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?


The "random dictator" baseline should not be interpreted as allowing the random dictator to dictate everything, but rather to dictate which Pareto improvement is chosen (with the baseline for "Pareto improvement" being "no superintelligence"). Hurting heretics is not a Pareto improvement because it makes those heretics worse off than if there were no superintelligence.


Yes. You will find more details in his paper, Provably safe systems with Steve Omohundro, in which I am listed in the acknowledgments (under my legal name, David Dalrymple).

Max and I also met and discussed the similarities in advance of the AI Safety Summit in Bletchley.


I agree that each of and has two algebraically equivalent interpretations, as you say, where one is about inconsistency and the other is about inferiority for the adversary. (I hadn’t noticed that).

The variant still seems somewhat irregular to me; even though Diffractor does use it in Infra-Miscellanea Section 2, I wouldn’t select it as “the” infrabayesian monad. I’m also confused about which one you’re calling unbounded. It seems to me like the variant is bounded (on both sides) whereas the variant is bounded on one side, and neither is really unbounded. (Being bounded on at least one side is of course necessary for being consistent with infinite ethics.)


These are very good questions. First, two general clarifications:

A. «Boundaries» are not partitions of physical space; they are partitions of a causal graphical model that is an abstraction over the concrete physical world-model.

B. To "pierce" a «boundary» is to counterfactually (with respect to the concrete physical world-model) cause the abstract model that represents the boundary to increase in prediction error (relative to the best augmented abstraction that uses the same state-space factorization but permits arbitrary causal dependencies crossing the boundary).

So, to your particular cases:

  1. Probably not. There is no fundamental difference between sound and contact. Rather, the fundamental difference is between the usual flow of information through the senses and other flows of information that are possible in the concrete physical world-model but not represented in the abstraction. An interaction that pierces the membrane is one which breaks the abstraction barrier of perception. Ordinary speech acts do not. Only sounds which cause damage (internal state changes that are not well-modelled as mental states) or which otherwise exceed the "operating conditions" in the state space of the «boundary» layer (e.g. certain kinds of superstimuli) would pierce the «boundary».
  2. Almost surely not. This is why, as an agenda for AI safety, it will be necessary to specify a handful of constructive goals, such as provision of clean water and sustenance and the maintenance of hospitable atmospheric conditions, in addition to the «boundary»-based safety prohibitions.
  3. Definitely not. Omission of beneficial actions is not a counterfactual impact.
  4. Probably. This causes prediction error because the abstraction of typical human spatial positions is that they have substantial ability to affect their position between nearby streets by simple locomotory action sequences. But if a human is already effectively imprisoned, then adding more concrete would not create additional/counterfactual prediction error.
  5. Probably not. Provision of resources (that are within "operating conditions", i.e. not "out-of-distribution") is not a «boundary» violation as long as the human has the typical amount of control of whether to accept them.
  6. Definitely not. Exploiting behavioural tendencies which are not counterfactually corrupted is not a «boundary» violation.
  7. Maybe. If the ad's effect on decision-making tendencies is well modelled by the abstraction of typical in-distribution human interactions, then using that channel does not violate the «boundary». Unprecedented superstimuli would, but the precedented patterns in advertising are already pretty bad. This is a weak point of the «boundaries» concept, in my view. We need additional criteria for avoiding psychological harm, including superpersuasion. One is simply to forbid autonomous superhuman systems from communicating to humans at all: any proposed actions which can be meaningfully interpreted by sandboxed human-level supervisory AIs as messages with nontrivial semantics could be rejected. Another approach is Mariven's criterion for deception, but applying this criterion requires modelling human mental states as beliefs about the world (which is certainly not 100% scientifically accurate). I would like to see more work here, and more different proposed approaches.

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


Kosoy's infrabayesian monad  is given by 

There are a few different varieties of infrabayesian belief-state, but I currently favour the one which is called "homogeneous ultracontributions", which is "non-empty topologically-closed ⊥–closed convex sets of subdistributions", thus almost exactly the same as Mio-Sarkis-Vignudelli's "non-empty finitely-generated ⊥–closed convex sets of subdistributions monad" (Definition 36 of this paper), with the difference being essentially that it's presentable, but it's much more like  than .

I am not at all convinced by the interpretation of  here as terminating a game with a reward for the adversary or the agent. My interpretation of the distinguished element  in  is not that it represents a special state in which the game is over, but rather a special state in which there is a contradiction between some of one's assumptions/observations. This is very useful for modelling Bayesian updates (Evidential Decision Theory via Partial Markov Categories, sections 3.5-3.6), in which some variable  is observed to satisfy a certain predicate : this can be modelled by applying the predicate in the form  where  means the predicate is false, and   means it is true. But I don't think there is a dual to logical inconsistency, other than the full set of all possible subdistributions on the state space. It is certainly not the same type of "failure" as losing a game.


Does this article have any practical significance, or is it all just abstract nonsense? How does this help us solve the Big Problem? To be perfectly frank, I have no idea. Timelines are probably too short agent foundations, and this article is maybe agent foundations foundations...

I do think this is highly practically relevant, not least of which because using an infrabayesian monad instead of the distribution monad can provide the necessary kind of epistemic conservatism for practical safety verification in complex cyber-physical systems like the biosphere being protected and the cybersphere being monitored. It also helps remove instrumentally convergent perverse incentives to control everything.

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