Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence

TL;DR

This paper explores how algorithmic randomness underpins inductive reasoning and artificial intelligence, highlighting its role in understanding unpredictability, quantifying simplicity, and addressing fundamental problems in AI development.

Contribution

It provides a personal overview of the significance of algorithmic randomness in AI, emphasizing its foundational role in inductive inference and the philosophy of science.

Findings

Algorithmic randomness helps quantify Ockham's razor.

It offers solutions to the induction problem.

The concepts relate to defining intelligence.

Abstract

This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.