Articles in 2020

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.

Microrobots are usually too small to contain traditional computing substrates that could control their behaviour. Dekanovsky and colleagues have developed a microrobot swarm that removes hormonal pollutants when it senses a chemical signal in its environment.

Metal–organic frameworks (MOFs) are attractive materials for gas capture, separation, sensing and catalysis. Determining their water stability is important, but time-intensive. Batra et al. use machine learning to screen water-stable MOFs and identify chemical features supporting their stability.

The thickness of the retina is an important medical indicator for diabetic retinopathy. Holmberg and colleagues present a self-supervised deep-learning method that uses cross-modal data to predict retinal thickness maps from easily obtainable fundus images.

The wealth of data gathered from single-cell RNA sequencing can be processed with deep learning techniques, but often those methods are too opaque to reveal why a single cell is labelled to be a certain cell type. Lifei Wang and colleagues present an RNA-sequencing analysis method that uses capsule networks and is interpretable enough to allow for identification of cell-type-specific genes.

Across disciplines, there is a rising interest in interpreting machine learning models to derive scientific knowledge from data. Genkin and Engel show that models optimized for predicting data can disagree with the ground truth and propose a new model selection principle to prioritize accurate interpretation.

Neural network models can predict the socioeconomic wealth of an area from aerial views, but fall short of explaining how visual features trigger a given prediction. The authors develop a pipeline for projecting class activation maps onto the underlying urban topology, to help interpret such predictions.

Autonomous driving technology is improving, although doubts about their reliability remain. Controllers based on compact neural architectures could help improve their interpretability and robustness.

Addressing the problems caused by AI applications in society with ethics frameworks is futile until we confront the political structure of such applications.

Robots can relieve humans of dangerous tasks. With the pandemic making physical contact potentially dangerous due to the risk of contagion, a new focus for robotic applications in healthcare has come into view.

To infer a previously unknown molecular formula from mass spectrometry data is a challenging, yet neglected problem. Ludwig and colleagues present a network-based approach to ranking possible formulas.

Inspired by the brain of the roundworm Caenorhabditis elegans, the authors design a highly compact neural network controller directly from raw input pixels. Compared with larger networks, this compact controller demonstrates improved generalization, robustness and interpretability on a lane-keeping task.

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.

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.

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.

Magnetic endoscopes have the potential to improve access, reduce patient discomfort and enhance safety. While navigation of magnetic endoscopes can be challenging for the operator, a new approach by Martin, Scaglioni and colleagues explores how to reduce this burden by offering different levels of autonomy in robotic colonoscopy.

Recent advances have increased the dimensionality and complexity of immunological data. The authors developed a machine learning approach to incorporate prior immunological knowledge and applied it on clinical examples and a simulation study. The approach may be useful for high-dimensional datasets in clinical settings where the cohort size is limited.

Classifying cells from single-cell RNA sequences is challenging for cells where only limited data is available. Hu and colleagues show here that a clustering approach using transfer learning can use the variability of one dataset to cluster a smaller target dataset with high-quality results.