Machine Learning and the Future of Realism

TL;DR

This paper discusses the evolution and impact of machine learning over three decades, highlighting its broad applications and suggesting it may challenge traditional notions of realism in science.

Contribution

It provides a historical overview of ML development and explores its potential to influence philosophical perspectives on realism.

Findings

ML has evolved from simple algorithms to complex problem-solving tools.

Machine learning's widespread adoption could impact philosophical views on realism.

The paper suggests ML may contribute to the triumph of anti-realism.

Abstract

The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational cognition to embrace all forms of problem solving, from voice recognition to medical diagnosis to automated scientific research and driverless cars, and it is now widely opined that the real industrial revolution lies less in mobile phone and similar than in the maturation and universal application of ML. Among the consequences just might be the triumph of anti-realism over realism.