Indeed,<p>Most of (relative) successes of machine learning have involved a circuit something like<p>(input data) --&gt; ML --&gt; (human acceptable output)<p>We see this in translation, image classification, face recognition.<p>But if we a circuit of<p>(input data) --&gt; ML --&gt; (data for further <i>significant</i> processing by computer)<p>We have a problem. The ML can&#x27;t be a real &quot;black box&quot; at all here in the sense it can&#x27;t learn the correct classifications without regard to how those classifications are going to be used. Since it&#x27;s learning &quot;what humans do&quot;, the code can&#x27;t be that much more reliable than humans but it also has the problem of not being adaptable in the fashion that humans (sometimes) are adaptable.