Review Articles

Flow matching has emerged as a promising solution to mapping arbitrary pairs of high-dimensional data distributions, well suited to problems in molecular and cell biology. Morehead et al. review the theoretical foundations of flow-matching-based models and applications of flow matching in computational biology, and discuss its role in developing methods towards an AI-based virtual cell.

While transformers and large language models excel at efficiently processing long sequences, new approaches have been proposed that incorporate recurrence to overcome the quadratic cost of self-attention. Tiezzi et al. discuss recurrent and state-space models and the promise they hold for future sequence processing networks.

A systematic review of peer-reviewed AI safety research reveals extensive work on practical and immediate concerns. The findings advocate for an inclusive approach to AI safety that embraces diverse motivations and perspectives.

Micaela Consens et al. discuss and review the recent rise of transformer-based and large language models in genomics. They also highlight promising directions for genome language models beyond the transformer architecture.

Machine unlearning techniques remove undesirable data and associated model capabilities while preserving essential knowledge, so that machine learning models can be updated without costly retraining. Liu et al. review recent advances and opportunities in machine unlearning in LLMs, revisiting methodologies and overlooked principles for future improvements and exploring emerging applications in copyright and privacy safeguards and in reducing sociotechnical harms.

Large general-purpose models are becoming more prevalent and useful, but also harder to train and find suitable training data for. Zheng et al. discuss how models can be used to train other models.

Forecasting epidemic progression is a complex task influenced by various factors, including human behaviour, pathogen dynamics and environmental conditions. Rodríguez, Kamarthi and colleagues provide a review of machine learning methods for epidemic forecasting from a data-centric computational perspective.

Machine learning approaches in micro- and nanorobotics promise to overcome challenges encountered by applying traditional control methods at the microscopic scale. Lidong Yang et al. review this emerging area in robotics and discuss machine learning developments in design, actuation, locomotion, planning, tracking and navigation of microrobots.

Data-driven generative methods have the potential to greatly facilitate molecular design tasks for drug design.

Skin-like flexible electronics (electronic skin) has great potential in medical practices to enable continuous tracking of physical and biochemical information. Xu et al. review the integration of AI methods and electronic skins, especially how data collected from sensors are processed by AI to extract features for human–machine interactions and health monitoring purposes.

Traditionally, 3D graphics involves numerical methods for physical and virtual simulations of real-world scenes. Spielberg et al. review how deep learning enables differentiable visual computing, which determines how graphics outputs change when the environment changes, with applications in areas such as computer-aided design, manufacturing and robotics.

There are numerous algorithms for generating Shapley value explanations. The authors provide a comprehensive survey of Shapley value feature attribution algorithms by disentangling and clarifying the fundamental challenges underlying their computation.

Deep space exploration missions will require new technologies that can support astronaut health systems, as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this second of two Review articles based on the findings from the workshop, the intersection between artificial intelligence and space biology is discussed.

Deep-space exploration missions require new technologies that can support astronaut health systems as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this first of two Review articles based on the findings from the workshop, a vision for autonomous biomonitoring and precision space health is discussed.

Applying deep reinforcement learning to robot control poses challenges. The authors review methods for transferring deep reinforcement learning policies learned in simulation to the real world.

Both proteins and natural language are essentially based on a sequential code, but feature complex interactions at multiple scales, which can be useful when transferring machine learning models from one domain to another. In this Review, Ferruz and Höcker summarize recent advances in language models, such as transformers, and their application to protein design.

GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

The development of extra fingers and arms is an exciting research area in robotics, human–machine interaction and wearable electronics. It is unclear, however, whether humans can adapt and learn to control extra limbs and integrate them into a new sensorimotor representation, without sacrificing their natural abilities. The authors review this topic and describe challenges in allocating neural resources for robotic body augmentation.

The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.