Why Can't Even Superintelligent AI Predict the Future?

Cliff Sosin says even superintelligent AI cannot predict most of what people want to know, because complex systems amplify tiny errors until a forecast falls apart. Intelligence fills in a thin scattering of facts. Mathematics can reach a closed form. Weather, mixing cream into coffee, and how groups of people interact cannot. A simulation carries error in the starting conditions or in each truncated step, and that error builds.

The weather model behind chaos theory is the concrete case. A forecaster ran it, reset it, and ran it again. The second run matched at first and then diverged. A few days out, one forecast was 70 degrees and sunny and the other was 20 degrees and pouring. The only change was a technician rounding the starting pressure and temperature by one decimal. People called that the butterfly effect: a butterfly in Africa could, in principle, lead to a hurricane in North America. Short of simulating the whole universe, much of it stays unknowable.

The climb from an amoeba to a monkey, a person, Einstein, and then God misses that limit. Intelligence is more like the roundness of a ball than the height of a tree. It rises until a super-smart system is already making every prediction it can, and that set is a small slice of the interesting questions. Past that, new facts have to come from experiments, and experiments take time.