Machine learning's own Industrial Revolution

TL;DR

This paper discusses how machine learning must undergo its own industrial revolution to overcome current challenges and fully realize its potential in broad industrial applications.

Contribution

It provides a perspective on the necessary steps for ML to achieve its industrial revolution and discusses strategies for rapid deployment and mass adoption.

Findings

ML faces significant challenges due to lack of standardized networks

Achieving ML's industrial revolution can enable rapid translation to mass production

New opportunities exist for scaling ML innovations across industries

Abstract

Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.