smountjoy

"Superalignment" is (was?) OpenAI's term, so it might be more accurate to say he is joining Anthropic to work on alignment.

No doubt leadership at OpenAI, Google, and Microsoft are in favor of any regulations that would cement their lead.

For the rest of us, the relevant question is: are the costs worth the benefits?

> The risk [...] is that radically new products and approaches in the arena never get a chance to be developed and benefit consumers.

Totally. And the opposite risk is that these companies deploy technologies that cause massive harm to people, without adequate testing, because they're caught up in a race.

I think some regulations would be helpful on balance -- like reporting of large training runs, as in SB 1047 -- and some wouldn't -- like (hypothetically) requiring a license to train small models.

I like to imagine that, as in Ender's Game, the top-ranked player is unknowingly allocating memory for some critical infrastructure somewhere.

You're right that LLMs can produce new knowledge, but you're misinterpreting the quote. The claim in the article is that LLMs won't produce anything new when asked for "a paper about Middlemarch". The rest of the article goes on to demonstrate how language models can create new knowledge.

Wouldn't we just get pop-ups saying, "please enable X-Allow-Tracking for this website"? Same thing that some websites do in response to ad blockers.

The difference in running time grows exponentially with the size of the input. I'd say that's "literally" exponential.

Interesting! I'd like to see a version where the double-time track is fading in continuously, instead of arriving all at once at the beginning of a measure (which would be a closer analogue of the Shepard Tone). Can't quite tell if the other reply is an example of that.

OP agrees with you: antithesis of non-public = public.