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RussellThor

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Putting down a prediction I have had for quite some time.
The current LLM/Transformer architecture will stagnate before AGI/TAI (That is the ability to do any cognitive task as effectively and cheaper than a human)

From what I have seen, Tesla autopilot learns >10,000 slower than a human datawise.

We will get AGI by copying nature, at the scale of a simple mammal brain, then scaling up, like this kind of project:

https://x.com/Andrew_C_Payne/status/1863957226010144791
https://e11.bio/news/roadmap
I expect AGI to be 0-2 years after a mammal brain is mapped. In terms of cost-effectiveness I consider such a connectome project to be far more cost effective per $ than large training runs or building a 1GW data center etc if you goal is to achieve AGI.

That is TAI by about 2032 assuming 5 years to scan a mammal brain. In this case there could be a few years when Moores law has effectively stopped, larger data centers are not being built and it is not clear where progress will come from.

3RussellThor2y

Perhaps LLM will help with that. The reason I think that is less likely is 1. Deep mind etc is already heavily across biology from what I gather from interview with Demis. If the knowledge was there already there's a good chance they would have found it 2. Its something specific we are after, not many small improvements, i.e. the neural code. Specifically back propagation is not how neurons learn. I'm pretty sure how they actually do is not in the literature. Attempts have been made such as the forward-forward algorithm by Hinton, but that didn't come to anything as far as i can tell. I havn't seen any suggestion that even with too much detail on biology we know what it is. i.e. can a very detailed neural sim with extreme processing power learn as data efficiently as biology? 3. If progress must come from a large jump rather than small steps, then LLM have quite a long way to go, i.e. LLM need to speed up coming up ideas as novel as the forward-forward algo to help much. If they are still below that threshold in 2026 then those possible insights are still almost entirely done by people. 4. Even the smartest minds in the past have been beaten by copying biology in AI. The idea for neural nets came from copying biology. (Though the transformer arch and back prop didn't)

3Nathan Helm-Burger2y

I've heard this viewpoint expressed before, and find it extremely confusing. I've been studying neuroscience and it's implications for AI for twenty years now. I've read thousands of papers, including most of what DeepMind has produced. There's still so many untested ideas because biology and the brain are so complex. Also because people tend to flock to popular paradigms, rehashing old ideas rather than testing new ones. I'm not saying I know where the good ideas are, just that I perceive the explored portions of the Pareto frontier of plausible experiments to be extremely ragged. The are tons of places covered by "Fog of War" where good ideas could be hiding. DeepMind is a tiny fraction of the scientists in the world that have been working on understanding and emulating the brain. Not all the scientists in the world have managed to test all the reasonable ideas, much less DeepMind alone. Saying DeepMind has explored the implications of biology for AI is like saying that the Opportunity Rover has explored Mars. Yes, this is absolutely true, but the unexplored area vastly outweighs the explored area. If you think the statement implies "explored ALL of Mars" then you have a very inaccurate picture in mind.

OK fair point. If we are going to use analogies, then my point #2 about a specific neural code shows our different positions I think.

Lets say we are trying to get a simple aircraft of the ground and we have detailed instructions for a large passenger jet. Our problem is that the metal is too weak and cannot be used to make wings, engines etc. In that case detailed plans for aircraft are no use, a single minded focus on getting better metal is what its all about. To me the neural code is like the metal and all the neuroscience is like the plane schematics. ... (read more)