Volume 3 Issue 1, January 2021

We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021.

Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

Autonomous flight is challenging for small flying robots, given the limited space for sensors and on-board processing capabilities, but a promising approach is to mimic optical-flow-based strategies of flying insects. A new development improves this technique, enabling smoother landings and better obstacle avoidance, by giving robots the ability to learn to estimate distances to objects by their visual appearance.

The annotation of the visual signs of emotions can be important for psychological studies and even human–computer interactions. Instead of only ascribing discrete emotions, Toisoul and colleagues use a single neural network that predicts emotional labels on a spectrum of valence and arousal without separate face-alignment steps.

Inspired by many examples in nature where organisms change shape to concur environments, there is much interest in designing robots that are capable of shape change. Shah et al. demonstrate a method for automatically discovering shape and gait changes for soft robots that can adapt to different terrains.

To remove artefacts from medical imaging, machine learning can be a useful tool, but supervised approaches need examples of the same image with and without artefacts. Liu et al. present a method to train an artefact removal network without needing matching images of corrupted and uncorrupted images.

Many approved drugs can be used to treat diseases other than the one they were developed for, which has the added benefit that the safety of the drug has already been tested. To identify possible candidates for re-purposing trials, Liu et al. have developed a method to use existing electronic patient data to simulate clinical trials and identify drugs that influence the progression of diseases with which they were not previously associated.

Reticular frameworks are crystalline porous materials with desirable properties such as gas separation, but their large design space presents a challenge. An automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder can efficiently explore this space.

Turbulence modelling is an essential flow simulation tool, but is typically dependent on physical insight and engineering intuition. Novati et al. develop a multi-agent reinforcement learning approach for learning turbulence models that can generalize across grid sizes and flow conditions.