Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning
&gt;&gt; Google Translate is often almost as accurate as a human translator.<p>This is the kind of overhyped reporting of results highlighted by Douglas Hofstadter in his recent article about Google Translate:<p><i>I’ve recently seen bar graphs made by technophiles that claim to represent the “quality” of translations done by humans and by computers, and these graphs depict the latest translation engines as being within striking distance of human-level translation. To me, however, such quantification of the unquantifiable reeks of pseudoscience, or, if you prefer, of nerds trying to mathematize things whose intangible, subtle, artistic nature eludes them. To my mind, Google Translate’s output today ranges all the way from excellent to grotesque, but I can’t quantify my feelings about it.</i><p><a href="https:&#x2F;&#x2F;www.theatlantic.com&#x2F;technology&#x2F;archive&#x2F;2018&#x2F;01&#x2F;the-shallowness-of-google-translate&#x2F;551570&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.theatlantic.com&#x2F;technology&#x2F;archive&#x2F;2018&#x2F;01&#x2F;the-s...</a><p>It&#x27;s funny how the article above is claiming to speak of &quot;the downsides&quot; to deep learning, yet it spends a few paragraphs repeating the marketing pitch of Google, Amazon and Facebook, that their AI is now as good as humans in some tasks (limited as they may be) and all thanks to deep learning. To me that goes exactly counter to the article&#x27;s main point and makes me wonder, what the hell is the author trying to say- and do they even know what they&#x27;re talking about?