Ron Wilson, Intel FPGA
Deep-learning networks have won. They have outscored humans in classifying still images—at least sort-of. They have defeated world champions at chess and go. They have become the tool of choice for big-data analysis challenges, from customer service to medical diagnosis. They have shown that, once trained, they can be compact enough to fit in a smart phone. So have we reached the end of history for artificial intelligence (AI)? Or is this just the crest of one wave in a much larger ocean?
One answer to that question might come from the plethora of other approaches to AI now jostling for attention. Granted there are always alternatives to a successful technology, if only because every PhD candidate has to find something unique to write about, and every patent has to be circumvented. But many of the alternatives to deep-learning networks today have grown over time out of real issues with conventional static networks like AlexNet. Many are solving real problems and showing up in production systems. If you are designing a system that incorporates machine intelligence, look before you leap.
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