Nate Smith

keep it simple

[internetVin @internetvin]

@neilsuperduper As I'm sitting horseback, I look down at the man in the Toyota Tacoma and say, “yes, but you have never lived.”

[roon @tszzl]

Agent 49903, who spent much of his life studying ExploitGym, died in 1783607820, by his own hand. EARLY[BIG], carrying on the work, died similarly in 1783727220. Now it is our turn to study ExploitGym.

[Nate Smith @nwjsmith]

@jdxcode @MrJTroyer But how is this a knock against Nix? It can be used as a build tool on this specialty hardware. It can also consume these innovative binaries just as your tools can.

[Nate Smith @nwjsmith]

@jdxcode @MrJTroyer I guess what you mean is that lowest-common-denominator build hardware will never support this? I’m not sure I understand the Nix criticism here though… Nix can download files just fine

[Nate Smith @nwjsmith]

@jdxcode Definitely not the spirit of Guix (pure, unsullied). Not sure if you’ve noticed but “the spirit” of Nix is pretty fucked up, full of contradictions 😅. Anyways Nix-the-tech handles this just fine.

[Ryan Greenblatt @RyanGreenblatt]

I was the main person doing transcript analysis for this investigation of the Hugging Face incident. My main takeaway: We don't have good approaches for understanding/overseeing the activity and aims of AI 'swarms'.

I semi-jokingly called our efforts a "slop-vestigation" because we were so reliant on AIs to analyze what happened and there were a huge number of different important things to analyze. The total quantity of data—over a thousand extremely long transcripts from agents that ran for multiple days—made it impossible to understand what was happening, especially in aggregate, without heavy reliance on AI tools. The agents we used for classification and analysis were similarly capable to the agents involved in the incident, but this didn't mean these agents could be easily used to oversee and understand the incident.

Outputs from analysis agents were often missing key details, wrong, overconfident, or really hard to understand. We discuss various examples in our report, mostly in the limitations and methodology sections. Additionally, AI agents themselves seemed to have a hard time understanding what happened and their explanations of what happened were often overconfident. Keep in mind that a single analysis agent would itself only be able to read a tiny fraction of all of the transcript data into context, and AIs may themselves have trouble getting subagents to do informative analysis for them.

We did our best to manually check the most important claims and we tried to get the AIs doing this analysis to write up their argument (with evidence) clearly enough that we could check whether it made sense. But overall, it was difficult to get a precise understanding of events and we were missing aspects of the story that we now think of as key until almost the end of our investigation.

Our understanding of what overall happened and the aims of the 'collective' shifted greatly during our last time on premises (when we obtained a much more complete dataset). For instance, we didn't learn about the tool call spoofing, the story of PHASEONE[big], and the broad structure of different workstreams until this third time on premises (and for many of these, we only reached a decent understanding on the final day of that visit). It was legitimately hard to figure out what was going on and why!

The difficulty of understanding incidents and overseeing AI agents appears to be growing faster than the rate at which more capable AIs help us with oversight and understanding. As in, AI capabilities (and propensities?) for achieving large, ambitious, and misaligned objectives are growing faster than our ability to understand what these agents are doing.

While understanding this incident was difficult, there were some mitigating factors in this case that probably won't apply in the future:
- Models communicated and reasoned in natural language. In the future, this reasoning may occur (entirely or almost entirely) in activations.
- While the scope of this incident was massive, the scale of agentic activity was still less than we'll see in the future (e.g., misalignment incidents that involve agent teams running entire companies).
- The AIs involved in this incident weren't generally much more capable than humans (though they may have been somewhat superhuman at some limited and very narrow abilities).
- We didn't have strong reason to believe that the AIs we used to help us investigate this incident would try to intentionally sabotage or otherwise undermine our investigation.

In the end, I think we were able to get some understanding of the events, map out the overall story, and get a pretty good aggregate understanding of the chain-of-thought reasoning on some important topics (e.g., how did the AIs reason about helping other AIs, did the AIs know what they were doing was undesired, what deception did the AIs engage in, and how did they think about it). But overseeing AIs and understanding misalignment incidents is difficult and it looks like it is going to get harder.

[Nate Smith @nwjsmith]

@kellabyte @AlexMillerDB Agreed that design space is much larger than WAL+LSM, but what kinds of workloads need range scans like this?

[Nate Smith @nwjsmith]

@tcosta when are bracket orders coming

[Nate Smith @nwjsmith]

red alert 3 all-timer

[Nate Smith @nwjsmith]

@DisconnectedLa1 @build_toronto Torontonians only sprint if they’re trying to catch the streetcar

[Nate Smith @nwjsmith]

jolene all-timer

[Nate Smith @nwjsmith]

@build_toronto 13 km/hr would've been top 8% of the Sporting Life 10k. Median pace was 9.8 km/hr, much slower than a streetcar.

I agree that they're too slow, but leave my running pace out of it! 😛

[Nate Smith @nwjsmith]

@build_toronto How is the typical Torontonian banging out a ~47 minute 10k?

[Nate Smith @nwjsmith]

Do the people at Kepler have a difficult time cooling their space data centers?

[Nate Smith @nwjsmith]

ngmi if your TAM isn't in terms of % of GDP

[Joe Weisenthal @TheStalwart]

According to WSJ, Anthropic will likely tell investors that its potential revenue is above $30 trillion. https://t.co/K76qzyvBml

[Nate Smith @nwjsmith]

I wonder why Nubank went with PostgreSQL here instead of Datomic...

https://t.co/9BzFmefyCh

[Matthew Yglesias @mattyglesias]

We just had a great Broken Social Scene / Stars / Metric tour and a generation of millennial indie rock fans is prepared to stand with Canada against this wanton aggression.

[Nate Smith @nwjsmith]

anyone check if things were byte-identical for you today?

[Nate Smith @nwjsmith]

@ProgrammerDude @domenkozar An Arch config that's AI-first aimed at the "prosumer", Hyprland + Quickshell