3 September 2026
3 September 2026
Kyle Cranmer, Neil Lawrence, Jessica Montgomery and Denis Thérien have published AI for Science: Reframing AI’s Role in Discovery in RSS: Data Science and Artificial Intelligence.
The paper asks how AI actually contributes to science, and warns that inflated claims hide both present limits and longer-term possibilities. It organises that contribution around task capabilities, integration into scientific workflows, and the constraints of particular domains. From there it opens questions the field has not settled: whether knowledge that people cannot inspect still counts as scientific understanding; what it would take to move systems from pattern matching toward causal reasoning; and which institutional changes responsible adoption requires.
The article marks the opening of a call for papers from RSS Data Science and AI. It continues a line of work that includes the 2023 Dagstuhl report and the 2024 Royal Society Open Science paper on accelerating AI for science.
7 June 2022
The Accelerate Science Programme and education technology company Cambridge Spark are offering researchers across Cambridge University a new self-learning module so they can learn programming in Python.
5 July 2022
(5 July 2022) The Accelerate Programme in collaboration with C2D3 is offering small grants for Cambridge University researchers pursuing innovative applications of AI, in research or real-world contexts. Funding can support a variety of activities, including events, workshops, teaching, software development, or research, with a focus on interdisciplinary collaboration.
8 March 2023
A report from Dagstuhl Seminar 22382 sets out how machine learning and domain science can work together on complex scientific systems.
