*A putative new idea for AI control; index here.*

**EDIT: I feel this post is unclear, and will need to be redone again soon.**

This post attempts to use the ideas developed about natural categories in order to get high impact from reduced impact AIs.

## Extending niceness/reduced impact

I recently presented the problem of extending AI "niceness" given some fact X, to niceness given ¬X, choosing X to be something pretty significant but not overwhelmingly so - the death of a president. By assumption we had a successfully programmed niceness, but no good definition (this was meant to be "reduced impact" in a slight disguise).

This problem turned out to be much harder than expected. It seems that the only way to do so is to require the AI to define values dependent on a set of various (boolean) random variables Z_{j} *that did not include X/¬X*. Then as long as the random variables represented natural categories, given X, the niceness should extend.

What did we mean by natural categories? Informally, it means that X should not appear in the definitions of these random variables. For instance, nuclear war is a natural category; "nuclear war XOR X" is not. Actually defining this was quite subtle; diverting through the grue and bleen problem, it seems that we had to define how we update X and the Z_{j} given the evidence we expected to find. This was put in equation as picking Z_{j}'s that minimize

**Variance{log[ P(X∧Z|E)*P(¬X∧¬Z|E) / P(X∧¬Z|E)*P(¬X∧Z|E) ]}**

where E is the random variable denoting the evidence we expected to find. Note that if we interchange X and ¬X, the ratio inverts, the log changes sign - but this makes no difference to the variance. So we can equally well talk about extending niceness given X to ¬X, or niceness given ¬X to X.

## Perfect and imperfect extensions

The above definition would work for an "perfectly nice AI". That could be an AI that would be nice, given any combination of estimates of X and Z_{j}. In practice, because we can't consider every edge case, we would only have an "expectedly nice AI". That means that the AI can fail to be nice in certain unusual and unlikely edge cases, in certain strange set of values of Z_{j} that almost never come up...

...or at least, that almost never come up, *given X*. Since the "expected niceness" was calibrated given X, the such an expectedly nice AI may fail to be nice if ¬X results in a substantial change in the probability of the Z_{j} (see the second failure mode in this post; some of the Z_{j} may be so tightly coupled to the value of X that an expected niceness AI considers them fixed, and this results in problems if ¬X happens and their values change).

One way of fixing this is to require that the "swing" of the Z_{j} be small upon changing X to ¬X or vice versa. Something like, for all values of {a_{j}}, the ratio P({Z_{j}=a_{j}} | X) / P({Z_{j}=a_{j}} | ¬X) is contained between 100 and 1/100. This means that a reasonably good "expected niceness" calibrated on the Z_{j} will transfer from X to ¬X (though the error may grow). This approach has some other advantages, as we'll see in the next section.

Of course, problems arise if the Z_{j} are defined by smashing together events relatively independent of X with some that are very strongly dependent on X, into the same variable. Thus we'll further require that Z_{j} cannot be decomposed into natural subvariables whose values swing more under changes from X to ¬X.

## High impact from reduced impact

With this, we are finally in a position to define a reduced impact AI design that can have a large impact. Imagine that an asteroid is coming to hit the Earth. Two AIs are charged with targeting a laser at it. The laser isn't perfectly effective, and the asteroid isn't perfectly certain to hit the Earth anyway: let's say aiming the laser correctly will reduce the odds of impact by a factor of 10 or so (well within the probability swing restrictions of the previous section).

In the original formulation of this, the AIs were tasked with outputting the x and y coordinates to aim the laser. Here, instead, they'll be tasked with constructing the motivation of a robotic subagent that will move around for a while, see things and interact, before physically aiming the laser in the right direction. This task is harder.

Then the top level AI charged with aiming the y coordinates is given the following task:

- It is motivated to cause the correct y-coordinate to be outputted.
- If the x-coordinate AI is not turned on (call this event ¬X), it is strongly motivated to have reduced impact.
- It must phrase the robotic subagent's utility function in terms of the Z
_{j}that obey all the properties above. - The evidence E considered for the equation governing the naturalness of Z
_{j}is considered (for example) to be a random sampling of space at the time both AIs are turned on. Most importantly, this is defined to be a theoretical process that the AI cannot affect physically.

So what will the AI do? It will produce a robot that will aim to aim the y-coordinate of the laser correctly, given ¬X, and not expand dangerously. However, we've setup things so that X is virtually certain, so the robot will see another, x-aiming robot, wander around. The chances of this happening, given ¬X, are minuscule, so the swing of the probability from ¬X to X, is huge. So, by the restrictions we've put on the Z_{j}, the robot will not be motivated to change its behaviour just because its sees the other robot.

And if the laser is fired, the impact on humanity will, conversely, not be that large, so well within the parameters of the Z_{j}. Thus the "general reduced impact" portion of the robot's motivation will be preserved.

And, of course, the x-robot will have the mirror image motivations, so they will both aim correctly, it seems. This should evade the problem described here.

## Failure modes

There are two ways the approach could fail: one bad way, and two graceful ways. The bad way is if the "no decomposition" requirements (one of them is presented above, the other is in this post) fail. In this case, the niceness or reduced impact are not guaranteed to be extended to ¬X, and the AI's behaviour can become undefined. Note that this is is not bad optimisation pressure, just an absence of optimisation pressure - but that could still be potentially dangerous.

The first graceful failure happens if the AI is not able to create a reduced impact sub-agent given the restrictions on the Z_{j} above. In that case, the AI does nothing. The second graceful failure happens if the AI evades our attempts to increase its impact, given ¬X. In that case, it simply becomes a reduced impact AI that does little. Not ideal, but not deadly.

**Overall status**: I'm not sure the idea is sound, at least not yet. Critiques welcome.