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The Who's Who of AI

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5 experts · 27 posts · 3d ago
Maxim Raginsky Ryan Moulton Colin tweety fish Marco Z
Matches Markets & investment
“100%. It leaves a little trail of legible story everywhere it goes. Investigating it is a constant Stroop test.” evidence ↗
Multiple readings Concern & critique · 2 Research & technical analysis · 1
tweety fish: here is a succinct thing I can say: "alignment"-style AI safety risks DO NOT EXIST or need remediation with LLMs as such (there are lots of other problems) and OpenAI and Anthropic are in the busin…
tweety fish: like this, I think, maybe sounds weird, but is in a strong sense literally true.
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3 experts · 19 posts · 5d ago
Colin tachikoma coty.bsky.social
Matches Markets & investment
“how do you know when to step up the security measure? how early do you invest in new infra, and what if that slows you down while everyone else operates unencumbered?” evidence ↗
Multiple readings Concern & critique · 1 Markets & investment · 1 Questions & unknowns · 1
Colin: They’re going to try to say that the agents are so smart that there’s simply nothing anyone could have done to prevent this and this is straightforwardly false. All of this was easy to prevent and …
Colin: Here is a tricky nuance that I’m not seeing a lot of people get right. It can simultaneously be the case that the agents got out of the sandbox in ways you didn’t anticipate AND you could have prev…
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4 experts · 6 posts · 3d ago
Colin tachikoma Max Woolf Matt Darling
Matches Markets & investment
“debatable because it wasn't obvious at the time LLMs would generalize to do 'real work' that would justify investment into scaling them up” evidence ↗
Multiple readings Markets & investment · 1 Research & technical analysis · 1
Matt Darling: It's like how Nokia invented cell phones so people could order toilet paper while they were on the toilet, then pivoted to cell phones only as demand grew.
tachikoma: debatable because it wasn't obvious at the time LLMs would generalize to do 'real work' that would justify investment into scaling them up
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3 experts · 11 posts · 5d ago
Eugene Vinitsky Colin Andrea Lathrop
Matches Markets & investment
“Like, what on earth is this reward function that returns reward for unintended infrastructure probing. Would you have predicted it would do that? You could, if you understood the reward-learning mi…” evidence ↗
Multiple readings Research & technical analysis · 1 Questions & unknowns · 1
Colin: well, I mean, I'm sorry but yes you can
Colin: the training data is really the only place that it can come from (acknowledging that RL feedback is itself training data)
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