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The collective motion of swarms, such as flocks of birds or flying robots, can be described well in terms of locally defined rules, where every agent regulates its motion with respect to a limited set of neighbours. This complex phenomenon can be mathematically modelled using potential fields, but for aerial robots, this is not sufficient to guarantee safety in environments with obstacles. In this issue, Enrica Soria et al.. describe how a predictive model can incorporate the robots' dynamics and environments to improve the speed, order and safety of a swarm of aerial robots. The model is validated with a swarm of five quadrotors navigating a real-world indoor cluttered environment.
Accurate and fair medical machine learning requires large amounts and diverse data to train on. Privacy-preserving methods such as federated learning can help improve machine learning models by making use of datasets in different hospitals and institutes while the data stays where it is collected.
Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.
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.
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.
Gaining access to medical data to train AI applications can present problems due to patient privacy or proprietary interests. A way forward can be privacy-preserving federated learning schemes. Kaissis, Ziller and colleagues demonstrate here their open source framework for privacy-preserving medical image analysis in a remote inference scenario.
The recent advances in computational chemistry rely on large data collections of chemical compounds and reactions. However, not all entries in these datasets are correct. Toniato and colleagues present here an automated approach to identify incorrect reactions, using the effect of catastrophic forgetting in neural networks.
Being able to precisely analyse behaviour is essential for the study of motor behaviour in health and disease, but is often time- and labour-intensive. Brattoli et al. develop an automatic approach based on deep learning for analysing motor behaviour and evaluate it on different species and diverse motor functions.
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.
Identifying cancer driver genes from high-throughput genomic data is an important task to understand the molecular basis of cancer and to develop new treatments including precision medicine. To tackle this challenge, EMOGI, a new deep learning method based on graph convolutional networks is developed, which combines protein–protein interaction networks with multiomics datasets.
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.
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.
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.