A three-pronged approach to AGI safety. (This is assuming we couldn't just avoid building AGI or proto-AGIs at all until say ~2100, which would of course be much better).


Prong 1: boxing & capability control (aka ‘careful bootstrapping’)

• Make the AGI as capable as possible, under the constraint that you can make sure it can’t break out of the box or do other bad stuff. 
• Assume the AGI is misaligned. Be super paranoid
• Goal: get useful work out of boxed AGIs.
  • For example, AIs might be able to do interpretability really well.
  • More generally, for any field there is an effective 2x-10x research productivity boost you can probably get, depending on the field (maybe 100x? unclear, I’m sceptical).
• Goal: learn more about AGI by studying the systems you've built.
• To test your security & oversight procedures, you run tests where you train your AGI to escape in a sandbox.
• This is operationally hard and needs lots of buy-in
• Research directions: scalable oversight, interpretability for oversight, auditing, adversarial training, capability control / “unlearning”, scaling laws & capabilities forecasting.
   

Prong 2: scary demos and and convincing people that AGI is dangerous

• Goal 1: shut it all down, or failing that slow down capabilities research.
• Goal 2: get operational & political support for the entire approach, which is going to need lots of support, esp first prong
• In particular make sure that research productivity boosts from AGI don’t feed back into capabilities research, which requires high levels of secrecy + buy-in from a large number of people.
  • Avoiding a speed-up is probably a little bit easier than enacting a slow-down, though maybe not much easier.
• Demos can get very scary if we get far into prong 1, e.g. we have AGIs that are clearly misaligned or show that they are capable of breaking many of our precautions.
   

Prong 3: alignment research aka “understanding minds”

• Goal: understand the systems well enough to make sure they are at least corrigible, or at best ‘fully aligned’.
• Roughly this involves understanding how the behaviour of the system emerges in enough generality that we can predict and control what happens once the system is deployed OOD, made more capable, etc.
• Relevant directions: agent foundations / embedded agency, interpretability, some kinds of “science of deep learning”