Machine Learning and the Future of Realism
Machine Learning
2017-04-18 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1704.04688,
title = {Machine Learning and the Future of Realism},
author = {Giles Hooker and Cliff Hooker},
journal= {arXiv preprint arXiv:1704.04688},
year = {2017}
}