So predicting the next word given all humanity’s knowledge is surely going to max out at slightly less good (we probably can’t get perfect data) than the best human in any specific field. What test does the AI do to be able to understand it is improving? At some point it becomes impossible to know that the output is actually better right?
All planners start with executing just the next step. You do too. You might be planning long term but you execute just one set of things at the current moment.
Deep NNs and LLMs should not work based on our theoretical understanding. The fact that they do should give us a pause instead of us flatly denying their unexpected performance.
Why would deep NNs or LLMs not work based on our theoretical understanding? What theoretical understanding are you referring to?
Tasks like using a CNN to detect digits has been well understood since the 2000s. The explosion in the capability of LLMs is very surprising, sure, but where is the concrete proof that such systems "should not work"?
andy_ppp · · focus · HN ↗
whatever1 · · focus · HN ↗
Deep NNs and LLMs should not work based on our theoretical understanding. The fact that they do should give us a pause instead of us flatly denying their unexpected performance.
kmeh · · focus · HN ↗
Tasks like using a CNN to detect digits has been well understood since the 2000s. The explosion in the capability of LLMs is very surprising, sure, but where is the concrete proof that such systems "should not work"?
chrisjj · · focus · HN ↗
token != step.
Just you try executing a complex command one word at a time.