We built machines that can write, hold a conversation, and work through a problem out loud, and we did it before we had a clear account of what any of those verbs mean. The engineering ran ahead of the understanding. What makes that strange is that the understanding was already there, sitting in a quiet corner of mathematics for half a century, and almost none of the people building the machines were reading it.

There is a clean answer to the question of what intelligence is. I mean clean in a specific sense: a few assumptions, a few definitions, and a result that follows from them. It arrives in three moves.

The first move is Bayes. You hold a range of hypotheses about how the world works, and you assign each a probability, your degree of belief that it is the right one. Evidence comes in. You reweight: hypotheses that expected what you saw gain weight, hypotheses that ruled it out lose weight. Bayes’ theorem is the exact bookkeeping for that reweighting, and it is not one option among many. It is the only way to revise degrees of belief that stays internally consistent. But it leaves a hole. It tells you how to update your beliefs, not which beliefs to start with. Where does the prior come from?

The second move, Solomonoff’s, fills the hole. Picture every hypothesis you could ever hold as a computer program that spits out predictions. Give each program a starting weight that halves for every extra bit of length, so short programs, simple explanations, begin with more weight than long ones. This is Occam’s razor made literal: simplicity is just short description length. Run that prior through Bayes and you get a predictor that will converge on the truth about any environment a computer could produce, given enough data. It is, in a precise sense, the best possible learner from experience. The price is steep. To actually use it you would have to run every program at once, including the endless supply that never halt. It is uncomputable. You can write down exactly what it is and never once execute it.