Volume 6 Issue 1, January 2024

For our fifth anniversary, we reconnected with authors of recent Comments and Perspectives in Nature Machine Intelligence and asked them how the topic they wrote about developed. We also wanted to know what other topics in AI they found exciting, surprising or worrying, and what their hopes and expectations are for AI in 2024—and the next five years. A recurring theme is the ongoing developments in large language models and generative AI, their transformative effect on the scientific process and concerns about ethical implications.

The implementation of particle-tracking techniques with deep neural networks is a promising way to determine particle motion within complex flow structures. A graph neural network-enhanced method enables accurate particle tracking by significantly reducing the number of lost trajectories.

Machine learning is increasingly applied for disease diagnostics due to its ability to discover differentiating features in data. However, the clinical applicability of these models remains a challenge. Pavlović et al. provide an overview of the challenges in using machine learning for biomarker discovery and suggest a causal perspective as a solution.

Single-cell transcriptomics has provided a powerful approach to investigate cellular properties at unprecedented resolution. Sha et al. have developed an optimal transport-based algorithm called TIGON that can connect transcriptomic snapshots from different time points to obtain collective dynamical information, including cell population growth and the underlying gene regulatory network.

Feed-forward neural networks have become powerful tools in machine learning, but their behaviour during optimization is still not well understood. Ciceri and colleagues find that during optimization, class representations first separate and then rejoin, prompted by specific elements of the training set.

Multi-animal behaviour quantification is pivotal for deciphering animal social behaviours and has broad applications in neuroscience and ecology. Han and colleagues develop a few-shot learning framework for multi-animal 3D pose estimation, identity recognition and social behaviour classification.

Drug design has recently seen immense improvements in computational methods, but models can still struggle generalizing across binding pockets. Feng and colleagues combine a language model with geometric deep learning to provide efficient generation of potential new drugs.

Designing antibodies and assessing their biophysical properties for potential therapeutic development is challenging with current computational methods. Ramon et al. have developed a deep learning approach called AbNatiV, based on a vector-quantized variational encoder that accurately assesses the nativeness of antibodies and nanobodies, which are small single-domain antibodies that have recently attracted considerable interest.

Magnetic microrobots are of considerable interest for non-invasive biomedical applications but it is challenging to develop a general strategy for controlling microrobot positions, for varying configurations and environments. Choi et al. develop a reinforcement learning control method, training the model in a simulation environment for initial exploration after which the learning process is transferred to a physical electromagnetic actuation system.

Accurate real-time tracking of dexterous hand movements and interactions has applications in human–computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging due to the large number of articulations and degrees of freedom. Tashakori and colleagues report accurate and dynamic tracking of articulated hand and finger movements using machine-learning powered stretchable, washable smart gloves.