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Comment by pockmarked19 | Hacker News Reader

Calling it a neural network was clearly a mistake on the magnitude of calling a wheel a leg.

This is an excellent analogy. Aside from “they’re both networks” (which is almost a truism), there’s really nothing in common between an artificial neural network and a brain.

Neurons also adjust the signal strength based on previous stimuli, which in effect makes the future response weighted. So it is not far off—albeit a gross simplification—to call the brain a weight matrix.

As I learned it, artificial neural networks were modeled after a simple model for the brain. The early (successful) models were almost all reinforcement models, which is also one of the most successful model for animal (including human) learning.

I don't really get where you're coming from..

Is your point that the capabilities of these models have grown such that 'merely' calling it a neural network doesn't fit the capabilities?

Or is your point that these models are called neural networks even though biological neural networks are much more complex and so we should use a different term to differentiate the simulated from the biological ?

The OP is comparing the "neuron count" of an LLM to the neuron count of animals and humans. This comparison is clearly flawed. Even you step back and say "well, the units might not be the same but LLMs are getting more complex so pretty soon they'll be like animals". Yes, LLMs are complex and have gained more behaviors through size and increased training regimes but if you realize these structure aren't like brains, there's no argument here that they will soon reach to qualities of brains.

Actually, I'm comparing the "neuron-neuron connection count," while admitting that the comparison is not apples-to-apples.

This kind of comparison isn't a new idea. I think Hans Moravec[a] was the first to start making these kinds of machine-to-organic-brain comparisons, back in the 1990's, using "millions of instructions per second" (MIPS) and "megabytes of storage" as his units.

You can read Moravec's reasoning and predictions here:

https://www.jetpress.org/volume1/moravec.pdf

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[a] https://en.wikipedia.org/wiki/Hans_Moravec

Your "not apples to apples" concession isn't adequate. You are essentially still saying that a machine running a neural network is compare to the brain of an animal or a person - just maybe different units of measurement. But they're not. It's a matter of dramatically different computing systems, systems that operate very differently (well, don't know exactly how animal brains work but we know enough to know they don't work like GPUs).

Your Moravec article is only looking at what's necessary for computers to have the processing power of animal brains. But you've been up and down this thread arguing that equivalent processing power could be sufficient for a computer to achieve the intelligence of an animal. Necessary vs sufficient is big distinction.

It might be sufficient. We do not know. We have no way of knowing.

Given their current scale, I don't think we can judge whether current AI systems "represent a dead end" -- or not.

I think he was approaching the concept from the direction of "how many mips and megabytes do we need to create human level intelligence".

That's a different take than "human level is this many mips and megabytes", i.e. his claims are about artificial intelligence, not about biological intelligence.

The machine learning seems to be modeled after the action potential part of neural communication. But biological neurons can communicate also in different ways, i.e. neuro transmitters. Afaik this isn't modeled in the current ml-models at all (neither do we have a good idea how/why that stuff works). So ultimately it's pretty likely that a ml with a billion parameters does not perform the same as an organic brain with a billion synapses

I never claimed the machines would achieve "human level," however you define it. What I actually wrote at the root of this thread is that we have no way of knowing in advance what the future capabilities of these AI systems might be as we scale them up.

Afaict OP's not comparing neuron count, but neuron-to-neuron connections, aka synapses. And considering each synapse (weighted input) to a neuron performs computation, I'd say it's possible it captures a meaningful property of a neural network.

Most simple comparisons are flawed. Even just comparing the transistor counts of CPUs with vastly different architectures would be quite flawed.

It was clearly a mistake because people start attempting to make totally incoherent comparisons to rat brains.

excellent analogy. piggybacking on this: a lot of believers (as they are like religious fanatics) claim that more data and hardware will eventually make LLMs intelligent, as if it's even the neuron count matters. There is no other animal close to humans in intelligence, and we don't know why. Somehow though a random hallucinating LLMs + shit loads of electricity would figure it out. This is close to pure alchemy.

I don’t disagree with your main point but I want to push back on the notion that “there is no other animal close to humans in intelligence”. This is only true in the sense that we humans define intelligence in human terms. Intelligence is a very fraught and problematic concept both in philosophy, but especially in the sciences (particularly psychology).

If we were dogs surely we would say that humans were quite skillful, impressively so even, in pattern matching, abstract thought, language, etc. but are hopelessly dumb at predicting past presence via smell, a crow would similarly judge us on our inability to orient our selves, and probably wouldn’t understand our language and thus completely miss our language abilities. We do the same when we judge the intelligence of non-human animals or systems.

So the reason for why no other animal is close to us in intelligence is very simple actually, it is because of the way we define intelligence.

Interesting point. Though I would say that you didn't disprove my point. Humans have a level of generalized intelligence that's not matched. We might be terrible at certain sensory tasks (smell), maybe all, compared to another animal. But the capability of thought, at the level of humans, is unmatched.

Just to clarify one point: I don't think intelligence is exclusive to humans. I only think that there's a big discrepency that cannot be explained with neuron counts oor the volume of the brain etc. which makes the argument of more hardware and more data will create AGI.

Like I said the term is very fraught both in philosophy and the sciences. Many volumes have been written about this in philosophy (IMO the only correct outlet for the discussion) and there is no consensus on what to do with it.

My main problem with the notion of generalized intelligence (in philosophy; I have tons of problems with it in psychology) is it turns out to be rather arbitrary what counts towards general intelligence. Abstract thought and project planning seems to an essential component, but we have no idea how abstract thought and project planning goes on in non-human systems. In nature we have to look at the results and infer what the goals were with the behavior. No doubt we are missing a ton of intelligent behavior among several animals—maybe even pants and fungi—just because we don’t fully understand the goals of the organism.

That said though, I think our understanding of the natural world is pretty unparalleled by other species, and using this knowledge we have produced some very impressive intelligent behavior which no other species is capable of. But I have a hard time believing that humans are uniquely capable of this understanding nor of applying this understanding. For examples, elephants have shown they are capable of inter-generational knowledge and culture. I don’t know if elephants had access to the same instruments as we, that they would be able to pass this knowledge down generations on build up on them.

I agree with you fully. Thank you for the interesting discussion.

In a fictional scenario each dog might have enough brain power to simulate the entire universe including eight billion human brains and humans would still consider themselves more intelligent.

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