Blaise Agüera y Arcas: evaluation

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…ied computation. It is computational. You cannot have life without having computation. So obviously not everything that is alive reproduces, but everything that is alive has to be able to make itself. It has to be able to do some combination of healing, growing, maintaining itself, reproducing. All of that is autopoiesis. All of that involves self construction and all of that necessarily involves a universal constructor. Now, what do I mean by embodied computation? This is a really important distinction between Von Neumann and Turing. In Turing, the symbols that the that the head writes are different from the head itself and the tape and, and the table of rules that the that the head follows. Whereas in von Neumann, it's it's more like a 3 d printer. The the memory is atoms, not abstract symbols. In other words, you know, you could think about a Turing machine as like this laptop, you know, which can't extrude another laptop out the side. But a von Neumann replicator is like a combination of a laptop and a 3 d printer that can print another laptop. So its memory is actually atoms. That's what I mean by embodied. So I don't mean embodied in the ways that a lot of roboticists talk about embodied. I mean that that there is a closure between the the medium in which the computation happens and the thing that is actually doing the computation. That's the key. So computation that is embodied in that sense and that is autopoietic is alive. You can't reproduce non trivially, evolvably without without computation. No computation, no life. I do wanna say a word briefly about what I mean by computation and in this I'm following the the work of, Susan Stepney, Dominic Horseman, Rob Wagner, Viv Kendon. This is from a nice paper they wrote in 2023 relating, the evolution of a physical system and the computation that it does. So, you know, on top you have logical gates, on the bottom you have, you know, transistors in your computer. This is important because, you know, there's there are no bits in a computer, there are just voltages that go up and down. In fact, even the voltages are an abstraction of something further, you know, if we go further down. But, you know, the the point is that you have to coarse grain those voltages into bits and then you have to have a logical machine that talks about how those bits evolve, what are the what are the what are the computational processes that those bits undergo, and there's a mapping from the physical system to the logical system and vice versa. When we say something computes, what we mean is that it is possible to construct such a mapping and that therefore as the physical system evolves, that is equivalent to the logical system evolving. So, you know, there are some caveats. You can have stochastic computation in which there's a little bit of randomness injected so it doesn't have to be fully deterministic. Another really important caveat is that you don't want that description to be infinitely complex. Otherwise, you could have the trivial case of saying like, you know, the water in the SEN is a computer and the longer my computation, I just need to make my description longer and longer in order to match. No. you could have the trivial case of saying like, you know, the water in the SEN is a computer and the longer my computation, I just need to make my description longer and longer in order to match. No. That doesn't work either. You need a kind of a Occam's razor, description that, for it to be valid. But this is a good definition of computation, but it emphasizes that there's something subjective about computation. You need to have a model for how the, how the physical system translates into the logical system in order for any of this stuff to work. There are implications about entropy, free energy and heat and so on in this model. And in particular, you as you all know, we've talked already, you know, Hector Zenil in his very elegant, talk of a couple of days ago talked about, and actually Chris Kempis also talked about the Landauer limit, and the fact that in a computational system you're constantly reducing the entropy of of your state space and in doing so you therefore require free energy. So, you know, you need to have free energy available and you need to eject waste heat. The exception in a way only proves the rule which is reversible computation. In reversible computation you generate ansyllabits and, and that's equivalent to just saying there's no exhaust But, you know, then you either have to keep on making your computer bigger and bigger and bigger as you accumulate these ancillibits or you have to, shrink what you consider to be the computer and then you're back to reversible to to non reversible computation once again. 3 important fallacies that I wanna point out before continuing. 1 of them I will call the Sapolsky Era. Robert Sapolsky, you know, has written famously about, people not having free will, because we're built on physical systems. You know, the physics is is, you know, if you like deterministic, let's set aside quantum mechanics and stuff like this. Let's imagine we live in a Newtonian universe. It's fine. It's good enough. The point is that physics is reversible. All of the basic physics that we understand, whether that's Newton's equations, Maxwell's equations, Einstein's equations, quantum mechanics, all of those are essentially time reversible. So you can move them either forward or back. Computation is not reversible. When I add, you know, 3 plus 5 to get 8, once I've got the 8, and I've, you know, haven't kept my ancillips around, let's say, I no longer know what was added in order to make the 8. Computation is inherently irreversible. And so to say that what is true of the physical system is also true of the of the computational system or the logical system is is not is not the case. And, reversibility would be 1 trivial example of how that is not the case. Causation, by the way, only makes sense in the light of irreversibility. Right? So if you have a purely physical system, then, you know, to say that a causes b is equivalent to saying that b causes because everything is kind of a block universe, you like, in that kind of setup. But in computation, you can talk about causality because there are ifs and thens in there. lent to saying that b causes because everything is kind of a block universe, you like, in that kind of setup. But in computation, you can talk about causality because there are ifs and thens in there. And this once again connects with the way Hector was talking about how essentially nothing in nothing in causation makes sense except in the light of computation, which I fully agree with. Another fallacy, we could call the the early Wittgenstein error. If we say something like birds exist in the world, line 1 of the Tractatus Logical Philosophia, you didn't say birds, but whatever. You can't say birds exist or birds don't exist in a way that is independent of a model of the universe. There are no birds in physics. There are no birds in this underlying dynamical system. When we start talking about birds, we already are talking about having some kind of some kind of model. And once we start talking about models, you've got causality, reversibility, all kinds of other irreversibility, all kinds of other things in play, and none of these statements are are are airtight. They all rely on on an observer. This is kind of Kant as well, I guess. And this leads to the the early Leibniz error, or the same error that the good old fi the good old fashioned AI practitioners had, which is that intelligence could be carried out by just having a series of programs of strictly logical deductions or inductions. That doesn't work. This is why good old fashioned AI never panned out. The reason is that that you can't start out with, like in math, with propositions that are self sufficient. Even math is not self sufficient, but let's pretend for a moment and just move from there and kind of do an algebra in order to work various things out. When when your propositions are not airtight when and you're looking only at regularities and patterns, this good old fashioned AI idea simply cannot work. That's that's why we never got it to work. Let's move now to to some of the artificial life experiments that that that I began playing with in at the 2023 and my team and I published in June 2024, so just about a a year ago. I think some of you many of you perhaps have heard of these. They're in the What is Life books and I've talked about them a few times. The the basic setup here is to try and get self replication to get, you know, abiogenesis, the emergence of life from non life to happen in a purely artificial life system. Okay. So the setup is to begin with a minimal Turing complete language, I used brain fuck, because I I really liked the idea of being able to talk at a conference and say brain fuck over and over and I'm fundamentally 12 years old on the inside. But but also because it's it's, it it very closely models, the Turing machine. You know, it's it's a it's a minimal programming language. Only only 8 instructions that, that looks very Turing machine like and moves the head back and forth. I should say that in its original version, brain fuck is not embodied computation. It has basically a separate data tape and code tape, and that means that it cannot make a copy of itself.…

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