# Mundane solutions to exotic problems

I’m looking for alignment techniques that are [indefinitely scalable](https://ai-alignment.com/directions-and-desiderata-for-ai-control-b60fca0da8f4) and that [work in any situation we can dream up](https://ai-alignment.com/my-research-methodology-b94f2751cb2c). That means I spend time thinking about “exotic” problems — like AI systems reasoning about their own training process or about humanity’s far future.

Yet I’m very optimistic about finding practical alignment strategies that are relatively simple and usable today. I expect the results of my research to look mundane and even boring.

These two stances may appear to be in tension. If I’m worrying about all kinds of exotic considerations, how can the end result be something mundane?

I’m not too concerned. These failures seem exotic because they involve exotic _capabilities._ I’m looking for a mundane algorithm that trains the model to use whatever capabilities it has for good purposes. So it will lead to exotic countermeasures if and when the model has exotic capabilities, i.e. just in time to avoid exotic failures (by which time they’ll no longer feel exotic).

I want to illustrate this phenomenon with a few examples.

#### Example 1: Gradient hacking

[_Gradient hacking_](https://www.lesswrong.com/posts/uXH4r6MmKPedk8rMA/gradient-hacking)  is a possible failure mode where a powerful misaligned model behaves in a way that is apparently benign but biases the gradient of the loss to point in an unfavorable direction, eventually trapping optimization at a bad local minimum. This feels exotic today because it requires the model to have an incredible amount of introspective access and control, as well as strategic awareness over long time horizons. (“[Forward Pass](http://karpathy.github.io/2021/03/27/forward-pass/)” by Andrej Karpathy explores some similar ideas.)

Normally I imagine using SGD to optimize a “behavioral” objective: to update the parameters θ you use them to make a randomized decision _y_ on an input _x_, then you calculate a reward _r_(_x_, _y_), then you compute an unbiased estimate for the gradient of the expectation _r_(_x_, _y_) with respect to θ.

Gradient hacking is troubling because it suggests that if we optimize a purely behavioral objective we could get a bad outcome even if the reward function _r_(_x_, _y_) perfectly captures the quality of the output _y_.

Rather than fixing this problem in a specific way, I’d like to rely on competitiveness. That is, assume that (throughout training) we have a question-answering policy Q → A which reflects _everything_ that our learned model “knows” about the world.

Then it seems like the best way to compute the system’s loss should be by by asking some question like “What should the loss be for the current parameters?”

Our usual behavioral loss then is a great _heuristic_ for computing the loss (which we can justify on principled Bayesian grounds if we want). In mundane situations it’s not clear we have anything further to say about what the loss should be, so “What should the loss be?” is almost the same as hard-coding the heuristic.

But when gradient hacking occurs, we are no longer in a mundane situation. By hypothesis, our learned model is able to reason introspectively about its own parameters and the structure of the loss landscape! It is thinking about how to change its behavior in order to affect the loss, change the optimization trajectory, and ultimately disempower humans.

That means that a competitive question-answerer _also_ is able to answer questions about its own parameters and the structure of the loss landscape. It is able to talk about how assigning a loss could mess up the optimization trajectory and ultimately disempower humans. If we incorporate all that knowledge into our assessment of “What should the loss be for the current parameters?” then it will assign a low loss to some of the actions that the gradient-hacker tried to sabotage. That is, the malicious AI can outsmart the behavioral heuristic, but it can’t outsmart an overseer _who knows everything that it knows_.

It’s not clear if this works (and my strong guess is that we’ll ultimately end up with an algorithm that looks quite different from this). But overall I think that if gradient hacking actually came up, it’s more likely than not that the core difficulty in avoiding it would be the same version of epistemic competitiveness that we need in more mundane situations. The only real change to our algorithm we need to make is applying a competitive overseer to the right question.

You might worry that the knowledge a model has about its own parameters is qualitatively different than the knowledge it has about the world. But I don’t think so — they are both produced by transfer from the task of “get a low training loss,” combined with a bunch of computation. It’s possible that after we have an epistemically competitive solution we’ll see that it doesn’t apply to a model’s introspective knowledge. If that happens then we may indeed need some more exotic solution that talks about introspection _per se_, but I personally doubt it.

#### Example 2: far future forecasts and inaccessible influence

Suppose that there’s [some domain that our AI can understand but we cannot](https://ai-alignment.com/inaccessible-information-c749c6a88ce), for vividness you can imagine humans without microscopes and an AI that has a working model of the microscopic world. For simplicity, imagine that the world factors nicely into an accessible part and an inaccessible part, and that there are influences in both directions but to us the the (inaccessible) → (accessible) influences just look like noise. (If you don’t know about the mechanics of biology, then events like “getting cancer” can just look like noise.)

One of my concerns is that an AI that understands the inaccessible part may be able to cause trouble in the very long term. Even if we’re able to prevent the AI from doing anything overtly bad in the accessible part for the next few years (like killing humans), it may still be that humans aren’t really in control of the situation because the real power is in the inaccessible part of the world, and that power will eventually assert itself.

So if we can only observe the accessible part of the world, then we would need to look _very_ far ahead to avoid problem. Basically we’d need to look far enough ahead that the future humans have done science and learned about the previously-inaccessible parts of the world. This kind of lookahead feels particularly exotic because it involves extreme generalization. Training typically involves feedback loops over seconds, but now we are talking about a model that makes good predictions about events that literally occur _generations_ from now.

That kind of extreme lookahead sounds exotic, but in some cases my current alignment plans require it.

As in the last example, I think this situation is only exotic because our model had exotic capabilities. We’re in this situation because we are trying to handle a malicious AI that makes plans that have no observable consequences for many generations, building up power in the inaccessible part of the world that it predicts will eventually interfere with humans. That AI is already apparently able to generalize well to extremely long time horizons — if it wasn’t, we’d have no problem at all.

So once again, to avoid this problem we really just need epistemically competitive question answering, i.e. we need to learn a Q → A function that generalizes as well to novel questions (e.g. about the future) as the underlying model’s knowledge. And that’s a pretty mundane problem that’s core to the idea of epistemic competitiveness that we need to solve for other reasons.

If the malicious model is able to generalize from its experiences to make plans that unfold over generations, and the Q → A model generalizes as well as the malicious model, then our Q → A system can also answer questions about what happens over generations. That only sounds exotic because we assumed some exotic capabilities. Nothing about the solution or training needs to deal with ultra-long horizons specifically.

#### Conclusion

In my work I don’t shy away from exotic problems (I often find them useful as extreme cases to illustrate some principle). At the same time, I’m aiming for mundane solutions and optimistic about finding them.

I think those positions are consistent because my intermediate goal is to ensure that the oversight process is able to leverage all of the capabilities developed by the model — so if the model develops exotic capabilities which pose exotic challenges, then we get an exotic oversight process automatically.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=395bad49fbe7)

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[Mundane solutions to exotic problems](https://ai-alignment.com/mundane-solutions-to-exotic-problems-395bad49fbe7) was originally published in [AI Alignment](https://ai-alignment.com) on Medium, where people are continuing the conversation by highlighting and responding to this story.