Volume 1 Issue 1, January 2019

Volume 1 Issue 1

Intelligent collaboration within reach

As robots are becoming skilled at performing complex tasks, the next step is to enable useful and safe interactions with humans. To effectively collaborate with and assist us, robots need to be able to understand human actions and intent. This issue of Nature Machine Intelligence features an Article describing a game theoretic approach for adaptive human–robot collaboration, as well as a Comment that considers how several trends in robotics and AI research are merging for a fresh take on collaborative robotics.

See Li et al., News & Views by Drnach & Ting and Comment by Goldberg

Image: Robert Adrian Hillman/Alamy Stock Vector (hands); GoMixer/Alamy Stock Vector (machine). Cover Design: Karen Moore.

Editorial

  • Editorial |

    As artificial intelligence, robotics and machine learning are high on the agenda everywhere, Nature Machine Intelligence launches to stimulate collaborations between different disciplines.

Comment & Opinion

  • Comment |

    Ken Goldberg reflects on how four exciting sub-fields of robotics — co-robotics, human–robot interaction, deep learning and cloud robotics — accelerate a renewed trend toward robots working safely and constructively with humans.

    • Ken Goldberg
  • Comment |

    Debate about the impacts of AI is often split into two camps, one associated with the near term and the other with the long term. This divide is a mistake — the connections between the two perspectives deserve more attention, say Stephen Cave and Seán S. ÓhÉigeartaigh.

    • Stephen Cave
    •  & Seán S. ÓhÉigeartaigh

Books & Arts

News & Views

  • News & Views |

    To be useful in a variety of daily tasks, robots must be able to interact physically with humans and infer how to be most helpful. A new theory for interactive robot control allows a robot to learn when to assist or challenge a human during reaching movements.

    • Luke Drnach
    •  & Lena H. Ting

Reviews

  • Perspective |

    A new vision for robot engineering, building on advances in computational materials techniques, additive and subtractive manufacturing as well as evolutionary computing, describes how to design a range of specialized robots uniquely suited to specific tasks and environmental conditions.

    • David Howard
    • , Agoston E. Eiben
    • , Danielle Frances Kennedy
    • , Jean-Baptiste Mouret
    • , Philip Valencia
    •  & Dave Winkler
  • Perspective |

    Arguably one of the most promising as well as critical applications of deep learning is in supporting medical sciences and decision making. It is time to develop methods for systematically quantifying uncertainty underlying deep learning processes, which would lead to increased confidence in practical applicability of these approaches.

    • Edmon Begoli
    • , Tanmoy Bhattacharya
    •  & Dimitri Kusnezov
  • Review Article |

    Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • , Jeff Clune
    • , Joel Lehman
    •  & Risto Miikkulainen

Research

  • Article |

    Not all mathematical questions can be resolved, according to Gödel’s famous incompleteness theorems. It turns out that machine learning can be vulnerable to undecidability too, as is illustrated with an example problem where learnability cannot be proved nor refuted.

    • Shai Ben-David
    • , Pavel Hrubeš
    • , Shay Moran
    • , Amir Shpilka
    •  & Amir Yehudayoff
  • Article |

    Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.

    • Can Li
    • , Zhongrui Wang
    • , Mingyi Rao
    • , Daniel Belkin
    • , Wenhao Song
    • , Hao Jiang
    • , Peng Yan
    • , Yunning Li
    • , Peng Lin
    • , Miao Hu
    • , Ning Ge
    • , John Paul Strachan
    • , Mark Barnell
    • , Qing Wu
    • , R. Stanley Williams
    • , J. Joshua Yang
    •  & Qiangfei Xia
  • Article |

    Most machine learning approaches extract statistical features from data, rather than the underlying causal mechanisms. A different approach analyses information in a general way by extracting recursive patterns from data using generative models under the paradigm of computability and algorithmic information theory.

    • Hector Zenil
    • , Narsis A. Kiani
    • , Allan A. Zea
    •  & Jesper Tegnér

Challenge Accepted

  • Challenge Accepted |

    Yuanfang Guan explains how taking part in data challenges has helped her learn new analytical techniques and creatively apply them on a variety of datasets.

    • Yuanfang Guan

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