Collection |

The multidisciplinary nature of machine intelligence

Machine Intelligence is a highly multidisciplinary and active field, combining computer science, robotics and cognitive science, with potentially transformative applications in many areas of science, industry and society. Current research aims to develop AI systems with broad applicability that will safely interact with humans and the physical world. Different concepts and approaches – machine learning, symbolic reasoning, cognitive science, developmental psychology, robot control engineering, human-machine interactions among others – are increasingly brought together for such goals. To mark the impending launch of Nature Machine Intelligence, this collection explores recent developments in the field and its wide impact in other areas.

Machine Learning

  • Nature | Article

    A computer Go program based on deep neural networks defeats a human professional player to achieve one of the grand challenges of artificial intelligence.

    • David Silver
    • , Aja Huang
    • , Chris J. Maddison
    • , Arthur Guez
    • , Laurent Sifre
    • , George van den Driessche
    • , Julian Schrittwieser
    • , Ioannis Antonoglou
    • , Veda Panneershelvam
    • , Marc Lanctot
    • , Sander Dieleman
    • , Dominik Grewe
    • , John Nham
    • , Nal Kalchbrenner
    • , Ilya Sutskever
    • , Timothy Lillicrap
    • , Madeleine Leach
    • , Koray Kavukcuoglu
    • , Thore Graepel
    •  &  Demis Hassabis
  • Nature | Review Article

    The application and development of machine-learning methods used in experiments at the frontiers of particle physics (such as the Large Hadron Collider) are reviewed, including recent advances based on deep learning.

    • Alexander Radovic
    • , Mike Williams
    • , David Rousseau
    • , Michael Kagan
    • , Daniele Bonacorsi
    • , Alexander Himmel
    • , Adam Aurisano
    • , Kazuhiro Terao
    •  &  Taritree Wongjirad
  • Nature | Letter

    A reinforcement learning approach allows a suitably equipped glider to navigate thermal plumes autonomously in an open field.

    • Gautam Reddy
    • , Jerome Wong-Ng
    • , Antonio Celani
    • , Terrence J. Sejnowski
    •  &  Massimo Vergassola
  • Nature | Letter

    Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts.

    • Bo Zhu
    • , Jeremiah Z. Liu
    • , Stephen F. Cauley
    • , Bruce R. Rosen
    •  &  Matthew S. Rosen
  • Nature | Review Article

    • Yann LeCun
    • , Yoshua Bengio
    •  &  Geoffrey Hinton
  • Nature Physics | Letter

    The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order.

    • Juan Carrasquilla
    •  &  Roger G. Melko

Bio-inspired Intelligence

  • Nature Electronics | Review Article

    This Review Article examines the development of organic neuromorphic devices, considering the different switching mechanisms used in the devices and the challenges the field faces in delivering neuromorphic computing applications.

    • Yoeri van de Burgt
    • , Armantas Melianas
    • , Scott Tom Keene
    • , George Malliaras
    •  &  Alberto Salleo
  • Nature Communications | Article | open

    Multi-layered neural architectures that implement learning require elaborate mechanisms for symmetric backpropagation of errors that are biologically implausible. Here the authors propose a simple resolution to this problem of blame assignment that works even with feedback using random synaptic weights.

    • Timothy P. Lillicrap
    • , Daniel Cownden
    • , Douglas B. Tweed
    •  &  Colin J. Akerman
  • Nature Communications | Article | open

    Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

    • Decebal Constantin Mocanu
    • , Elena Mocanu
    • , Peter Stone
    • , Phuong H. Nguyen
    • , Madeleine Gibescu
    •  &  Antonio Liotta
  • Nature Methods | Article

    FreemoVR is a virtual reality system for freely moving animals. The versatile platform is demonstrated in various experiments with Drosophila, zebrafish, and mice.

    • John R Stowers
    • , Maximilian Hofbauer
    • , Renaud Bastien
    • , Johannes Griessner
    • , Peter Higgins
    • , Sarfarazhussain Farooqui
    • , Ruth M Fischer
    • , Karin Nowikovsky
    • , Wulf Haubensak
    • , Iain D Couzin
    • , Kristin Tessmar-Raible
    •  &  Andrew D Straw


Machine Intelligence and Society

  • Nature Communications | Article | open

    Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.

    • Jacob W. Crandall
    • , Mayada Oudah
    • , Tennom
    • , Fatimah Ishowo-Oloko
    • , Sherief Abdallah
    • , Jean-François Bonnefon
    • , Manuel Cebrian
    • , Azim Shariff
    • , Michael A. Goodrich
    •  &  Iyad Rahwan
  • Nature Human Behaviour | Comment

    Self-driving cars offer a bright future, but only if the public can overcome the psychological challenges that stand in the way of widespread adoption. We discuss three: ethical dilemmas, overreactions to accidents, and the opacity of the cars’ decision-making algorithms — and propose steps towards addressing them.

    • Azim Shariff
    • , Jean-François Bonnefon
    •  &  Iyad Rahwan
  • Nature Human Behaviour | Comment

    The development of autonomous weapon systems, by removing the human element of warfare, could make war crimes and atrocities a thing of the past. But if these systems are unable to respect the principles of humanitarian law, we might create a super-intelligent predator that is beyond our control.

    • James Dawes