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It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so?
Machine learning has shown remarkable success across many application areas
in recent years, leveraging advances in computing power and the availability
of large sets of training data. It provides a tremendous opportunity to
deploy data-driven systems in more complex and interactive tasks including
personalized autonomy, agile robotics, self-driving vehicles, and smart
cities. Despite dramatic progress, the machine learning community still
lacks an understanding of the trade-offs and mathematical limitations of
related technologies for a given domain, problem, or dataset.