(For the general concept of an agent, see standard agent properties.)
"Sufficiently advanced Artificial Intelligences" are the subjects of AI alignment theory; machine intelligences potent enough that:
Some example properties that might make an agent sufficiently powerful for 1 and/or 2:
Since there's multiple avenues we can imagine for how an AI could be sufficiently powerful along various dimensions, 'advanced agent' doesn't have a neat necessary-and-sufficient definition. Similarly, some of the advanced agent properties are easier to formalize or pseudoformalize than others.
As an example: Current machine learning algorithms are nowhere near the point that they'd try to resist if somebody pressed the off-switch. That would happen given, e.g.:
So the threshold at which you might need to start thinking about 'shutdownability' or 'abortability' or corrigibility as it relates to having an off-switch, is 'big-picture strategic awareness' plus 'cross-domain consequentialism'. These two cognitive thresholds can thus be termed 'advanced agent properties'.
The above reasoning also suggests e.g. that general intelligence is an advanced agent property, because a general ability to learn new domains could lead the AI to understand that it has an off switch.
One reason to keep the term 'advanced' on an informal basis is that in an intuitive sense we want it to mean "AI we need to take seriously" in a way independent of particular architectures or accomplishments. To the philosophy undergrad who 'proves' that AI can never be "truly intelligent" because it is "merely deterministic and mechanical", one possible reply is, "Look, if it's building a Dyson Sphere, I don't care if you define it as 'intelligent' or not." Any particular advanced agent property should be understood in a background context of "If a computer program is doing X, it doesn't matter if we define that as 'intelligent' or 'general' or even as 'agenty', what matters is that it's doing X." Likewise the notion of 'sufficiently advanced AI' in general.
The goal of defining advanced agent properties is not to have neat definitions, but to correctly predict and carve at the natural joints for which cognitive thresholds in AI development could lead to which real-world abilities, corresponding to which alignment issues.
An alignment issue may need to have been already been solved at the time an AI first acquires an advanced agent property; the notion is not that we are defining observational thresholds for society first needing to think about a problem.
Absolute-threshold properties (those which reflect cognitive thresholds irrespective of the human position on that same scale):
Relative-threshold advanced agent properties (those whose key lines are related to various human levels of capability):
Sufficiently sophisticated models and predictions of human minds potentially leads to:
A behaviorist AI is one with reduced capability in this domain.
Probably requires generality (see below). To grasp a concept like "If I escape from this computer by hacking my RAM accesses to imitate a cellphone signal, I'll be able to secretly escape onto the Internet and have more computing power", an agent needs to grasp the relation between its internal RAM accesses, and a certain kind of cellphone signal, and the fact that there are cellphones out there in the world, and the cellphones are connected to the Internet, and that the Internet has computing resources that will be useful to it, and that the Internet also contains other non-AI agents that will try to stop it from obtaining those resources if the AI does so in a detectable way.
Contrasting this to non-primate animals where, e.g., a bee knows how to make a hive and a beaver knows how to make a dam, but neither can look at the other and figure out how to build a stronger dam with honeycomb structure. Current, 'narrow' AIs are like the bee or the beaver; they can play chess or Go, or even learn a variety of Atari games by being exposed to them with minimal setup, but they can't learn about RAM, cellphones, the Internet, Internet security, or why being run on more computers makes them smarter; and they can't relate all these domains to each other and do strategic reasoning across them.
So compared to a bee or a beaver, one shot at describing the potent 'advanced' property would be cross-domain real-world consequentialism. To get to a desired Z, the AI can mentally chain backwards to modeling W, which causes X, which causes Y, which causes Z; even though W, X, Y, and Z are all in different domains and require different bodies of knowledge to grasp.
Many dangerous-seeming convergent instrumental strategies pass through what we might call a rough understanding of the 'big picture'; there's a big environment out there, the programmers have power over the AI, the programmers can modify the AI's utility function, future attainments of the AI's goals are dependent on the AI's continued existence with its current utility function.
It might be possible to develop a very rough grasp of this bigger picture, sufficiently so to motivate instrumental strategies, in advance of being able to model things like cellphones and Internet security. Thus, "roughly grasping the bigger picture" may be worth conceptually distinguishing from "being good at doing consequentialism across real-world things" or "having a detailed grasp on programmer psychology".
An AI that can crack the protein structure prediction problem (which seems speed-uppable by human intelligence); invert the model to solve the protein design problem (which may select on strong predictable folds, rather than needing to predict natural folds); and solve engineering problems well enough to bootstrap to molecular nanotechnology; is already possessed of potentially pivotal capabilities regardless of its other cognitive performance levels.
Other material domains besides nanotechnology might be pivotal. E.g., self-replicating ordinary manufacturing could potentially be pivotal given enough lead time; molecular nanotechnology is distinguished by its small timescale of mechanical operations and by the world containing an infinite stock of perfectly machined spare parts (aka atoms). Any form of cognitive adeptness that can lead up to rapid infrastructure or other ways of quickly gaining a decisive real-world technological advantage would qualify.
If the AI's thought processes and algorithms scale well, and it's running on resources much smaller than those which humans can obtain for it, or the AI has a grasp on Internet security sufficient to obtain its own computing power on a much larger scale, then this potentially implies rapid capability gain and associated context changes. Similarly if the humans programming the AI are pushing forward the efficiency of the algorithms along a relatively rapid curve.
In other words, if an AI is currently being improved-on swiftly, or if it has improved significantly as more hardware is added and has the potential capacity for orders of magnitude more computing power to be added, then we can potentially expect rapid capability gains in the future. This makes context disasters more likely and is a good reason to start future-proofing the safety properties early on.
On complex tractable problems, especially those that involve real-world rich problems, a human will not be able to cognitively 'contain' the space of possibilities searched by an advanced agent; the agent will consider some possibilities (or classes of possibilities) that the human did not think of.
The key premise is the 'richness' of the problem space, i.e., there is a fitness landscape on which adding more computing power will yield improvements (large or small) relative to the current best solution. Tic-tac-toe is not a rich landscape because it is fully explorable (unless we are considering the real-world problem "tic-tac-toe against a human player" who might be subornable, distractable, etc.) A computationally intractable problem whose fitness landscape looks like a computationally inaccessible peak surrounded by a perfectly flat valley is also not 'rich' in this sense, and an advanced agent might not be able to achieve a relevantly better outcome than a human.
The 'cognitive uncontainability' term in the definition is meant to imply:
Particularly surprising solutions might be yielded if the superintelligence has acquired domain knowledge we lack. In this case the agent's strategy search might go outside causal events we know how to model, and the solution might be one that we wouldn't have recognized in advance as a solution. This is Strong cognitive uncontainability.
In intuitive terms, this is meant to reflect, e.g., "What would have happened if the 10th century had tried to use their understanding of the world and their own thinking abilities to upper-bound the technological capabilities of the 20th century?"
(Work in progress)