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  • AI systems operating in the real world unavoidably encounter unexpected environmental changes and need a built-in robustness and capability to learn fast, making use of advances such as lifelong and few-shot learning. Kejriwal et al. discuss three categories of such open-world learning and discuss applications such as self-driving cars and robotic inspection.

    • Mayank Kejriwal
    • Eric Kildebeck
    • Abhinav Shrivastava
  • Tailoring the alignment of large language models (LLMs) to individuals is a new frontier in generative AI, but unbounded personalization can bring potential harm, such as large-scale profiling, privacy infringement and bias reinforcement. Kirk et al. develop a taxonomy for risks and benefits of personalized LLMs and discuss the need for normative decisions on what are acceptable bounds of personalization.

    • Hannah Rose Kirk
    • Bertie Vidgen
    • Scott A. Hale
  • An emerging research area in AI is developing multi-agent capabilities with collections of interacting AI systems. Andrea Soltoggio and colleagues develop a vision for combining such approaches with current edge computing technology and lifelong learning advances. The envisioned network of AI agents could quickly learn new tasks in open-ended applications, with individual AI agents independently learning and contributing to and benefiting from collective knowledge.

    • Andrea Soltoggio
    • Eseoghene Ben-Iwhiwhu
    • Soheil Kolouri
  • As the impacts of AI on everyday life increase, guidelines are needed to ensure ethical deployment and use of this technology. This is even more pressing for technology that interacts with groups that need special protection, such as children. In this Perspective Wang et al. survey the existing AI ethics guidelines with a focus on children’s issues, and provide suggestions for further development.

    • Ge Wang
    • Jun Zhao
    • Nigel Shadbolt
  • Training a machine learning model with multiple tasks can create more-useful representations and achieve better performance than training models for each task separately. In this Perspective, Allenspach et al. summarize and compare multi-task learning methods for computer-aided drug design.

    • Stephan Allenspach
    • Jan A. Hiss
    • Gisbert Schneider
  • Machine learning algorithms play important roles in medical imaging analysis but can be affected by biases in training data. Jones and colleagues discuss how causal reasoning can be used to better understand and tackle algorithmic bias in medical imaging analysis.

    • Charles Jones
    • Daniel C. Castro
    • Ben Glocker
  • 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.

    • Milena Pavlović
    • Ghadi S. Al Hajj
    • Geir K. Sandve
  • 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.

    • Edgar A. Duéñez-Guzmán
    • Suzanne Sadedin
    • Joel Z. Leibo
  • 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.

    • Fergus Imrie
    • Robert Davis
    • Mihaela van der Schaar
  • 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.

    • Sören Dittmer
    • Michael Roberts
    • Carola-Bibiane Schönlieb
  • 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.

    • Achuta Kadambi
    • Celso de Melo
    • Stefano Soatto
  • 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.

    • Maxwell T. West
    • Shu-Lok Tsang
    • Muhammad Usman
  • 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.

    • Mai Ha Vu
    • Rahmad Akbar
    • Dag Trygve Truslew Haug
  • 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.

    • Sandra Steyaert
    • Marija Pizurica
    • Olivier Gevaert
  • 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.

    • Yasha Ektefaie
    • George Dasoulas
    • Marinka Zitnik
  • 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.

    • Mona Sloane
    • Ian René Solano-Kamaiko
    • Julia Stoyanovich
  • 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.

    • Robin Mitra
    • Sarah F. McGough
    • Ben D. MacArthur
  • A goal of AI is to develop autonomous artificial agents with a wide set of skills. The authors propose the immersion of intrinsically motivated agents within rich socio-cultural worlds, focusing on language as a way for artificial agents to develop new cognitive functions.

    • Cédric Colas
    • Tristan Karch
    • Pierre-Yves Oudeyer
  • Finding good benchmarks is an important and pervasive problem in machine learning for healthcare. This Perspective highlights key aspects that require scrutiny in the whole process of benchmark generation and use, including problem formulation, creation of datasets, development of a suite of machine learning models and evaluation of these models.

    • Diana Mincu
    • Subhrajit Roy