@theo you don’t need to look far, just look at frontier math. One I think of are the hilbert problems. You can translate these problems into code (initial state), llms can give you a satisfying initial condition (example code) and ofc it’s verifiable!
@PlastiqSoldier@Mihonarium One of the most important developments leading up to agi (if it’s even possible) would be recursive self improvement. anthropic engineers a yearish ago agreed with me that learning from user data could achieve that albeit to lesser extent. Likely changed minds since then
Many in my community hold Anthropic in high regard. Sadly, they should not. I wrote a post showing why.
Anthropic in its current form is not trustworthy. The leadership is sometimes misleading and deceptive; they contradict themselves and lobby against regulations just like everyone else, while not really being accountable to anyone except perhaps their investors.
The post discloses a number of facts that had not previously been reported on and combines them with publicly available information in an attempt to paint an image of Anthropic more accurate than the picture Anthropic’s leadership likes to present.
Read: https://t.co/IjKE5VQZC0
Singularity may look nothing like what industry leaders predict. The coming years will be pivotal in determining whether its trajectory benefits humanity as a whole or consolidates power for a select few corporations
session #2 of the NYC AI Reading Group w/ @qw3rtman@willcb is in order!
> where: @haizelabs hq
> when: thursday 5/29 @ 7:30 pm
> what: sft memorizes, rl generalizes: a comparative study of foundation model post-training
> who: awesome people like yourself :^)
> also: PIZZA🍕