Reviews & Analysis

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.

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.

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.

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.

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.

Cancer diagnosis and treatment decisions often focus on one data source. Steyaert and colleagues discuss the current status and challenges of data fusion, including electronic health records, molecular data, digital pathology and radiographic images, in cancer research and translational development.

One of the main advances in deep learning in the past five years has been graph representation learning, which enabled applications to problems with underlying geometric relationships. Increasingly, such problems involve multiple data modalities and, examining over 160 studies in this area, Ektefaie et al. propose a general framework for multimodal graph learning for image-intensive, knowledge-grounded and language-intensive problems.

An increasing number of regulations demand transparency in automated decision-making processes such as in automated online recruitment. To provide meaningful transparency, Sloane et al. propose the use of ‘nutritional’ labels that display specific information about an automated decision system, depending on the context.

Gathering big datasets has become an essential component of machine learning in many scientific areas, but it is unavoidable that some data values are missing. An important and growing effect that needs careful attention, especially when heterogeneous data sources are combined, is that of structured missingness, where data values are missing not at random, but with a specific structure.