AI is always less impressive than is claimed
I opened the link expecting a discussion of flawed metrics that fail to capture uncertainty or failure in replication of claimed studies, or complex architecture turning out to be shallow combinations of simpler ones.<p>Instead the post attacks a straw man argument about &quot;General intelligence&quot;, something that no AI researcher is claiming.<p>Also the author is clearly clueless as evidenced by:<p>&quot;Ask a medical researcher to estimate the odds that a Google-funded AI-driven cancer moonshot will succeed, and I expect you will be met with a snicker. &#x27;<p>Google employs several researchers [1] who are well versed in both medicine as well as ML. Who are able to competently judge capabilities and limits of their tools and challenges involved in doing medical research. Rather than creating straw mental images of &quot;medical researcher&quot; &quot;snickering&quot; at &quot;moonshots&quot;, the author should to engage in some self reflection before writing about scientific fields he has zero knowledge of.<p>AND<p>&quot;I promise you, Facebook did not just work it all out in PHP, and they will not do so in my lifetime or yours.&quot;<p>I won&#x27;t comment on absurdity  of the paragraph about FB or language &amp; meaning. But if any reader was confused, Facebook AI team uses Torch&#x2F;Lua (for deep learning) or Python (in FBLearnerFlow) and surely not PHP.<p>[1] <a href="http:&#x2F;&#x2F;www.nimh.nih.gov&#x2F;about&#x2F;dr-tom-insel-to-step-down-as-nimh-director.shtml" rel="nofollow">http:&#x2F;&#x2F;www.nimh.nih.gov&#x2F;about&#x2F;dr-tom-insel-to-step-down-as-n...</a>