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Published · transcript-backedKeith Duggar: recommendation
10 Sept 2025 Machine Learning Street Talk Karl Friston - Why Intelligence Can't Get Too Large (Goldilocks principle)
“You install that cause effect structure into your computer architecture, which is again the argument against von Neumann architectures, which is why 1 might, I think, look to all the processing in memory, neuromorphic photonics, possibly quantum computation, but think that's gone off the boil recently.”
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- Keith Duggar
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- recommendation
- Recorded
- 10 Sept 2025
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- Machine Learning Street Talk
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…I think it is. I'm answering in an almost trivial way that if you want to talk about something, you have to be able to define its mark off blanket. So yes, you have to be able to draw lines and bounds and lines around things to talk about them. I think that your question though speaks to something that we were just been covering which is the separation of scales and the everything, everything literally from the point of view of the free energy principle that is equipped with the Markov blanket is contextualized by a scale above. And the same rules apply to the scale above as to the thing itself. So you have to have a context in which everything is operating. So the virus may not be intelligent but it certainly has to be living in a world which is conducive to its existence and that world and the states of that particular world at scale above, for example the host cell and the host organism, has to comply and be intelligent in some sense in order for the virus to be there even if the virus itself is not intelligent. So applying that to the plant but we should come back to the extended cognition and Andy Clark. But to the plant thing you made an interesting point. Is it in the DNA or is it in the morphology and the phototaxis, everything else that plants possess and we infer goes on on the inside. First of all I think you're absolutely right to say yes, is in the morphology and in a sense that's what I was getting at when talking about the importance of mortal computation for machine consciousness. That you have to to realize a Bayesian mechanics or self evidencing, you physically have to parameterize your conditional probability distributions, your Bayesian beliefs about the environment to which you are coupled. So the substrate is the parameterization, so by definition it's substrate dependent computation, which means the structural form, the morphology, is the structure of the generative model in the spirit of structural learning. So that to be part and very much in spirit of the Code Regulator Theorem, you know, to be in my world, I have to physically encode and embody a model of the structure of my world, which means that I have to have, if you like if you think your world is scale free, that means that my brain must have some scale free hierarchical aspect. And indeed, it does. And you can actually write that down and model it with a renormalization group. And that's just a reflection of the fact that I immersed in a world that has some scale invariance in it. So that is certainly true for the plant. And just 1 little aside here. That paper was written before David Attenborough's series on the life of plants where he was able to speed up by a factor of 10 or a 100. Of course, if you look at plants doing stuff, they're conspecifics by sending their little roots off in a particular direction or eating insects or whatever. If you speed it up, these things are very animalistic and they look you'd be hard pressed to say conspecifics by sending their little roots off in a particular direction or eating insects or whatever. If you speed it up, these things are very animalistic and they look you'd be hard pressed to say that they weren't intelligent irrespective of whether they're conscious or not, but they certainly start to look much more like you and me when you speed things up. So yeah, have their morphology matters. Matters more than than 1 could possibly imagine because this is how you become a good regulator. You model your environment. You are. You install that cause effect structure into your computer architecture, which is again the argument against von Neumann architectures, which is why 1 might, I think, look to all the processing in memory, neuromorphic photonics, possibly quantum computation, but think that's gone off the boil recently. But all that processing in memory stuff, memorysters for example, I think that's where the answer will be. Is it in the DNA? So does the DNA cause a structure? Okay, your comment the expression to make that point. Yeah. So I think I think some of the hang up or crux may actually be, like, what we mean by model. And I'm sure this is a whole philosophical debate that I'm probably not qualified to, you know, decide. But I think, for example, you know, if you're flying an airplane, like, airplane internally has a computational model of the airplane, and you're interacting with this computer and through controls that then are enacted through all kinds of gears and cables and levers and things like that. But I think of the model as being the thing that's in the computer, and then it's modeling the the airplane. Right? And so in that sense, you know, the DNA of a plant species must incorporate the code for the model. Right? Because that's what unfurls is the plant. It's the same DNA in every cell that's enacting a program that's essentially happening through gene expression, you know, and so a particular cell, maybe some salt content is in there, which causes it to release a hormone that binds to all the other cells in the plant, which activates certain genetic pathways. So I kind of think about in that same analogy, you know, the the core of the model is is the code, you know, that's running and operating and controlling these processes. And then I think of the embodiment of the plant as the stuff that the model is modeling. Right? But I guess, you know, there's some vagueness there too.…
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