[klabb3]

> The LLM skeptics need to point out what differs with code compared to Chess, DoTA, etc from a RL perspective.

I see the burden of proof has been reversed. That’s stage 2 already of the hubris cycle.

On a serious note, these are nothing alike. Games have a clear reward function. Software architecture is extremely difficult to even agree on basic principles. We regularly invalidate previous ”best advice”, and we have many conflicting goals. Tradeoffs are a thing.

Secondly programming has negative requirements that aren’t verifiable. Security is the perfect example. You don’t make a crypto library with unit tests.

Third, you have the spec problem. What is the correct logic in edge cases? That can be verified but needs to be decided. Also a massive space of subtle decisions.

[zoogeny]

> I see the burden of proof has been reversed.

Isn't this just a pot calling the kettle black? I'm not sure why either side has the rightful position of "my opinion is right until you prove otherwise".

We're talking about predictions for the future, anyone claiming to be "right" is lacking humility. The only think going on is people justifying their opinions, no one can offer "proof".

[klabb3]

> Isn't this just a pot calling the kettle black?

New expression to me, thanks.

But yes, and no. I’d agree in the sense that the null hypothesis is crucial, possible the main divider between optimists and pessimists. But I’ll still hold firm that the baseline should be predicting that transformer based AI differs from humans in ability since everything from neural architecture, training, and inference works differently. But most importantly, existing AI vary dramatically in ability across domains, where AI exceeds human ability in some and fail miserably in others.

Another way to interpret the advancement of AI is viewing it as a mirror directed at our neurophysiology. Clearly, lots of things we thought were different, like pattern matching in audio- or visual spaces, are more similar than we thought. Other things, like novel discoveries and reasoning, appear to require different processes altogether (or otherwise, we’d see similar strength in those, given that training data is full of them).

[mrkstu]

I think the difference it that computers tend to be pretty good at thing we can do autonomically- ride a bike, drive a car in non-novel/dangerous sitations and things that are advanced versions of unreasoned speech - regurgitations/reformulations of things it can gather from a large corpus and cast into it’s neural net.

They fail at things requiring novel reasoning not already extant in its corpus, a sense of self, or an actual ability to continuously learn from experience, though those things can be programmed in manually as secondary, shallow characteristics.