Researchers in machine learning argue that computers trained on mountains of data can learn just about anything—including common sense—with few, if any, programmed rules. These experts "have a blind spot, in my opinion," says Gary Marcus, a developmental cognitive scientist at New York University. He says computer scientists are ignoring decades of work in the cognitive sciences and developmental psychology showing that humans have innate abilities—programmed instincts that appear at birth or in early childhood—that help us think abstractly and flexibly. He believes AI researchers ought to include such instincts in their programs.
Yet many computer scientists, riding high on the successes of machine learning, are eagerly exploring the limits of what a naïve AI can do. "Most machine learning people, I think, have a methodological bias against putting in large amounts of background knowledge because in some sense we view that as a failure," says Thomas Dietterich, a computer scientist at Oregon State University in Corvallis. He adds that computer scientists also appreciate simplicity and have an aversion to debugging complex code.
In the longer term, computer scientists expect AIs to take on tougher tasks that require flexibility and common sense. Some computer scientists are already trying.
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