University Of Cambridge Announces AI & Machine Learning Accelerator Programme

In an attempt to equip non-Computer Science students with valuable machine learning and artificial intelligence (AI) skills, the University Of Cambridge has announced a new accelerator programme.

Dubbed the ‘Accelerate Programme for Scientific Discovery’, the new initiative will be supported by a donation from Schmidt Futures and headed up by Professor Neil Lawrence, DeepMind Professor of Machine Learning.

While initially aimed at STEMM (science, technology, engineering, mathematics and medicine) students, the programme will eventually be expanded to include arts, humanities and social science, allowing students from these areas to learn how to use machine learning and AI skills to accelerate their research.

Cambridge University is hoping that the programme will help level the playing field for young researchers across a variety of disciplines and enable them to take advantage of some of the powerful techniques that can potentially accelerate discovery.

The five-year programme will be designed and delivered by four new early-career specialists, who will work alongside researchers from the Department of Computer Science and Technology, as well as industry collaborators.

In the first year, 32 PhD students and postdoctoral researchers will receive structured training in machine learning techniques. Over the course of five years, the programme is expected to provide training to a total of 160 PhD students and postdocs.

The programme will also benefit from in-kind support from world-leading British AI company DeepMind, which has also contributed to the programme’s development. Participants will benefit from guest lectures by DeepMind’s research team and have the opportunity to apply for internship positions with the firm.

Speaking about the Accelerate Programme, University Of Cambridge Vice-Chancellor, Professor Stephen Toope, said: “As the intellectual home of Alan Turing, the father of artificial intelligence and modern computer science, Cambridge has long fostered technological innovation and invention. This programme will help ensure that Cambridge continues to be a beacon for the very best young global researchers and that we’re giving them the tools they need to thrive.”

The University Of Cambridge hopes that the programme will lead to the creation of a network of machine learning experts across the institution. It is hoped that the PhD students and postdoctoral researchers who benefit from it initially will share their knowledge with colleagues, building up capacity throughout Cambridge at scale.

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