The full Solomonoff measure is uncomputable. So a real-world AI would have a computable approximation of that measure, meaning that there are (rare) worlds that punish it badly.
But you don’t get the Free Lunch from the optimality of Solomonoff’s Measure, but instead from the fact that it lets you avoid giving weight to the adversarial reality functions and distributions normally constructed in the proof of the NFL Theorem.
The full Solomonoff measure is uncomputable. So a real-world AI would have a computable approximation of that measure, meaning that there are (rare) worlds that punish it badly.
But you don’t get the Free Lunch from the optimality of Solomonoff’s Measure, but instead from the fact that it lets you avoid giving weight to the adversarial reality functions and distributions normally constructed in the proof of the NFL Theorem.