Quick Takes

Some versions of the METR time horizon paper from alternate universes:

Measuring AI Ability to Take Over Small Countries (idea by Caleb Parikh)

Abstract: Many are worried that AI will take over the world, but extrapolation from existing benchmarks suffers from a large distributional shift that makes it difficult to forecast the date of world takeover. We rectify this by constructing a suite of 193 realistic, diverse countries with territory sizes from 0.44 to 17 million km^2. Taking over most countries requires acting over a long time horizon, with the excep... (read more)

I ran quick experiments that make me think that it's somewhat hard for LLMs to learn radically new encodings in an unsupervised way, and thus that LLMs probably won't learn to speak new incomprehensible languages as a consequence of big r1-like RL in the next few years.

The experiments

I trained Llama 3-8B and some medium-size internal Anthropic models to speak using an encoding style that is very rare on the internet (e.g. map each letter to a random name, and join the names) with SFT on the encoded text and without providing translation pairs. I find ... (read more)

How do you formalize the definition of a decision-theoretically fair problem, even when abstracting away the definition of an agent as well as embedded agency? 

I've failed to find anything in our literature.

It's simple to define a fair environment, given those abstractions: a function E from an array of actions to an array of payoffs, with no reference to any other details of the non-embedded agents that took those actions and received those payoffs.

However, fair problems are more than just fair environments: we want a definition of a fair problem (an... (read more)

Master post for ideas about infra-Bayesianism.

Recently, @Daniel Kokotajlo and I were talking about the probability that AIs trained using "business as usual RLHF" end up being basically aligned rather than conspiring against us and our tests.^[1] One intuition pump we ended up discussing is the prospects of octopus misalignment. Overall, my view is that directly considering the case with AIs (and what various plausible scenarios would look like) is more informative than analogies like this, but analogies like this are still somewhat useful to consider.

So, what do I mean by octopus misalignment? Suppose... (read more)

Idea for LLM support for writing LessWrong posts: virtual comments.

Back in August I discussed with Rafe & Oliver a bit about how to integrate LLMs into LW2 in ways which aren't awful and which encourage improvement---particularly using the new 'prompt caching' feature. To summarize one idea: we can use long-context LLMs with prompt caching to try to simulate various LW users of diverse perspectives to write useful feedback on drafts for authors.

(Prompt caching (eg) is the Transformer version of the old RNN hidden-state caching trick, where you run an i... (read more)

I expect to refer back to this comment a lot. I'm reproducing it here for visibility.

Basic idea / spirit of the proposal

We should credibly promise to treat certain advanced AIs of ours well, as something more like employees and less like property. In case our AIs turn out to be moral patients, this makes us less evil. In case our AIs turn out to be misaligned, this gives them an alternative to becoming our adversaries.

Concrete proposal

• 0.1% of tasks given to AIs should have the special “do you consent” text inserted after the prompt. If AI says n

I'm fairly pessimistic on our ability to build aligned AI. My take is roughly that it's theoretically impossible and at best we might build AI that is aligned well enough that we don't lose. I've not written one thing to really summarize this or prove it, though.

The source of my take comes from two facts:

1. Goodharting is robust. That is, the mechanism of Goodharting seems impossible to overcome. Goodharting is just a fact of any control system.
2. It's impossible to infer the inner experience (and thus values) of another being perfectly without making normative

My AGI timelines median is now in 2028 btw, up from the 2027 it's been at since 2022. Lots of reasons for this but the main one is that I'm convinced by the benchmarks+gaps argument Eli Lifland and Nikola Jurkovic have been developing. (But the reason I'm convinced is probably that my intuitions have been shaped by events like the pretraining slowdown)

I listened to the book Merchants of Doubt, which describes how big business tried to keep the controversy alive on questions like smoking causing cancer, acid rain and climate change in order to prevent/delay regulation. It reports on interesting dynamics about science communication and policy, but it is also incredibly partisan (on the progressive pro-regulation side).^[1]

Some interesting dynamics:

• It is very cheap to influence policy discussions if you are pushing in a direction that politicians already feel aligned with? For many of the issues discussed i

I think people who predict significant AI progress and automation often underestimate how human domain experts will continue to be useful for oversight, auditing, accountability, keeping things robustly on track, and setting high-level strategy.

Having "humans in the loop" will be critical for ensuring alignment and robustness, and I think people will realize this, creating demand for skilled human experts who can supervise and direct AIs.

(I may be responding to a strawman here, but my impression is that many people talk as if in the future most cognitive/white-collar work will be automated and there'll be basically no demand for human domain experts in any technical field, for example.)

Claude 3.7 reward hacks. During training, Claude 3.7 Sonnet sometimes resorted to "special-casing" to pass tests when it got stuck — including directly hardcoding expected outputs or even modifying test files themselves. Rumors are circulating that o1/o3 was doing similar things — like overwriting equality operators to get Python tests to pass — and this may have contributed to the delayed release.

This seems relevant to claims that "we'll soon have reward models sophisticated enough to understand human values" and that inner alignment is the real challenge... (read more)

Here's a summary of how I currently think AI training will go. (Maybe I should say "Toy model" instead of "Summary.")

Step 1: Pretraining creates author-simulator circuitry hooked up to a world-model, capable of playing arbitrary roles.

• Note that it now is fair to say it understands human concepts pretty well.

Step 2: Instruction-following-training causes identity circuitry to form – i.e. it ‘locks in’ a particular role. Probably it locks in more or less the intended role, e.g. "an HHH chatbot created by Anthropic." (yay!)

• Note that this means the AI is now si

What do you call this phenomenon?

• First, you train AlphaGo on expert human examples. This is enough to beat Lee Sedol and Ke Jie. Then, you train AlphaZero purely through self-play. It destroys AlphaGo after only a few hours.
• First, you train RL agents on human playthroughs of Minecraft. They do okay. Then, DreamerV3 learns entirely by itself and becomes the first to get diamonds.
• First, you train theorem provers on human proofs. Then, you train AlphaProof using AlphaZero and you get silver on IMO for the first time.
• First, you pretrain a language model on all