As anyone with an even cursory knowledge of AI history knows, there have been several AI Winters, periods of cooling of interest (and drop in funding) in AI research. All of these came about after the realization that at the time dominant AI paradigms were somehow limited. 1/7
For at least a decade now we have been enjoying an unprecedented AI Springtime. A perfect storm of major advances in algorithms (deep learning), computational architecture (GPUs) and availability of large high quality datasets has enabled the field to grow - exponentially! 2/7
However, in the real world there is no such thing as an endless exponential growth. What may seem like an exponential curve, inevitably turns out to be the fast rising part of a logistic curve. It is hard to speculate when the fastest part of the growth will end though. 3/7
Deep Learning architectures seem to be getting close to their optimum, but there still might be some surprises ahead. We are getting near to what is physically possible with the current computational architectures, 4/7
but we are only scratching the surface of what massive parallelization can achieve. Once your datasets contain all of human knowledge, it's hard to imagine where else you can find more data to feed your algorithms with. 5/7
We are still well in the "exploitation" part of the current paradigm, and there are still many gains to be made. But we should keep our eyes open to the alternatives, and devote at least a fraction of our attention, funding and other resources to explore them. 6/7
Because it is very likely that this wave of AI is not the final one, and a new AI Winter may arrive at some point. Maybe even much sooner than most people realize. 7/7
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