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Search: L773:2470 9476 > (2022)

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1.
  • Sandamirskaya, Yulia, et al. (author)
  • Neuromorphic computing hardware and neural architectures for robotics
  • 2022
  • In: SCIENCE ROBOTICS. - : American Association for the Advancement of Science (AAAS). - 2470-9476. ; 7:67
  • Journal article (peer-reviewed)abstract
    • Neuromorphic hardware enables fast and power-efficient neural network-based artificial intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be further developed following neural computing principles and neural network architectures inspired by biological neural systems. In this Viewpoint, we provide an overview of recent insights from neuroscience that could enhance signal processing in artificial neural networks on chip and unlock innovative applications in robotics and autonomous intelligent systems. These insights uncover computing principles, primitives, and algorithms on different levels of abstraction and call for more research into the basis of neural computation and neuronally inspired computing hardware.
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2.
  • Ziemke, Tom, et al. (author)
  • Do we really want AI to be human-like?
  • 2022
  • In: SCIENCE ROBOTICS. - : AMER ASSOC ADVANCEMENT SCIENCE. - 2470-9476. ; 7:68
  • Journal article (other academic/artistic)abstract
    • Behavioral variability can be used to make robots more human-like, but we propose that it may be wiser to make them less so.
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  • Result 1-2 of 2
Type of publication
journal article (2)
Type of content
other academic/artistic (1)
peer-reviewed (1)
Author/Editor
Ziemke, Tom (1)
Conradt, Jörg (1)
Thellman, Sam (1)
Sandamirskaya, Yulia (1)
Kaboli, Mohsen (1)
Celikel, Tansu (1)
University
Royal Institute of Technology (1)
Linköping University (1)
Language
English (2)
Research subject (UKÄ/SCB)
Engineering and Technology (2)
Natural sciences (1)
Year
2022 (2)Undo refine

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