Reviews & Analysis

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  • 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
  • The ethical use of publicly available datasets with human data for which consent has not been explicitly given needs urgent attention from researchers, funders, research institutes and publishers. A specific challenging case is research involving hacked data and this Perspective discusses whether and under what conditions it is morally and ethically justified to conduct such research.

    • Marcello Ienca
    • Effy Vayena
  • Selecting interesting proton–proton collisions from the millions taking place each second in the Large Hadron Collider is a challenging task. A neural network optimized for a field-programmable gate array hardware enables 60 ns inference and reduces power consumption by a factor of 50.

    • David Rousseau
    News & Views
  • Finding the optimum design of a complex auction is a challenging and important economic problem. Multi-agent deep learning can help find equilibria by making use of inherent symmetries in bidding strategies.

    • David C. Parkes
    News & Views
  • Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

    • Vishwali Mhasawade
    • Yuan Zhao
    • Rumi Chunara
  • As highly automated systems become pervasive in society, enforceable governance principles are needed to ensure safe deployment. This Perspective proposes a pragmatic approach where independent audit of AI systems is central. The framework would embody three AAA governance principles: prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements.

    • Gregory Falco
    • Ben Shneiderman
    • Zee Kin Yeong
  • Traditional sensing techniques apply computational analysis at the output of the sensor hardware to separate signal from noise. A new, more holistic and potentially more powerful approach proposed in this Perspective is designing intelligent sensor systems that ‘lock-in’ to optimal sensing of data, making use of machine leaning strategies.

    • Zachary Ballard
    • Calvin Brown
    • Aydogan Ozcan
  • Online targeted advertising fuelled by machine learning can lead to the isolation of individual consumers. This problem of ‘epistemic fragmentation’ cannot be tackled with current regulation strategies and a new, civic model of governance for advertising is needed.

    • Silvia Milano
    • Brent Mittelstadt
    • Christopher Russell
  • Drug repurposing provides a way to identify effective treatments more quickly and economically. To speed up the search for antiviral treatment of COVID-19, a new platform provides a range of computational models to identify drugs with potential anti-COVID-19 effects.

    • Ania Korsunska
    • David C. Fajgenbaum
    News & Views
  • Modern machine learning approaches, such as deep neural networks, generalize well despite interpolating noisy data, in contrast with textbook wisdom. Mitra describes the phenomenon of statistically consistent interpolation (SCI) to clarify why data interpolation succeeds, and discusses how SCI elucidates the differing approaches to modelling natural phenomena represented in modern machine learning, traditional physical theory and biological brains.

    • Partha P. Mitra
  • 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