Reviews & Analysis

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  • Machine reading and knowledge extraction methods can be used to mine the scientific literature and reveal the direction and robustness of discoveries. Such efforts now point to the importance of independent tests of reported claims.

    • Luís A. Nunes Amaral
    News & Views
  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
    Perspective
  • GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

    • Mohit Pandey
    • Michael Fernandez
    • Artem Cherkasov
    Review Article
  • Graph convolutional networks have become a popular tool for learning with graphs and networks. We reflect on the reasons behind the success story.

    • Mostafa Haghir Chehreghani
    News & Views
  • Predicting the performance of a tactile sensor from its composition and morphology is nearly impossible with traditional computational approaches. Machine learning can not only predict device-level performance, but also recommend new material compositions for soft machine applications.

    • James T. Glazar
    • Vivek B. Shenoy
    News & Views
  • Drug resistance in tropical diseases such as malaria requires constant improvement and development of new drugs. To find potential candidates, generative machine learning methods that can search for novel bioactive molecules can be employed.

    • David A. Winkler
    News & Views
  • Machine learning applications in agriculture can bring many benefits in crop management and productivity. However, to avoid harmful effects of a new round of technological modernization, fuelled by AI, a thorough risk assessment is required, to review and mitigate risks such as unintended socio-ecological consequences and security concerns associated with applying machine learning models at scale.

    • Asaf Tzachor
    • Medha Devare
    • Seán Ó hÉigeartaigh
    Perspective
  • Although artificial reinforcement learning agents do well when rules are rigid, such as games, they fare poorly in real-world scenarios where small changes in the environment or the required actions can impair performance. The authors provide an overview of the cognitive foundations of hierarchical problem-solving, and propose steps to integrate biologically inspired hierarchical mechanisms to enable problem-solving skills in artificial agents.

    • Manfred Eppe
    • Christian Gumbsch
    • Stefan Wermter
    Perspective
  • Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.

    • Pat Pataranutaporn
    • Valdemar Danry
    • Misha Sra
    Perspective
  • Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

    • Kenneth Atz
    • Francesca Grisoni
    • Gisbert Schneider
    Review Article
  • Newly sequenced organisms present a challenge for protein function prediction, as they lack experimental characterisation. A network-propagation approach that integrates functional network relationships with protein annotations, transferred from well-studied organisms, produces a more complete picture of the possible protein functions.

    • Yingying Zhang
    • Shayne D. Wierbowski
    • Haiyuan Yu
    News & Views
  • Substantial advances have been made in the past decade in developing high-performance machine learning models for medical applications, but translating them into practical clinical decision-making processes remains challenging. This Perspective provides insights into a range of challenges specific to high-dimensional, multimodal medical imaging.

    • Rohan Shad
    • John P. Cunningham
    • William Hiesinger
    Perspective
  • The development of extra fingers and arms is an exciting research area in robotics, human–machine interaction and wearable electronics. It is unclear, however, whether humans can adapt and learn to control extra limbs and integrate them into a new sensorimotor representation, without sacrificing their natural abilities. The authors review this topic and describe challenges in allocating neural resources for robotic body augmentation.

    • Giulia Dominijanni
    • Solaiman Shokur
    • Silvestro Micera
    Review Article
  • The radiomics features of disease lesions can be learned from medical imaging data, but is it possible to identify interpretable biomarkers that can help make clinical predictions across heterogeneous diseases and data from different modalities?

    • Yue Wang
    • David M. Herrington
    News & Views
  • Functional subsystems of the macroscale human brain connectome are mapped onto a recurrent neural network and found to perform optimally in a critical regime at the edge of chaos.

    • Nabil Imam
    News & Views
  • Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

    • Charlotte Frenkel
    News & Views
  • When the training data for machine learning are highly personal or sensitive, collaborative approaches can help a collective of stakeholders to train a model together without having to share any data. But there are still risks to the privacy of the data. This Perspective provides an overview of potential attacks on collaborative machine learning and how these threats could be addressed.

    • Dmitrii Usynin
    • Alexander Ziller
    • Jonathan Passerat-Palmbach
    Perspective