New human AI research teams could be the future of research, addressing future societal challenges

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The University of Manchester is developing unique research teams to help solve humanity’s increasingly complex future health and societal challenges by pairing researchers with artificial intelligence (AI).

Today, Professor Sami Kaski from the University of Manchester was named among Turing’s first world-renowned artificial intelligence (AI) research fellows. The scholarships, named after AI pioneer Alan Turing, are part of the UK’s commitment to further strengthen its position as a world leader in the field.

With his fellowship, Professor Kaski aims to overcome a fundamental limitation of current AI systems, which is that they require detailed specification of the goal before they can help. Machine learning, where solutions to problems are automatically learned from data, is a very promising form of AI for addressing a number of challenges. This includes healthcare, where AI can detect patterns associated with diseases and health conditions by studying medical records and other data.

As part of this new AI-driven approach, the University of Manchester has also today received a share of UKRI’s £4.4m research funding, in addition to partner contributions and university totaling over £10 million. With these investments, the university is further strengthening its fundamental research in AI.

For healthcare applications, the AI ​​activity will draw on multimillion-pound technology from the Christabel Pankhurst Institute for Health. The aim of the Institute is to capitalize on the University’s strengths in digital health, AI and advanced materials and to develop innovative products and services for the health sector. This, in turn, will stimulate business growth and employment, as well as long-term health benefits for the urban region.

Artificial intelligence is further limited by the fact that human intervention is required to set appropriate goals and rewards to tell AI systems what outcomes are desired. It is difficult when one only partially knows the goal, as is the case at the beginning of scientific research.

In drug design, for example, today’s most advanced tools are able to generate candidate molecules if we can specify a precise objective function for them. However, for us humans, that’s hard to do – and if our specs aren’t perfect, the smart system will very intelligently produce results we don’t want. This is where the tools of the new approaches will help us.

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