A government-sponsored review into the potential impact of artificial intelligence (AI) on the UK economy is urging a comprehensive programme of support for the discipline.

Published under the joint aegis of the departments for Business, Energy and Industrial Strategy and for Digital, Culture, Media and Sport, the report – Growing the artificial intelligence industry in the UK – is positioned as “a contribution to the government’s industrial strategy, for which a whitepaper will be published later in 2017”.

Authors Wendy Hall and Jérôme Pesenti have been working on the report since March 2017, and Hall referred to it in her recent testimony before the House of Lords select committee on artificial intelligence. Hall is professor of computer science at the University of Southampton, and Pesenti is the chief executive of BenevolentTech, an AI supplier.

The report says the review took input from meetings and workshops that involved more than 100 AI experts drawn from the university sector, the IT industry and the civil service.

Hall said, in a statement: “I was very honoured to be asked to co-chair this review ... I’m particularly keen to ensure that we use it to inform the establishment of initiatives and programmes to help us extract the most value from artificial intelligence for the country; that includes an emphasis on increasing and improving our skill levels to prepare the workforce for the number of jobs the industry will need for the future.

“AI has been around for a very long time as a concept, and this latest surge of technological development is likely to see automation continue to escalate and accelerate in every walk of life.”

Pesenti said: “Our proposals are deliberately specific and boil down to three fundamentals – enable better access to data, create a greater supply of AI skills and promote the uptake of AI. I am looking forward to working with government, academia and industry to drive these changes.”

Build on UK’s historical success in AI The report advances the view that the UK enjoys a comparative advantage in the field of artificial intelligence due to a heritage exemplified by the Cambridge mathematician Alan Turing, who was one of the leading cryptanalyst at the government’s code-breaking centre at Bletchley Park during the Second World War. The report also cites other British pioneers in AI, such as fellow Bletchley alumnus Donald Michie, of the University of Edinburgh, and Christopher Strachey of the University of Manchester. The report recommends that The Alan Turing Institute, set up in 2015 at The British Library, should become the “national institute for artificial intelligence and data science”. It also recommends that Turing AI Fellowships be established and funded by government to ensure “the UK is open to any and all of the eligible experts from around the world”. The report’s authors state they are “convinced that because of the UK’s current and historical strengths in this area we are in a strong position to lead rather than follow in both the development of the technology and its deployment in all sectors of industry, education and government. “We have a choice. The UK could stay among the world leaders in AI in the future, or allow other countries to dominate.” The report uses a capacious definition of artificial intelligence from The Engineering and Physical Science Research Council to inform its advocacy: “Artificial intelligence technologies aim to reproduce or surpass abilities (in computational systems) that would require ‘intelligence’ if humans were to perform them. These include: learning and adaptation; sensory understanding and interaction; reasoning and planning; optimisation of procedures and parameters; autonomy; creativity; and extracting knowledge and predictions from large, diverse digital data.” And it explicitly uses AI as an “umbrella term to cover a set of complementary techniques that have developed from statistics, computer science and cognitive psychology. While recognising distinctions between specific technologies and terms – for example, artificial intelligence versus machine learning, versus deep learning – it is useful to see these technologies as a group, when considering how to support development and use of them”.