work. And honestly, it's so funny because everyone's like, now I'm going to work. And it was just like, because the chemist was saying, of course, there are complicated molecules out there, you can detect that just form randomly.

I was like, really, really? That's like, that was like, you know, as a bit like a, I don't know, someone saying, of course, Darwin textbook was just written randomly by some monkeys in a typewriter. It was just for me, it was like, really? And and and I pushed a lot on the chemist now, and I think most of them are on board, but not totally. I really, it really had some big arguments, but the copy number caught there. Cause I think I confused the chemist by saying one off. And then

when I made clear about the copy number, I think that made it a little bit easier. Just to clarify, chemists might say that, of course, out there outside of earth, there's complex molecules. Yes. Okay. And then you're saying, wait a minute, that's like saying, of course, there's aliens out there. Yeah. Exactly that. Okay. Exactly. But you're you say, you clarify that that's actually a very interesting question. And we should be looking for complex molecules

of which the copy number is two or greater. Yeah. Exactly. So on earth, the coming back to earth, what we did is we took a whole bunch of samples, we and we were running pre-bodic chemistry experiments in the lab. We took various inorganic minerals and extracted them. Look at the volatile because there's a special way of treating minerals and polymers in assembly theory where in this in our life machine, we're looking at molecules. We don't care about polymers because they don't

volatile. You can't hold them. And how can you make if you can't discern that they're identical, then it's very difficult for you to to to work out if this undergone selection or they're just around a mess. Same with some minerals, but we can come back to that. So basically what you do, we got a whole load of samples inorganic ones. We got a load of we got Scotch whiskey and also got this. It took a hard bag, which is one of my favorite whiskeys, which is very peaty. And another

whizzes, PD mean. So the way that on in Scotland in Eilert, which is little island, the Scotch, the whiskeys, let to mature in barrels. And it said that the peak, the complex molecules in the peat might find their way through into the whisky. And that's what gives it this intense brown color and really complex flavor is literally molecular complexity that does that. And so, you know, vulcars, the complete opposite is just pure, right? What are the whiskey that hire these?

The higher the semi-innocent, the better the whiskey. That's what I mean, I really love deep PT Scottish whiskeys. Near my house, there is a one of the lowland distilleries called Glen going. It's still beautiful whiskey, but not as complex. So for fun, I cooked up some Glen going whiskey in our bag and put them into the mass spec at measure of the assembly index. I also got Ecoli. So the way we do it, take the Ecoli, break the cell apart, take it all apart, and also got some

beer. And people were ridiculing us saying, oh, beer is evidence of complexity. One of the one of the computational complexity people was just throwing, yeah, we were kind of he's very vigorous in his disagreement of assembly theory was just saying, you know, you don't know what you're

doing, even beer is more complicated than human. We didn't realize it's not beer, per se, it's taking the East extract, taking the extract, breaking the cells, extracting the molecules, and just looking at the profile of the molecules, see if there's anything over the threshold. And we also put in a really complex molecule tax on. So we took all of these, but also NASA gave us, I think, five samples. And they wouldn't tell us what they are. They said, no, we don't believe

you can get this to work. And they really, you know, they gave us some super complex samples. And they gave us two fossils, one that was a million years old and one was at 10,000 years old. See, but something from Antarctica, see, bed, they gave us an emergency and meteor, right? And a

few others. Put them through the system. So we took all the samples, treated them all identically, put them into mass spec, fragmented them, counted, and in this case, implicit in the measurement was, we, you, in mass spec, you only detect peaks when you've got more than say, let's say 10,000 identical molecules. So the copy numbers already baked in, there wasn't quantified, which is

super important there. This is in the first paper, because I guess abundant, of course. And when you then took it all out, we found that the biological samples gave you molecules that had an assembly index greater than 15. And all the abart examples were less than 15. And then we took the NASA samples, and we looked at the ones that were more than 15, less than 15. And we gave them back to NASA and

they're like, oh gosh, yep, dead, living, dead, living, you got it. And, and that's what we found on Earth. That's a success. Yeah. Oh, yeah, resounding success. Well, can you, can you, can you, I'll just go back to the beer and the E. coli? So what, what's the sum index on those? So what you were able to do is like the assembly index of, we found high assembly index molecules originating from the beer sample and the E. coli sample. So I mean, I didn't know which one was

higher. We wouldn't really do any detail there because now we are doing that because one of the things we've done, it's a secret, but I can tell you. No, nobody's listening. Well, is that we've just mapped the tree of life using a assembly theory because everyone said, oh, that you can't do in phimbology. And what we're able to do is, so you, I think there's three way, well, two ways doing tree of life traffic. Well, three ways actually. Yeah, what's the tree of life?

So the tree of life is basically tracing back the history of life on Earth for all different species going back, what, who evolved from what? And it all goes all the way back to the first kind of life forms and they branch off and like you have plant kingdom, the animal kingdom, the fungi, exist the kingdom, you know, and different, different branches all the way up. And the way this was classically done, and I'm no evolutionary biologist, the evolution

biologist, it's a very, tell me every day, at least 10 times. I want to be one though. I kind of like biologists, kind of cool, but yeah, it's very cool. But basically what Darwin and Mendelay have and all these people do is just they draw pictures, right? And they they tax it. They just can't, they were able to draw pictures and say and say, oh, these look like common classes. Then there are artists really, they're just, you know, but they're they're they're

able to find out a lot, right? And looking at verbrates and verbrates, camera and explosion, all this stuff. And then, then came the genomic revolution and suddenly everyone used gene sequencing and Craig Ventors is a good example. We are thinking he's gone around the world and he's yacht just taking up samples looking for new species, where he's just found new species of life just

from sequencing. It's amazing. So you have taxonomy, you have sequencing, and you can also do a little bit of kind of molecular kind of archaeology like, you know, measure the samples and and kind of form some inference. What we did is we were able to fingerprint through to the load of random

samples from all of biology. And we use mass spectrometry. And what we did now is not just look for individual molecules, but we looked for coexisting molecules where they had to look at their joint assembly space and where we were able to cut them apart and undergo recursion in the mass spec and infer some relationships and were able to recapitulate the tree of life using mass spectroscopy, no sequencing and no drawing. All right. Can you try to say that again,

with a little more detail? So recreating what does it take to recreate the tree of life? What does the reverse engineering process look like here? So what you do is you take an unknown sample, you pung it into the mass spec, you get a, because this comes from what you're asking like, what you see in E. coli. And so in E. coli, you don't just see, it's not it's not it's not that the most sophisticated cells on on Earth make the most sophisticated molecules. It is the

coexistence of lots of complex molecules above a threshold. And so what we realized is you could fingerprint different life forms. So fungi make really complicated molecules. Why can they can't move? They have to make everything on site. Whereas, you know, some animals are like lazy. They can just go eat the fungi. They don't need to make very much. And so what you do is you look at the, so you take, I don't know, the fingerprint, maybe the top number of high molecular white

molecules you find in the sample, you fragment them to get their assembly indices. And then what you can do is you can infer common origins of molecules. You can do a kind of molecular, when the reverse engineering of the assembly space, you can infer common roots and look at what's called the joint assembly space. But what let's translate that into the experiment. Take a sample, bung it in the mass spec, take the top, say, 10 molecules, fragment them. And then, and that gives

you one fingerprint. Then you do it for another sample, you get another fingerprint. Now the question is you say, Hey, are these samples the same or different? And that's what we've been able to do. And by basically looking at the assembly spaces, these molecules create without any knowledge of assembly theory, you are unable to do it. With an knowledge of assembly theory, you can reconstruct the tree. How does, how does knowing if they're the same or different, give you the tree?

Let's go to two leaves on different branches on the tree, right? What you can do by counting the number of differences, you can estimate how far away that origin was. And that's all we do. And it just works. But when we realized you could even use assembly theory to recapitulate the tree of life from no gene sequencing, we were like, So this, this is looking at samples that exist today in the world. What about like things that are no longer exist. I mean, the tree contains information about

the past. I would, some of it is gone. Yeah, yeah, absolutely. I would love to get old fossil samples and apply assembly theory mass spec and see if we can find new forms of life that have, there are no longer amenable to gene sequencing because the DNA is all gone. There's DNA DNA and RNA is quite unstable. But some of them are complex molecules might be there, they might give you a hint, something

new. Or wouldn't it be great if you if you find a sample that's worth really persevering and doing, you know, doing the proper extraction to reek to, you know, PCR and so on and then sequence it and then put it together. So one thing dies, you can still get some information about this complexity. Yeah. And we can, and it appears that you can do some dating. Now, there are really good techniques as radiocarbon dating. There is longer dating going looking at

radioactive minerals and so on. And you can also in bone, you can look at the, what happens in after something dies is the, you get what's called rastomization where the chirality in the polymers basically changes and you just get, you get decomposition. And the rate of the deviation from the pure, in antimer to the mixture, you can have a heart, you give you a time,

time scale on it, half life. So you can date when it died. I want to use assembly theory to see if I can date, use it date death and things and trace the tree of life and also decomposition and molecules. Do you think it's possible? Oh, yeah. Without a doubt. It may not be better than what, because like the, I was just at conference, whereas some brilliant people were looking at isotope enrichment and looking at how life enriches isotopes and they're really sophisticated stuff

that they're doing. But I think there's some fun to be had there because it gives you another dimension of dating. How old is this molecule? In terms of more importantly, how long ago was this molecule produced by life? The more complex the molecule, the more prospect for decomposition, oxidation, reorganization, loss of chirality and all that jazz. But what life also does is it enriches, as you get older, the amount of carbon 13 and you goes up because of the way the

metabolite, because of the way the bonding is in carbon 13. So it has a slightly different strength. Bond strength and you is called a kinetic isotope effect. So you can literally date how old you are, you know, or when you stop metabolizing. So you could date someone's death, how old they are, I think I'm making this up. This might be right. But I think it's roughly right. The amount of carbon 13 you have in you, you can kind of estimate how old you are.

How old living organs are? Humans are. Yeah, like you could say, all this person is 10 years old and this person is 30 years old because they'll be metabolizing more carbon and they've accumulated it. That's the basic idea. It's probably completely wrong timescale. Signatures of chemistry are fascinating. So you've been saying a lot of chemistry examples for assembly theory. What if we zoom out and look at a bigger scale of an object?

You know, like really complex objects, like humans or living organisms that are made up of, you know, millions or billions of other organisms. How do you try to apply assembly theory to that? At the moment, we should be able to do this to morphology in cells. So we're looking at cell surfaces and really trying to extend further. It's just that, you know, we work so hard to get this paper out and people to start discussing the ideas. But it's kind of funny because I think the

penny is falling on this. So, yeah, so was it mean for a penny? No, the penny's dropped, right? Because a lot of people are like, it's rubbish, it's rubbish, you've insulted me, it's wrong. And I'm, and you know, I mean, the paper got published on the fourth of October. It had 2.3 million engagements on Twitter, right? And it's been downloaded over a few hundred thousand times. And someone actually said to me, wrote to me and said, this is an example of really bad writing

and what not to do. And I was like, if all of my papers got read this much because that's the objective of I have a publishing of people and people to read it, I want to write that badly again. I don't know what's the deep inside here about the negativity in the space. I think it's probably the immune system of the scientific community making sure that there's no bullshit that gets published. That's, and then it can overfire, it can do a lot of damage, it can shut down conversations in a

way that's not productive. We go back coming on to your question about the hierarchy assembly. But let's go back to the perception. People saying that paper was badly written. I mean, of course, we could improve it. We can always improve the clarity. Let's go there before we go to the hierarchy. It has been criticized quite a bit the paper. What has been some criticism that you found most

powerful? That you can understand and can you explain it? Yes, the most exciting criticism came from the evolutionary biologist telling me that they thought that origin of life was a solved problem. That was like, whoa, we're really on something because it's clearly not. When you poke them on that, they just said, no, you don't understand evolution. I said, no, no, I don't think

you understand the evolution had to occur before biology. There's a gap. That was really, for me, that misunderstanding and that that did cause an immune response, which was really interesting. The second thing was the fact that physicists were actually really polite, right? Really nice about it. They just said, we're not really sure about the initial conditions thing, but this is a really big debate that we should certainly get into because the emergence of life was not encoded in the

initial conditions of the universe. It can't, and I think assembly theory shows why it can't be. That's sure. If you could say that again. The emergence of life was not and cannot imprinsible being coded in the initial conditions of the universe. It's the cleofower mean by life is like what high assembly index objects? Yeah. This goes back to your favorite subject. What's that? Time. Right. So why? What does time have to do with it?

Probably we can come back to it later, but I think it might be, if we have time. Yeah. But I think I now understand how to explain how lots of people got angry with the assembly paper, but also the ramifications of this is how time is fundamental in the universe, and this notion of combinatorial spaces. There are so many layers on this, but you have to become an intuitionist mathematician and you have to abandon platonic mathematics. And also platonic

mathematics is their physics astray, but there's a lot back there. So we can go to the... Atonic mathematics. Okay. There's okay. The evolution of biologists criticize because the origin of life is understood and not it doesn't require an explanation of the world's physics. Yeah. It pays there. Well, I mean, it was, I think they said lots of confusing statements. Basically, I realized the evolutionary biology community that were vocal and some of

them really rude, really spiteful and needlessly so, right? Because like, you know, I didn't, people misunderstand publication as well. Some of the people has said, how dare this be published in nature? This is, you know, how to call it a terrible journal. And I, and it really, and I want to say that people look, this is a brand new idea that's not only potentially going to change the way we look at biology, it's going to change the way we look at the universe.

And everyone's like saying, how dare you? How dare you be so grandiose? I'm like, no, no, no, this is not hype. We're not, we're not like saying we've invented some, I don't know, we've discovered alien in a closet somewhere just for hype. We've genuinely mean this to genuinely have the impact or ask the question. And the way people jumped on that was a really bad precedent for young people want to actually do something new because this makes a bold claim. And the chances are

that it's not correct. But what I wanted to do is a couple of things as I want to make a bold claim that was precise and testable and correctable, not a woolly, another woolly information in biology argument information, cheering machine blah, blah, blah, blah, a concrete series of statements that can be falsified and explored. And either the theory could be destroyed or built upon. What about the criticism of you're just putting a bunch of sexy names and something that's

already obvious? Yeah, that's really good. So, so the assembly index of a molecule is not obvious, no one has measured it before. And no one has thought to quantify selection complexity and copy number before in such a primitive quantifiable way. I think the nice thing about this paper, this paper is a tribute to all, we're not to all the people that understand that the biology

does something very interesting. Some people call it neg entropy. Some people call it think about organizational principles that lots of people were not shocked by the paper because they've done it before. A lot of the arguments we got, some people said, oh, it's rubbish. Oh, by the way, I had this idea 20 years before. I was like, which one? Is it your rubbish part or the really revolutionary part? So this kind of plucked two strings at once. It plucked the, there is something

interesting the biology as we can see around this, but we haven't quantified yet. And what this is, the first stab at quantifying that. So the fact that people said this is obvious, but it's also, so with it's obvious, why have you not done it? Sure, but there's a few things to say there. One is, you know, this is in part of philosophical framework because, you know, it's not like you can apply this generally to any object in the universe. It's very chemistry focused. Yeah, well,

I think you will be able to. We just haven't got their robustly. So we can say, how can we, let's go up a level. So we go up from level, we go up, let's go up from molecules to cells because you jump to people and I jumped from motorcons and both are good and they will be a single cells. Yeah, we go from it. If we go from, so if we go from molecules to assemblies and let's take a cellar assembly, a nice thing about a cell is you can tell the difference between

a u-carrier and a pro-carrier, right? The organelles are specialized differently. We then look at the cell surface and the cell surface has different glycosolation patterns and these cells will stick together. Now, let's go up a level with multicellular creatures. You have cellular differentiation. Now, if you think about how embryos develop, you go all the way back, those cells undergo differentiation on a causal way that's biomechanically a feedback between the genetics,

biomechanics. I think we can use assembly theory to apply to tissue types. We can even apply it to different cell disease types. So that's what we're doing next, but we're trying to walk, you know, the thing is I'm trying to leap ahead, I want to leap ahead to go, whoa, we'll apply it to culture, but clearly you can apply it to memes and culture and we've also applied assembly

theory to CAs and not as you think. So, cellular term, yeah, yeah, to say what I'm not just as you think, different CA rules were invented by different people at different times and one of my, one of my co-workers very talented chap basically was like, oh, I can realize that different people had different ideas with different rules and they copied each other and made slightly different

bit different cellular automata rules and they and public and looked at them online. And so he was able to further assembly index and copy number of rule whatever doing this thing, but I digress, but it does show you can apply it at a higher scale. So what do we need to do to apply assembly theory to things? We need to agree there's a common set of building blocks. So in a cell,

well, in a in a multicellular creature, you need to look back in time. So there is the initial cell, which the creature is fertilized and then starts to grow and then there is cell differentiation and you have to then make that causal chain both on those. So I requires development of the organism in time or if you look at the cell surfaces and the cell types,

they've got different features on the cell, what walls and inside the cell. So we're building up, but obviously I want a leap to things like emoticons, language, mathematical theory. But that's a very large number of steps to get from a molecule to the human brain. Yeah, and I think they are related, but in hierarchies of emergence, right? So you shouldn't compare them. I mean, the assembly index of a human brain, what does that even mean? Well, maybe

we can look at the morphology of the human brain. Say all human brains have these number of features in common. If they have those numbers and then let's look at a brain in a whale or a dolphin or a chimpanzee or a bird, say, okay, let's look at the assembly indices, number of features in these. And now the copy number is just a number of how many birds are there, how many chimpanzees are there, how many humans are there? Then you have to discover for that the features that you would be looking

for. Yeah, and that means you need to have a unit have some idea of the anatomy. But is there an automated way to discover features? I guess so. I mean, and I think this is a good way to apply machine learning and image recognition to specific characteristics. So apply compression to it to see what emerges and then use the thing the features used as part of the compression as the measurement of as the thing that is searched for when you're measuring

assembly index and copy number. And the compression has to be remember the assembly universe, which is you have to go from assembly possible to assembly contingent and that jump from a say because assembly possible or possible brains or possible features all the time. But we know that on the tree of life and also on the lineage of life going back to Luca, the human brain just didn't spring into existence yesterday, it is a long lineage of brains going all the way back.

And so if we could do assembly theory to understand the development, not just an evolutionary history, but in biological development as you grow, we're going to learn something more. What would be amazing is if you can use a assembly theory, this framework to show the increase in the assembly index associated with I don't know, cultures or pieces of text like language or images and so on and illustrate without knowing the data ahead of time, just kind of like you did with NASA

that you able to demonstrate that it applies in those other contexts. I mean, and that probably wouldn't at first and you have to evolve the theory somehow, you have to change it, you have to expand it. I think so. But like that, I guess this is as a paper at first step in saying, okay, can we create a general framework for measuring complexity of objects, for measuring life, the complexity of living organisms? Yeah, that's what this is reaching for.

That is the first step. And also to say, look, we have a way of quantifying selection and evolution in a fairly, not mundane, but a fairly mechanical way. Yeah. Because before now, it wasn't the ground truth for it was very subjective, whereas here we're talking about clean observables. And there's going to be layers on that. I mean, we've

collaborated right now. We already think we can do assembly theory on language. And not only that, wouldn't it be great if we can put so that if we can figure out how under pressure, language is going to evolve and be more efficient because you're going to want to transmit things. And again, it's not just about compression. It is about understanding how you can make the most of the in the architecture you've already built. And I think this is something beautiful that

evolution does. We're reusing those architectures. We can't just abandon our evolutionary history. And if you don't want to abandon your evolutionary history and you know that evolution has been happening, then assembly theory works. And I think that's that's a key comment I want to make. Is that assembly theory is great for understanding where evolution has been used. The next jump is when we go to technology. Because of course, if you take the M3 processor,

I want to buy it. I haven't bought one yet. I can't justify it, but I want to at some point. The M3 processor arguably is there's quite a lot of features, a quite large number. The M2 came before it then the M1 all the way back. You can apply assembly theory to micro processor architecture. It doesn't take a huge leap to see that. I'm a Linux guy, by the way. So your examples go away. Is that like a is that a fruit company or some sort? I don't even know.

Yeah, there's a lot interesting stuff to ask about language. You could look at how that work. You could look at GPT1, GPT2, GPT3, 354, and try to analyze the kind of language it produces. I mean, that's almost trying to look at assembly index of intelligent systems.

Yeah, I mean, I think the thing about large language models, and this is a whole hobby horse, I have at the moment, is that obviously they're all about the evidence of evolution in the in the large language model comes from all the people that produced all the language. And that's really interesting. And all the corrections in the in the mechanical Turk. That's part of the history, part of the memory of the system.

Exactly. So it would be really interesting to basically use an assembly-based approach to making language in a hierarchy. My guess is that we might be able to build a new type of large language model that uses assembly theory that it has more understanding of the past and how things were created. Basically, the thing with LLMs is they're like everything everywhere, all at once, splat, and make the user happy. So there's not much intelligence in the model.

The model is how the human interacts with the model, but wouldn't it be great if we could understand how to embed more intelligence in the system? What do you mean by intelligence, though? You seem to associate intelligence with history. Yeah. Well, I think selection produces intelligence. You're almost implying that selection is intelligence. No. Kind of. I would go out and lim and say that, but I think it's a little bit more.

Human beings have the ability to abstract and they can break beyond selection. And this is what Darwinian selection, because the human being doesn't have to basically do trial and error. They can think about it and say, oh, that's a bad idea. When do that and then technologies and so on? We escaped Darwinian evolution. And now we're on to some other kind of evolution. I guess higher. Yeah. Higher level level. And then we'll assembly theory will measure that as well,

right? Because it's all lineage. Okay. Another piece of criticism, or by way of question, is how is assembly theory or maybe assembly index different from comagorov complexity? So for people who don't know, comagorov complexity of an object is the length of a shortest computer program that produces the object as output. Yeah. I seem to. There seems to be a disconnect between the computational approach. So yeah. So comagolor of measure requires a cheering machine,

requires a computer. And that's one thing. And the other thing is assembly theory is supposed to trace the process by which life, evolution, emerged. Right. There's a main thing there. There are lots of other layers. So so comagolor of complexity, you can you can approximate comagolor of complexity, but it's not really telling you very much about the actual, it's really telling you about like your date, your data set compression of your

data set. And so that doesn't really help you identify the the turtle in this case is the computer. And so what assembly theory does is I'm going to say, this is a trigger warning for anyone listening

is who loves complexity theory. I think that we're going to show that AIT is a very important subset of assembly theory, because here's what happens that I think that assembly theory allows us to build, understand when we're selections occurring, selection produces factories and things, factories in the end produce computers and you can go to an algorithmic information theory comes out of that. The frustration I've had with with looking at life through this kind of information

theory is it doesn't take into account causation. So the main difference between assembly theory and all these complexity measures is there's no cortisol chain. And I think that's the main. As the causal chain is at the core of assembly theory. Exactly. And if you're in if you've got your data in a computer memory, all the data is the same. You can access it in the same type of way, there's you don't care, you just compress it and you either look at the program runtime or the

shortest program. And that for me, it is absolutely not capturing what it is what its selection does. But assembly theory looks at objects. It doesn't have information about the object history. It's going to try to infer that history by looking for the shortest history. The object, the object doesn't like have a Wikipedia page that goes with it. Oh, I would say it does in a way and it is fascinating.

Look, so you've just got the objects and you have no one information about the object. What assembly theory allows you to do with just what the object is to and the word infer is correct. I agree with him. For those you like say, well, that's not the that's not the history, but something really interesting comes from this. The shortest path is inferred from the object. That is the worst case scenario if you have no machine to make it. So that tells you about the depth of that object

in time. And so what assembly theory allows you to do without considering any other circumstances to say from this object, how deep is this object in time if we just treat the object as itself without any other constraints? And that's super powerful because the shortest path then says, allows you to say, oh, this object wasn't just created randomly, there was a process. And so assembly theory is not meant to, you know, one up, AIT or to ignore the factory, it's just to say,

it's just to say, hey, there was a factory. How big was that factory and how deep in time is it? But it's still computationally very difficult to compute that history for complex objects. It is and becomes harder. One of the things that's super nice is that it constrains your initial conditions, right? It constrains where you're going to be. So if you take say, imagine, so one of the things we're doing right now is applying assembly theory to drug discovery.

Now, what everyone's doing right now is taking all the proteins and looking at the proteins and and looking at molecules, docker proteins. Why not instead take the, look at the molecules that are involved in interacting with the receptors over time, rather thinking about and use the molecules evolve over time as a proxy for how the proteins evolved over time and then use that to constrain your drug discovery process. You flip the problem, one AIT and focus on the molecule

evolution rather than the protein. So you can guess in the future what might happen. So you rather than having to consider all possible molecules, you know where to focus. And that's the same thing if you're looking at an assembly space as for an object where you don't know the entire history, but you know that in the history of this object, it's not going to have

some other motif that there that doesn't apply, it doesn't appear in the past. But just even for the drug discovery point you made, don't you have to simulate all of chemistry for to figure out how to come up with constraints? No. No. The molecules and the, I mean, I don't know enough about protein. Well, this is another thing that I think causes, because this paper goes across 70 boundaries. So chemists have looked at this and said, this is not a react, this is not correct reaction.

It's like, no, it's a graph. Sure, there's a assembly index and shortest path examples here on chemistry. Yeah. And so, and what you do is you look at the minimal constraints on that graph. Of course, there has some mapping to the synthesis, but actually you don't have to know all of chemistry. You just have to understand you can build up the constraint space rather nicely. But this is just at the beginning, right? There are so many directions this could go in and I said

it, it could all be wrong, but hopefully it's less wrong. What about the little criticism I saw by way of question? Do you consider the different probabilities of each reaction in the chain? So like that there could be different, when you look at a chain of events that led up to the creation of an object, doesn't it matter that some parts in the chain are less likely than others? No, it doesn't matter. No, no, well, let's go back. So no, not less likely, but react. So,

so no, so let's go back to what we're talking about. So the assembly index is the minimal path that could have created that object probabilistically. So imagine you have all your atoms in a plasma, you've got enough energy, you've got enough, there's collisions. What is the quickest way you could zip out that molecule with no reaction constraints? How do you define quickest there then? It's just basically what a walk on a random graph. So we make an assumption that basically

the time scale for forming the bonds. So no, I don't want to say that because it's going to have people getting obsessing about this point and your criticism is a really good one. What we're trying to say is like this puts a lower bound on something. Of course, some reactions are less possible than others, but actually, I don't think chemical reactions exist. Oh boy. What does that mean? Why don't chemical reactions exist? I'm writing a paper right now that I keep being told I have

to finish. It's called the origin of chemical reactions. And it merely says that reactivity exists as controlled by the laws of quantum mechanics. And we put names, chemists put names on reactions. So you could have like, I don't know, the vitic reaction, which is by, you know, vitic, you could have the Suzuki reaction, which is by Suzuki. Now, what are these reactions? So these reactions are constrained by the following. They're constrained by the fact that they're on planet

Earth, 1G, 298 Kelvin, 1 bar. So these are constraints. They're also constrained by the chemical composition of Earth, oxygen, availability, all this stuff. And that then allows us to focus in our chemistry. So when a chemist does a reaction, that's a really nice, compressed, short hand for constraint application, glass flask, pure reagent, temperature pressure, boom, boom, boom, boom, control control control control control. So of course, we have bond energies.

This, so the bond energies are kind of intrinsic in a vacuum, if you say that. So the bond energy, you have to have a bond. And so for assembly theory to work, you have to have a bond, which means that bond has to give the molecule certain life, a half life. So you're probably going to find later on that some bonds are weaker. And that you are going to miss in mass

spectrum. When you count, look at the assembly of some molecules, you're going to miscount the assembly of the molecule because it falls apart too quickly because the bond is just formed. But you can solve that with looking infrared. So when people think about the probability, they're kind of misunderstanding. Assembly theory says nothing about the chemistry, because chemistry is chemistry and their constraints are put in bi biology. There was no chemist

in the origin of life, baking unless you believe in the chemist in the sky. And they were, you know, it's like Santa Claus. I had a lot of work to do. But chemical reactions do not exist in the constraints that allow chemical transformations to occur, do exist. Okay. Okay. So it's constrained to replicate. So there's no chemical reactions. It's all constraint application. Yep. Which enables the emergence of the, of what's the different word for chemical reaction?

Transformation. Transformation. Yeah. Like a function. It's a function. But no, but I love chemical reactions as a shorthand. And so the chemists don't all go mad. I mean, of course, chemical reactions exist on earth. It's a shorthand for these constraints. For it right. So assuming all these constraints that we've been using for so long, that we just assume that that's always the case in natural language conversation. Exactly.

The grammar of chemistry, of course, emerges in reactions and we can use them reliably. But I do not think the vitic reaction is accessible on Venus. Right. And this is useful to remember, you know, to frame it as constraint application is useful for when you zoom out to the bigger picture of the universe and looking at the chemistry of the universe and then starting to apply something with theory. Yeah. That's interesting. That's really interesting.

But we've also pissed off the chemists now. Oh, I'm a, that's pretty happy. But what most of them? No, everybody, everybody deep down is happy. I think they're just sometimes feisty. That's how they show. That's how they have fun. Everyone is grumpy on some days when when you challenge the problem with this paper is you what is like, it's almost like I went to a part. It's like you, I

do used to do this occasionally. You want to go to a meeting and just find a way to find, find, offend everyone at the meeting simultaneously, even the, even the factions that don't like each other, they're all unified in their hatred of you, just defending them. This paper, it feels like the person that went to the party and offended everyone simultaneously, so stop fighting with themselves and just focus on this paper. Maybe just a little insider, interesting information.

What were the editors of Nisha, like what, their reviews and so on? How difficult was that process? This is a pretty like big paper. Yeah. I mean, the, so, um, we, when we originally sent the paper, um, we sent the paper and the editor said, um, that, you know, this was like, this is a quite a long process. We sent the paper and the editor gave us some feedback and said, you know, I don't think

is that interesting. It's not, you know, it's hard. It's, it's, it's a, it's hard concept. And we asked, and the editor gave us some feedback. Um, and we, and Sarah and I took a year to rewrite the paper. Was the nature of the feedback very specific and like this part of this part? Or is it like, like, what, what do you guys smoke? Yeah, it was kind of the latter. What are you smoking? Okay. And, you know, you, you know, we're polite in this promise. Yeah. Well, the thing is,

the, the, the, the editor was really critical, but in a, but in a really professional way. Yeah. And I mean, for me, this was the way science should happen. So when it came back, you know, we had too many equations in the paper. If you look at the preprint, they're just equations everywhere, like the 23 equations. And when I said to Abyshek, he was the first author, we've got to remove all the equations. But my assembly equations, staying in Abyshek was like,

you know, no, we can't. I said, well, look, if we want to explain this to people, it's a real challenge. And so Sarah and I went through the, I think it was actually 160 versions of the paper, but we basically, we got to version 40 or something, we said, right, zero, it start again. So we wrote the whole paper again. We knew the entire, amazing. And we just went spit by bit by bit. So what is it we want to say? And then we send the paper in. And to us, we expected it to be

rejected and not even go to review. And then the, we got notification back at Gondra review. And we were like, oh my god, it's so going to get rejected. How's it going to get rejected? Because the first assembly paper that were on the mass spec, we sent to nature, got went through six rounds of review and rejected. Right. And there's, by a chemist, just said, I don't believe you, you must be committing fraud. A long story, probably a boring

story. But in this case, it went out to review the comments came back and the comments were incredibly, they were very, they were very deep comments from all the reviewers. They were, and but the, but the, but the, but the, but the nice thing was the reviewers were kind of very critical, but not dismissive. They were like, oh, really? Explain this, explain this, explain this. Explain this. Are you sure it's not comagolar off? Are you sure it's not this? And we went

through, I think three rounds of review pretty quick. And, and the editor went, yeah, yeah, it's in. Um-hmm. But maybe you could just comment on the whole process. You've published some pretty huge papers and all kinds of topics within chemist chain beyond. Some of them have some little spice in them, a little spice of crazy. Like Tomway says, I like my Tom a little drop

of poison. There's, you know, it's not a mundane paper. So where, what's it like psychologically to go through all this process to keep getting rejected to, to get reviews from people that don't get the paper or all that kind of stuff just from a question of a scientist. Like, what, what is that like? Um, it's, I think it's, I mean, this paper for me kind of, because this wasn't the

first time we tried to publish assembly theory at the highest level. The Nature Communications paper we on the mass spec on the, on the idea went through, went to nature and got rejected, went through six rounds of review and got rejected. And, and, and it's, and I, I just was so confused when the, when the chemist said this can't be possible. I do not believe you can measure complex at using mass spec. And also by the way, molecules, molecules, complex molecules can

randomly form. And we're like, but look at the data, the data says, and they said, no, no, we don't believe you. And, and we went and we, and I just wouldn't give up the edit and the edit in the end was just like the different editors actually, right? Right. What's behind that never giving up is you're like, when you're sitting there 10 o'clock in the evening, there's a melancholy feeling that comes over you and you're like, okay, this is rejection number five.

Or it's not rejection, but maybe it feels like a rejection because of the, you know, the, the comments are, the you totally don't get it. Like what gives you strength to keep going there? Yeah, I don't know. I don't normally get emotional about papers, but, um, it's not about giving it up because we want to get it published because we want the glory or

anything. It's just like, why don't you understand? And so, um, um, so why did, so why would just try to be as, as, as, um, as rational as possible and say, yeah, you didn't like it. Um, tell me why. And then, um, sorry. Silly. Any of it. Never get emotional about papers normally, but, but you, but I think what we do, you just compressed like five years of angst from this. So it's been, it's been rough. It's not just rough. It's like, it happened, you know, I came up with the assembly equation,

you know, remote from Sarah in Arizona and the people SFI, I feel like it was a mad person. Like, you know, the guy in depicted in, you know, in a beautiful mind who was just like, not, not the actual genius part, but just the, just the, just the, because I kept writing expanded and I have no mathematical ability at all. And I was expand, I was making these mathematical expansions where I kept seeing the same motif again. I was like, oh, I think this is a copy number. The same string

is carrying again and again. I kept, I couldn't do the math. And then I realized the copy number fell out of the equation and everything collapsed down. I was like, oh, that works kind of. So we submitted the paper. And then when it was almost accepted, right, the mass spec one. And it was astrobiologists at Gray, you know, a mass spectroscopist at Gray in the chemist went nonsense, like biggest pilot

nonsense ever fraud, you know. And I was like, why fraud? And they just said, just because. And I was like, well, and so, so, and the, and I could not convince the editor in this case. The edit was just soapist off because they see it as like a kind of, you know, a, you're wasting my time. And I

would not give up. I wrote, I went and dissected, you know, all the parts. And I think, although, I mean, I got upset about it, you know, it was kind of embarrassing actually, but, but I guess it's beautiful. But it was just trying to understand why they didn't like it. So they were part of me was like really devastated. And the part of me was super exciting. I'm like, huh, they can't tell me why I'm

wrong. And this kind of goes back to, you know, when I was at school, I was in a kind of learning difficulties class and I kept going to the teacher and say, you know, you know, how, what do I do today to prove I'm smart? And they were like, nothing, you can't. I was like, give me a job, you know, give me something to do. Give me a job to do something to do as we, and I kind of felt like that a bit when I was arguing with the, I'm not arguing. There's no ad hominem. I wasn't telling

the editor. They were idiots, reading something like this or the, the reviewers, I kept it strictly like factual. And all I did is I just kept knocking it down bit by bit by bit by bit by bit. It was ultimately rejected and it got published elsewhere. And then the actual experiment or data. So this is kind of in this paper, the experimental justification was already published. So when we did this one and we went through the versions and, and then we sent it in and in the

end it just got accepted. We were like, well, that's kind of cool, right? This is kind of like, you know, some days you had, you know, the, the, the students, sorry, the, the first author was like, I can't believe it got accepted. I was like, no, my, it's great. It's like, it's good. And then when the paper was published, I was not expecting the backlash. I was expecting computational, well, no, actually it was just expecting one person had been trolling me for a while about it,

just to carry on trolling. But I didn't expect the backlash. And then I wrote, wrote the editor and apologized. And the edit was like, well, you're apologizing for what was a great paper. Of course, it's going to get backlash. You said some controversial stuff, but it's awesome. And so it's, I think it's a beautiful story of perseverance. And the backlash is just a negative word for discourse, which I think is beautiful. I think you, as I said to, you know, when it got accepted

and people were saying, we're kind of like hacking on it. And I was like, papers are not gold medals. The reason I wanted to publish that paper in nature is because it says, hey, there's something before biological evolution. You have to have that if you're not a creationist, by the way. This is an approach. First time someone has put a concrete mechanism or sorry, a concrete

quantification. And what comes next, you're pushing on is a mechanism. And that's what we need to get to is an auto-candidic set, self-replicating molecules, some other features that come in. And the fact that this paper has been so discussed for me is a dream come true. Like, it doesn't get better than that. If you can't accept a few people hating it. And the nice thing is the thing that I really makes me happy is that no one has attacked the actual physical content. Like, you can measure

the assembly index. You can measure selection now. So either that's right or it's, well, either that's helpful or unhelpful. If it's unhelpful, this paper will sink down and no one will use it again. If it's helpful, it'll help people scaffold on it and we'll start to converge for a new paradigm. So I think that that's the thing that I wanted to see, you know, my colleagues, authors, collaborators. And people were like, you've just published this paper, you're a chemist.

Why have you done this? Like, who are you to be doing evolutionary theory? Like, well, I don't know, I mean, sorry, did I need to get anyone to do anything? Well, I'm glad you did. Let me just before coming back to the origin of life and these kinds of questions. You mentioned learning difficulties. I didn't know about this. So what was it like? I wasn't very good at school, right? This is when you're very young. Yeah, yeah. One but in primary school, my handwriting was really poor.

And apparently I couldn't read and my mathematics was very poor. So I just said, this is a problem. They identified it. My parents kind of at the time were confused because I was busy taking things apart, buying electronic junk from a shop, trying to build computers and things. And then what's I got out of when I was I think about the major transition in my stupidity, like, you know, I, everyone thought I wasn't that stupid. Well, I basically everyone thought I was faking. I like

stuff and I was faking wanting to be it. So I always want to be a scientist. So five, six, seven years old, a big scientist, take things apart. And everyone's like, yeah, this guy wants to be a scientist, but he's an idiot. And so, and so everyone was really confused, I think at first. But I wasn't smarter than I, you know, it was claiming to be. And then I just basically didn't do one in the attest. I went down and down and down and down and then, and I was kind of like,

this is really embarrassing. I really like maths and everyone says I can't do it. I really like kind of, you know, physics and chemistry and all that in science. And people say, you're not, you can't, you can't read and write. And so I found myself in a learning difficulties class at the end of primary school in the beginning of secondary school in the UK secondary school is like 11, 12 years old. And I remember being put in the in the remedial class. And the remedial class

was basically full of, were two types, three types of people. There were people that had quite violent, right? And there were people who couldn't speak English. And there were people that really had learning difficulties. So, the one thing I can objectively remember was, I mean, I could read. I like reading. I read a lot. But something in me, I'm a bit of a rebel. I refused to read while I was told to read. And I found it difficult to read individual

words in the way that I told. But anyway, I got caught one day teaching someone else to read. And they said, okay, there, we don't understand this. I, I always know what to be a scientist, but didn't really know what that meant. And I realized you had to get university. And I thought, I can just go university, it's like curious people like, no, no, no, you need to have these,

you have to be able to enter these exams to get this great point average. And the fact is, the exams you've been entered into, you're not, you're, you're, you're just going to get C, D or E. You can't even get A, B or C, right? This is the UK G's, D or C's. And I was like, oh, shit. And I said, can you just put me into the high exam? I said, no, no, you're going to fail. There's no chance. So my, my father, I'm in to V and said, you know, just let him go in the exams.

And they said, he's definitely going to fail. It's a waste of time, waste of money. And he said, well, what if we paid? So they said, well, okay. So you didn't actually have to pay, you had to pay if I failed. So I took the exams and passed them, fortunately. I didn't get the top grades, but I, you know, I got into A levels. But then that also kind of limited what I could do

at A levels. I wasn't allowed to do A level maths. Because I had such a bad math grade from my GCSE, and he had a C. But I, they wouldn't let me go into the ABC for maths, because of some kind of coursework requirement back then. So the top grade I could have got was a C, so C, D or E. So I got a C. And then let me do a kind of A S level maths, which is this half intermediate and get to go university. But in the I could like to chemistry, I had a good chemistry teacher. So in the end,

I got to university to do chemistry. So through that kind of process, I think, for kids in that situation, it's, it's easy to start believing that you're not, well, how do I put it? They're year stupid. And basically give up that you're just not good at math. You're not good at school. So this is by way of advice for people, for interesting people, for interesting young kids right now, experience in the same thing. Where was the place? What was the source of you not giving up there?

I have no idea. Other than I, I was really, I really like not understanding stuff. For me, when I not understand something, I didn't understand, I feel like I didn't understand anything. But, but now, but back then, I was so, I remember when I was like, I don't know, I, I, I trialled it, I tried to build a laser when I was like eight. And I thought, how hard could it be? Like, and I basically, I was going to build a C, I was going to build a CO2 laser. And I was like,

right, I think I need some partially coated mirrors and need some carbon dioxide. And I need a high high high high voltage. So I kind of, and I was like, I didn't have a, and I was so stupid, right? I was kind of so embarrassed. I had to make enough CO2, I actually set a fire and try to filter the flame. Oh, nice. So, to crap it off CO2. Yeah. And I was like completely, completely failed. And I bent, but half the, the garage down. So my parents were not very happy

about that. But so that was one thing. I was like, I really like first principle thinking. And so, you know, so I remember being super curious and being determined at finances. And so the kind of, when people do a give advice about this, why are asked for advice about this? I don't really have that much advice, rather than don't give up. And one of the things I try to do as a, as a chemistry professor in my group is I, I don't, I hire people that I think that, you know, I'm kind of home. I,

if there's persistent enough, who am I to deny them the chance? Because, you know, people gave me a chance. And I was able to do stuff. Do you believe in yourself essentially? I'm, I like, so I love being around smart people. And I love confusing smart people. And when I'm confusing smart people, you know, not by stealing their wallets and hiding it somewhere. But if I can confuse smart people, that is the one piece of hope that I might be doing something interesting.

Well, that's quite brilliant. Because a gradient to optimize, yeah, hang out with smart people and confuse them. Yeah. And the more confusing it is, the more, there's something there. And as long as they're not telling you just a complete idiot, and, and they give you different reasons. Yeah. And I mean, I'm, you know, if everyone, it's like with assembly theory and people said, oh, it's wrong. And I was like, why? And they

like, and no one could give me a consistent reason. They said, oh, because it's been done before, or it's just comagolar after, or it's just there and the other. So I think the, the, the thing that I like to do is, and in academia, it's hard, right? Because people are critical. But I mean, you know, the criticism, I mean, although I got kind of upset about it earlier, which is kind of silly, but not silly, because obviously it's hard work being on your own or with a team,

spatially separated, like, during lockdown. And try to keep everyone on board and, and, and, and be, and I have some faith that I've always wanted to have a new idea. And so, you know, I like a new idea. And I want to, I don't want, I want to nurture it as long as possible. And if someone can give me actionable criticism, that's why I think I was trying to say earlier when I was kind of like stuck for words, give me actionable criticism. You know, it's wrong. Okay, why is it wrong?

Say, oh, it doesn't, your, your equations incorrect for this or your method is wrong. And then, say, if, and so why try and do is get enough criticism from people to then try and go back. And I've been very fortunate in my life that I've got great colleagues, great collaborators, funders, mentors and people that will take the time to say, you're wrong because. And then, why you have to do is integrate the wrongness and go, oh, cool. Maybe I can fix that. And I think

criticism is really good. People have a go at me because I'm really critical. And like, but I'm not criticizing, you know, you as a person, I'm just criticizing the idea and trying to make it better and say, well, what about this? And, you know, and sometimes I'm kind of, you know, my filters are kind of, you know, truncated in some ways. I'm just like, that's wrong. That's wrong. That's wrong. What do they do? And people are like, oh my god, you just told me you destroyed my

life's work. I'm like, relax. No, I'm just like, let's make it better. And I think that we don't do that enough because we're, we're, you know, we, we're, we're, we're either personally critical, which isn't helpful. Or we don't give any criticism at all because we're too scared. Yeah, I, yeah, the, I've seen you be pretty aggressively critical, but it's every time I've seen it, it's the idea and not the person. I'm sure I make mistakes on that. I mean, I, you know, I argue,

I argue lots with, with lots, I mean, I argue lots with Sarah and she's like kind of shocked. I've argued with Yashar in the past and he's like, you're just making, Gashabark and you're like, you're just making up. I'm like, no, not, not quite. But kind of, yeah. Um, you know, I had a big argument with Sarah about time. She's like, no, time, time doesn't exist. I'm like, no, time does exist.

And now, and as, as she realized, the her conception of assembly theory and my conception of assembly theory was the same thing necessitated us to abandon the fact that time is eternal to actually really fundamentally question how the universe produces combinatorial novelty. So, time is fundamental for some of the theory. I'm just trying to figure out where you and Sarah converge. So I, I think assembly theory is fine in this time right now, but I think it helps us understand that something

interesting is going on. So there's, and I mean, really inspired by a guy called Nick Gizzen. I'm going to butcher his argument, but I love his argument a lot. So, however he forgives me, if he hears about it, but basically, um, if you want free will, time has to be fundamental. And we can go and, um, if you want time to be fundamental, um, you have to give up on

the tonic mathematics. And you have to use intuitions mathematics by the way, um, and again, I'm going to butcher this, but basically Hilbert said that, you know, infinite numbers are allowed. And I think it was Brower said, no, you can't, all numbers are finite. So they're kind of like, with, so let's go back a step because I was like, people are going to say, assembly theory seems to explain that combinatorial large combinatorial space, um, allows you to produce things like life

and technology. And that large combinatorial space is so big, is not even accessible to a Sean Carroll, David Deutsch, multiverse, that physicists saying that, um, that all of, all of the universe already exists in time is probably, provably, strong word, not correct. The, we are going to know that the universe, as it stands, the present, the way the present builds the future, so big, the universe can't ever contain the future. And this is a really interesting

thing. I think Max Techmark has this mathematical universe. He says, you know, the universe is kind of like a block universe. And I apologize to Max if I'm getting it wrong, but people think you can just move. You have the stat, you have the initial conditions and you can run the universe right to the end and go backwards and forwards in that universe. That is not correct. Let me load that in. The universe is not big enough to contain the future.

Yeah. That's why that's it. So that, that's another, that's a beautiful way of saying that time is fundamental. Yes. And the, and that you can have, and that's what, this is why, um, the, it's the law of the excluded middle, something as true or false only works in the past.

Is it going to slow in New York next week or in Austin? You might, in Austin say, probably not in New York, you might say, yeah, if you go forward to next week and say, did it snow in New York last week, true or false, you can answer that question. The fact that the law of the excluded middle cannot apply to the future explains why time is

fundamental. Well, I mean, that that's a good example, intuitive example, but it's possible that we might be able to predict, you know, whether it's going to snow if we had perfect information. I think we're saying it not impossible. Impossible. So here's why I'll make a quick, really quick argument. And this argument isn't mine. It's, it's next and a few other people can you can you explain his view on fundamental and time being fundamental? Yeah, so I'll give my

view, which kind of resonates with his, but basically, um, it's very simple actually. He would say that free will, your ability to design and do an experiment is an exercising free will. So he used that full process. I never really thought about it that way, um, and that you actively make decisions. I do think that I used to think that free will was I kind of kind of consequence of just selection, but I'm kind of understanding that human free will something really interesting.

And he very much inspired me, but I think that Sarah Walker said that inspired me as well, that that these all converge is that I think that the universe in the universe is very big, huge, but actually the own, the place is largest in the universe right now. The largest place in the universe is earth. Yeah, I've seen you say that. And boy, does that, that's a, that's an interesting one of the process. What do you mean by that earth is the biggest place in the universe?

Because we have this combinatorial scaffolding going all the way back from Luka. So you've, you've got cells that can self replicate. And then you go all the way to terraforming the earth. You've got all these architectures, the amount of selection that's going on biological selection just to be clear, biological evolution. And then you have multicellularity, then animals and abstraction and we're not abstraction. There was another kick because you can then build architectures and

computers and cultures and language. And these things are the biggest things exist in the universe because we can just build architectures that couldn't naturally arise anywhere. And the further that distance goes in time and this kind of is just, it's gigantic. And from a complexity perspective, yeah. Okay, wait a minute. But I mean, I know you're being poetic, but how do you know there's not other earth like, like, how do you know you're basically saying earth is really special. It's

awesome stuff as far as we look out. There's nothing like it going on. But how do you know there's not nearly infinite number of places where cool stuff like this is going on? I agree. And I would say, I'll say again, that earth is the most gigantic thing we know in the universe, commentarily, we know. We know. Now, now, I guess this is just purely a guess. I have no data, but other than hope. Well, maybe not hope. Maybe, no, I have some data that every star in the sky

probably has planets. Yep. And life is probably emerging on these planets. But the amount of contingency that has looked associated with life is that I think the commentorial space associated with these planets is so different. We are never going to, our causal cones are never going to overlap or not easily. And this is a thing that makes me sad about alien life, why we have to create

alien life in the lab as quickly as possible. Because I don't know if we are going to be able to be able to build architectures that will intersect with alien intelligence and architectures. And just say, you don't mean in time or space. Time and the ability to communicate. The ability to communicate. Yeah. My biggest fear in a way is that life is everywhere, but we become infinitely more lonely because of our scaffolding in that commentorial space.

Because it's so big. And because you're saying the constraints created by the environment that led to the factory of Darwinian evolution are just like the little tiny cone in a nearly infinite commentatorial space. Exactly. So there's other cones like it. And why can't we communicate with other like just because we can't create it. Doesn't mean we can't appreciate the creation, right? Like that. I did say detect the creation. I truly don't know, but it's an excuse for me to

ask for people to give me money to make a planet simulator. Yeah. Right. If I can make with a different different kind of like another shameless thing. It's like, give me money. I need to say this was all a long plug for a planet simulator. It's like, it's like, you know, hey, I won't be the first in my. My my my my my Rick. My Rick garage has run out of room. Yeah. No. Um, and this is a planet simulator. You mean like a different kind of planet? Yeah.

Or different sets of environments and pressures. Exactly. If we could basically recreate the selection before biology. As we know it, that gives rise to a different biology. We should be able to put the constraints on where I look in the universe. So here's a thing. Here's my here's my dream. Yeah. My dream is that by creating life in the lab, based upon constraints, we understand. Like this is like a Venus type life or earth type life or something. Again, do

F2.0. Screw it. Let's do it. F2.0. And F2.0 has a different genetic alphabet. Fine. That's fine. Debt different protein at alphabet. Fine. Have cells and evolution and all that stuff. We will then be out. Say, okay, life is a more general phenomena. Selection is more general than the what we think is the chemical constraints on life. And we can point the James Webb and other telescopes to other planets that we are in that zone. We are most likely to combinatorially overlap with.

Right. So because you know, we basically so they're a chemistry. You're looking for some overlap. And then we can then basically shine light on them literally and white look at light coming back and apply advanced assembly theory to land general theory of language that we will get.

And say, huh, we in that signal, it looks random, but there's a copy number. Oh, this random set of things that shouldn't be that looks like a true random number generator has structure as a not common common goal are off a it type structure, but evolutionary structure given by assembly theory. And we start to, but I would say that because I'm a shameless assembly theorist. Yeah. I just feels like the the cone that might be misusing the word cone here,

but the width of the cone is growing faster. It's going really fast to where eventually all the cones overlap. Even in a very, very, very large combinatorial space. It just, but then again, if you're saying the universe is also growing very quickly in terms of possibilities. That's really I hope that as we build as we build abstractions, the main I mean one one idea is that as we go to intelligence, intelligence allows us to look at the regularities around us in the universe.

And that gives us some common grounding to to discuss with aliens. And you might be right that that that we will overlap there, even though we have completely different chemistry, literally completely different chemistry, that we will be at a past information from one another. But it's not a given. And I have to kind of try and divorce hope and emotion away from what I can logically justify. But it's just hard to intuit a world, a universe where there's nearly infinite complexity

objects and they somehow can't detect each other. But the universe is expanding. But the nice thing is that I would say I would look, you see, I think Carl Sagan did the wrong thing. Well, not the wrong thing. He flicked the Voyager pro brand and pale blue dot. So I'd look at how big the universe is. I was done it the way around said, look at the Voyager pro that came from planet Earth that came from Luka. Look at how big Earth is. They produced that. It produced that.

Yeah. And that I think is like completely amazing. And then that should allow people on earth to think about, well, probably we should try and get causal chains, offer on to Mars, onto the moon, wherever. Well, it's human life or Martian life that we create, it doesn't matter. But I think this commentorial space tells us something very important about the universe. And I realized in the assembly theory that the universe is too big to contain itself.

And I think this is, and now coming back and I want to kind of change your mind about time, because I'm guessing that your time is just coordinate. So I'm going to change your mind. I'm guessing you're one of those. I'm going to change your mind in real time, at least attempt. Oh, in real time. There you go. I already got the tattoo. So this is going to be embarrassing if you change your mind. But you can just add, you can just add an arrow, a time one to it, right?

True. Just a monster. Or raise it a bit. So, and the argument that I think that is really most interesting is like, people say the initial conditions specify the future of the universe. Okay, fine. Let's say that's the case for a moment. Now let's go back to Newtonian mechanics. Now, the uncertainty between, for example, in Newtonian mechanics is this.

If I give you the coordinates of your of an object moving in space, and the coordinates of another object, and they collide in space, and you know those initial conditions, you should know exactly what's going to happen. However, you cannot specify these coordinates to infinite precision. Now everyone said, you know, oh, this is kind of like, you know, the chaos theory argument. No, no, it's deeper than that. Here's a problem with numbers. This is how this is where Hilbert

and Broward fell out. To have the coordinates of this object, to give an object as a colliding, you have to have them to infinite precision. That's what Hilbert says. This is no problem. Infinite precision is fine. Let's just take that for granted. But when the object is finite, and it can't store its own coordinates, what do you do? So, in principle, if a finite object cannot be specified to infinite precision, in principle, the initial conditions don't apply.

Well, how do you know it can't store its, or how do you store it, infinitely long number in a finite size? Well, we're using infinity very loosely here. No, no, we're using infinite precision. I mean, not loosely, but very precisely. You think infinite precision is required. Well, let's take the object. Let's say the object is a golf ball. Golf balls, a few centimeters in diameter. We can work out how many atoms are on the golf ball. And let's say we

can store numbers down to atomic dislocations. So we can work out how many atoms are on the golf ball. And we can store the coordinates in that golf ball down to that number. But beyond that, we can't. Let's make the golf ball smaller. And this is where I think that we think that we get randomness in quantum mechanics. And some people say you can't get randomness in quantum mechanics deterministic. But, aha, this is where we realize that classical mechanics and quantum mechanics

suffer from the same uncertainty principle. And that is the inability to specify the condition, the initial conditions to precise enough degree to give you determinism. The universe is intrinsically too big. And that's why time exists. It's non-deterministic. Looking back into the past, you can look at the, you can use logical arguments because you can say, was it true or false? You really know. But this is the fact we are unable to predict the

future with the precision is not evidence of lack of knowledge. It's evidence the universe is generating new things. Okay, so to you, first of all, quantum mechanics, you can just say statistically what's going to happen when two golf balls hit each other statistically. But that, but sure, I can say statistic what's going to happen. But then what they do happen. And then you keep nesting it together. You can't, I mean, it goes almost back to look at, look at, let's think about

entropy in the universe. So how do we, how do we, how do we understand entropy change? Well, we could do the look at or process. We can use the agurgetic hypothesis. We can also have, we can also have the counterfactuals where we have all the different states. And we can even put that in the multiverse, right? But both those are kind of, they're non physical. The multiverse

kind of collapses back to the same problem about the precision. So all that, what you, if you accept, you don't have to have true and false going forward into future, the real numbers are real. They're just, they're just, they're, they're observables. We're trying to see exactly where time being fundamental sneaks in in this difference between the golf ball can't contain its own position perfectly precisely. If how that leads to time needing to be fundamental. Let me, I've,

I've quit. Do you believe or do you accept you have free will? Yeah, I think at this moment in time, I believe that I have free will. So then you are, then you have to believe that time is fundamental. I understand that's the state where you've made it. Well, no, that we can logically follows because if you don't have free will, so like if you're in a, if you're in a universe that has no time, universe is deterministic. If it's deterministic, then you have no free will.

I think the space of how much we don't know is so vast. They're saying the universe is deterministic from that jumping. There's no free will. It's just too difficult to believe. No, I logically follows. No, no, no, I don't disagree. I'm not saying any, I mean, it's deep and it's important.

All I'm, all I'm saying and it's the difference of, it's actually different what I've said before is that if you don't require platonistic mathematics and accepts that non-determinism is how the universe looks and that gives us our creativity in the way the universe is getting novelty, it's kind of really deeply important in assembly theory because assembly theory starts to actually give you a mechanism why you go from boring time, which is basically initial condition,

specify everything to a mismatch in creative time. And I hope we'll do experiments. I think it's really important to, I would love to do an experiment that prove that time is fundamental and the universe is generating novelty. I don't know all the features of that experiment yet, but by having these conversations openly and getting people to think about the problems in a new way, better people, more intelligent people, with good mathematical backgrounds can say, oh,

hey, I've got an idea. I'd love to do an experiment that shows that the universe, I mean, universe is too big for itself going forward in time. And I really, you know, this is why I really hate the idea of the Boltzmann brain. The Boltzmann brain makes me super kind of like, you know, everyone's having a free lunch. It's like saying, it's like, let's break

the order laws of physics. So a Boltzmann brain is this idea that in a long enough universe, a brain will just emerge in the universe as conscious without neglects the causal chain of evolution required to produce that brain. And this is where the computational argument really falls down because the computation is because I can calculate the probability of a Boltzmann brain. And I can and they'll give you probability, but I can calculate the probability of a Boltzmann

brain zero. Just because the space of our ability is so large. Yeah, it's like when we start falling ourselves with numbers that we can't actually measure and we can't ever conceive of, I think it doesn't give us a good explanation. And I've become, I want to explain why life is in the universe. I think life is actually novelty minor. I mean, life basically mines novelty almost from the future and makes it actualizes in the present. Okay, life is a novelty minor

from the future that is actualized in the present. Yeah. I think so. novelty minor. First of all, novelty. What's the origin of novelty when you go from boring time to creative time? Where is that? Is it as simple as randomness like you refer into? I'm really struggling with randomness because I had a really good argument with Yasha Bach about randomness. And he said randomness doesn't give you free. Well, that's insane because

you just be random. But I think and I think he's right at that level. But I don't think we, I don't think he is right on another level. And it's not about randomness is about, it's about constrained. I'm going to sound looks constrained. I'll put you making this up as I go along. So making this up constrained opportunity. So what I mean is like, so you have to have so that the novelty, what is novelty? You know, this is what I think is the funny thing.

Well, you ever want to discuss AI? Why I think everyone's kind of gone AI mad? Is that they're misunderstanding novelty. But let's think about novelty. Yes, what is novelty? So I think novelty is a genuinely new configuration that is not predicted by the past. Right? And that you discover in the present, right? And that is truly different. Right? Now everyone says that some people say that novelty doesn't exist. There's always with president. I want to do experiments

that show that that is not the case. And it goes back to a question you asked me a few moments ago, which is where is the factory? Right? Because I think the same mechanism that gives us a factory gives us novelty. And I think that that is why I'm so deeply hung up on time. I mean, of course,

I'm wrong, but how wrong? And I think that life opens up that combinatorial space in the way that that that that our current laws of physics, although as contrived in a deterministic initial condition universe, even with the get out of the multiverse, David Deutsch style, which I hate love, by the way, but I don't think is correct. But it's it's kind of it's really beautiful. But the model that David Deutsch is conception of the multiverse is kind of like given.

And but I think that the problem with wave particle duality and quantum mechanics is not about the multiverse is about understanding how determined the past is. Why don't you think just think that actually this is a discussion I was having with Sarah about that, right? Which she was like, Oh, I think we're debating this for a long time now about how we how do we reconcile novelty to determinism in determinism? So just to clarify, you both human Sarah think the universe

is not deterministic. I won't speak for Sarah, but I roughly can't I think that the universe I think the universe is deterministic looking back in the past, but undetermined going future going forward in the future. So I'm kind of having my cake and eat it, eat it here. This is because I fundamentally don't understand randomness, right? As Yasha told me or other people told me.

But if I adopt a new view now, which the new view is the universe is just non deterministic, but I'd like to refine that and say the universe appears deterministic going back in the past. But it's it, but it's undetermined going forward in the future. So how can we have a determinist universe that has deterministically look looking rules? This non-determined going in the future.

It's this breakdown and precision in the initial conditions. And we have to just stop using initial conditions and start looking at trajectories and how how the commentorial space behaves in expanding universe in time and space. And assembly theory helps us quantify the transition to biology. And biology appears to be in novelty mining because it's making crazy stuff. You know, I'm that we are unique to earth, right? There are objects on the earth that are unique to earth.

They will not be found anywhere else because you can do the commentorial math. What was that statement you made about life? It's novelty mining from the future. Yeah. What's the what's the little element of time that you're introducing? So what I'm kind of meaning is because the future is bigger than the present in a deterministic universe, how do you go from the how do the how do the how do the states go from one to another?

I mean, there's a mismatch, right? Yeah. So so that must mean that you have a little bit of indeterminism, whether that's randomness or something else, I don't understand. I want to do experiments to formulate a theory to refine that as we go forward that my helpers explain that. And I and I think is why I'm so determined to try and crack the the non-life to life transition looking at networks

and molecules and that might help us think about it, the mechanism. But certainly the future is bigger than the past in my conception of the universe and some conception of the universe. By the way, that's not obvious, right? That's what was just kind of the future being bigger than the past. Well, that that's one statement and the statement that the universe is not big enough to contain the future is another statement. Yeah. Yeah. Yeah. Yeah. That one is a big one. That was a really big one.

I think so. I think it, but I think it's entirely because look, we have the second law. And right now, I mean, I'm we don't need the second law if the future is bigger than the past. It follows naturally. Right. Right. So why are we retrofitting all these these sticking past is onto our reality to hold on to a timeless universe? Yeah, but that's because it's kind of difficult to imagine the universe that's they can't contain the future. But it's not really exciting.

It's very exciting, but it's it's hard. I mean, we're we're humans on earth and we have a very kind of four dimensional conception of the world of three deep last time. It's just hard to intuit a world where what does they even mean? A universe that can't contain the future. Yeah, it's kind of it's kind of crazy, but obvious. I mean, I suppose it sounds obvious. Yeah,

if it's true. But the nice thing is you can so why I mean, so the reason why assembly theory turned me on to that was that you let's let's just start in the present and look at all the complex molecules and go backwards in time and understand how evolutionary processes go gave rise to them. It's not it's not at all obvious. The tax cell, which is a complex one the most complex natural products produced by biology, was going to be invented by biology. It's an accident.

You know, tax always unique to earth. There's no taxile elsewhere in the universe and taxile was not decided by the initial conditions. It was decided by this kind of this interplay between the so the past simply is embedded in the present. It gives some features, but why the past doesn't map to the future one to one is because the universe is too big to contain itself. That gives

space for creativity, novelty and and on some things which are unpredictable. Well, okay, so given that you're disrespecting the power of the initial conditions, let me ask you about what I'd explain that cellular terminary able to produce such incredible complexity given just basic rules and basic initial conditions. I think that you've you've this falls into the Broward Hilbert trap. So how do you get a cellular automata producing complexity? You have a computer,

you generate a display and you map the change of that in time. There are some CAs repeat like functions like it's fascinating to me that for Pi, there is a there is a formula where you can go to the the millionth decimal place of Pi and read out the the number without having to go there. But there are some numbers where you can't do that. You have to just crank through. This

whether it's Wolframian computation, we reduce ability or some other thing. That doesn't matter, but these CAs that complexity is that just complexity or a number that is basically you're mining that number in time. Is that just a display screen for that number, that function? Well, can she see the same thing whether complexity and earth then? No, because the complexity on earth has a copy number and an assembly index associated with it.

That CAs just a number running. You don't think it has a copy number? Wait a minute. Well, it does in the human where we're looking at humans producing different rules, but then it's nested on selection. So those CAs are produced by selection. Yeah. I mean, the CAs such a fascinating pseudo complexity generator. What I would love to do is understand, quantify the degree of surprise in a CA, right? That long enough.

But what that I guess that means is we have to instantiate, we have to have a number of experiments where we're generating different rules and running them time-spare steps. But oh, got it. CAs are mining novelty in the future by iteration. And you're like, oh, that's great. That's great. You didn't predict it. Some rules you can predict the what's going to happen. Other rules you can't. So for me, if anything, CAs are evidence that

the universe is too big to contain itself. Because otherwise, you'd know what the rules are going to do forever more. Right. I guess you were saying that the physicists saying that all you need is the initial conditions and the rules of physics is somehow missing the bigger picture. Yeah. And if you look at CAs, all you need is the initial condition and the rules and then run the thing. You need three things. You need the

initial conditions. You need the rules and you need time iteration to mine out without the coordinate. You can't get it out. Sure. And that's that that to use for them. And you can't predict it from initial conditions. Yeah. If you could, then it'd be fine. And that time is a resource. A foundation of this is the history, the memory of each of the things that created. Has to have that memory of all the things that led up to it. I think it's a yeah, you have to have

the resource. Yeah. Because time is a fundamental resource. And yeah, I'm becoming I think I had a major epiphany about randomness, but I keep doing that every two days and then that goes away again. It's random. You're you're a time fundamentalist. You should be as well. If you believe in free will, the only conclusion is there is time as fundamental. Otherwise, you cannot have free will. It logically follows. Well, my my foundation, my belief of free will is just is observation driven.

But that's I think if you use logic, it's like logically seems like the universe is deterministic. Looking back was in time. And that's correct. The universe is. And then everything else is is a kind of leap. It requires a leap. I mean, I I I think that it's kind of this is what I think machine learning is going to provide a big chunk of that, right? Because it helps us explain this. So the way I say, if you take that's interesting. Why? Well, let's let's just my favorite one is

because I'm the AI dooms are driving me mad. And in fact, that we don't have any intelligence. Yeah, I call AI autonomous informatics just to make people grumpy. Yeah. And they're because you're saying we're quite far away from AGI. I think that we have no conception of intelligence. And I think that we don't understand how the human brain does what it does. I think that we are neurosciences making great advances. But I think that we have no idea about AGI. So I am a technological,

I guess, optimist. I believe we should do everything. The whole regulation of AI is nonsensical. I mean, why would you regulate Excel other than the fact that Clippy should come back and I love Excel 97 because we can play, you know, we can do the flight flight simulator. I'm sorry, Excel. Yeah. Have you not played the flight simulator in night? Excel. Yeah. Yeah. What does that look like? It's like wireframe very, very basic. But basically, I think it's X

zero, Y zero shift. And it opens up and you can play the flight simulator. Oh, well, wait, is it using Excel Excel? Excel 97. Okay. I resurrected it the other day and saw Clippy again for the first time in a long time. Well, Clippy is definitely coming back. But you're saying we don't have a great understanding of what is intelligence. What is the intelligence? I am very frustrated. I'm underpinning the human mind. I'm very frustrated by the way that we're AI

dooming right now. And people are bestowing some kind of magic. Now, let's go back a bit. So you said, AGI, are we far away from AGI? Yes, I do not think we're going to get to AGI any time soon. I've seen no evidence of it. And the AI doom scenario is nonsensical in the extreme. Yeah. And the reason why I think it's nonsensical. But it's not non. And I don't think there isn't things we should do and be very worried about. Right. I mean, there are things we need to

worry about right now. What AI doing, whether it's fake data, fake users, right? I want authentic people, authentic data. I don't want everything to be faked. And I think it's a really big problem. And I'm absolutely want to go on the record to say, I really worry about that. What I'm not worried about is that some fictitious entity is going to turn us all to paper clips or detonate nuclear bombs. I don't know. Maybe I don't know. Anything you can't think of. Why is this?

Is a I'll take a very simple series of logical arguments. And and this is the the AI dooms are have not had the correct. They do not have the correct epistemology. They do not understand what knowledge is. And until we understand what knowledge is, they're not going to get anywhere because they're applying things falsely. So let me give you a very simple argument. People talk about the probability P doom AI. I can we can work out the probability of a asteroid hitting

the planet. Why? Because it's happened before. We know the mechanism. We know that there's a gravity well or that space time is bent and stuff falls in. We don't know the probability of AI because we have no mechanism. So let me give you the another one, which is like, I'm really worried about AG. What's AG? AG is anti-gravity. One day we could wake up an anti-gravity. You know, it's discovered. We're all going to die. The atmosphere is going to float away. We're going to float away. We're

all doomed. What is the probability of AG? We don't know because there's no mechanism for AG. Do we worry about it? No. And I don't understand the current reason for the for certain people in certain areas to be generating this nonsense. I think they're not doing it maliciously. I think we're observing the emergence of new religions. How religions come because religions are about some controls. You've got the optimist saying AI is going to curate or AI is going to kill us all.

What's the reality? Well, we don't have AI. We have really powerful machine learning tools. And they will allow us to do interesting things. And we need to be careful about how we use those tools in terms of manipulating human beings and faking stuff. Right? Right. Well, let me, let me try to sort of steal man the AI Dumer's argument. Actually, I don't know. Our AI Dumer's in the Karski camp saying definitely going to kill us because there's a spectrum. 95% I think is

the limit. Yeah. And they 5% plus. No, not plus. I think I don't know. I was seeing on Twitter today various things. But I think your Karski is at 95%. But to belong to the AI Dumer club, is there a threshold? I don't know what the membership may be. And what are the fees? I think, well, I think it's got Aronson. I was quite surprised. I saw this online. It could be wrong. So sorry if it's wrong. It says 2%. But the thing is, if you were to get, if someone said,

there's a 2% chance you're going to die going into the lift. Would you go into the lift? In the elevator for the elevator. Yeah, you're an American, English speaking audience. Well, no, not for the elevator. So I would say anyone higher than 2%. I mean, like, I mean, I think there's a 0% chance of AI to zero. Just to push back on the argument where the end of zero on the AGI, we can see on Earth that there's increasing levels of intelligence of organisms.

We can see what humans with extra intelligence were able to do to the other species. So that is a lot of samples of data what a Delta in intelligence gives you. When you have an increase in intelligence, how you able to dominate a species on Earth. And so the idea there is that if you have a being that's 10x smarter than humans, we're not going to be able to predict what that's going to. What that being is going to be able to do, especially if it has the power to hurt humans.

Which you can imagine a lot of trajectories in which the more benefit AI systems give, the more control would give to those AI systems over our power grid, over our nuclear weapons, or weapons of any sort. And then it's hard to know what an ultra intelligence system would be able to do in that case. You don't find that convincing. I think this is it. I would fail that argument 100%. Here's a number of reasons to fail it on. First of all, we don't know where the intention comes from.

The problem is that people think they keep, you know, with all the, I mean, watching all the Huxters online with the prompt engineering and all this stuff. Where, when I talk to a typical AI computer scientist, they keep talking about the AI is having some kind of decision-making ability. That is a category error. The decision-making ability comes from human beings. We have no understanding of how humans make decision. We've just been discussing free will for last half an hour,

right? We don't even know what that is. So the intention, I totally agree with you, people who intend to do bad things, can do bad things, and we should not let that risk go. That's totally here and now. I do not want that to happen, and I'm happy to be regulated to make sure that systems I generate, whether they're like computer systems or, you know, I'm working on a new project called

Chem Machiner. Yeah, yeah, which is basically a, for people who don't understand the point of the ex-Markiner, there's a great film about, I guess, CGI embodied and chemistry version of that. And I only know one way to embody intelligence lasting chemistry in human brains. So category error number one is agents that they have agency. Category error number two is saying that assuming that anything we make is going to be more intelligent.

Now you didn't say super intelligent. I'll put the words into our mouths here super intelligent. Sure. That I think that there is no reason to expect that we are going to make systems that are more intelligent, more capable, you know, when people play chess computers, they don't expect to win now, right? They just the chess computer is very good at chess. That doesn't mean it's super intelligent. So I think that super intelligence, I mean, I think even Nick Bostrom is pulling back on this now,

because he invented this. So I see this a lot. When did it see first happen? Eric Drexler, Nia Technology, atomically precise machines. He came up with a world where we had these atom cogs everywhere. They were going to make self-replicating nanobots. Not possible. Why? Because there's no resources to build these self-replicating nanobots. You can't get the precision. It doesn't work. It was a major category error in taking engineering principles down to the molecular level.

The only functioning molecular technology we know, sorry, the only functioning nanomolecular technology we know produced by evolution. There. Now let's go forward to AI. What is AI? We don't know. It's super. It can do this. So humans can't think that I would argue the only AI is that exist in the universe produced by evolution. Sure, we may be able to make our working memory better. We might be able to do more things. Human brain is the most compact computing unit in the universe.

It uses 20 watts. It uses a really limited volume. It's not like a chat GPT cluster, which has to have thousands of watts, some model that's generated, and it has to be corrected by human beings. You are autonomous and embodied intelligence. So I think that there are so many levels that we're missing out. We've just kind of went, oh, we've discovered fire. Oh gosh, the planet's just going to burn one day randomly. I mean, I just don't understand that leap.

There are bigger problems we need to worry about. So what is the motivation? Why are these people? Let's assume they have their earnest, have this conviction. Well, I think it's kind of, they're making leaps that they're trapped in a virtual reality. That isn't reality. Well, I can continue to set arguments here, but also it is true that ideologies that fear monger are dangerous because you can then use it to control, to regulate in a way that

holds progress, to control people, to cancel people, all that kind of stuff. So you have to be careful because you reason ultimately wins, right? But there is a lot of concerns with super intelligent systems, very capable systems. I think when I, when you hear the word super intelligent, you're hearing like it's smarter than humans in every way that humans are smart. But the paperclip manufacturing system doesn't need to be smart in every way. You just need to be smart.

I said that's specific ways. And the more capable the systems become, the more you could see us giving them control over, like I said, our power grid, a lot of aspects of human life. And that means they will be able to do more and more damage when there's unintended consequences that come to life. I think that that's right. The unintended consequences we have to think about. And I'm that I fully agree with. But let's go back a bit. Sentient, I mean, I'm going on far away from

my comfort zone and all this stuff. But hey, let's talk about it because I'll give myself a qualification. Yeah, we're both qualified in sentience, I think. Yeah, so as much as anyone else. I think the paperclip scenario is just such a poor one because let's think about how that would happen. And also, let's think about we are being so unrealistic about how much of the Earth's surface we have common dead. And you know, for papermit clip manufacturing to really happen, I mean, do the

math. It's like, it's not going to happen. There's not enough energy. There's not enough resource where they're all going to come from. I think that what happens in evolution is really why is why is a killer virus not killed out all of you, not killed all life on Earth? What happens is sure super killer viruses that kill the ribosome have emerged. You know what happens? They nuke a small space because they can't propagate. They will die. So there's this interplay between evolution

and propagation, right? And death. And so in evolution, you don't think it's possible to engineer, for example, sorry to interrupt, but like a perfect virus. No, there's deadly enough. No, I'm not sensible. Okay. I think that just wouldn't, again, it wouldn't work. It was too deadly. I would just kill the radius and not replicate it. Yeah. I mean, you don't think it's possible to get a... I mean, if you were soup, I mean, if you were... Not kill all of life on Earth, but kill all

humans. There's not many of us. There's only like 8 billion. There's so much more ants. I mean, I don't, I so many more ants. And they're pretty smart. I think we, the nice thing about what we, where we are, I would love for the AI crown to take a leaf out of the book of the bio warfare, chemical warfare crown. I mean, not love because actually people have been killed with chemical weapons in the first and second world war. And people and bio weapons have been made. And you know,

we can argue about COVID-19 and all this stuff. Let's not go there just now. But I think there is a consensus that some certain things are bad and we shouldn't do them, right? And, and sure, it would be possible for a bad actor to engineer something bad. But the damage would be, we would see it coming and we would be able to do something about it. Now, I guess what I'm trying to say is, when people talk about doom and they just, when you ask them for the mechanism, they just say, you know,

they just make something up. I mean, in this case, I'm, we, Jan LeCoon. I think we could put out a very good point about trying to regulate jet engines before we've even vented them. And I think that's what I'm saying. I'm not saying we should, I just don't understand why these guys are going around making, literally making stuff up about us all dying. When basically we need to actually really focus on. Now, let's say there's some actors are earnest, right? Let's say Yodakowski is

being earnest, right? And he really cares. But he loves it. And then you're going to die. It's like, you know, why don't we try and do the same thing? Say, you could do this and then you're going to be happy forever after. Yeah. Well, I think there's several things to say there. One, I think there is a role in society for people that say we're all going to die. Because I think it filters through as a message, as a viral message that gives us the proper amount of concern. Okay. All right.

Meaning not the, it's not 95%. But when you say 95% and it filters through society, you'll give an average of like a 0.03% an average. So it's nice to have people that are like, we're all going to die. Then we'll have a proper concern. Like for example, I do believe we're not properly concerned about the threat of nuclear weapons currently. Like that, it just seems like people have forgotten that that's the thing. And you know, there's a war in Ukraine with the

nuclear power involved. There's nuclear power throughout the world. And it just feels like we're in the brink of a potential world war to a percentage that I don't think people are properly calibrating like in their head. We're all thinking it's a Twitter battle as opposed to like actual threat. So like it's nice to have that kind of level of concern. But to me, like what I when I hear AI rumors, what I'm imagining is with unintended consequences, a potential situation where

let's say 5% of the world suffers deeply because of a mistake made of unintended consequences. I don't imagine the entirety human civilization dying, but there could be a lot of suffering if this is done. I understand that. And I'm kind of I guess I mean, I'm involved in the whole hype cycle. Like why I would like us to I don't want us to. So what's happening right now is this seems to be so let me let's say having some people saying AI AI do is a worry fine. Let's give

them that. But it what seems to be happening is there seems to be people who don't think AI is doing. They're trying to use that to control regulation and to push people to regulate where which which stops humans generating knowledge. And I am an advocate for generating as much knowledge as possible. When it comes to nuclear weapons, I grew up in the 70s and 80s where the nuclear doom a lot of adults really had existential threat. Almost as bad as now with AI do they were

really worried right. There was some great were not great. There was some horrific documentaries. I think there's one called Fred's that was generated in the UK wishing it was like it was terrible was like so scary. And I think that the correct thing to do is obviously get rid of nuclear weapons. But let's think about unintended consequences. We've got rid of this. We've got

rid of all the sulfur particles in the atmosphere right or the other sir. And what's happened in the last couple years is global warming is accelerated because we've cleaned up the atmosphere too much.

So sure I mean the same thing if you get rid of nuclear weapons you can. Exactly that's my point is so if so what we could do is if we actually started to put the AI in charge which is I really like an AI to be in charge of all world politics and this sounds ridiculous just a second hang on but if we could all agree on the ad we're just woke up yeah yeah yeah yeah that statement but I really don't like politicians who are basically just looking at local sampling but if you could say globally

look here's some game theory here there's what is the minimum number of nuclear weapons we need to just distribute around the night the world to everybody to basically reduce war to zero. I mean just the start experiment of the United States and China and Russia and major nuclear powers get together and say all right we're going to distribute nuclear weapons to everybody every single

nation on earth yeah oh boy I mean that has a probably greater than 50 percent chance of eliminating major military conflict yeah yeah but it's not 100 percent but I don't think anyone will use them because I think I think and look what you've got to try and do is like to qualify

for these nuclear weapons this is a great idea the game theorist could do this right I think the question is this I really buy your question we have too many nukes from just from a feeling point of view that we've got too many of them so let's reduce the number but not go rid of them because

we'll have too much conventional warfare so then what is the minimum number of nuclear weapons we can just do it around to remove what humans hurting each other is something we should stop doing it's in it's not out with our conceptual capability but right now what about the nation's

certain nations that are being exploited for their natural resources in the future because for a short term gain because we don't want to generate knowledge and and so if everybody had an equal doomsday switch I predict the quality of life your every human will go up faster I am an optimist

and I believe humanity is going to get better and better and better that we're going to eliminate more problems but I think yeah let's but the probability of a bad actor of one of the nations setting off a nuclear weapon I mean you have to you have to integrate that into the

but we we get we just give you the nuke-look-nukes like population right we give what we do is we but anyway let's just just go there let's say so if a if a small nation with a couple of nukes uses one because they're a bit bored or annoyed they're gonna they the likelihood that

they are going to be pummeled out of existence immediately is 100% and yet they've only they've only nuked one other city I know this is crazy and I apologize for no no I think this just to be clear we're just having a thought experiment that's interesting but you know there's terrorist

organizations that would take that would take would take that trade yeah I mean like I'm and we have to ask ourselves a question of how many which percentage of humans would be suicide bombers essentially where they would sacrifice their own life to to because they hate another group of

people and that I believe it's a very small fraction but is it large enough to uh if you give out nuclear weapons I can predict a future where we take all nuclear material when we burn it for energy right as well because we're getting there and the other thing you can do is say look there's a gap

so if we get all the countries to sign up to the virtual nuclear agreement where we all exist we have a simulation yeah where we can nuke each other in the simulation and the and the economic consequences are catastrophic sure in the simulation I love it it's not gonna kill all humans is just

going to have economic consequences yeah yeah I don't know I just made it up it seems like no it's interesting I mean it's interesting whether that would have as much power and human psychology as actual physical nuclear it's possible but people don't take economic consequences as seriously I think

as actual nuclear weapons I think they're doing Argentina and they're doing Somalia and they're doing a lot of these places where no I I think this is a great idea I'm a strong advocate now for so what we come up with burning burning all the nuclear material to have energy and before we do

that because mad is good mutually assured destruction is very powerful let's take it into the metaverse and then get people to kind of subscribe to that and if they actually nuke each other even for fun in the metaverse there are dire consequences yeah yeah so it's like a video game well

to join this metaverse video game yeah I can't believe it's our economic consequences I don't know how and it's all run by AI as you mentioned which so the AI tumors are really terrified at this point now they're happy they have a job for another 50 years right oh if you're

a man great yeah yeah yeah we got I'm a believer in equal employment you've mentioned that what you call cam machina yeah yeah so you've mentioned that a chemical brain is something you're interested in creating and that's the way to get conscious AI soon can you explain

what a chemical brain is I want to understand the mechanism of intelligence has gone through evolution right because the way that the way that intelligence was produced by evolution appears to be the following origin of life multicellularity locomotion sensors once you can start to see things come

coming towards you and you can remember the past and interrogate the present and imagine the future you can do something amazing right so and I think only in recent years did humans become cheering complete right yeah yeah yeah right we'll go and so that cheering completeness kind of gave

us another kick up but our ability to process that information as produced in a wet brain and I think that we are not getting going we do not have the correct hardware architectures to have the domain flexibility and the inter the ability to integrate information I think intelligence

also comes at a massive compromise of data right now we're obsessing about getting more and more data more and more processing more and more tricks to get dopamine hits so we're going when we look back on this going oh yeah that was really cool because when I've chat our chat GPT

it made me it made me really feel really happy I got a hit from it but actually it just exposed how little intelligence I use in every moment because I'm easily fooled so what I would like to do is to say well hey hang on what is it about the brain so the brain has this incredible connectivity

and it has the ability to you know as I said earlier about my nephew you know I just I went from Bill to Billy and he went all right Leroy like how did he make that leap they he was able to basically without any training I extended his name he went gay and he doesn't like

he wants me called Bill he went back and said you like to be called Lee I'm going to call you Leroy so human beings have a brilliant ability or intelligent beings appear to have a brilliant ability to integrate across all domains all at once and to synthesize something which allows

us to generate knowledge and and becoming cheering complete on our own I don't although AIs are built and cheering complete things they're they're thinking is not cheering complete in that they are not able to build universal explanations and that lack of universal explanation

means that they're just inductivists inductivism it doesn't get you anywhere in not not it's just basically a party trick it's like you know the the I like the I think it's in the fabric a reality from David Deutsch where basically you know the farmer is feeding the chicken every day and the chicken's getting fat and happy and the chicken's like I'm really happy every time the farmer comes in it feeds me and then one day the farmer comes in and doesn't instead of feeding the chicken

just rings its neck you know and that's kind of and had the chicken had an alternative understanding of why the farmer was feeding it it's interesting though because we don't know what's special about the human mind that's able to come up with these kind of generalities this universal theories of

things so that's what come up with novelty I can imagine because you give an example in you know about William and Leroy I I feel like example like that will be able to see in future versions of large language models will be really really really impressed by the humor the insights all of it

because it's fundamentally trained on all the incredible humor and insights that's available out there on the internet right so we'll be impressed I think we'll be impressed oh I'm impressed right I'm impressed increasingly so but we're mining the past yes and what the human brain

appears to be out of do is mine the future yes so novelty it it is interesting whether these large language models will ever be able to come up with something truly novel I can show on the back of a piece of paper what that's impossible and it's like the problem is that and again

there's a domain experts kind of bullshitting each other the term generative yes right average person they are as general as no no no if look if I take the numbers between zero and one thousand and I train a model to pick out the prime numbers by giving all the prime numbers between zero and a

thousand he doesn't know what prime number is occasionally if I can get a bit it will start to guess that it never will produce anything out with the dataset because you mine the past the thing that I'm getting to is I think that actually current machine learning technologies might actually

help reveal why time is fundamental it's like I'm even saying because they tell you about what's happened in the past but they can never help you understand what's happening in the future without training examples sure if that thing happens again it's like um so I think so let's think about

what large language models are doing we have the we have the language we have all the internet as we know it you know language but also they're doing something else we're having human beings correcting it all the time those models are being corrected um steered corrected modified tweaked

it's well yeah but I mean cheating well you could say the training on human data in the first place is cheating well let me but the humans in the loop sorry to interrupt yes so human is definitely in the loop but it's not just human is in the loop a very large collection of humans look

are turning and that could be I mean to me it's not intuitive that you said prime numbers that the system can generate an algorithm right that the algorithm that can generate prime numbers or the algorithm that can tell you for numbers prime and so on and generate algorithms that generate

algorithms that generate algorithms that I can start to look a lot like human reasoning you know I don't think I think again we can show that on a piece of paper that sure I think there has you have to have so this is the failure in epistemiology like I'm glad I even say that word

then what it means for us is set of multiple times I know it's like three times now without failure oh you're a little while you're ahead still say it again you did really well it thanks so I but I think the so what is reasoning so coming back to the chemical brain if I could basically

if I could show the inner because I mean I'm never going to make an intelligence in in cam macchina because if we don't have brain cells they don't have guile cells they don't have neurons but if I can make if I can take a matre gel and engineer the gel to have it be a hybrid

hardware for reproperry programming which I think I know how to do I will be I process a lot more information and train that they train models billions of times cheaper and use cross domain knowledge and there's certain techniques I think we can do but they're still missing though the the abilities

of human beings have to become true and complete and so I guess the question to give back at you and I is like how do you tell the difference between trial and error and the generation of new knowledge I think the way you can do it is this is that you come up with a theory and explanation

inspiration comes from out yeah and then you then test that and then you see that's going towards a truth and human beings are very good at doing that in and the transition between philosophy mathematics physics and natural sciences where and I think that we we can see that where I get

confused is why people miss appropriate the term artificial intelligence to say hey there's something else going on here because I think you and I both agree machine learning is really good it's only get better we're gonna get happier with the outcome but why would you ever think the

model is thinking or reasoning reasoning requires intention and the intention if the model isn't reasoning the intentions come from the prompt her and the intention has come from the person who programmed it to do it so I but don't you think you can prompt it to have intention basically start

with the initial conditions and get it going where the you know currently large language models chat GPT only talks to you when you talk to it there's no reason why you can't just start it talking but with but those initial conditions conditions came from someone starting it yes and

that calls will chain in there so that intention comes from the outside I think that there is something in that calls will chain of intention that's super important I don't disagree we're going to get to a GI is a matter of when and what hardware I think we're not going to do it in this hardware

and I think we're unnecessarily fetishizing really cool outputs and dopamine hits because obviously that's what people want to sell us well but there could be a GI is a lot of term but there could be incredibly super impressive intelligent systems on the way to a GI so these large language models I

mean if it appears conscious if it appears super intelligent poor we to say it's not I agree but I the super intelligence I want I want to I want to be able to have a discussion with it about coming up with fundamental new ideas at generate knowledge and if the if the super intelligence

regenerate can mind novel even the future that I didn't see in its training set in the past I would agree there's something really interesting is coming on I'll say that again if the if the intelligent system be a human being a chat chat bar something else is able to produce something

truly novel that we I could not predict even having full audit trail from the past then I'd be sold well so we should be clear that it can currently produce it can currently produce things that are in a shallow sense novel that are not in the training set but you're saying truly novel I think

they are in the training set I think everything it produces comes from a training set they might be in turn there's a difference between interpret novelty and interpolation we do not understand where these leaps come from yet that is what intelligence is I would argue those leaps and some people say

no it's actually just what will happen if you just do cross-training training and all that staff and that may be true and I may be completely wrong but right now the human mind is able to mind novelty in a way that artificial intelligence systems cannot and this is why we still have a job

and we're still doing staff and you know I use chat Gbt for a few people this is cool and then it took me to I had to I will what happened is it took me too much time I correct it then it got really good and now they've they've done something to it it's not actually that good yeah right

I don't know what's going on the censorship yeah it's so I mean that's interesting but it will push us humans to characterize novelty better like characterize the novel like what is novel what is truly novel what's the difference to novelty and interpolation I think that this this is the

thing that makes me most excited about these technologies is they're going to help me demonstrate to you that time is fundamental and the unit future is bigger than the than the than the present which is why we we are human beings are quite good at generating novelty because we have to expand

our data set and and to cope with unexpected things in our environment our environment throws them all at us again we have to survive in that environment and I mean either I never say never I would be very interested in how we can get cross-domain training cheaply in chemical systems

because I'm a chemist and a great the only thing I know of is human brain but maybe that's just me being boring predictable and not novel yeah you mentioned GPD for election audacity so a GPD like system for generating molecules that combine to host automatically I mean that's that's

interesting that's really interesting applying this same kind of transform mechanism yeah for I mean this is one it goes my team I try and do things unknown obvious but not obvious in certain areas and one of the things I was always asking about in chemistry people like to represent molecules

as graphs and it's quite difficult it's really hard in a if you're doing AI in chemistry you really want to basically have good representations you can generate new molecules are interesting and I was thinking well molecules aren't really graphs and they're not continuously different

iable could I do something that was continuously differentiable I was like well molecules are actually made up of electron density so they got thinking say well okay could there be a way where we could just basically take a take a database of readily solved electron densities for a in a

millions of molecules so we took the electron density for millions of molecules and just train the model to put to learn what electron density is and so what we built was a system that you literally could give it a let's say you could take a protein that has a particular active psi or you know

a cup of a certain hole in it you pour noise into it and with a GPT you turn the noise into electron density and then in this case it hallucinates like all of them do the hallucinations are good because it means I don't have to train on such a large num such a huge dataset because these datasets are

very expensive because how do you produce it so so go back a step so you've got all these molecules in this dataset but what you've literally done is a quantum mechanical calculation we produce electron densities for each molecule so you say oh this representation of this molecule has these

electron densities associated with it so you know what the representation is and you train the neural network to know what electron density is so then you give it an unknown pocket you pour in noise and you say right produce me electron density it produces electron density that doesn't

look ridiculous and what we did in this case is we produce electron density that maximizes the electrostatic potentials to the stickiness but minimizes what we call the steric hindrance so the overlap so it's repulsive so you know make the perfect fit and then we then use the kind of

a kind of like a chat GPT type thing to turn that electron density into what's called a smile a smile string is a is a is a a way of representing a molecule and letters and then we can then just just generate them just generate some and then the other thing is then we bung that into

the computer and then just makes it yeah the computer being the thing that get you right that the robot we've got that can basically just do chemists really needs so kind of we've kind of got this end to end drug discovery machine where you can say oh you want to bind to this active

site here you go I mean it's a bit leaky and things kind of break but it's a proof of principle well what were the hallucinations what are those still accurate well the hallucinations are really great in this case because in the case of a large amount of the hallucinations just like just make

everything up to when it doesn't just make everything up but it gives you an output that you're plausibly comfortable with and thinks you're doing probabilistically the problem on these electron density models is it's very expensive to solve a shredding equation going up to many heavy

atoms and and large molecules and so we wondered if we trained the the the system on up to nine heavy atoms whether it would go beyond nine and it did it started to generate molecules or 12 no problem they look pretty good and I was like well this hallucination I will take for free

thank you very much because it just basically this is a case where interpolation extrapolation worked relatively well and we were able to generate the really good molecules and then what we were able to do here is and this is a really good point what I was trying to say earlier that

we were able to generate new molecules from the known dataset that would bind to the the host so a new guest would bind were these truly novel not really because they they were constrained by the host were they new to us yes so I do understand I can concede that machine learning systems

artificial intelligence systems can generate new entities but how novel are they it remains to be seen yeah and how novel the things that humans generate is also difficult to quantify they seem novel that's what a lot of people say like you know so the way to really get to

generating novelty and the assembly theory shows you the way is to have different causal chains overlap and this really this and it really resonates with the the the time is fundamental argument and if you're bringing together a couple of object objects with different initial conditions coming

together when they interact the more different their histories the more novelty they generate in time going forward and so it could be that genuine novelty is basically about mix mix it up a little and the human brain is able to mix it up a little and all that stimulus comes from the

environment but all I think I'm saying is the universe is deterministic going back in time non deterministic going forward in time because the future is took the universe is too big in the future to contain in the present therefore these collisions of known things generate unknown things

that then become part of your data set and don't appear weird that's how we give ourselves comfort the past looks consistent with this initial condition hypothesis but actually we're generating more and more novelty and that's how it works simple so it's hard to quantify novelty looking

backwards I mean the present and the future of the novelty generators but I like this whole idea of mining novelty I think it is it is going to reveal why the limitations of current AI is a bit like a printing press right everyone thought that when book when the printing press came that writing

books is going to be terrible that you had evil spirits and all this they were just books and saying with be with AI yeah but there I think they're just a scale you can achieve in terms of impact with AI systems is pretty never-racking but that's what the big companies want you to

think but not like in terms of destroy all humans but you could have major consequences in the way social media has had major consequences both positive and negative and so you have to kind of think about and worry about it but yeah people that fear monger you know

my pet theory yeah for this you want to know yeah is I think that a lot and maybe I'm being and I think I really do respect you know a lot of the people out there who are trying to have discourse about the positive future so open AI guys met a guys and all this and what I wonder if

they're trying to cover up for the fact that social media has had a pretty disastrous effect at some level yeah and they're just trying to say yeah we should do this because and covering up for the fact that we have got some problems with you know teenagers and Instagram and Snapchat

and you know all this stuff and maybe they're just overreacting now yeah it's like oh yeah sorry we made the bubonic played and gave it to you all and you're all dying and oh yeah but look at this over here it's even worse yeah there's a little bit of that but there's also not enough

celebration of the positive impact that all these technologies have had tend to focus on the negative and tend to forget that it in part because it's hard to measure like it's very hard to measure the positive impacts of social media had on the world yeah I agree but if what I worry about right

now is like I'm really I do care about the ethics of what we're doing and then one the reasons why I'm so open about the things we're trying to do in the lab make life look at intelligence all this is so people say what are the consequences of this and you say what the consequences are not

doing it and I think that what worries me right now in the present is lack of authentic AI users and authenticate data and human users yeah human I still think that there will be AI agents that appear to be conscious but they would have to be also authenticated and labeled as such

so there's too much there's too much value in that you know like friendships with the AI systems there's too much meaningful human experiences to have with the AI systems that I just but that's like a tool right it's a bit like a meditation tool right some people have a meditation

tool makes them feel better but I'm not sure you can ascribe sent in some legal rights to a chatbot that makes you feel less lonely sentience yes I think legal rights no I think it's the same you can have a really deep meaningful relationship with a dog and with a dog sent in yes the chat

bots not what right now using the technology we use is not going to be sent in this is going to be a fun continue conversation on Twitter that I look forward to since you've had also from another place some debates that were inspired by the assembly theory paper let me ask you about God

is there any room for notions of God in assembly theory um I was God yeah I don't know what God is a I mean so God exists in our minds created by selection so human beings have created the concept of God in the same way that human beings have created the concept of superintelligence sure but

does it does it mean does it not it still could mean that that's a projection from the real world with like we're just assigning words and concepts the thing that is fundamental to the real world that there is something out there that is a creative force underlying the universe um I think the

universe it there is a creative force in the universe but I don't think it's it's sent in I mean I think the so I do not understand the universe so who am I to say you know um that that God doesn't exist I am an atheist but I'm not an angry atheist right I have lots of

I have lots of there's some people I know that are angry atheists say you know you know thank you I would say that religious people are stupid I don't think that's the case um I have faith in some things because I don't I mean when I was a kid I kept like you know it's like

well I need to know what the charge of electron is like I can't make it the charge of electron that was you know I was just gave up and had faith okay you know resistors worked so when it comes to I want to know why the universe is growing in the future and what humanity is going to become

and I've seen that the the acquisition of knowledge via the generation of novelty to produce technology has uniformly made humans lives better I would love to continue that tradition and you said that there's that creative force do you do you think just to think on that point do

you think there's a creative force like is there like a thing like a driver that's like that's creating stuff yeah I think that so I think that I'm aware what what is it what you can describe it like mathematics I think selection I think selection is the

voice selection is the force in the universe it creates novelty so it's selection somehow fundamental like what yeah yeah I think persistence of objects that could decay into nothing mm-hmm through operations that maintain that structure I mean think about it if it's amazing

that things exist at all that we're just not a big comment or a mess yes so the fact that exist and they put the thing that exists persistent time yeah may I mean let's think maybe the universe is actually in the present the things everything that can exist in the present does exist well that would

mean is deterministic right no I think the university's mind so the universe started super small the past was deterministic there wasn't much going on it was able to mind mind mind mind mind mind and so the the process I mean that is somehow generating universe is basically I can't I'm trying

to put this into what you just say there's no free will though no I didn't say that as if I'm sorry I'm sorry I said there is free will I think I think I I I'm saying that three will my curse at the boundary between the the present future the past in the future yeah I got you but

everything that can exist does exist everything that is so everything that's possible to exist at this so no really a lot of loaded words there so what I mean is there's a time element loaded into that I think that the universe is able to do what it can in the present right yeah and then I think

in the future there are other things that could be possible we can imagine lots of things but they don't all happen sure so what that's what I guess we're using again free will right there yeah so I guess what I'm saying is what what we exist is a calm is a convolution of the past with the present

and the free will going into the future well we can still imagine stuff right we can imagine stuff in the wrap and it's amazing force because you're imagining this is a the most important thing that we don't understand is our imaginations can actually change the future in a tangible way which is

what fit the fit the initial conditions and physics cannot predict like your imagination has a causal consequence in the future is that weird to yeah breaks breaks the laws of physics as we know them right now yeah so you think the

imagination is a causal effect in the future yeah but it does exist in there in the head I mean there must be a lot of power in whatever's going on there could be a lot of power whatever's going on in there if we then go back to initial conditions yeah and that's it's simply not possible

that can happen but if we go into if we go into a universe where we accept that there is a finite ability to represent numbers and you have round it we're not rounding errors you have some that the some what happens the your ability to make decisions imagine and do stuff is that that

interface between the certain and the uncertain it's not as yasha was saying to me randomness goes and you just you know randomly do random stuff it is that you are set free a little on your trajectory free will is about being able to explore on this narrow trajectory and that allows you

to build you have a choice about what you build all that choice is you interacting with a future in the present what do you most beautiful about this whole thing the universe the the the fact it seems to be very undecided very open and the fact that every time I think I'm getting

towards an answer to a question there are so many more questions that make the the chase you know do you hate that it's going to be over at some point no I well for me I don't so I I think if you think about it is it over for Newton now Newton has had causal consequences in the future we discuss

him all the time is ideas but not the person the person just had a lot of causal power when he was alive but oh my god one of the things I want to do is leave as many easter eggs in the future when I'm gone to go that's cool would you be very upset if somebody made it like a good late large

language model that's fine tuned to lecon it would be quite boring because I mean I mean I'm a novelty generation I would I mean it if it's a faithful representation of what I've done in my life that's great that's that's a interesting artifact but I think the most most interesting thing about

not eating each others we don't know what we're going to do next sure sure I mean within some constraints I've got you know you might I can predict some things about you you can predict some things about me we can't predict everything everything and it's because we can't predict everything is why we're exciting to come back and discuss and see it's so yeah I'm I'm I'm kind of happy that it will be interesting that some things that I've done can be captured but I'm pretty sure that my angle

on mining novelty for the future will not be captured yeah yeah so that that's what life is is just some novelty generation and then you're done each one of us just generally a little bit I think I have a capacity to at least I think life is a selection produces life and life affects

universe in the universe is with life in them are materially physically fundamentally different in universes of our life and that's super interesting and I have no beginnings of understanding I think maybe this is like in a thousand years there'll be a new

discipline in humans we are yeah of course this is how it all works right and in retrospect there will all be obvious I think I think a seventy theories obvious that's why a lot of people got angry right they were like oh my god this is such nonsense yeah you know and I oh yeah

now actually it's not quite but the writing's really bad well I can't wait to see where it evolves Lee and I'm glad I get to exist in this universe with you you're fascinating human this is always a pleasure I hope to talk to you many more many more times and I'm a huge

fan of just watching you create stuff in this world and thank you for talking today it's a pleasure always like thanks for having me on thanks for listening to this conversation with Lee Kronin to support this podcast please check out our sponsors in the description and now let me leave

you some words from Carl Sagan we can judge your progress by the courage of our questions and the depth of our answers our willingness to embrace what is true rather than what feels good thank you for listening and hope to see you next time