"These are three of the biggest problems facing today's AI." The need for huge amounts of data, the inability to...
Originally shared by Wayne Radinsky
"These are three of the biggest problems facing today's AI." The need for huge amounts of data, the inability to learn multiple tasks, and the difficulty of seeing into the neural networks to see why they make the decisions they do.
"These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more." "Right now, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon’s AI team, data is like coal was in the early years of the Industrial Revolution. He gives the example of Thomas Newcomen -- an Englishman who, in 1712, invented a primitive version of the steam engine that ran on coal, about 60 years before James Watt did. Newcomen's invention wasn't very good: compared to Watt's machine, it was inefficient and costly to run. That meant it was put to work only in coalfields -- where the fuel was plentiful enough to overcome the machine's handicaps."
http://www.theverge.com/2016/10/10/13224930/ai-deep-learning-limitations-drawbacks
"These are three of the biggest problems facing today's AI." The need for huge amounts of data, the inability to learn multiple tasks, and the difficulty of seeing into the neural networks to see why they make the decisions they do.
"These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more." "Right now, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon’s AI team, data is like coal was in the early years of the Industrial Revolution. He gives the example of Thomas Newcomen -- an Englishman who, in 1712, invented a primitive version of the steam engine that ran on coal, about 60 years before James Watt did. Newcomen's invention wasn't very good: compared to Watt's machine, it was inefficient and costly to run. That meant it was put to work only in coalfields -- where the fuel was plentiful enough to overcome the machine's handicaps."
http://www.theverge.com/2016/10/10/13224930/ai-deep-learning-limitations-drawbacks
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