A proposal meant to produce value-aligned agents is 'advanced-safe' if it succeeds, or fails safely, in scenarios where the AI becomes much smarter than its human developers.

Definition

A proposal for a value-alignment methodology, or some aspect of that methodology, is alleged to be 'advanced-safe' if that proposal is claimed robust to scenarios where the agent:

Knows more or has better probability estimates than us

Learns new facts unknown to us

Searches a larger strategy space than we can consider

Confronts new instrumental problems we didn't foresee in detail

Gains power quickly

Has access to greater levels of cognitive power than in the regime where it was previously tested

Wields strategies that wouldn't make sense to us even if we were told about them in advance

Importance

It seems reasonable to expect that there will be difficulties of dealing with minds smarter than our own, doing things we didn't imagine, that will be qualitatively different from designing a toaster oven to not burn down a house, or from designing an AI system that is dumber than human. This means that the concept of 'advanced safety' will end up importantly different from the concept of robust pre-advanced AI.

Concretely, it has been argued to be foreseeable for several difficulties including e.g. programmer deception and unforeseen maximums, that they won't materialize before an agent is advanced, or won't materialize in the same way, or won't materialize as severely. This means that practice with dumber-than-human AIs may not train us against these difficulties, requiring a separate theory and mental discipline for making advanced AIs safe.

We have observed in practice that many proposals for 'AI safety' do not seem to have been thought through against advanced agent scenarios; thus, there seems to be a practical urgency to emphasizing the concept and the difference.

Key problems of advanced safety that are new or qualitatively different compared to pre-advanced AI safety include:

Edge instantiation

Unforeseen maximums

Context change problems

Programmer deception

Programmer maximization

Philosophical competence

Non-advanced-safe methodologies may conceivably be useful if a known algorithm nonrecursive agent can be created that is (a) powerful enough to be relevant and (b) can be known not to become advanced. Even here there may be grounds for worry that such an agent finds unexpectedly strong strategies in some particular subdomain - that it exhibits flashes of domain-specific advancement that break a non-advanced-safe methodology.

Omni-safety

As an extreme case, an 'omni-safe' methodology allegedly remains value-aligned, or fails safely, even if the agent suddenly becomes omniscient and omnipotent (acquires delta probability distributions on all facts of interest and has all describable outcomes available as direct options). See: real-world agents should be omni-safe.