Studying human learning patterns, according to Domingos, requires a broad, multidisciplinary approach. In other words, to fully grasp the potential of artificial intelligence, it is necessary to deconstruct human intelligence and learning.ĭomingos said he agrees with the sentiment of Yann LeCun, director of AI at Facebook, that: “Most of the knowledge in the future will be extracted by machines and will reside in machines.” But we have a long way to go to perfect that learning, he said. But which principles should we follow? Figuring out the best approach falls to scholars like Pedro Domingos.ĭomingos, a professor of computer science at the University of Washington and the author of The Master Algorithm(Basic Books, 2015), said that in the past few decades, five schools of thought have dominated the understanding of machine learning, each with its own master algorithm and each with its own flaws.Īt a recent MIT IDE seminar, he explained these “five tribes of machine learning,” and how they each contribute to the ultimate goal of a unified, “master algorithm” that will combine many parts into a scalable model. First, we need to understand the underlying principles of where knowledge comes from, and how humans learn. This aphorism holds true when it comes to understanding how machine learning works. Philosophers have said that to know your present, look to the past, then, imagine the future.
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