zlacker

>>davidb+(OP)
Ilya booted him https://twitter.com/karaswisher/status/1725702501435941294

>>johnwh+Uc1
Jeremy Howard called ngmi on OpenAI during the Vanishing Gradients podcast yesterday, and Ilya has probably been thinking the same: LLM is a dead-end and not the path to AGI.

https://twitter.com/HamelHusain/status/1725655686913392933

>>dwd+zL1
Did we ever think LLMs were a path to AGI...? AGI is friggin hard, I don't know why folks keep getting fooled whenever a bot writes a coherent sentence.

>>erhaet+1O1
It's mostly a thing among the youngs I feel. Anybody old enough to remember the same 'OMG its going to change the world' cycles around AI every two or three decades knows better. The field is not actually advancing. It still wrestles with the same fundamental problems they were doing in the early 60s. The only change is external, where computer power gains and data set size increases allow brute forcing problems.

>>Rugged+9P1
Deep learning was an advance. I think the fundamental achievement is a way to use all that parallel processing power and data. Inconceivable amounts of data can give seemingly magical results. Yes, overfitting and generalizing are still problems.

I basically agree with you about the 20 year hype-cycle, and but when compute power reaches parity with human brain hardware (Kurzweil predicts by about 2029), one barrier is removed.

>>hypert+Uq2
Human and computer hardware are not comparable, after all even with the latest chips the computer is just (many) von Neumann machine(s) operating on a very big (shared) tape. To model the human brain in such a machine would require the human brain to be discretizable, which, given its essentially biochemical nature, is not possible - certainly not by 2029.

>>somewh+xM2
It depends on the resolution of discretization required. Kurzweil's prediction is premised on his opinion of this.

Note that engineering fluid simulation (cfd) makes these choices in discretization of pde's all the time, based on application requirements.