“Everybody uses different techniques, but you definitely need multiple examples to train off of. And the nature of machine learning is the more data you train with the better the performance generally is, which is one of the reasons why some of the companies like Google and Uber are exploring large scale, real-world tests so they can experience as much as possible and train their systems on as much variation as possible,” Aaron Steinfeld, an associate research professor at the Robotics Institute at Carnegie Mellon, told Business Insider.
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