Basic Problem

If I read about current AI Alignment research ideas, I see a lot of discussion about high capability scenarios, in which AI would be too smart to align simply. It would deceive operators, hack through communication channels and goodheart it's way over to infinity.

However if I block the part of my mind that likes to think about cool superintelligence overlords, and try to understand why high capability should introduce an actual complexity shift... it doesn't seem that intuitive.

At basic level - any time an AI does something we don't want, it's a plain and simple failure of matching one reward function to another. In high capability AI (HAI) we can only imagine what reward functions would be, and how they would be represented. Right now, our simple low capability models (LAI) have reward functions in the shape of big mess of parameters and connections that randomly walked into the first region in which loss is small enough. Are we sure that they are aligned ? I think it's pretty probable that if we take a general consequentialist and bind him to this reward function (whatever it represents) the result would be quite catastrophic. Of course this example is a little contrived, but I think image of separate consequentialist vs. reward function is helpful here.

It seems to me a real alignment failure structure is shared between HAI and LAI. The first just has more wiggle room, so potential misalignments propagate to the level when we can easily notice them.

To be more precise, I think that base level alignment challenge (make actual reward function of a model to be what we want) has some fixed (but high) level of complexity mostly independent of capability.

Let's assume this is somewhat true, then what?

Next steps

Then we should do more low capability research:

It will be more anchored in reality.
1. We can try to test that our current alignment ideas work on LAI.
It consumes less compute, so it can:
1. Produce more insights for the same money/time.
2. Be more relevant to current AI research, which would increase chances of adoption.
3. We can perform more complicated experiments.

There is some current research directions which are working at LAI level. I think a lot of Interpretability work are in this category.

Also isn't the whole AI research industry working on this problem? AI that works ok and can be commercially deployed is more aligned than one whic...

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