Problems in AI, their roots in philosophy, and implications for science and society

TL;DR

This paper emphasizes the importance of philosophical understanding in AI development, critiques misconceptions about knowledge like Bayesianism, and discusses implications for science and society, including perspectives on AGI.

Contribution

It links philosophical theories of knowledge to current AI practices, highlighting misconceptions and proposing a more philosophically informed approach to AI and AGI.

Findings

Current AI practices mirror mistaken philosophies of knowledge.

Misconceptions like Bayesianism influence public discourse on AI.

Philosophical analysis suggests new directions for AI and AGI development.

Abstract

Artificial Intelligence (AI) is one of today's most relevant emergent technologies. In view thereof, this paper proposes that more attention should be paid to the philosophical aspects of AI technology and its use. It is argued that this deficit is generally combined with philosophical misconceptions about the growth of knowledge. To identify these misconceptions, reference is made to the ideas of the philosopher of science Karl Popper and the physicist David Deutsch. The works of both thinkers aim against mistaken theories of knowledge, such as inductivism, empiricism, and instrumentalism. This paper shows that these theories bear similarities to how current AI technology operates. It also shows that these theories are very much alive in the (public) discourse on AI, often called Bayesianism. In line with Popper and Deutsch, it is proposed that all these theories are based on mistaken philosophies of knowledge. This includes an analysis of the implications of these mistaken philosophies for the use of AI in science and society, including some of the likely problem situations that will arise. This paper finally provides a realistic outlook on Artificial General Intelligence (AGI) and three propositions on A(G)I and philosophy (i.e., epistemology).