Lee Cronin
speaker
545 appearances
2 recordings
1 series
first heard Dec 2023
last heard Jun 2024
Lee Cronin’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsNo recordings in the last 12 months.Older appearances are listed below; set an alert to hear about the next one.
Appearances
Yeah. Memory. I think selection produces intelligence.
wait you're almost implying that selection is intelligence no yeah kind of i would go that i would go out on a limb 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 like darwinian selection because the human being doesn't have to basically do trial and error like they can think about they say oh that's a bad idea won't do that and then technologies and so on
And assembly theory will measure that as well, right? Because it's all a lineage.
Yeah, there seems to be a disconnect between the computational approach. So a Kolmogorov measure requires a Turing 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. There's a main thing there. There are lots of other layers.
So Kolmogorov complexity, you can approximate Kolmogorov complexity, but it's not really telling you very much about... the actual... It's really telling you about your data set, compression of your data set. And so that doesn't really help you identify the turtle, in this case, is the computer. And so what assembly theory does is... I'm going to say...
Trigger warning for anyone listening 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. I think that assembly theory allows us to understand when were selections occurring. Selection produces factories and things.
Factories in the end produce computers and then algorithmic information theory comes out of that. The frustration I've had 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 causal chain. Yeah.
Exactly. And if you've got all your data in a computer memory, all the data is the same. You can access it in the same way. 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.
I would say it does in a way, and it is fascinating to look at. So you've just got the object.
Mm-hmm.
and you have no other information about the object, what assembly theory allows you to do just with the object is to, and the word infer is correct, I agree with infer, you say, well, 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. Mm-hmm. And so what assembly theory allows you to do is 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, 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 one-up AIT or to ignore the factory. It's just to say, hey, there was a factory. How big was that factory and how deep in time is it? Mm-hmm.
It is. It becomes harder. But one of the things that's super nice is that it constrains your initial conditions, right? Sure. 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. Mm-hmm.
Now, what everyone's doing right now is taking all the proteins and looking at the proteins and looking at molecules docked with proteins. Why not instead look at the molecules that are involved in interacting with the receptors over time, rather than thinking about and use the molecules that evolve over time as a proxy for how the proteins evolved over time?
Mm-hmm.
and then use that to constrain your drug discovery process. You flip the problem 180 and focus on the molecule evolution rather than the protein. And so you can guess in the future what might happen. So rather than having to consider all possible molecules, you know where to focus.
And that's the same thing if you're looking in assembly spaces 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 there that doesn't appear in the past.
No.
Well, this is another thing that I think causes, because this paper goes across so many boundaries. So chemists have looked at this and said, this is not a correct reaction. It's like, no, it's a graph.
Showing 221–240 of 545 · page 12 of 28
← Previous
Next →