Volume 5 Issue 5, May 2023

Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

Metaverse-enabled healthcare is no longer hypothetical. Developers must now contend with ethical, legal and social hazards if they are to overcome the systematic inefficiencies and inequities that exist for patients who seek care in the real world.

A new geometric deep learning method can reconstruct cellular and subcellular trajectories and characterize mobility in microscopic imaging, for a broad range of challenging scenarios.

Language models trained on proteins can help to predict functions from sequences but provide little insight into the underlying mechanisms. Vu and colleagues explain how extracting the underlying rules from a protein language model can make them interpretable and help explain biological mechanisms.

Online commerce is increasingly relying on pricing algorithms. Using a network-based approach inspired by adversarial machine learning, a firm can learn the strategy of its competitors and use it to unilaterally increase all firms’ profits. This approach, termed as ‘adversarial collusion’, calls for new regulatory measures.

Particle tracking velocimetry to estimate particle displacements in fluid flows in complex experimental scenarios is a challenging task and often comes with high computational cost. Liang and colleagues propose a graph neural network and optimal transport-based algorithm that can greatly improve the accuracy of existing tracking algorithms in real-world applications.

Memory efficient online training of recurrent spiking neural networks without compromising accuracy is an open challenge in neuromorphic computing. Yin and colleagues demonstrate that training a recurrent neural network consisting of so-called liquid time-constant spiking neurons using an algorithm called Forward-Propagation Through Time allows for online learning and state-of-the-art performance at a reduced computational cost compared with existing approaches.

To detect phenotype-related cell subpopulations from single-cell data, appropriate feature sets need to be chosen or learned simultaneously. Ren et al. present here a tool based on Learning with Rejection, a method that during training learns features from cells that can be predicted with high confidence, while cells that the model is not yet certain about are rejected.

Deep learning can be used to predict molecular properties, but such methods usually need a large amount of data and are hard to generalize to different chemical spaces. To provide a useful primer for deep learning models models, Fang and colleagues use contrastive learning and a knowledge graph based on the Periodic Table and Wikipedia pages on chemical functional groups.

The organizers of the TinyML Design Contest describe the top machine-learning-based real-time detection algorithms for ventricular arrhythmia.