Volume 2 Issue 8, August 2020

As robot swarms move from the laboratory to real-world applications, a routine checklist of questions could help ensure their safe operation.

The part that artificial intelligence plays in climate change has come under scrutiny, including from tech workers themselves who joined the global climate strike last year. Much can be done by developing tools to quantify the carbon cost of machine learning models and by switching to a sustainable artificial intelligence infrastructure.

Bayesian networks can capture causal relations, but learning such a network from data is NP-hard. Recent work has made it possible to approximate this problem as a continuous optimization task that can be solved efficiently with well-established numerical techniques.

This review covers the history of procedural content generation (PCG) approaches for video games, and how these approaches are now used to generate training data and environments for machine learning models. The authors then discuss how PCG may be crucial for training agents which generalise well.

Robot-assisted microsurgery promises high stability and accuracy for instance in eye- or neurosurgery applications. A new miniature robotics device, based on an origami-inspired design, can make complex 3D motions and reaches a precision of around 26 micrometres.

Gene expression is regulated by a variety of mechanisms, which have been difficult to study in a unified way. The authors propose a flexible framework that can integrate different types of data for studying their joint effects on gene expression. The framework uses a general network representation for data integration, metapaths for inputting prior knowledge of gene regulatory mechanisms, and embedding techniques for capturing complex structures in the data.

When designing new drugs, there are countless ways to create molecules, yet only a few interact with biological targets. Beker and colleagues provide here a graph neural network based metric for drug-likeness that can guide the search.

The role of DNA methylation on N⁶-adenine (6mA) in eukaryotes is a challenging research problem. Tan et al. develop a deep-learning-based algorithm to predict 6mA sites from sequences at single-nucleotide resolution, and apply the method to three representative model organisms. The method is further developed to visualize regulatory patterns around 6mA sites.

Deep learning approaches can show excellent performance but still have limited practical use if they learn to predict based on confounding factors in a dataset, for instance text labels in the corner of images. By using an explanatory interactive learning approach, with a human expert in the loop during training, it becomes possible to avoid predictions based on confounding factors.