&gt; I think it&#x27;s about machines learning how to do things and improve at them rather than the decision parameters being hard coded.<p>Yes, it&#x27;s just that there&#x27;s nothing <i>necessarily</i> intelligent about learning through statistical clustering. I mean humans don&#x27;t learn something by looking at many thousands of examples; usually a couple suffices. Maybe that&#x27;s just a higher-level statistical induction that yields intelligent deduction[1], but we have no idea. We know that statistical clustering, at least in biological brains, is a more primitive computation than, say, bubble-sort, but we don&#x27;t know if it is <i>necessarily</i> the primitive building block for an &quot;intelligent&quot; algorithm. It&#x27;s also true that we don&#x27;t have a good definition of what intelligence is, but we do have a probably-reasonable intuition about it. Now, don&#x27;t get me wrong, I understand why people feel like this should be called AI (for various reasons, both backward- and forward-looking), but it&#x27;s important to remember that we have no idea where these algorithms are placed on the spectrum between bubble-sort and human intelligence.<p>If anything, machine learning is more of a testament to how effective statistical algorithms can be (which is either &quot;very&quot; or &quot;hardly&quot;, depending on the problem and the problem) without doing anything that could be called intelligent. This is not too surprising. Insects are also extremely effective -- and adaptive -- at some tasks without being intelligent at all.<p>[1]: Word embedding (vector representation of words) is probably the closest we&#x27;ve ever got to deduction, but we don&#x27;t know that this model generalizes well.