agent that perceives, and acts in order to maximize, its expected utility. Subfields such as logical planning, robotics, and natural-language understanding are special cases of the general paradigm. AI has incorporated probability theory to handle uncertainty, utility theory to define objectives, and statistical learning to allow machines to adapt to new circumstances. These developments have created strong connections to other disciplines that build on similar concepts, including control theory, economics, operations research, and statistics. In both the logical-planning and rational-agent views of AI, the machine’s objective—whether in the form of a goal, a utility function, or a reward function (as in reinforcement learning)—1s specified exogenously. In Wiener’s words, this is “the purpose put into the machine.” Indeed, it has been one of the tenets of the field that AI systems should be general-purpose—i.e., capable of accepting a purpose as input and then achieving it—rather than special-purpose, with their goal implicit in their design. For example, a self-driving car should accept a destination as input instead of having one fixed destination. However, some aspects of the car’s “driving purpose” are fixed, such as that it shouldn’t hit pedestrians. This is built directly into the car’s steering algorithms rather than being explicit: No self-driving car in existence today “knows” that pedestrians prefer not to be run over. Putting a purpose into a machine which optimizes its behavior according to clearly defined algorithms seems an admirable approach to ensuring that the machine’s “conduct will be carried out on principles acceptable to us!” But, as Wiener warns, we need to put in the right purpose. We might call this the King Midas problem: Midas got exactly what he asked for—namely, that everything he touched would turn to gold—but too late he discovered the drawbacks of drinking liquid gold and eating solid gold. The technical term for putting in the righ