AI winter is well on its way
This is a deep, significant post (pardon pun etc).<p>The author is clearly informed and takes a strong, historical view of the situation. Looking at what the really smart people who brought us this innovation have said and done lately is a good start imo (just one datum of course, but there are others in this interesting survey).<p><i>Deepmind hasn&#x27;t shown anything breathtaking since their Alpha Go zero.</i><p>Another thing to consider about Alpha Go and Alpha Go Zero is the vast, vast amount of computing firepower that this application mobilized. While it was often repeated that ordinary Go program weren&#x27;t making progress, this wasn&#x27;t true - the best, amateur programs had gotten to about 2 Dan amateur using Makov Tree Search. Alpha Go added CNNs for it&#x27;s weighting function and petabytes of power for it&#x27;s process and got effectiveness up to best in the world, 9 Dan professional, (maybe 11 Dan amateur for pure comparison). [1]<p>Alpha Go Zero was supposedly even more powerful, learned without human intervention. BUT it cost petabytes and petabytes of flops, expensive enough that they released a total of ten or twenty Alpha Go Zero game to the world, labeled &quot;A great gift&quot;.<p>The author convenniently reproduces the chart of power versus results. Look at it, consider it. Consider the chart in the context of Moore&#x27;s Law retreating. The problems of Alpha Zero generalizes as described in the article.<p>The author could also have dived into the troubling question as of &quot;AI as ordinary computer application&quot; (what does testing, debugging, interface design, etc mean when the app is automatically generated in an ad-hoc fashion) or &quot;explainability&quot;. But when you can paint a troubling picture without these gnawing problems appearing, you&#x27;ve done well.<p>[1] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Go_ranks_and_ratings" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Go_ranks_and_ratings</a>