Articles in 2021

Experimental benchmarks such as ImageNet and Atari games play an important part in advancing artificial intelligence research. An analysis of results and papers linked to 25 popular benchmarks shows that research dynamics beyond conventional co-authorship has developed in this area.

The movement of drone swarms can be coordinated using virtual potential fields to reach a global goal and avoid local collisions. Soria et al. propose here to extend potential fields with a predictive model that takes into account the agents’ flight dynamics to improve the speed and safety of the swarm.

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.

Although technologies enable large-scale profiling of chromatin accessibility at the single-cell level, there are methodological challenges due to high dimensionality and high sparsity of data. Liu and colleagues describe a computational tool for the simultaneous determination of latent representation and clustering of cells from single-cell ATAC-seq data using a pair of generative adversarial networks.

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.

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?

We spoke with Mariarosaria Taddeo, an associate professor and senior research fellow at the Oxford Internet Institute and Dstl Ethics Fellow at the Alan Turing Institute working on digital and AI ethics about two recent reports from the UK and the US on using AI in national defence and security.

There is an urgent need to identify drugs that may be effective against SARS-CoV-2. A platform with a range of machine learning models is made available to predict anti-COVID-19 activity in candidate drugs and to help prioritize compounds for virtual screening.

Tackling scientific problems often requires computational models that bridge several spatial and temporal scales. A new simulation framework employing machine learning, which is scalable and can be used on standard laptops as well as supercomputers, promises exhaustive multiscale explorations.

Citizen scientists are empowered by mobile technology to collect data and crowdsource knowledge. Furthermore, automated machine learning tools allow non-experts in AI to analyse data. Ethical and regulatory questions arise, however, as data collection and AI technologies become enmeshed in people’s lives.

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.

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.

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?

Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.

Bats with sophisticated biosonar systems move their ears at a high speed to help localize sound sources. Yin and Müller present a system inspired by this strategy, which can localize sounds with high accuracy and with a single detector, using a flexible silicone model of a bat’s ear and a deep convolutional neural network to process the complex Doppler signatures.

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.