Buck's Shortform

[epistemic status: I think I’m mostly right about the main thrust here, but probably some of the specific arguments below are wrong. In the following, I'm much more stating conclusions than providing full arguments. This claim isn’t particularly original to me.]

I’m interested in the following subset of risk from AI:

• Early: That comes from AIs that are just powerful enough to be extremely useful and dangerous-by-default (i.e. these AIs aren’t wildly superhuman).
• Scheming: Risk associated with loss of control to AIs that arises from AIs scheming
  • So e.g. I exclude state actors stealing weights in ways that aren’t enabled by the AIs scheming, and I also exclude non-scheming failure modes. IMO, state actors stealing weights is a serious threat, but non-scheming failure modes aren’t (at this level of capability and dignity).
• Medium dignity: that is, developers of these AIs are putting a reasonable amount of effort into preventing catastrophic outcomes from their AIs (perhaps they’re spending the equivalent of 10% of their budget on cost-effective measures to prevent catastrophes).
• Nearcasted: no substantial fundamental progress on AI safety techniques, no substantial changes in how AI wo

[this is a draft that I shared with a bunch of friends a while ago; they raised many issues that I haven't addressed, but might address at some point in the future]

In my opinion, and AFAICT the opinion of many alignment researchers, there are problems with aligning superintelligent models that no alignment techniques so far proposed are able to fix. Even if we had a full kitchen sink approach where we’d overcome all the practical challenges of applying amplification techniques, transparency techniques, adversarial training, and so on, I still wouldn’t feel that confident that we’d be able to build superintelligent systems that were competitive with unaligned ones, unless we got really lucky with some empirical contingencies that we will have no way of checking except for just training the superintelligence and hoping for the best.

Two examples: 

• A simplified version of the hope with IDA is that we’ll be able to have our system make decisions in a way that never had to rely on searching over uninterpretable spaces of cognitive policies. But this will only be competitive if IDA can do all the same cognitive actions that an unaligned system can do, which is probably false, eg cf In

I think people in the safety community underrate the following possibility: early transformatively-powerful models are pretty obviously scheming (though they aren't amazingly good at it), but their developers are deploying them anyway, either because they're wildly irresponsible or because they're under massive competitive pressure.

In this world, it's not very valuable to have better techniques for determining whether models are scheming (because they obviously are), but it is valuable to have better techniques for reducing the risk associated with deploying known-scheming models.

One background assumption that leads to this belief is that I think it might be pretty hard for early transformatively-powerful schemers to escape; it wouldn't shock me if the schemer only has like 1% chance of escaping even a pretty irresponsible lab per time that it tries. In this regime, it's very plausible that people at the lab think the model is obviously scheming before its cumulative probability of escape is very high, but SGD isn't sample-efficient enough to make it stop.

See catching AIs red-handed for more takes on what you might do in this situation.

[this is not a particularly original take; I'm writing it here so that I have somewhere to link people when I want to make this point]

From my perspective, one of the main avenues by which adversarial robustness might reduce AI x-risk is making weak trusted models more robust to attempts from scheming models to cause concentrated failures by jailbreaking or otherwise tricking them.

That is: if you’re using weak trusted models to review the actions of more powerful models to ensure they aren’t causing egregious problems (we call this trusted monitoring), one of the main ways the powerful models might be able to defeat your safety measures is by jailbreaking the trusted model into saying that some action is safe when it actually isn’t. You have similar problems with many other control protocols that require trusted models to be run on inputs that contain generations from untrusted models.

So I’d say the main applications of research into adversarial robustness are:

• Improving robustness of models to attacks from scheming AIs (and other AI attacks).
• Improving robustness of models to human attacks.
• Making models themselves less likely to take intentional catastrophic actio

Something I think I’ve been historically wrong about:

A bunch of the prosaic alignment ideas (eg adversarial training, IDA, debate) now feel to me like things that people will obviously do the simple versions of by default. Like, when we’re training systems to answer questions, of course we’ll use our current versions of systems to help us evaluate, why would we not do that? We’ll be used to using these systems to answer questions that we have, and so it will be totally obvious that we should use them to help us evaluate our new system.

Similarly with debate--adversarial setups are pretty obvious and easy.

In this frame, the contributions from Paul and Geoffrey feel more like “they tried to systematically think through the natural limits of the things people will do” than “they thought of an approach that non-alignment-obsessed people would never have thought of or used”.

It’s still not obvious whether people will actually use these techniques to their limits, but it would be surprising if they weren’t used at all.

AI safety people often emphasize making safety cases as the core organizational approach to ensuring safety. I think this might cause people to anchor on relatively bad analogies to other fields.

Safety cases are widely used in fields that do safety engineering, e.g. airplanes and nuclear reactors. See e.g. “Arguing Safety” for my favorite introduction to them. The core idea of a safety case is to have a structured argument that clearly and explicitly spells out how all of your empirical measurements allow you to make a sequence of conclusions that establish that the risk posed by your system is acceptably low. Safety cases are somewhat controversial among safety engineering pundits.

But the AI context has a very different structure from those fields, because all of the risks that companies are interested in mitigating with safety cases are fundamentally adversarial (with the adversary being AIs and/or humans). There’s some discussion of adapting the safety-case-like methodology to the adversarial case (e.g. Alexander et al, “Security assurance cases: motivation and the state of the art”), but this seems to be quite experimental and it is not generally recommended. So I think it’s ve... (read more)

[epistemic status: speculative]

A lot of the time, we consider our models to be functions from parameters and inputs to outputs, and we imagine training the parameters with SGD. One notable feature of this setup is that SGD isn’t by default purposefully trying to kill you--it might find a model that kills you, or a model that gradient hacks and then kills you, but this is more like incompetence/indifference on SGD’s part, rather than malice.

A plausible objection to this framing is that much of the knowledge of our models is probably going to be produced in other ways than SGD. For example, the models might write down various notes (in natural language or in neuralese) that they then read later, and they might have internal structures like economies that produce and consume information. Does this introduce new alignment problems?

Here’s a way I’ve been thinking about this recently. I’m writing this in way that might feel obnoxiously overwrought because this is the way that I think would have conveyed my current intuition to me two months ago.

In SGD, we update our weights by something like:

weights <- weights + alpha * (d loss/d weights)

You might think that this is fundamental. But a... (read more)

I used to think that slower takeoff implied shorter timelines, because slow takeoff means that pre-AGI AI is more economically valuable, which means that economy advances faster, which means that we get AGI sooner. But there's a countervailing consideration, which is that in slow takeoff worlds, you can make arguments like ‘it’s unlikely that we’re close to AGI, because AI can’t do X yet’, where X might be ‘make a trillion dollars a year’ or ‘be as competent as a bee’. I now overall think that arguments for fast takeoff should update you towards shorter timelines.

So slow takeoffs cause shorter timelines, but are evidence for longer timelines.

This graph is a version of this argument: if we notice that current capabilities are at the level of the green line, then if we think we're on the fast takeoff curve we'll deduce we're much further ahead than we'd think on the slow takeoff curve.

For the "slow takeoffs mean shorter timelines" argument, see here: https://sideways-view.com/2018/02/24/takeoff-speeds/

This point feels really obvious now that I've written it down, and I suspect it's obvious to many AI safety people, including the people whose writings I'm referencing here. Thanks to various people for helpful comments.

I think that this is why belief in slow takeoffs is correlated with belief in long timelines among the people I know who think a lot about AI safety.

Here's a math problem I'd like solved:

I want to know the Nash equilibrium of the following two player game between players Alice and Bob.

Alice chooses the rates of two Poisson processes; we call these rates lam_1 and lam_2.

Bob needs to run each of these processes for 0.5 seconds. Bob can run each of the processes in "shielded" mode or "unshielded" mode, but can only afford to use $t$ seconds of shielded mode in total between the two processes. (So Bob chooses $t_1$ and $t_2$ such that $t_1+t_2=t$.)

The game goes as follows:

• We run process 1 for time $t_1$ and process 2 for time $t_2$, in shielded mode. If either event occurs during these time periods, Bob wins.
• Then we run process 1 for time $0.5-t_1$ and process 2 for time $0.25-t_2$, in unshielded mode. If either event occurs during these time periods, Alice wins.
• If the event never occurred, Bob wins.

Here’s a Sympy implementation of this game (where I parameterize with $t_1=pt$ and $t_2=(1-p)t$.)

import sympy as sp

lam_1, lam_2, p, t = sp.symbols('lambda_1 lambda_2 p t', real=True, positive=True)

time_2_overseen = t * (1 - p)
time_2_not_overseen = sp.Rational(1, 2) - time_2_overseen

Sometimes people talk about problems where “your AI is taking actions that you can’t distinguish from the actions an aligned AI would take”. There are two different things people sometimes mean when they say this, and I think it’s good to disambiguate between them:

• Given access to actions from both an aligned AI and an unaligned AI, you can’t tell which is which.
• Given just access to actions from an unaligned AI, you can't tell which it is.