Every learning algorithm is just a prior. Generalization is Bayesian inference over the space of computable models. Everything else is an approximation.
I will soon be introducing a bill to give the public a 50% ownership stake in the largest AI companies in America.
This would guarantee that the trillions created by AI are used to improve the lives of all of us — and block oligarch decisions that harm the American people.
I’ve always believed the No.1 application of AI should be to improve human health.
That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease!
We are turbocharging that goal with $2.1B in new funding.
@xiaosun86@Figure_robot why would that be an RL artifact if the nodding behavior didn't improve their cumulative reward? it's probably something different, like rewarding human legibility. maybe it's not even being explicitly rewarded but their wireless communication is limited or not in the RL loop.
@asknbid@Grady_Booch@rafacastilloc@claudeai there's a lot of interesting discussion on "mortal compution" (see: human brain) especially for learning algorithms. a learning algorithm that can exploit the analog properties of its computing substrate can be far more efficient albeit you lose the ability to copy+run anywhere.
@asknbid@Grady_Booch@rafacastilloc@claudeai have a passing familiarity with EBMs (yann lecun is/was working on them). i think i'll use this opportunity to read the article and learn more about them. thank for the link.
@asknbid@Grady_Booch@rafacastilloc@claudeai haven't went nearly as far as you (curious to hear more), but computation seems strangely dependent on the relationship between an observer and a process said to be computing. is there a way define computation without making every physical process satisfy the definition?
@BartenOtto hrm... a lot to unpack there. "secure" software is another kind of alignment problem? often don't even know what we want our software to do. (conversely, if you can precisely define the problem, the solution is often trivial)
@euxoa@salinasdanielf@ChrisHayduk looped LM seems independent of CoT. CoT is amenable to rewarding sequences of outputs, looped LM is still pretraining where you're trying to learn the training distribution with standard loss. likely though there's no hard boundary and i'm not really sure how to think about it.
“A whole civilization will die tonight, never to be brought back again.” -Trump
Article III
The following acts shall be punishable:
(a) Genocide;
(b) Conspiracy to commit genocide;
(c) Direct and public incitement to commit genocide;
(d) Attempt to commit genocide;
(e) Complicity in genocide.
@fchollet NFL: 7+-2 is an inductive bias tuned to the ancestral niche, not a universal optimum. AlphaFold works because the answer lives in dimensions we can't fit through working memory. We just get feelings. Sample efficiency that can't grok what it builds is a strange kind of optimal.
@ESYudkowsky Read the Chinese Room 20+ years ago. My reaction: treat the man as a neuron. No neuron understands Chinese. The argument draws force from making you identify with the part instead of the whole. Classic fallacy of composition. The topic is still confusing, but the argument isn't.
200+ Google and OpenAI staff have signed this petition to share Anthropic's red lines for the Pentagon's use of AI
let's find out if this is a race to the top or the bottom
https://t.co/3qgmaLfM0i