In Part Two, The Problem of Inference, I argue that the only type of inference—thinking, in other words—that will work for human-level AI (or anything even close to it) is the one we don’t have a clue how to program or engineer. The problem of inference goes to the heart of the AI debate because it deals directly with intelligence, in people or machines. Our knowledge of the various types of inference dates back to Aristotle and other ancient Greeks, and has been developed in the fields of logic and mathematics. Inference is already described using formal, symbolic systems like computer programs, so a very clear view of the project of engineering intelligence can be gained by exploring inference. There are three types. Classic AI explored one (deduction), modern AI explores another (induction). The third type (abduction) makes for general intelligence, and, surprise, no one is working on it—at all.1 Finally, since each type of inference is distinct—meaning, one type cannot be reduced to another—we know that failure to build AI systems using the type of inference undergirding general intelligence will result in failure to make progress toward artificial general intelligence, or AGI.