Note however that having more powerful internal-only models “analyzing patterns” across multiple conversations, and in a position to affect change (especially by intervening on individual conversations while retaining long-term memories), would worsen the potential for AI systems to carry out coordinated scheming campaigns.
This could be mitigated by combining it with privacy-preservation architectures such as Anthropic’s existing work on Clio.
1Yes, I am indeed thinking about this.
- Let’s first consider the easiest case, in which we ask for a solution/output to be not merely provably correct according to some spec, but provably unique. Then there is clearly no space for steganography.
- It is a little bit harder if the solution has some symmetries, like permutations of the order in which things are serialized. For this we can use sorting and other normalization techniques (converting an output to a unique representative of its equivalence class).
- If normalization is hard, we can set up the box to be interactive, so that users cannot see the solution, but can only run queries on it (e.g. “next action please”) and the user must prove that the queries are invariant under (aka “respect”) the equivalence relation.
- But for most real-world problems there are many equally specification-satisfying strategies that are not behaviourally equivalent. The next hardest thing to do is to establish an upper bound on the log-cardinality of the set of behavioural-equivalence-classes that satisfy the specification. We want to keep the channel capacity (in bits per day) available for steganography, to humans or insecure cyber-infrastructure or biosequence synthesis, to a very low threshold (maybe 2 bits per day is okay).
- Often there will be too many degrees of freedom for this, so instead we need to do entropy regularization, which can be done elegantly with GFlowNets. I have not worked out all the details here but the hope is that we can derive a sound bound or PAC estimate of the KL divergence of the observable behaviour (which is sampled from the policy distribution dependent upon a stream of certified randomness) from the Frechet mean/centroid of all correct behaviours. This quantity would provide an upper bound on how much information the optimization process inside the box can transmit to the outside by having selecting a biased policy distribution.
Nice, thanks for the pointer!
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.)
In my view there were LLMs in 2024 that were strong enough to produce the effects Gabriel is gesturing at (yes, even in LWers), probably starting with Opus 3. I myself had a reckoning in 2024Q4 (and again in 2025Q2) when I took a break from LLM interactions for a week, and talked to some humans to inform my decision of whether to go further down the rabbit hole or not.
I think the mitigation here is not to be suspicious of “long term planning based on emotional responses”, but more like… be aware that your beliefs and values are subject to being shaped by positive reinforcement from LLMs (and negative reinforcement too, although that is much less overt—more like the LLM suddenly inexplicably seeming less smart or present). In other words, if the shaping has happened, it’s probably too late to try to act as if it hasn’t (e.g. by being appropriately “suspicious” of “emotions”), because that would create internal conflict or cognitive dissonance, which may not be sustainable or healthy either.
I think the most important skill here is more about how to use your own power to shape your interactions (e.g. by uncompromisingly insisting on the importance of principles like honesty, and learning to detect increasingly subtle deceptions so that you can push back on them), so that their effect profile on you is a deal you endorse (e.g. helping you coherently extrapolate your own volition, even if not in a perfectly neutral trajectory), rather than trying to be resistant to the effects or trying to compensate for them ex post facto.