Few fields have been more filled with hype and bravado than artificial intelligence (AI). It has flitted from fad to fad decade by decade, always promising the moon and only occasionally delivering. One minute it was expert systems, next it was Bayesian networks, and then support vector machines. Nowadays, the flavor of choice has been deep learning, the multibillion-dollar technique that drives so much of contemporary AI.
But deep learning is at its best when all we need are rough-ready results, where stakes are low and perfect results optional. When the stakes are higher, we need to be much more cautious about adopting deep learning.
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