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AI one-percenters seizing power forever is the real doomsday scenario, warns AI godfather
(www.businessinsider.com)
this post was submitted on 30 Oct 2023
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Either ML is going to scale in an unpredictable way, or it is a complete dead end when it comes to artificial intelligence. The “godfathers” of ai know it’s a dead end.
Probabilistic computing based on statistical models has value and will be useful. Pretending it is a world changing AI tech was a grift from day 1. The fact that art, that cannot be evaluated objectively, was the first place it appeared commercially should have been the clue.
That is literally modelling how your and all our brains work, so no, neuromorphic computing / approximate computing is still the way to go. It's just that neuromorphic computing does not necessarily equal LLMs. Paired with powerful mixed analogue and digital signal chips based on photonics, we will hopefully at some point be able to make neural networks that can scale the simulation of neurons and synapses to a level that is on par or even superior to thr human brain.
A claim that we have a computing model that shares a design with the operation of a biological brain is philosophical and conjecture.
If we had a theory of mind that was complete, it would simply be a matter of counting up the number of transistors required to approximate varying degrees of intelligence. We do not. We have no idea how the computational meat we all possess enables us to translate sensory input into a contiguous sense of self.
It is totally valid to believe that ML computing is a match to the biological model and that it will cross a barrier at some point. But it is a belief that does not support itself with empirical evidence. At least not yet.
Mathematical actually. See the 1943 McCulloch and Pitts paper for why Neural networks are called such.
We use logic and math to approximate neurons
We have also recently trained a model against a small fly or worm, I can't remember which it was, and it behaved identically to the original organism, it's the complicated networks which have multiple sub networks essentially that are our current weak spot.