# Machine Learning and the Future of Realism

arXiv: https://arxiv.org/abs/1704.04688 · HTML: https://mlsanity.com/paper/1704.04688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena](https://mlsanity.com/paper/2206.05487/) (2022)
- [Deep Learning Opacity in Scientific Discovery](https://mlsanity.com/paper/2206.00520/) (2022)
- [Targeted Learning for Variable Importance](https://mlsanity.com/paper/2411.02221/) (2024)
- [Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representations](https://mlsanity.com/paper/2511.18633/) (2025)

## Abstract (arXiv)

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.