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

机译：深入兔子洞：从机器学习和人工智能中解开深度学习

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摘要

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.
机译：最近，来自行业和普通大众对深度学习，机器学习和人工智能的兴趣达到了高潮。但是，这些术语经常被滥用，混淆和混淆。通过简要探讨深度学习的历史背景，其公开存在以及对这些技术的局限性的日益增长的关注，本文对那些对这些越来越有影响力的概念有较高了解的人士提供了非技术指导。第一步，定义人工智能和机器学习。接下来，概述深度学习的历史背景，揭示了其广泛的范围和深厚的渊源。提出了一个主要的深度学习实施案例研究，以分析由专注于技术的公司塑造的公众看法。最后，对深度学习局限性的回顾说明了系统漏洞和对这些系统的日益增长的担忧。

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