Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

Keywords: critical data studies, history, deep learning, machine learning, artificial intelligence

Abstract

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

Author Biography

Niel Chah, University of Toronto
Niel is a PhD student at the University of Toronto, Faculty of Information (iSchool). His research interests include knowledge graphs, data science, and machine learning.
Published
2019-02-01
How to Cite
Chah, N. (2019). Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence. First Monday, 24(2). https://doi.org/10.5210/fm.v24i2.8237