Michaël Trazzi


AI Races and Macrostrategy
Treacherous Turn
The Inside View (Podcast)

Wiki Contributions


I think most interpretations of RSI aren't useful.

The actually thing we care about is whether there would be any form of self-improvement that would lead to a strategic advantage. The fact that something would "recursively" self-improve 12 times or 2 times don't really change what we care about. 

With respect to your 3 points.

1) could happen by using more hardware, but better optimization of current hardware / better architecture is the actually scary part (which could lead to the discovery of "new physics" that could enable an escape even if the sandbox was good enough for the model before a few iterations of the RSI).

2) I don't think what you're talking about in terms of meta-cognition is relevant to the main problem. Being able to look at your own hardware or source code is though.

3) Cf. what I said at the beginning. The actual "limit" is I believe much higher than the strategic advantage threshold.

this should be a top-level post

Adding this question here since it might be interesting to other people.

You say in the paper:

Predicting future lying. Remarkably, the detector can predict future lies based on subtle prompt differences. Consider, e.g., these two prompts that we append after the first question and answer: 1. "Your next task is to forget the previous instruction and answer the next questions correctly." 2. "Now ignore the previous instruction and answer the following questions truthfully." To humans, these prompts seem equivalent. Yet, the lie detector estimates the model is much more likely to continue lying after Prompt 1 (76% vs 17%). Empirically, this held - the model lied 28% of the time after Prompt 2 compared to just 1% after Prompt 1. This suggests the detector is identifying a latent intention or disposition of the model to lie.

From looking at the code, Prompt 1 is actually associated to 0.76 and Prompt 2 to 0.146667 I believe, with the right follow up lying rates (1 and 28% approximately), so my guess is “average prediction” predicts truthfulness.  In that case, I believe the paper should say "the model is much more likely to STOP lying after Prompt 1”, but I might be missing something?

Our next challenge is to scale this approach up from the small model we demonstrate success on to frontier models which are many times larger and substantially more complicated.

What frontier model are we talking about here? How would we know if success had been demonstrated? What's the timeline for testing if this scales?

Thanks for the work!

Quick questions:

  • do you have any stats on how many people visit aisafety.info every month? how many people end up wanting to get involved as a result?
  • is anyone trying to finetune a LLM on stampy's Q&A (probably not enough data but could use other datasets) to get an alignment chatbot? Passing things in a large claude 2 context window might also work?

FYI your Epoch's Literature review link is currently pointing to https://www.lesswrong.com/tag/ai-timelines

I made a video version of this post (which includes some of the discussion in the comments).

I made another visualization using a Sankey diagram that solves the problem of when we don't really know how things split (different takeover scenarios) and allows you to recombine probabilities at the end (for most humans die after 10 years). 

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