Comment by pfannkuchen
7 hours ago
Okay so by the same logic can’t we say that we can implement human intelligence on a 90s era single core processor? Its instruction set is Turing complete! Now all that’s left is we just have to figure out how the brain works!
Turing completeness applies to a model of computation, not to a physical instantiation of a machine. The stumbling block of "figure out how the brain works" applies more to the argument like the one I was responding to. How a person can know that a general model of computation can't implement the way people think, if we don't know how people think?
The existing LLM training methods on the other hand give the results that are hard to distinguish from "thinking like people," judging by the end results.
So your argument is that scale is also necessary? I can see that, we don’t expect that a single neuron is human intelligence.
That's not the counterargument one might wish, as LLM deep nets are actually implemented on von Neumann hardware, without true understanding of natural intelligence, just our taking inspiration from neurobiology.
The connectionist models are basically a proposed highest possible abstraction of naturally evolved intelligences so it is in retrospect not surprising that passing some hardware scaling threshold they will start doing things that humans and animals do
It's more that formal Turing equivalence plus the Church-Turing thesis tells us that we're not allowed to assume counterarguments based on magic, there's no magic sauce barrier that prevents AI from running on CPU models. The algorithms exist and most of us thought discovering them would be hard.
The empirical surprise was that human intelligence is maybe not that computationally complex after all. (The entirety of academia was basically caught off guard.) That's one not unreasonable interpretation given recent events.