I don&#x27;t know what his point was that it was predictable. If you were closely following the field, sure. There were some <i>super</i> promising papers published at the end of 2014. Heck if you just saw the wave of deep learning results that were crushing various AI domains, you could have predicted Go would be on the chopping block soon. I was really sure that Go would be beaten by the end of 2015, which didn&#x27;t happen. However almost everyone I spoke to was skeptical of that, and the outside world was totally unaware. And it still represents relatively fast progress, even if the progress is predictable.<p>Anyway I think drawing strong conclusions about AI progress from this single example is wrong. You need to look at general trends, which is definitely rapid improvement.<p>Here is a study about how fast algorithms are improving in general, after accounting for hardware speedups: <a href="https:&#x2F;&#x2F;intelligence.org&#x2F;files&#x2F;AlgorithmicProgress.pdf" rel="nofollow">https:&#x2F;&#x2F;intelligence.org&#x2F;files&#x2F;AlgorithmicProgress.pdf</a><p>&gt;Chess programs have improved by around fifty Elo points per year over the last four
decades. Estimates for the significance of hardware improvements are very noisy but are
consistent with hardware improvements being responsible for approximately half of all
progress. Progress has been smooth on the scale of years since the 1960s, except for the
past five.<p>&gt;Go programs have improved about one stone per year for the last three decades. Hardware
doublings produce diminishing Elo gains on a scale consistent with accounting for
around half of all progress.