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PaperclipPerfector  8d ago (text post)   32183 thread views

Culture War Roundup for the week of March 24, 2025

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You're correct that I'm being generous. Expecting a system as macroscopic and noisy as the brain to rely on quantum effects that go away if you look at them wrong is a stretch. I wouldn't say that's impossible, just very, very unlikely. It's the kind of thing you could present at a neuroscience conference, without being kicked out, but everyone would just shake their heads and tut the whole time.

If this were true, then entering an MRI would almost certainly do crazy things to your subjective conscious experience. Quantum coherence holding up to a tesla-strong field? Never heard of that, at most it's incredibly subtle and hard to distinguish from people being suggestible (transcranial magnetic stimulation does do real things to the brain). Even the brain in its default state is close to the worst case scenario when it comes to quantum-only effects with macroscopic consequences.

And even if the brain did something funky, that's little reason to assume that it's a feature relevant to modeling it. As you've mentioned, there's a well behaved classical model. We already know that we can simulate biological neurons ~perfectly with their ML counterparts.

We know for a fact that the electron transport chain of mitochondria relies on quantum tunneling to move electrons between complexes and MRI doesn't seem to effect that very much, so I wouldn't be surprised if an MRI had no effect on conscious experience (although I couldn't tell you, I've never had one).

I don't buy the claim that we can simulate biological neurons perfectly with their ML counterparts. We can barely simulate the function of an entire bacterial cell, which for context, is about as big as a mitochondria. Can we approximate neuronal function? Sure. But something is clearly lost: what else would explain the great efficiency of biological versus human systems in terms of power consumption.

https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/

They started by creating a massive simulation of the input-output function of a type of neuron with distinct trees of dendritic branches at its top and bottom, known as a pyramidal neuron, from a rat’s cortex. Then they fed the simulation into a deep neural network that had up to 256 artificial neurons in each layer. They continued increasing the number of layers until they achieved 99% accuracy at the millisecond level between the input and output of the simulated neuron. The deep neural network successfully predicted the behavior of the neuron’s input-output function with at least five — but no more than eight — artificial layers. In most of the networks, that equated to about 1,000 artificial neurons for just one biological neuron.

But something is clearly lost: what else would explain the great efficiency of biological versus human systems in terms of power consumption.

I really don't see this as a good explanation. We know that there are brains that are more efficient, by volume. Avian neurons are smaller, packed more densely, but can create a very smart animal that uses tools. You can find a wide range of "efficiency" values in biology, the human brain isn't particularly special.

I think it's far more likely that the brain's architecture is just very well optimized for its constraints, and power draw is a very important constraint. Running an LLM that is human level in reasoning (or close enough) uses a lot more power, but an 8B model can be surprisingly smart and run on a smartphone. I bet a model like that is "smarter" than a human child in relevant domains for far less power. Silicon computers are also far faster in terms of clock speed. They are incredibly reliable in terms of error rate and consistency. You've chosen IQ/watt as the metric in advance (and it's important), but silicon computers have enormous advantages that biology can only dream of. You can imagine the analogous situation, trying to convince people that it's important to simulate things like electron tunneling causing power draw in very small transistors (which is true), when that has no relevance to actually emulating a computer.

None of this requires quantum effects to be an irreducible aspect of human computation. Nor that it's responsible for the relative efficiency.

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