“It felt like philosophy could transition from vague discussions about the mind and brain to something more concrete, algorithmic, and logical.
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“Thinking about thought as a logical process and realizing that computers had arisen from software and hardware implementations of logic, I saw a parallel to the mind and the brain," Jordan says. Russell explored thought as a logical mathematical process. He was inspired to study psychology and statistics after reading British logician Bertrand Russell's autobiography. “There's not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities." JOINING A MOVEMENTĪs a child of the '60s, Jordan has been interested in philosophical and cultural perspectives on how the mind works. “While the science-fiction discussions about AI and super intelligence are fun, they are a distraction," he says. Moreover, he says, it is the first engineering field that is humancentric, focused on the interface between people and technology. He draws parallels to the emergence of chemical engineering in the early 1900s from foundations in chemistry and fluid mechanics, noting that machine learning builds on decades of progress in computer science, statistics, and control theory. He says he believes that developments in machine learning reflect the emergence of a new field of engineering.
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In recent years, he has been on a mission to help scientists, engineers, and others understand the full scope of machine learning. Last year he received the IEEE John von Neumann Medal for his contributions to machine learning and data science. Jordan's contributions have earned him many awards including this year's Ulf Grenander Prize in Stochastic Theory and Modeling from the American Mathematical Society. Unsupervised learning plays an important role in scientific applications where there is an absence of established theory that can provide labeled training data. 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. In 2016 he was ranked as the most influential computer scientist by a program that analyzed research publications, Science reported. The IEEE Fellow is one of the world's leading authorities on machine learning. Jordan should know the difference, after all.
“We don't have that, but people are talking as if we do." “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.
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. 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. 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. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley. Jordan, a leading researcher in AI and machine learning. 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. THE INSTITUTE Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction.
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