Dorian, Ah, well I'm sympathetic to an approach looking at this as a problem of dynamics (though I'm not sure what "memory-energy" coupling could mean.)
Colin Hales, who I think has corresponded here, has an electrodynamic field model which would seem to be more strictly embodied. He says it must be implemented as electrodynamic fields. Lee Cronin seems to have a similar argument, but from a chemistry perspective. George Lakoff, embodiment... basically as neurons, anyway. (The whole embodiment thing became something in the fields of linguistics which retained a basis in data after Chomsky: Functional, Cognitive Linguistics, and indeed generally in the "corpus based" fields most directly connected with machine learning. And quite rightly. They saw something. Insisting on basis in a "corpus" is an embodied form of non-compression. "Corpus" = body. Embodiment is a form of the non-compressibility argument, as I say. You need the whole corpus, said Corpus Linguistics. This as opposed to Chomsky, who argued language could be... not compressed... the whole point became that it could not be compressed, that's why Chomsky still rejects machine learning. Chomsky insisted any abstraction must be innate, exactly because it could not be compressed/learned. At one level, observable compressions contradicted. So linguistics resolved to embodied, or innate. But then LLMs ignored linguistics and went and "learned" over corpora anyway!) But you're not trapped by the embodiment interpretation of this. Good. Let alone Chomsky's "unlearnable" innate structure. You see a solution in dynamics. Good. (Once a dynamical system becomes chaotic, it does become embodied in a sense, but chaos is not limited to one embodiment.) So what are the parameters of this dynamics? You say "certain energy-structured conditions (e.g., coherence, memory capacity, signal velocity) are necessary". "Coherence" I'm sympathetic to. I've been pushing the idea of a dynamical system solution parameterized by the "coherence" of oscillations. Driven by network symmetries of prediction. A dynamical system parametrized by similarity of context, anyway. A dynamical system in the sense the patterns grow and change. Actually I think its evolution is probably chaotic on some level. Concurring with Walter Freeman on that, from the neuroscience field. Checking your link, you try memristors. "emergent, quantized intelligence, analogous to a phase transition" sounds good. Phase transitions being a big theme of Walter Freeman. "adaptive behavior arises intrinsically from the physics of the substrate". Yes. Personally I think the crucial parameters are the ones already identified by LLMs: shared context or shared prediction. If you can harness dynamics which depend on those, it doesn't matter what your substrate is. I got focused on the potential of (neural activation) oscillations in a network of neurons representing language sequences. Just because synchronized oscillations struck me as dynamics to capture those parameters of shared context/prediction. >From a casual glance at your paper I'm unable to tell if the parameters of your dynamics are also shared context/prediction. If they are, then it might be good. I feel you might be looking at static attractors in some sense though. Some kind of "bubble memory". Which then has meaning... how? I can't find the word "meaning" anywhere in your paper. Instead you have "adaptive, feedback-driven reconfiguration". So it seems you make something of the novelty of reconfiguration, but this has value only because of "feedback". So the system will give meaning to these reconfigurations by some kind of feedback from the environment? That sounds to me like George Edelman's Neural Darwinism. Endless random reconfigurations, which are selected for meaning by the environment (like his Nobel Prize winning immune system insight.) Of interest to me, I recently came across Eugene Izhikevich's "polychronizations". Stable sequences appearing (from the dynamics) spontaneously in networks of neurons. But Izhikevich didn't attribute meaning to these based on the way the sequences shared contexts either. It looks to me like you have the insights about dynamical systems, and the power of reordering/reconfiguration. But (like Edelman and Izhikevich) you may be missing shared context/prediction, as the key parameter. Making "meaning" internal to the system too, and not needing to be imposed by any external feedback. -R On Mon, Sep 1, 2025 at 2:23 AM Dorian Aur <[email protected]> wrote: > Electrodynamic Intelligence doesn’t argue that learning is tied to a > specific biological embodiment or that there’s only one valid substrate. > What it does argue is that *learning occurs from the physical dynamics of > signal propagation and memory-energy coupling,* not from abstract > optimization rules. This isn't an anti-compression or anti-symbolic stance, > it is a move toward *physically embedded, self-organizing systems *that > learn because of how they are built, not because of what we train them to > do. > We agree that “can’t be compressed” doesn’t imply “must be embodied in one > way.” EDI doesn’t claim there's only one valid realization of learning or > intelligence. Rather, it highlights that certain energy-structured > conditions <https://zenodo.org/records/16997063>(e.g., coherence, memory > capacity, signal velocity) are necessary for physically grounded > intelligence to occur.. > Propagation-driven learning is not about memorizing complexity but about > allowing local physical dynamics to shape the system's functional structure > over time. Compression is secondary, what's primary is whether the system > can self-organize adaptively through physical interaction to generate > self-learning and active memory consolidation. > > --- Dorian Aur > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-M2ce345fbb62815fcb868ce18 Delivery options: https://agi.topicbox.com/groups/agi/subscription
