Home → Magazine Archive → February 2023 (Vol. 66, No. 2) → Software Engineering of Machine Learning Systems → Abstract

Software Engineering of Machine Learning Systems

By Charles Isbell, Michael L. Littman, Peter Norvig

Communications of the ACM, Vol. 66 No. 2, Pages 35-37

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Machine learning (ML) is ubiquitous, contributing to society-facing applications that each day impact how we work, play, communicate, live, and solve problems. In 2015, after Marc Andreesen proclaimed "software is eating the world," others countered "machine learning is eating software." While there have been exciting ACM A.M. Turing-award-winning worthy advances in the science of ML, it is important to remember ML is not just an academic subject: it is a technology used to build software that is consequential in the real world.

Failures of ML systems are commonplace in the news: IBM's Oncology Expert Advisor project is canceled for poor results after a $60 million investment; the chatbot Tay learns abusive language; an Uber self-driving car runs a red light; a Knightscope security robot knocks over a toddler;8 facial recognition systems unfairly make three times more errors for non-white non-males.2


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