Andreas Stuhlmüller

Researcher at Ought

Wiki Contributions


Competition: Amplify Rohin’s Prediction on AGI researchers & Safety Concerns

Rohin has created his posterior distribution! Key differences from his prior are at the bounds:

  • He now assigns 3% rather than 0.1% to the majority of AGI researchers already agreeing with safety concerns.
  • He now assigns 40% rather than 35% to the majority of AGI researchers agreeing with safety concerns after 2100 or never.

Overall, Rohin’s posterior is a bit more optimistic than his prior and more uncertain.

Ethan Perez’s snapshot wins the prize for the most accurate prediction of Rohin's posterior. Ethan kept a similar distribution shape while decreasing the probability >2100 less than the other submissions.

The prize for a comment that updated Rohin’s thinking goes to Jacob Pfau! This was determined by a draw with comments weighted proportionally to how much they updated Rohin’s thinking.

Thanks to everyone who participated and congratulations to the winners! Feel free to continue making comments and distributions, and sharing any feedback you have on this competition.

Ought: why it matters and ways to help

Thanks for this post, Paul!

NOTE: Response to this post has been even greater than we expected. We received more applications for experiment participant than we currently have the capacity to manage so we are temporarily taking the posting down. If you've applied and don't hear from us for a while, please excuse the delay! Thanks everyone who has expressed interest - we're hoping to get back to you and work with you soon.

Factored Cognition

What I'd do differently now:

  • I'd talk about RL instead of imitation learning when I describe the distillation step. Imitation learning is easier to explain, but ultimately you probably need RL to be competitive.
  • I'd be more careful when I talk about internal supervision. The presentation mixes up three related ideas:
    • (1) Approval-directed agents: We train an ML agent to interact with an external, human-comprehensible workspace using steps that an (augmented) expert would approve.
    • (2) Distillation: We train an ML agent to implement a function from questions to answers based on demonstrations (or incentives) provided by a large tree of experts, each of which takes a small step. The trained agent is a big neural net that only replicates the tree's input-output behavior, not individual reasoning steps. Imitating the steps directly wouldn't be possible since the tree would likely be exponentially large and so has to remain implicit.
    • (3) Transparency: When we distill, we want to verify that the behavior of the distilled agent is a faithful instantiation of the behavior demonstrated (or incentivized) by the overseer. To do this, we might use approaches to neural net interpretability.
  • I'd be more precise about what the term "factored cognition" refers to. Factored cognition refers to the research question whether (and how) complex cognitive tasks can be decomposed into relatively small, semantically meaningful pieces. This is relevant to alignment, but it's not an approach to alignment on its own. If factored cognition is possible, you'd still need a story for leveraging it to train aligned agents (such as the other ingredients of the iterated amplification program), and it's of interest outside of alignment as well (e.g. for building tools that let us delegate cognitive work to other people).
  • I'd hint at why you might not need an unreasonably large amount of curated training data for this approach to work. When human experts do decompositions, they are effectively specifying problem solving algorithms, which can then be applied to very large external data sets in order to generate subquestions and answers that the ML system can be trained on. (Additionally, we could pretrain on a different problem, e.g. natural language prediction.)
  • I'd highlight that there's a bit of a sleight of hand going on with the decomposition examples. I show relatively object-level problem decompositions (e.g. Fermi estimation), but in the long run, for scaling to problems that are far beyond what the human overseer could tackle on their own, you're effectively specifying general algorithms for learning and reasoning with concepts, which seems harder to get right.