Reviews & Analysis

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.

New research reveals a duality between neural network weights and neuron activities that enables a geometric decomposition of the generalization gap. The framework provides a way to interpret the effects of regularization schemes such as stochastic gradient descent and dropout on generalization — and to improve upon these methods.

A framework for training artificial neural networks in physical space allows neuroscientists to build networks that look and function like real brains.

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.

Advances in machine intelligence often depend on data assimilation, but data generation has been neglected. The authors discuss mechanisms that might achieve continuous novel data generation and the creation of intelligent systems that are capable of human-like innovation, focusing on social aspects of intelligence.

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.

Recommender systems are a predominant feature of online platforms and one of the most widespread applications of artificial intelligence. A new model captures information dynamics driven by algorithmic recommendations and offers ways to ensure that users are exposed to diverse content and information.

New algorithms allow researchers to decode words the brain is hearing with a non-invasive method, outside the scalp.

Efficient quantum-control protocols are required to utilize the full power of quantum computers. A new reinforcement learning approach can realize efficient, robust control of quantum many-body states, promising a practical advance in harnessing present-day quantum technologies.

Limited interpretability and understanding of machine learning methods in healthcare hinder their clinical impact. Imrie et al. discuss five types of machine learning interpretability. They examine medical stakeholders, highlight how interpretability meets their needs and emphasize the role of tailored interpretability in linking machine learning advancements to clinical impact.

A ‘programming’-like approach provides a one-step algorithm to find network parameters for recurrent neural networks that can model complex dynamical systems.

Sustainability awareness is lacking in the development of AI systems and algorithms for healthcare. The authors discuss resource sustainability issues in energy, storage and domain knowledge, and present potential solutions.

An in vitro biological system of cultured brain cells has learned to play Pong. This feat opens up an avenue towards the convergence of biological and machine intelligence.

With the explosion of machine learning models of increasing complexity for research applications, more attention is needed for the development of good quality codebases. Sören Dittmer, Michael Roberts and colleagues discuss how to embrace guiding principles from traditional software engineering, including the approach to incrementally grow software, and to use two types of feedback loop, testing correctness and efficacy.

Although computer vision techniques are often data-driven, they can be enhanced by including the physical models underlying image formation as constraints. Achuta Kadambi et al. provide an overview of various techniques to incorporate physics into data-driven vision pipelines.

To fulfil the potential of quantum machine learning for practical applications in the near future, it needs to be robust against adversarial attacks. West and colleagues give an overview of recent developments in quantum adversarial machine learning, and outline key challenges and future research directions to advance the field.

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.

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.

There is a continuing demand for high-quality, large-scale annotated datasets in medical imaging supported by machine learning. A new study investigates the importance of what type of instructions crowdsourced annotators receive.

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.