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Eight beliefs I have about technical alignment research

Written up quickly; I might publish this as a frontpage post with a bit more effort.

  1. Conceptual work on concepts like “agency”, “optimization”, “terminal values”, “abstractions”, “boundaries” is mostly intractable at the moment.
    • Success via “value alignment” alone— a system that understands human values, incorporates these into some terminal goal, and mostly maximizes for this goal, seems hard unless we’re in a very easy world because this involves several fucked concepts.
  2. Whole brain emulation probably won’t happen in time because the brain is complicated and biology moves slower than CS, being bottlenecked by lab work.
  3. Most progress will be made using simple techniques and create artifacts publishable in top journals (or would be if reviewers understood alignment as well as e.g. Richard Ngo).
  4. The core story for success (>50%) goes something like:
    • Corrigibility can in practice be achieved by instilling various cognitive properties into an AI system, which are difficult but not impossible to maintain as your system gets pivotally capable.
    • These cognitive properties will be a mix of things from normal ML fields (safe RL), things that rhyme with normal ML fields (unlearning, faithfulness), and things that are currently conceptually fucked but may become tractable (low impact, no ontological drift).
    • A combination of oversight and these cognitive properties is sufficient to get useful cognitive work out of an AGI.
    • Good oversight complements corrigibility properties, because corrigibility both increases the power of your most capable trusted overseer and prevents your untrusted models from escaping.
  5. Most end-to-end “alignment plans” are bad for three reasons: because research will be incremental and we need to adapt to future discoveries, because we need to achieve several things for AI to go well (no alignment magic bullet), and because to solve the hardest worlds that are possible, you have to engage with MIRI threat models which very few people can do well [1].
    • e.g. I expect Superalignment’s impact to mostly depend on their ability to adapt to knowledge about AI systems that we gain in the next 3 years, and continue working on relevant subproblems.
  6. The usefulness of basic science is limited unless you can eventually demonstrate some application. We should feel worse about a basic science program the longer it goes without application, and try to predict how broad the application of potential basic science programs will be.
    • Glitch tokens work probably won’t go anywhere. But steering vectors are good because there are more powerful techniques in that space.
    • The usefulness of sparse coding depends on whether we get applications like sparse circuit discovery, or intervening on features in order to usefully steer model behavior. Likewise with circuits-style mechinterp, singular learning theory, etc.
  7. There are convergent instrumental pressures towards catastrophic behavior given certain assumptions about how cognition works, but the assumptions are rather strong and it’s not clear if the argument goes through.
    • The arguments I currently think are strongest are Alex Turner’s power-seeking theorem and an informal argument about goals.
  8. Thoughts on various research principles picked up from Nate Soares
    • You should have a concrete task in mind when you’re imagining an AGI or alignment plan: agree. I usually imagine something like “Apollo program from scratch”.
    • Non-adversarial principle (A safe AGI design should not become unsafe if any part of it becomes infinitely good at its job): unsure, definitely agree with weaker versions
    • To make any alignment progress we must first understand cognition through either theory or interpretability: disagree
    • You haven’t engaged with the real problem until your alignment plan handles metacognition, self-modification, etc.: weakly disagree; wish we had some formalism for “weak metacognition” to test our designs against [2]

[1], [2]: I expect some but not all of the MIRI threat models to come into play. Like, when we put safeguards into agents, they'll rip out or circumvent some but not others, and it's super tricky to predict which. My research with Vivek often got stuck by worrying too much about reflection, others get stuck by worrying too little.

I'm worried that "pause all AI development" is like the "defund the police" of the alignment community. I'm not convinced it's net bad because I haven't been following governance-- my current guess is neutral-- but I do see these similarities:

  • It's incredibly difficult and incentive-incompatible with existing groups in power
  • There are less costly, more effective steps to reduce the underlying problem, like making the field of alignment 10x larger or passing regulation to require evals
  • There are some obvious negative effects; potential overhangs or greater incentives to defect in the AI case, and increased crime, including against disadvantaged groups, in the police case
  • There's far more discussion than action (I'm not counting the fact that GPT5 isn't being trained yet; that's for other reasons)
  • It's memetically fit, and much discussion is driven by two factors that don't advantage good policies over bad policies, and might even do the reverse. This is the toxoplasma of rage.
    • disagreement with the policy
    • (speculatively) intragroup signaling; showing your dedication to even an inefficient policy proposal proves you're part of the ingroup. I'm not 100% this was a large factor in "defund the police" and this seems even less true with the FLI letter, but still worth mentioning.

This seems like a potentially unpopular take, so I'll list some cruxes. I'd change my mind and endorse the letter if some of the following are true.

  • The claims above are mistaken/false somehow.
  • Top labs actually start taking beneficial actions towards the letter's aims
  • It's caused people to start thinking more carefully about AI risk
  • A 6 month pause now is especially important by setting anti-racing norms, demonstrating how far AI alignment is lagging behind capabilities, or something
  • A 6 month pause now is worth close to 6 months of alignment research at crunch time (my guess is that research at crunch time is worth 1.5x-3x more depending on whether MIRI is right about everything)
  • The most important quality to push towards in public discourse is how much we care about safety at all, so I should endorse this proposal even though it's flawed

There are less costly, more effective steps to reduce the underlying problem, like making the field of alignment 10x larger or passing regulation to require evals

IMO making the field of alignment 10x larger or evals do not solve a big part of the problem, while indefinitely pausing AI development would. I agree it's much harder, but I think it's good to at least try, as long as it doesn't terribly hurt less ambitious efforts (which I think it doesn't).

It's incredibly difficult and incentive-incompatible with existing groups in power

Why does this have to be true? Can't governments just compensate existing AGI labs for the expected commercial value of their foregone future advances due to indefinite pause? 

This seems good if it could be done. But the original proposal was just a call for labs to individually pause their research, which seems really unlikely to work.

Also, the level of civilizational competence required to compensate labs seems to be higher than for other solutions. I don't think it's a common regulatory practice to compensate existing labs like this, and it seems difficult to work out all the details so that labs will feel adequately compensated. Plus there might be labs that irrationally believe they're undervalued. Regulations similar to the nuclear or aviation industry feel like a more plausible way to get slowdown, and have the benefit that they actually incentivize safety work.

The independent-steps model of cognitive power

A toy model of intelligence implies that there's an intelligence threshold above which minds don't get stuck when they try to solve arbitrarily long/difficult problems, and below which they do get stuck. I might not write this up otherwise due to limited relevance, so here it is as a shortform, without the proofs, limitations, and discussion.

The model

A task of difficulty n is composed of  independent and serial subtasks. For each subtask, a mind of cognitive power  knows  different “approaches” to choose from. The time taken by each approach is at least 1 but drawn from a power law,  for , and the mind always chooses the fastest approach it knows. So the time taken on a subtask is the minimum of  samples from the power law, and the overall time for a task is the total for the n subtasks.

Main question: For a mind of strength ,

  • what is the average rate at which it completes tasks of difficulty n?
  • will it be infeasible for it to complete sufficiently large tasks?

Results

  • There is a critical threshold  of intelligence below which the distribution of time to complete a subtask has infinite mean. This threshold depends on .
    • This implies that for an n-step task, the median of average time-per-subtask grows without bound as n increases. So (for minds below the critical threshold) the median time to complete a whole task grows superlinearly with n.
  • Above the critical threshold, minds can solve any task in expected linear time.
  • Some distance above the critical threshold, minds are running fairly close to the optimal speed, and further increases in Q cause small efficiency gains.
  • I think this doesn't depend on the function being a power law; it would be true for many different heavy-tailed distributions, but the math wouldn't be as nice.

I'm planning to write a post called "Heavy-tailed error implies hackable proxy". The idea is that when you care about  and are optimizing for a proxy , Goodhart's Law sometimes implies that optimizing hard enough for  causes  to stop increasing.

A large part of the post would be proofs about what the distributions of  and  must be for , where X and V are independent random variables with mean zero. It's clear that

  • X must be heavy-tailed (or long-tailed or something)
  • X must have heavier tails than V

The proof seems messy though; Drake Thomas and I have spent ~5 person-days on it and we're not quite done. Before I spend another few days proving this, is it a standard result in statistics? I looked through a textbook and none of the results were exactly what I wanted.

Note that a couple of people have already looked at it for ~5 minutes and found it non-obvious, but I suspect it might be a known result anyway on priors.

Doesn't answer your question, but we also came across this effect in the RM Goodharting work, though instead of figuring out the details we only proved that it when it's definitely not heavy tailed it's monotonic, for Regressional Goodhart (https://arxiv.org/pdf/2210.10760.pdf#page=17). Jacob probably has more detailed takes on this than me. 

In any event my intuition is this seems unlikely to be the main reason for overoptimization - I think it's much more likely that it's Extremal Goodhart or some other thing where the noise is not independent

We might want to keep our AI from learning a certain fact about the world, like particular cognitive biases humans have that could be used for manipulation. But a sufficiently intelligent agent might discover this fact despite our best efforts. Is it possible to find out when it does this through monitoring, and trigger some circuit breaker?

Evals can measure the agent's propensity for catastrophic behavior, and mechanistic anomaly detection hopes to do better by looking at the agent's internals without assuming interpretability, but if we can measure the agent's beliefs, we can catch the problem earlier. Maybe there can be more specific evals we give to the agent, which are puzzles that can only be solved if the agent knows some particular fact. Or maybe the agent is factorable into a world-model and planner, and we can extract whether it knows the fact from the world-model.

Have the situational awareness people already thought about this? Does anything change when we're actively trying to erase a belief?