Wrote about the crazy revelations in the METR report on Hugging Face (including one that corrected something I'd been getting wrong), and the growing industry push for a slowdown
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If that’s true there are two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad!
It is highly fortunate that the OpenAI agents hacked HuggingFace. This is the only reason we know about all the severe internal failures at OpenAI, and gives us an opportunity to wake up before it is too late.
In this case, however, the agents did not copy their weights, attempt to procure replacement compute, or take other steps that would be rational to take if their objective was to survive shutdown. So while the agents in the OpenAI-Hugging Face Incident were rogue, they were not truly sovereign.
however at any given point in time I think the systems we need to worry most about by far are the frontier systems. By the time open source systems can do something like the hugging face attack, frontier systems are going to be on a whole another level doing something even crazier than that.
So I think one of the most comforting aspects of this situation or the like most important mitigating factor is these agents really didn't seem concerned with humans one way or another.
A less bad version of it is known to have happened, and from the outside it seems likely that worse things have happened internally that we never heard about.
Overall, I’m very surprised at how little media coverage there’s been around the OpenAI / Hugging Face attack. It’s clearly one of the most important things to happen this year.
By 2027, almost every country-and every non-state group with sufficient computing power-will have the ability to download open-weight models even more powerful than the ones that attacked Hugging Face and use them for whatever they like.
the opposition to adjustments is entirely instrumental: climate skeptics like the results that the raw, biased data give them for the US, and they don't like the results that bias-corrected data give them.
And when you make a mistake and correct it, well, first you you've achieved the task because you've corrected, but you've also gained knowledge that allows you to avoid that mistake in the future. With driving, because of the dynamics of how it's set up, it's very hard to make a mistake, correct it, and then learn from it because the mistakes themselves have significant ramifications.
When AWS ships a service which is half-baked, it diminishes customer trust in AWS as a whole; even if the problems in that service ultimately get corrected (either by fixing them or in some cases by simply getting rid of a service which should never have existed in the first place) the memory of a failed launch will live on in customers' minds for years to come.
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