In recent months, evidence has continued to mount that artificial neural networks of the "deep learning" variety are significantly better than previous techniques at a diverse range of visual understanding tasks.
For example, Yannis Assael and colleagues from Oxford have demonstrated a deep learning algorithm for lip reading that is dramatically more accurate than trained human lip readers, and much more accurate than the best previously published algorithms.
Meanwhile, Andre Esteva, Brett Kuprel and colleagues at Stanford described a deep learning algorithm for diagnosing skin cancer that is as accurate as typical dermatologists (who, in the U.S., complete 12 years of post-secondary education before they begin practicing independently).
Even for tasks where classical computer vision algorithms have been successful, deep learning is raising the bar. Examples include optical flow (estimating motion in a sequence of video frames) and stereo matching (matching features in images captured by a pair of stereo cameras).
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