Volume 4 Issue 9, September 2022

Policymakers and researchers consistently call for greater human accountability for AI technologies. We should be clear about two distinct features of accountability.

In animals, both body and neural control have co-evolved to be adaptable to the environment. While a newborn foal learns quickly how to use its legs, traditional robotic approaches require careful engineering and calibration for stable walking robots. Bio-inspired robotics aims to bridge this gap.

PROTACs can directly and selectively degrade proteins, which opens promising applications in the design of novel drugs, but designing effective PROTACs is a hard challenge due to the complexity of pharmacokinetics. Zheng et al. use a deep generative model to create likely candidates and screen them further to identify a novel BRD4-degrading PROTAC.

Changing weather conditions pose a challenge for autonomous vehicles. Almalioglu and colleagues use a geometry-aware learning technique that fuses visual, lidar and radar information, such that the benefits of each can be used under different weather conditions.

Genomic sequencing offers a wealth of information that could be analysed with deep neural networks. But despite good performance, neural networks can choose random features for their prediction. Kassani et al. present a method to stabilize the features selected by a DNN to make it more interpretable.

To understand reactions in organic chemistry, ideally simple rules would help us predict the outcome of new reactions, but in reality such rules are not easily identified. Chen and Jung extract generalized reaction templates from data and show that they can be used in graph neural networks to predict the outcome of reactions and, despite simplification, still represent a high percentage of existing reactions.

Producing high-quality 3D refractive index maps from 2D intensity-only measurements is a long-standing objective in computational microscopy, with many applications involving the visualization of cellular and subcellular structures. A new method can reconstruct high-contrast and artefact-free images by employing the neural fields technique, which can learn a continuous 3D representation using a neural network that maps spatial coordinates to the refractive index values.

The performance of machine learning models is usually compared via the mean value of a selected performance measure such as the area under the receiver operating characteristic curve on a specific benchmark data set. However, this measure, its mean value or even relative differences between models do not provide a good prediction of whether the results can translate to other data sets. Gosiewska and colleagues present here a comparison based on Elo ranking, which offers a probabilistic interpretation of how much better one model is than another.