Diverse Consequences of Algorithmic Probability

TL;DR

This paper explores the broad implications of algorithmic probability across AI, philosophy, and society, arguing that Solomonoff's framework has formalized AI as a scientific discipline and connecting it to incremental learning and complexity philosophy.

Contribution

It advocates that Solomonoff's axiomatization has established AI as a rigorous scientific field and relates algorithmic probability to various interdisciplinary applications.

Findings

Algorithmic probability underpins diverse AI applications.

Solomonoff's axiomatization formalizes AI as a scientific discipline.

Connections between algorithmic probability, incremental learning, and complexity philosophy.

Abstract

We reminisce and discuss applications of algorithmic probability to a wide range of problems in artificial intelligence, philosophy and technological society. We propose that Solomonoff has effectively axiomatized the field of artificial intelligence, therefore establishing it as a rigorous scientific discipline. We also relate to our own work in incremental machine learning and philosophy of complexity.