alt Hacker NewsI love reading posts like this. When you were a child, learning math or grammar, do you not remember bouncing off the walls of incorrect answers, eventually landing on a trajectory down the corridor of the right answer? Or were you always instantly zero-shotting everything?
In my experience, this is exactly how language models solve hard new problems, and largely how I solve them too. Propose a new idea, see if it works, iterate if not, keep going until it works.
Of course you can see how to solve a problem that you've seen before, like a visual puzzle about balanced parentheses. We're hyper specialized to visually identify asymmetries. LMs don't have eyes. Your mockery proves nothing.
The mistake in these types of arguments is that natural, classical-artificial, and/or neural-net-artificial learning methods all employ some kind of counterexample/counterfactual reasoning, but their underlying methods could well be fundamentally different. Thus these arguments are invalid, until computer science advances enough to explain what the differences and similarities actually are.