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:
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.
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.
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 ?