News & Views

Various machine learning models have been developed in recent years for the discovery of crystal structures. Matbench Discovery, a new benchmark, offers an efficient way to identify the most promising architectures.

Generative deep learning models offer a fundamentally new approach for simulating stochastic processes in turbulent flows.

More than 30 years have passed since the advent of omics technologies revolutionized biological and medical research. Research now highlights the unique opportunity to integrate and decode complex biological mechanisms for health and diseases with machine learning.

Users often overestimate the accuracy of large language models (LLMs). A new approach examines user perceptions and finds that aligning LLM explanations with the models’ internal confidence improves user perception.

The immunogenic binding interactions of antigens are complex and interconnected. A new transformer-based model can simultaneously predict the bindings of antigens to two main receptors.

The development of comprehensive benchmarks to assess the performance of algorithms on causal tasks is an important, emerging area. The introduction of two physical ‘causal chamber’ systems serves as a firm step towards future, more reliable benchmarks in the field.

The performance of omics prediction models can be significantly improved by combining limited patient proteomic data with widely available electronic health records.

As powerful institutions increasingly promote AI systems, efforts to align those systems with human morality have grown. An open-source AI system aims to predict human moral judgments across a broad spectrum of everyday situations expressed in natural language. Identifying the limitations of such systems offers important insights for future work.

Tackling partial differential equations with machine learning solvers is a promising direction, but recent analysis reveals challenges with making fair comparisons to previous methods. Stronger benchmark problems are needed for the field to advance.

Social learning is a powerful strategy of adaptation in nature. An interactive rat-like robot that engages in imitation learning with a freely behaving rat opens a way to study social behaviours.

A deep learning-based method shows promise in issuing early warnings of rate-induced tipping, of particular interest in anticipating effects due to anthropogenic climate change.

Recurrent neural networks are efficient and capable agents for discovering new peptides with strong self-organizing capabilities.

A self-decoupling tactile sensor dramatically reduces calibration time for three-dimensional force measurement, scaling from cubic (N³) to linear (3N). This advancement facilitates robotic tactile perception in human–machine interfaces.

Using deep reinforcement learning, flexible skills and behaviours emerge in humanoid robots, as demonstrated in two recent reports.

Training data are crucial for advancements in artificial intelligence, but many questions remain regarding the provenance of training datasets, license enforcement and creator consent. Mahari et al. provide a set of tools for tracing, documenting and sharing AI training data and highlight the importance for developers to engage with metadata of datasets.

Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. A recent study demonstrates that a self-attention neural network using predictive coding can generate an environmental map in its latent space as an agent that navigates the environment.

Differential privacy offers protection in medical image processing but is traditionally thought to hinder accuracy. A recent study offers a reality check on the relationship between privacy measures and the ability of an artificial intelligence (AI) model to accurately analyse medical images.

A classic question in cognitive science is whether learning requires innate, ___domain-specific inductive biases to solve visual tasks. A recent study trained machine-learning systems on the first-person visual experiences of children to show that visual knowledge can be learned in the absence of innate inductive biases about objects or space.

AI tools such as ChatGPT can provide responses to queries on any topic, but can such large language models accurately ‘write’ molecules as output to our specification? Results now show that models trained on general text can be tweaked with small amounts of chemical data to predict molecular properties, or to design molecules based on a target feature.

Recent work has demonstrated important parallels between human visual representations and those found in deep neural networks. A new study comparing functional MRI data to deep neural network models highlights factors that may determine this similarity.