Induction -> models -> prediction -> decision -> action
For example, finding a number in the telephone directory.
Inducts that numbers are sorted by alphabetical order of names from the data.
Models or imagines a straight line domain for all data.
Predicts at what part of the book the number can be found.
Takes decision and action to open that part.
Again infers from data and updates its predictions, decisions, and acts accordingly with the new information.
MARCUS HUTTER approach
It is not necessary for a theory to be computable in general, at least in most of the sciences. But as the Artificial intelligence sits as a branch of computer science, most of AI researchers looks for the theory of AI from the perspective of its computability. MARCUS HUTTER suggests that at least a small chunk of researchers should look for theory of intelligence without considering computational resource constraints and then we can approximate that theory to computability once we find it.
Solomonoff’s theory of induction – Algorithmic probability and Kolmogorov complexity.