"To embrace the poisonous nonsense of degrowth now — to shut down nuclear power plants, to regulate the AI industry out of existence, to forcibly shorten working hours, to bar the construction of houses and factories, etc. — would be to cripple one of the last few remaining economic engines of the free world, at precisely the time when it’s under its greatest external challenge." @Noahpinion open.substack.com/pub/noahpi…

In her Nobel Prize acceptance speech in 1983, the octogenarian geneticist Barbara McClintock asked the question, “What does a cell know of itself?” Forty years later, scientists are realizing that the answer might be: Much more than we thought. quantamagazine.org/what-can-…

@StuartHameroff We have explained in detail what generates the information-rich electric field: all electrically charged structures and all processes that produce currents like electrodiffusion, mechanotransduction etc, including microtubules, please see pubmed.ncbi.nlm.nih.gov/3721…

This idea that memory is distributed across regions of the brain is interesting but I wish it were more concrete to help my own understanding. Does this mean a memory (ex: driving your car yesterday) is broken into bits and the bits are combined and reassembled during recall?

Hi @StuartHameroff See below for a biological correlate of GC : Ephaptic coupling strength , could involve microtubules too pubmed.ncbi.nlm.nih.gov/3742… Building on @anilkseth 's work. W/@MillerLabMIT

Google DeepMind CEO Demis Hassabis on the central question of his life: what can humans do that AI never will? Asked about the limits of artificial intelligence, Hassabis doesn't hesitate to say this is the question that drives him: "Yes, it is." His starting point is one of his heroes, Alan Turing. He explains that Turing described theoretical constructs—Turing machines—that underpin all modern computers, machines "that are able to compute anything that's computable… anything that can be described as an algorithm." The provocative leap comes when Hassabis turns that lens on biology. The AI systems his teams build are Turing machines, and he suspects the brain may be one too: "A lot of neuroscientists including me think that maybe the brain… a good model for the brain is an approximate Turing machine." He's careful to note this isn't settled. Some of his peers see it differently, including physicist Roger Penrose: "Friends of mine like Roger Penrose… believe there might be some quantum effect in the brain." But Hassabis points to where the evidence currently stands: "So far neuroscience hasn't found any quantum effects in the brain… people have looked quite carefully and we haven't found any." His conclusion from that absence is striking. If the brain runs on ordinary computation, then there may be no hard ceiling on what AI can eventually match: "It looks like most of what's going on in the brain is kind of classical computation… so therefore it's not clear what the limit would be in terms of eventually what an AI system could do and could mimic." For @demishassabis, building intelligence is a mirror held up to ourselves, not only a feat of engineering. He describes the project as a kind of experiment that reveals what we are: "I think we'll have almost like a control study comparison to the human mind. And then I think we'll see in this journey what are the differences and what's unique about the mind." He stays genuinely open about what that comparison might expose. Some things, he suspects, may never transfer: "There could be unique things and certainly unique connections between humans that will never be replicated by these AI systems." But the capabilities we tend to treat as distinctly human, he believes, are within reach: "A lot of things that we currently are not in reach, like long-term planning and reasoning and maybe some forms of creativity... I think eventually AI systems will be able to do."

Dimensionality reduction likely corresponds to a shift toward lower-frequency dynamics. This reduced representation may function as a top-down pointer and be organized by these slower frequencies. doi.org/10.1016/j.cobeha.202… osf.io/preprints/psyarxiv/z4…

And what is that exactly? @StuartHameroff

A second or longer would be the most important number in the field if it survives replication, because it’s finally falsifiable. That’s the whole game to me!! A coherence time long enough to matter is also long enough to be cleanly measured and cleanly killed if it’s wrong. So I’ll hold the enthusiasm until Dogariu and Tuszynski are out and reproduced. But I’ll say this. You just stated a number most people in this debate carefully avoid stating. That’s the difference between a position and a mood.

2/2 Our new @PhysRevE paper extends the method to a wide class of dynamical (Langevin) systems, with many applications. But it remains a *statistical analysis method*, not a phenomenon (like, e.g., consciousness) in need of a biological mechanism or correlate. Hope that's clear.

Even if decoherence rates are correct, the question may be misframed. In scale-free critical systems, the micro-macro gap that makes amplification necessary may not be structurally present.

Stepwise is the key admission, because steps are testable in a way a smooth gradient isn’t. If the downshift is discrete, each step should have a signature you can locate, not just model. Negative resonance gives you a mechanism for the descent. What it doesn’t yet give you is a way to read off which step the system is on from the outside, independent of the substrate you attribute it to. That’s the gap I keep circling: a stepwise process is exactly the kind that a clean measurement could pin down.

You and Miller are both describing a descent: high-dimensional, high-frequency activity collapsing toward something lower. The interesting question is whether that descent is monotonic and drivable on purpose. If consciousness tracks reduction in dimensionality, then a system walked stepwise down its own excitation axis should show the reduction as an ordered gradient, not a single threshold. Time crystal or microtubule, the discriminating test is the same: does the observable move by steps as you descend, or all at once at the bottom?

1/2 The reason I quoted Maslow is that Granger casuality (GC) has absolutely nothing to do with Orch OR or microtubules. GC is a statistical method for estimating directed functional connectivity (or 'information flow') in time series data. (scholarpedia.org/article/Gra…)

1/2 The reason I quoted Maslow is that Granger casuality (GC) has absolutely nothing to do with Orch OR or microtubules. GC is a statistical method for estimating directed functional connectivity (or 'information flow') in time series data. (scholarpedia.org/article/Gra…)

No, it's the process of shifting to better epistemic standards than "microtubule". dottheory.co.uk

Maslow (1966): if all you have is a hammer, everything looks like a nail.

Michael Levin & Earl Miller: Platonic Space of Minds, Bioelectricity, Cell Intelligence & Neurobots Youtube: youtu.be/6mU5nN5nlLA Spotify: open.spotify.com/episode/6qJ… Skool Ad Free: skool.com/the-giants-shoulde…

5/5 (iii) extension of GC maps and GC rates to classical *deterministic* systems (via shrinking the noise globally to zero, while retaining its "ghost" as noise variance-covariance maps in phase space). Here's the paper: journals.aps.org/pre/abstrac… (funded by @ERC_Research CONSCIOUS)