Reviews & Analysis

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  • 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
  • Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

    • José Jiménez-Luna
    • Francesca Grisoni
    • Gisbert Schneider
    Review Article
  • Evidence syntheses produced from the scientific literature are important tools for policymakers. Producing such evidence syntheses can be highly time- and labour-consuming but machine learning models can help as already demonstrated in the health and medical sciences. This Perspective describes a machine learning-based framework specifically designed to support evidence syntheses in the area of agricultural research, for tackling the UN Sustainable Development Goal 2: zero hunger by 2030.

    • Jaron Porciello
    • Maryia Ivanina
    • Haym Hirsh
  • Finding states of matter with properties that are just right is a main challenge from metallurgy to quantum computing. A data-driven optimization approach based on gaming strategies could help.

    • Eliska Greplova
    News & Views
  • The proper response to an ever-changing environment depends on the ability to quantify elapsed time, memorize short intervals and forecast when an upcoming experience may occur. A recent study describes the encoding principles of these three types of time using computational modelling.

    • Hugo Merchant
    • Oswaldo Pérez
    News & Views
  • Recent developments in machine learning have seen the merging of ensemble and deep learning techniques. The authors review advances in ensemble deep learning methods and their applications in bioinformatics, and discuss the challenges and opportunities going forward.

    • Yue Cao
    • Thomas Andrew Geddes
    • Pengyi Yang
    Review Article
  • Bayesian networks can capture causal relations, but learning such a network from data is NP-hard. Recent work has made it possible to approximate this problem as a continuous optimization task that can be solved efficiently with well-established numerical techniques.

    • Yunan Luo
    • Jian Peng
    • Jianzhu Ma
    News & Views
  • Developing swarm robots for a specific application is a time consuming process and can be alleviated by automated optimization of the behaviour. Birattari and colleagues discuss that there are two fundamentally different design approaches; a semi-autonomous one, which allows for situation specific tuning from human engineers and one that needs to be entirely autonomous.

    • Mauro Birattari
    • Antoine Ligot
    • Ken Hasselmann