&gt; In contrast, &#x27;ML&#x27; assumes from the start that computing resources will be available.<p>Right, but I&#x27;m not sure that &quot;better, cleaner names for things&quot; actually follows. Instead, I find that the ML folks just hacked their way to similar results as traditional statistics, but in many cases were comfortable with the algorithms as &quot;black boxes&quot; rather than having a clear understanding of <i>why</i> the algorithms worked. In that sense, the author&#x27;s &quot;unprincipled&quot; criticism is valid. This is less true today, but the new research in convolutional neural nets shows how ML starts from hacking things until they produce practical results then backing into the theory of why. This habit has resulted in much duplication of effort and naming schemes. My ML prof at Georgia Tech (Isbell was awesome! <a href="http:&#x2F;&#x2F;www.cc.gatech.edu&#x2F;~isbell&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.cc.gatech.edu&#x2F;~isbell&#x2F;</a>) constantly trashed &quot;genetic algorithms&quot; for being a silly form of randomized hill-climbing.<p>The beneficial side of these less-principled techniques is that they happen to work on larger scale datasets. It turns out approximate results are more scalable than exact results.