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.
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And so he was able to refer an 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 that 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, walls and inside the cell. So we're building up.
But obviously I want a leap to things like emoticons, language, mathematical theorems.
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.
Mm-hmm.
If they have those number of, and then let's look at a brain in a whale or a dolphin or a chimpanzee or a bird and 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?
Yeah. And that means you need to have some idea of the anatomy.
I guess so. I mean, and I think this is a good way to apply machine learning and image recognition just to basically characterize things.
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... Because assembly possible, all possible brains, all 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 in evolutionary history, but in biological development as you grow, we are going to learn something more.
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. Because before now, the ground truth for it was very subjective.
Mm-hmm.
Whereas here, we're talking about clean observables. And there's going to be layers on that. I mean, with collaborators right now, we already think we can do assembly theory on language. And not only that, wouldn't it be great 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 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 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 haven't bought one yet.
I can't justify it, but I want to at some point. The M3 processor, arguably, 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 microprocessor architecture. It doesn't take a huge leap to see that.
Yeah, well, whatever.
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 large language model comes from all the people that produced all the language. And that's really interesting. And all the corrections in the Mechanical Turk.
That's part of the history, part of the memory of the system. Exactly. It would be really interesting to basically use an assembly-based approach to making language in a hierarchy. My guess is that
you could 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 well 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 them in the system
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