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Slashdot: Stop Calling Everything AI, Machine-Learning Pioneer Says

Stop Calling Everything AI, Machine-Learning Pioneer Says
Published on April 01, 2021 at 02:55AM
An anonymous reader shares a report: Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley. He notes that the imitation of human thinking is not the sole goal of machine learning -- the engineering field that underlies recent progress in AI -- or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large data sets in much the way that a search engine augments human knowledge by organizing the Web. Machine learning also can provide new services to humans in domains such as health care, commerce, and transportation, by bringing together information found in multiple data sets, finding patterns, and proposing new courses of action. "People are getting confused about the meaning of AI in discussions of technology trends -- that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans," he says. "We don't have that, but people are talking as if we do." Jordan should know the difference, after all. The IEEE Fellow is one of the world's leading authorities on machine learning. In 2016 he was ranked as the most influential computer scientist by a program that analyzed research publications, Science reported. Jordan helped transform unsupervised machine learning, which can find structure in data without preexisting labels, from a collection of unrelated algorithms to an intellectually coherent field, the Engineering and Technology History Wiki explains. Unsupervised learning plays an important role in scientific applications where there is an absence of established theory that can provide labeled training data.

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