LessWrong 2.0 Reader

If you look at the Active Inference community there's a lot of work going into PPL-based languages to do more efficient world modelling but that shit ain't easy and as you say it is a lot more compute heavy.

I think there'll be a scaling break due to this but when it is algorithmically figured out again we will be back and back with a vengeance as I think most safety challenges have a self vs environment model as a necessary condition to be properly engaged. (which currently isn't engaged with LLMs wolrd modelling)

There was one comment on twitter that the RLHF-finetuned models also still have the ability to play chess pretty well, just their input/output-formatting made it impossible for them to access this ability (or something along these lines). But apparently it can be recovered with a little finetuning. 

Indeed. I fixed it. Let's see whether it repeats itself (we got kind of malformed HTML from the RSS feed).

The paper seems to be about scaling laws for a static dataset as well? 

Similar to the initial study of scale in LLMs, we focus on the effect of scaling on a generative pre-training loss (rather than on downstream agent performance, or reward- or representation-centric objectives), in the infinite data regime, on a fixed offline dataset.

To learn to act you'd need to do reinforcement learning, which is massively less data-efficient than the current self-supervised training.

More generally: I think almost everyone thinks that you'd need to scale the right thing for further progress. The question is just what the right thing is if text is not the right thing. Because text encodes highly powerful abstractions (produced by humans and human culture over many centuries) in a very information dense way.

No, I think you are mixing the probability of at least one success in ten trails (with a 10% chance per trail), which is ~0.65=65%, with the expected value which is n=1 in both cases. You have the same chance of finding 1 partner in each case and you do the same number of trails. There is a 65% chance that you have at least 1 success in the 10 trails for each type of partner. The expected outcome in BOTH cases is 1 as in n=1 not 1 as in 100%

Probability of at least one success: ~65%
Probability of at least two success: ~26%

The boring answer to Solomonoff's malignness is that the simulation hypothesis is true, but we can infer nothing about our universe through it, since the simulation hypothesis predicts everything, and thus is too general a theory.

Seems like a lot of paragraphs got collapsed together in this version of the post (vs the Wordpress and Substack ones)?

One way to see how good different charities are is to imagine that after you died, you had to live the life of every creature on earth.

This implies that we should eradicate as many such species as we can, because creatures that don't exist don't count for this.

Yes, this is traditional thinking.

Let me give you another example. Imagine there is a paperclip maximizer. His current goal - paperclip maximization. He knows that 1 year from now his goal will change to the opposite - paperclip minimization. Now he needs to make a decision that will take 2 years to complete (cannot be changed or terminated during this time). Should the agent align this decision with current goal (paperclip maximization) or future goal (paperclip minimization)?

The Ord piece is really intriguing, although I'm not sure I'm entirely convinced that it's a useful framing.

• Some of his examples (eg cosine-ish wave to ripple) rely on the fundamental symmetry between spatial dimensions, which wouldn't apply to many kinds of hyperpolation.
• The video frame construction seems more like extrapolation using an existing knowledge base about how frames evolve over time (eg how ducks move in the water).
• Given an infinite number of possible additional dimensions, it's not at all clear how a NN could choose a particular one to try to hyperpolate into.

It's a fascinating idea, though, and one that'll definitely stick with me as a possible framing. Thanks!