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DEEP TROUBLE FOR DEEP LEARNING

Nature (London), 2019-10, Vol.574 (7777), p.163-166 [Peer Reviewed Journal]

Copyright Nature Publishing Group Oct 10, 2019 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/d41586-019-03013-5

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  • Title:
    DEEP TROUBLE FOR DEEP LEARNING
  • Author: Heaven, Douglas
  • Subjects: Artificial intelligence ; Calibration ; Colleges & universities ; Computer & video games ; Computer simulation ; Deep learning ; Machine learning ; Neural networks ; Neurons ; Researchers ; Robotics ; Scientists
  • Is Part Of: Nature (London), 2019-10, Vol.574 (7777), p.163-166
  • Description: AIs that play games can be sabotaged: in 2017, computer scientist Sandy Huang, a PhD student at the University of California, Berkeley, and her colleagues focused on DNNs that had been trained to beat Atari video games through a process called reinforcement learning8. When an AI sees a doctored image of a lion as a library, a person still sees a lion because they have a mental model of the animal that rests on a set of high-level features - ears, a tail, a mane and so on - that lets them abstract away from low-level arbitrary or incidental details. What's more, the data might not be reliable, because the calibration of sensors can change over time and hardware can degrade. Because of this, most robotics work that involves deep learning still uses simulated environments to speed up the training. In robotics, for instance, computer scientist Kristen Grauman at Facebook AI Research in Menlo Park, California, and the University of Texas at Austin is teaching robots how best to explore new environments for themselves.
  • Publisher: London: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 0028-0836
    EISSN: 1476-4687
    DOI: 10.1038/d41586-019-03013-5
  • Source: ProQuest One Psychology
    ProQuest Central
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