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

Niel Chah

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.


Keywords


critical data studies; history; deep learning; machine learning; artificial intelligence

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DOI: https://doi.org/10.5210/fm.v24i2.8237



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