8 March 2023
8 March 2023
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg and Jessica Montgomery have published AI for Science: an emerging agenda, the report of Dagstuhl Seminar 22382, “Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling”.
The seminar treated AI for science as a meeting point: expertise from machine learning and from application domains, modelling knowledge alongside engineering practice, and collaboration across disciplines and between people and machines. The report argues that the next wave of progress will come from that community — machine learning researchers, domain experts, citizen scientists and engineers — designing and deploying tools together, not from technical advances alone.
The report is available on arXiv. It sits alongside the Accelerate Science programme’s work on interdisciplinary use of AI in research.
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.
21 August 2024
A Royal Society Open Science paper sets out a framework for moving AI from isolated successes to wider use in research.
