Reviews & Analysis

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  • A challenge for multiscale simulations is how to link the macroscopic and microscopic length scales effectively. A new machine-learning-based sampling approach enables full exploration of macro configurations while retaining the precision of a microscale model.

    • Shangying Wang
    • Simone Bianco
    News & Views
  • Deep learning applied to genomics can learn patterns in biological sequences, but designing such models requires expertise and effort. Recent work demonstrates the efficiency of a neural network architecture search algorithm in optimizing genomic models.

    • Yi Zhang
    • Yang Liu
    • X. Shirley Liu
    News & Views
  • State of the art neural network approaches enable massive multilingual translation. How close are we to universal translation between any spoken, written or signed language?

    • Marta R. Costa-jussà
    News & Views
  • Hyperspectral imaging can reveal important information without the need for staining. To extract information from this extensive data, however, new methods are needed that can interpret the spatial and spectral patterns present in the images.

    • Rohit Bhargava
    • Kianoush Falahkheirkhah
    News & Views
  • Medical artificial intelligence and machine learning technologies marketed directly to consumers are on the rise. The authors argue that the regulatory landscape for such technologies should operate differently when a system is designed for personal use than when it is designed for clinicians and doctors.

    • Boris Babic
    • Sara Gerke
    • I. Glenn Cohen
  • The dynamical properties of a nonlinear system can be learned from its time-series data, but is it possible to predict what happens when the system is tuned far away from its training values?

    • Daniel J. Gauthier
    • Ingo Fischer
    News & Views
  • 3D image reconstruction is important for the understanding of materials and their function in devices. A generative adversarial network architecture reconstructs 3D materials microstructures from 2D images.

    • Alejandro A. Franco
    News & Views
  • At the heart of many challenges in scientific research lie complex equations for which no analytical solutions exist. A new neural network model called DeepONet can learn to approximate nonlinear functions as well as operators.

    • Irina Higgins
    News & Views
  • Neuromorphic computing could unlock low-power machine learning that can run on edge devices. A new algorithm that implements an artificial neuron emitting a sparse number of spikes could help realize this goal.

    • Tara Hamilton
    News & Views
  • Computational models that capture the nonlinear processing of the inner ear have been prohibitively slow to use for most machine-hearing systems. A convolutional neural network model replicates hallmark features of cochlear signal processing, potentially enabling real-time applications.

    • Laurel H. Carney
    News & Views
  • Many researchers have become interested in implementing artificial intelligence methods in applications with socially beneficial outcomes. To provide a way to study and benchmark such ‘AI for social good’ applications, Josh Cowls et al. use the United Nations’ Sustainable Development Goals to systematically analyse AI for social good applications.

    • Josh Cowls
    • Andreas Tsamados
    • Luciano Floridi
  • The Conference on Neural Information Processing Systems (NeurIPS) introduced a new requirement in 2020 that submitting authors must include a statement on the broader impacts of their research. Prunkl and colleagues discuss challenges and benefits of this requirement and propose suggestions to address the challenges.

    • Carina E. A. Prunkl
    • Carolyn Ashurst
    • Allan Dafoe
  • Chemical reactions can be grouped into classes, but determining what class a specific reaction belongs to is not trivial on a large-scale. A new study demonstrates data-driven automatic classification of chemical reactions with methods borrowed from natural language processing.

    • Jonas Boström
    News & Views
  • Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

    • Risto Miikkulainen
    • Stephanie Forrest
  • The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.

    • Ge Wang
    • Jong Chul Ye
    • Bruno De Man
    Review Article
  • DNN classifiers are vulnerable to small, specific perturbations in an input that seem benign to humans. To understand this phenomenon, Buckner argues that it may be necessary to treat the patterns that DNNs detect in these adversarial examples as artefacts, which may contain predictive information.

    • Cameron Buckner
  • Microrobots can interact intelligently with their environment and complete specific tasks by well-designed incorporation of responsive materials. Recent work demonstrates how swarms of microbots with specifically tuned surface chemistry can remove a hormone pollutant from a solution by coalescing it into a web.

    • Dongdong Jin
    • Li Zhang
    News & Views
  • Deep learning has resulted in impressive achievements, but under what circumstances does it fail, and why? The authors propose that its failures are a consequence of shortcut learning, a common characteristic across biological and artificial systems in which strategies that appear to have solved a problem fail unexpectedly under different circumstances.

    • Robert Geirhos
    • Jörn-Henrik Jacobsen
    • Felix A. Wichmann
  • Autonomous driving technology is improving, although doubts about their reliability remain. Controllers based on compact neural architectures could help improve their interpretability and robustness.

    • Michael Milford
    News & Views
  • Robots could play an important part in transforming healthcare to cope with the COVID-19 pandemic. This Perspective highlights how robotic technology integrated in a range of tasks in the surgical environment could help to ensure a continuation of medical services while reducing the risk of infection.

    • Ajmal Zemmar
    • Andres M. Lozano
    • Bradley J. Nelson