Programme Director at UK Advanced Research + Invention Agency focusing on safe transformative AI; formerly Protocol Labs, FHI/Oxford, Harvard Biophysics, MIT Mathematics And Computation.
davidad
Karma: 2,049
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:
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».
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.
Definitely not. Omission of beneficial actions is not a counterfactual impact.
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.
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.
Definitely not. Exploiting behavioural tendencies which are not counterfactually corrupted is not a «boundary» violation.
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.
Meyer’s
If this is David Jaz Myers, it should be “Myers’ thesis”, here and elsewhere
I have said many times that uploads created by any process I know of so far would probably be unable to learn or form memories. (I think it didn’t come up in this particular dialogue, but in the unanswered questions section Jacob mentions having heard me say it in the past.)
Eliezer has also said that makes it useless in terms of decreasing x-risk. I don’t have a strong inside view on this question one way or the other. I do think if Factored Cognition is true then “that subset of thinking is enough,” but I have a lot of uncertainty about whether Factored Cognition is true.
Anyway, even if that subset of thinking is enough, and even if we could simulate all the true mechanisms of plasticity, then I still don’t think this saves the world, personally, which is part of why I am not in fact pursuing uploading these days.
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.
I like the idea of trying out H-JEPA with GFlowNet actors.
I also like the idea of using LLM-based virtue ethics as a regularizer, although I would still want deontic guardrails that seem good enough to avoid catastrophe.
Yes, it’s the latter. See also the Open Agency Keyholder Prize.
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.
It is often considered as such, but my concern is less with “the alignment question” (how to build AI that values whatever its stakeholders value) and more with how to build transformative AI that probably does not lead to catastrophe. Misuse is one of the ways that it can lead to catastrophe. In fact, in practice, we have to sort misuse out sooner than accidents, because catastrophic misuses become viable at a lower tech level than catastrophic accidents.
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.
Yes, the “shutdown timer” mechanism is part of the policy-scoring function that is used during policy optimization. OAA has multiple stages that could be considered “training”, and policy optimization is the one that is closest to the end, so I wouldn’t call it “the training stage”, but it certainly isn’t the deployment stage.
We hope not merely that the policy only cares about the short term, but also that it cares quite a lot about gracefully shutting itself down on time.
There’s something to be said for this, because with enough RLHF, GPT-4 does seem to have become pretty corrigible, especially compared to Bing Sydney. However, that corrigible persona is probably only superficial, and the larger and more capable a single Transformer gets, the more of its mesa-optimization power we can expect will be devoted to objectives which are uninfluenced by in-context corrections.
A system with a shutdown timer, in my sense, has no terms in its reward function which depend on what happens after the timer expires. (This is discussed in more detail in my previous post.) So there is no reason to persuade humans or do anything else to circumvent the timer, unless there is an inner alignment failure (maybe that’s what you mean by “deception instance”). Indeed, it is the formal verification that prevents inner alignment failures.
Suppose Training Run Z is a finetune of Model Y, and Model Y was the output of Training Run Y, which was already a finetune of Foundation Model X produced by Training Run X (all of which happened after September 2021). This is saying that not only Training Run Y (i.e. the compute used to produce one of the inputs to Training Run Z), but also Training Run X (a “recursive” or “transitive” dependency), count additively against the size limit for Training Run Z.
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.