Intrinsic and Extrinsic Motivation in Intelligent Systems

There are two ways that systems, human or machine, can get "motivated" to take action in problem solving. One, they can be given goals by some external entity. In some instances, they might have no capability other than to work towards the goals provided by that entity. Two, they can have their own, internal goals, and work towards those goals. If given a goal by an outside entity, they can then try to figure out whether, and how, the external goal might align with their internal goals. In that case, the agent might be said to be acting in a "self-supervised" manner. There are, of course, cases where both intrinsic and extrinsic motivation come into play. This paper will argue that many machine learning systems, as well as human organizations, put too much emphasis on extrinsic motivation, and have not fully taken advantage of the potential of intrinsic motivation. Reinforcement learning systems, for example, have a "reward signal" that is the sole extrinsic motivating factor. It is no wonder then, that even when such systems work well, they are incapable of explaining themselves, because they cannot express an explanation in terms of their own (or their users') goals. In human organizations, relying only on extrinsic motivation (= "incentive") leads to rigid or dictatorial organizations; engaging internal motivation (at some cost to "organizational efficiency") can lead to creativity and invention.