Skip to yearly menu bar Skip to main content


Poster

Can Machines Learn the True Probabilities?

Jinsook Kim

Hall C 4-9 #1408
[ ] [ Paper PDF ]
Thu 25 Jul 2:30 a.m. PDT — 4 a.m. PDT

Abstract:

When there exists uncertainty, AI machines are designed to make decisions so as to reach the best expected outcomes. Expectations are based on true facts about the objective environment the machines interact with, and those facts can be encoded into AI models in the form of true objective probability functions. Accordingly, AI models involve probabilistic machine learning in which the probabilities should be objectively interpreted. We prove under some basic assumptions when machines can learn the true objective probabilities, if any, and when machines cannot learn them.

Live content is unavailable. Log in and register to view live content