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The Who's Who of AI

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Established AI field signal Analysis 19d ago
⚡ 7 h early
AI development pace essay

In AI, Nothing Ever Happens. Wait, It’s Happening!

3 experts across 3 network communities independently surfaced this.

3 experts 3 communities 1 sources clustered
Maxim Raginsky John Herrman Colin

“One can always count on @jwherrman.bsky.social to make good sense of what’s happening in and around AI:”

“The AI industry is prone to massive, frequent, and intensifying narrative mood swings. Part of this is typical of a boom. But I think it's also intrinsic to the tech itself nymag.com/intelligence...”

3 experts discussed this · 9 posts
John Herrman: The AI industry is prone to massive, frequent, and intensifying narrative mood swings. Part of this is typical of a boom. But I think it's also intrinsic to the tech itself nymag.com/intelligence...
John Herrman: The constant, direct experience of enchantment, disenchantment, and re-enchantment with AI — including at the highest levels of the industry — helps explain a lot about the last few years, and what…
John Herrman: this is basically unhelpful as a whole (of course you/we want to maintain control over perceptions of tech). but for certain actors, in particular positions, being maximally open to technological e…
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Primary source nymag.com ↗
Debates gaining attention

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4 experts · 20 posts · 13d ago
Eugene Vinitsky Colin rev. howard arson Mary Branscombe
Multiple readings Policy & governance · 2 Research & technical analysis · 1 Questions & unknowns · 1
rev. howard arson: working on this in 2019, the reason we delayed releasing any LLMs to the public was specifically because "if we fuck this up or handle it irresponsibly, we will ruin first the web, then society, an…
rev. howard arson: 2022, almost instantaneously, coinciding with Sergey Brin having gone completely fucking insane during the pandemic and then returning to involvement at Alphabet. (he spent almost a decade prior hi…
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4 experts · 13 posts · 15d ago
SE Gyges Colin Carlos Scheidegger Doll
Shared emphasis Research & technical analysis · 2
SE Gyges: the thing about LLMs training on LLM output messing them up isn't true btw. provided the LLM doing the output is good and it is reacting to diverse prompts, this is extremely delicious data
SE Gyges: we train them on their own outputs a lot right now! whether this is eliciting skills they already have or creates new ones is controversial. i am on the second team
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3 experts · 14 posts · 6d ago
Ben Recht Benjamin Riley Colin
Shared emphasis Questions & unknowns · 2
Colin: It seems like there are a number of discoveries, like solutions to quasi-famous (but not too famous) open problems in math and 0day security vulnerabilities, that are low-hanging fruit in the sense…
Colin: I used to be skeptical of the billion-phds-in-a-data-center idea, and I still am, on the grounds that it’s not really a billion phds, it’s a billion identical copies of the same phd, who’s going to…
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4 experts · 7 posts · 18d ago
Christian Wolf David Picard John Herrman Colin
Multiple readings Building & implementation · 1 Research & technical analysis · 1
Christian Wolf: To me this is not new and not surprising: (Applied) ML always had a very strong experimental component and finding out what trained models can do is akin to studying biological systems. This has on…
David Picard: To me this is not new and not surprising: (Applied) ML always had a very strong experimental component and finding out what trained models can do is akin to studying biological systems. This has on…
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