The ML scene was more rigorous 10-15-20 years ago, because it was mostly confined to the world of academia and industrial R&D, and we had much less wide-reachin

The ML scene was more rigorous 10-15-20 years ago, because it was mostly confined to the world of academia and industrial R&D, and we had much less wide-reaching problems to work towards. As tech evolves, more data is being generated, which in turn creates more problems that increases the demand for solutions. To put it short: Solving "real world" problems is more/better rewarded than figuring out the underlying technology, so it's no wonder that we see a larger portion of practitioners that may lack the academic background - and that is understandable. You could be a ML Ph.D (in Academia) for 5-10 year, earning gov. worker salary while trying to reach the next step - or hack together some ML-based product, until desired accuracy, and cash in 50-100 times the pay. Now, with that said, I understand that Ali targeted his speech at the NIPS folk - but the industry / academia crossover in ML is massive, and it still stands that much of industry problems are of commercial interest - so the motivation for many is still the same.