Emergent Goals

"As well as optimizing a subtly wrong goal, systems can develop harmful instrumental goals in the service of a given goal—without these emergent goals being specied in any way [434, 218, 339, 17]. For instance, a theorem in reinforcement learning suggests that optimal and near-optimal policies will seek power over their environment under fairly general conditions [560]. This power-seeking behavior is plausibly the worst of these emergent goals [92], and may be an attractor state for highly capable systems, since most goals can be furthered through gaining resources, self-preservation, preventing goal modication, and blocking adversaries [426, 449]. Presently, power-seeking is not common, because most systems are unable to plan and understand how actions affect their power in the long term [414]."

"Emergent Goals. Ascribing goals to a system is not always straightforward. For our present purposes, it will suffice to adopt a Dennetian perspective (Dennett, 1971), ascribing goals and intentions only when it is useful (i.e., predictive) to do so.51 While it might not be helpful to describe individual narrow AI tools as having goals, their combination may act as a (seemingly) goal-directed collective. For example, a group of moderation bots on a major social networking site could subtly but systematically manipulate the overall political perspectives of the user population, even though, individually, each agent is programmed to simply increase user engagement or filter out dis-preferred content."