[uh_uh]

We don't really have a clue what they are and aren't capable of. Prior to the LLM-boom, many people – and I include myself in this – thought it'd be impossible to get to the level of capability we have now purely from statistical methods and here we are. If you have a strong theory that proves some bounds on LLM-capability, then please put it forward. In the absence of that, your sceptical attitude is just as sus as the article's.

[Baeocystin]

I majored in CogSci at UCSD in the 90's. I've been interested and active in the machine learning world for decades. The LLM boom took me completely and utterly by surprise, continues to do so, and frankly I am most mystified by the folks who downplay it. These giant matrixes are already so far beyond what we thought was (relatively) easily achievable that even if progress stopped tomorrow, we'd have years of work to put in to understand how we got here. Doesn't mean we've hit AGI, but what we already have is truly remarkable.

[chihuahua]

The funny thing is that 1/3 of people think LLMs are dumb and will never amount to anything. Another third think that it's already too late to prevent the rise of superhuman AGI that will destroy humanity, and are calling for airstrikes on any data center that does not submit to their luddite rules. And the last third use LLMs for writing small pieces of code.

[Workaccount2]

Pretty much until 2022, the de facto orthodoxy for AI was "The creative pursuits will forever be outside the reach of computers".

People are pretty quiet about creative pursuits actually being the low hanging fruit on the AI tree.

[kubav027]

LLM also have no idea what it is capable of. This feels like difference to humans. Having some understanding of the problem also means knowing or "feeling" the limits of that understanding.

[uh_uh]

1. Many humans don't have an idea of the limits of their competence. It's called the Dunning–Kruger effect.

2. LLMs regularly tell me if what I'm asking for is possible or not. I'm not saying they're always correct, but they seem to have at least some sense of what's in the realm of possibility.

[kubav027]

1. Dunning-kruger effect describes difference in expected and real performance. It is not saying that humans confidently give wrong answers if they do not know correct ones.

2. That is not my experience. Almost half of the time LLM gives wrong answer without any warning. It is up to me to check correctness. Even if I follow up it often continues to give wrong answers.

[uh_uh]

1. "It is not saying that humans confidently give wrong answers if they do not know correct ones." And I didn't say that they do either, so you might have hallucinated that.

2. What are you arguing about? I didn't say they're always correct obviously.