Sorry Matt for the late reply. You're not misunderstanding, your reading is thoughtful, and you're raising valid concerns.
The paper <https://doi.org/10.5281/zenodo.16998027> proposes a *theoretical model*, not an empirical threshold, for when systems may cross into a higher "cognitive phase" based on energetic and dynamical constraints. The figure of 10^−7J represents the *total memory-energy* (Mtot) of a localized cognitive system (e.g., neural module or neuromorphic unit), not energy per bit. The *70% efficiency* (η) and the *normalized propagation velocity* (ϕ^2 ≳1) are derived from biological and likely current neuromorphic benchmarks- plausible upper bounds in the model’s saturating functions. The paper doesn't claim a *universal phase transition*, but rather defines a *dimensionless quotient *(Q) to quantify when a system may enter a self-sustaining information-processing regime. So, these thresholds are not predictions, they are reference values used to ground the model and allow comparison across different physical systems. *---Dorian Aur* On Sun, Aug 31, 2025 at 8:17 PM Matt Mahoney <[email protected]> wrote: > Maybe I am misunderstanding the paper. But my understanding after staring > at the equations for a long time is that it claims (without evidence) that > an intelligent system undergoes some kind of phase transition to self > learning when it uses less than 10^-7 joules per bit per second with 70% > efficiency and the information propagation velocity squared exceeds 1 meter > per second. Is this correct? And where do these numbers come from? > > -- Matt Mahoney, [email protected] > > On Sun, Aug 31, 2025, 8:44 PM Rob Freeman <[email protected]> > wrote: > >> 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 <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + > delivery options <https://agi.topicbox.com/groups/agi/subscription> > Permalink > <https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-M637794d719218156ae6efa55> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-Mb541c19a030bdd389ca437ab Delivery options: https://agi.topicbox.com/groups/agi/subscription
