> Second, regarding the question of how academic groups should respond scientifically to DeepMind’s entry, I suspect the right answer comes from evolution: adap

> Second, regarding the question of how academic groups should respond scientifically to DeepMind’s entry, I suspect the right answer comes from evolution: adapt. Focus on problems that are less resource intensive, and that require key conceptual breakthroughs and less engineering. I disagree with this notion a bit - the idea that academic researchers interested in managing their career should consider focusing on problems that are less resource intensive in the future to avoid having to compete with the resource advantage of a Deep Mind ... I feel I've seen this position analogously expressed in various forms over time in the internet space "don't try to compete with $huge_tech_company on the internet because $scale challenges that they invested $megabucks into solving". This kind of statement was made over and over again as the internet explosion was going on. If you actually go back and look at the details you'll find stories like 'google built $inhouse technology so they could scale to 100 * x visits per day" for increasing values of x depending on the article publish date. But if you look again at that article in the context of a year or two after it was published, getting to x scale for the cited problem has become trivial with nearly off the shelf hardware and software designs. It seems to me that computing resource advantage is something that should actually _nearly always diminish_ over time, especially when there is widely known/appreciated understanding of the value of the resource ... Scientific research (in many fields) has been performance and design-test-efficiency bottlenecked for a long, long time -- the fact that there are now wider software trends able to support breaking past those bottlenecks in specific problems is not evidence to me that there is now a resource Emperor to whom all others must cow in fear, forever unable to compete head to head in related spaces ... if anything, the fact that more efficient computation approaches are viable _right now_ and known to be valuable, can spread into the academic groups as easily as the domain expertise of academia spread into Deep Mind's first foray int this problem space ...