davidad (David A. Dalrymple)

Programme Director at UK Advanced Research + Invention Agency focusing on safe transformative AI; formerly Protocol Labs, FHI/Oxford, Harvard Biophysics, MIT Mathematics And Computation.

Wiki Contributions


Paralysis of the form "AI system does nothing" is the most likely failure mode. This is a "de-pessimizing" agenda at the meta-level as well as at the object-level. Note, however, that there are some very valuable and ambitious tasks (e.g. build robots that install solar panels without damaging animals or irreversibly affecting existing structures, and only talking to people via a highly structured script) that can likely be specified without causing paralysis, even if they fall short of ending the acute risk period.

"Locked into some least-harmful path" is a potential failure mode if the semantics or implementation of causality or decision theory in the specification framework are done in a different way than I hope. Locking in to a particular path massively reduces the entropy of the outcome distribution beyond what is necessary to ensure a reasonable risk threshold (e.g. 1 catastrophic event per millennium) is cleared. A FEEF objective (namely, minimize the divergence of the outcomes conditional on intervention from the outcomes conditional on filtering for the goal being met) would greatly penalize the additional facts which are enforced by the lock-in behaviours.

As a fail-safe, I propose to mitigate the downsides of lock-in by using time-bounded utility functions.

It seems plausible to me that, until ambitious value alignment is solved, ASL-4+ systems ought not to have any mental influences on people other than those which factor through the system's pre-agreed goals being achieved in the world. That is, ambitious value alignment seems like a necessary prerequisite for the safety of ASL-4+ general-purpose chatbots. However, world-changing GDP growth does not require such general-purpose capabilities to be directly available (rather than available via a sociotechnical system that involves agreeing on specifications and safety guardrails for particular narrow deployments).

It is worth noting here that a potential failure mode is that a truly malicious general-purpose system in the box could decide to encode harmful messages in irrelevant details of the engineering designs (which it then proves satisfy the safety specifications). But, I think sufficient fine-tuning with a GFlowNet objective will naturally penalise description complexity, and also penalise heavily biased sampling of equally complex solutions (e.g. toward ones that encode messages of any significance), and I expect this to reduce this risk to an acceptable level. I would like to fund a sleeper-agents-style experiment on this by the end of 2025.

Re footnote 2, and the claim that the order matters, do you have a concrete example of a homogeneous ultradistribution that is affine in one sense but not the other?

For the record, as this post mostly consists of quotes from me, I can hardly fail to endorse it.

I think AI Safety Levels are a good idea, but evals-based classification needs to be complemented by compute thresholds to mitigate the risks of loss of control via deceptive alignment. Here is a non-nebulous proposal.

That’s basically correct. OAA is more like a research agenda and a story about how one would put the research outputs together to build safe AI, than an engineering agenda that humanity entirely knows how to build. Even I think it’s only about 30% likely to work in time.

I would love it if humanity had a plan that was more likely to be feasible, and in my opinion that’s still an open problem!

OAA bypasses the accident version of this by only accepting arguments from a superintelligence that have the form “here is why my proposed top-level plan—in the form of a much smaller policy network—is a controller that, when combined with the cyberphysical model of an Earth-like situation, satisfies your pLTL spec.” There is nothing normative in such an argument; the normative arguments all take place before/while drafting the spec, which should be done with AI assistants that are not smarter-than-human (CoEm style).

There is still a misuse version: someone could remove the provision in 5.1.5 that the model of Earth-like situations should be largely agnostic about human behavior, and instead building a detailed model of how human nervous systems respond to language. (Then, even though the superintelligence in the box would still be making only descriptive arguments about a policy, the policy that comes out would likely emit normative arguments at deployment time.) Superintelligence misuse is covered under problem 11.

If it’s not misuse, the provisions in 5.1.4-5 will steer the search process away from policies that attempt to propagandize to humans.

That being said— I don’t expect existing model-checking methods to scale well. I think we will need to incorporate powerful AI heuristics into the search for a proof certificate, which may include various types of argument steps not limited to a monolithic coarse-graining (as mentioned in my footnote 2). And I do think that relies on having a good meta-ontology or compositional world-modeling framework. And I do think that is the hard part, actually! At least, it is the part I endorse focusing on first. If others follow your train of thought to narrow in on the conclusion that the compositional world-modeling framework problem, as Owen Lynch and I have laid it out in this post, is potentially “the hard part” of AI safety, that would be wonderful…

I think you’re directionally correct; I agree about the following:

  • A critical part of formally verifying real-world systems involves coarse-graining uncountable state spaces into (sums of subsets of products of) finite state spaces.
  • I imagine these would be mostly if not entirely learned.
  • There is a tradeoff between computing time and bound tightness.

However, I think maybe my critical disagreement is that I do think probabilistic bounds can be guaranteed sound, with respect to an uncountable model, in finite time. (They just might not be tight enough to justify confidence in the proposed policy network, in which case the policy would not exit the box, and the failure is a flop rather than a foom.)

Perhaps the keyphrase you’re missing is “interval MDP abstraction”. One specific paper that combines RL and model-checking and coarse-graining in the way you’re asking for is Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning.

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