> Statistics? As in David Freedman's books? Uh, statistics tries to be applied probability with some assumptions, theorems, proofs, results, and applications.

> Statistics? As in David Freedman's books? Uh, statistics tries to be applied probability with some assumptions, theorems, proofs, results, and applications. E.g., the assumptions for regression analysis that let us use and F-ratio and t-tests hypothesis testing and confidence intervals. Does current AI with neural networks have such assumptions and results? Maybe with the test data could develop some such results. And AI seems to have and assumption that the training data and the test data are independent and identically distributed (i.i.d.) random variables. Uh, arguing i.i.d. for some enormous collections of data might be difficult? How 'bout some concept of approximately independent? M. Talagrand has some such in his "A New Look at Independence"?