Volume 3 Issue 3, March 2021

The COVID-19 pandemic has highlighted key challenges for patient care and health provider safety. Adaptable robotic systems, with enhanced sensing, manipulation and autonomy capabilities could help address these challenges in future infectious disease outbreaks.

To truly understand the societal impact of AI, we need to look beyond the exclusive focus on quantitative methods, and focus on qualitative methods like ethnography, which shed light on the actors and institutions that wield power through the use of these technologies.

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.

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.

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.

Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

Neural networks are known as universal approximators of continuous functions, but they can also approximate any mathematical operator (mapping a function to another function), which is an important capability for complex systems such as robotics control. A new deep neural network called DeepONet can lean various mathematical operators with small generalization error.

Spiking neural networks could offer a low-energy consuming solution to deep learning applications on the edge and in mobile devices. Using temporal coding, where the timing of spikes carries extra information, a new method efficiently converts conventional artificial neural networks to spiking networks.

Self-driving vehicles must reliably detect the drivable area in front of them in any weather condition. An actively developed sensor approach is camera-based road segmentation, but it is limited by the visible spectrum. Radar-based approaches are a promising alternative and a new method extracts the drivable area from raw radar data by training a deep neural network using paired camera data, which can be labelled automatically using pretrained computer vision models.

In drug discovery and repurposing, systematic analysis of genome-wide gene expression of chemical perturbations on human cell lines is a useful approach, but is limited due to a relatively low experimental throughput. Computational, deep learning methods can help. In this work a graph neural network called Deep Chemical Expression is developed that can predict chemical-induced gene expression profiles. It is applied to identify drug repurposing candidates for COVID-19 treatments.

Model interpretability is important in genomics. Koo and Ploenzke show that exponential activations in the first layer of convolutional neural networks lead to interpretable and robust representations of genomic sequence motifs.

Advanced electron microscopy and spectroscopy techniques can reveal useful structural and chemical details at the nanoscale. An unsupervised deep learning approach helps to reconstruct 3D images and observe the relationship between optical and structural properties of semiconductor nanocrystals, of interest in optoelectronic applications.