New research funding will support The University of Manchester in its drive to create human-AI research teams to meet future health needs.
The University of Manchester is partnering researchers with Artificial Intelligence (AI) in an attempt to overcome the limitations of current AI systems.
Professor Sami Kaski, from The University of Manchester, has been appointed amongst the first Turing Artificial Intelligence (AI) research fellows. Through his fellowship, Professor Kaski aims to overcome the challenge attached to AI systems that they require a detailed specification of the goal before they can help.
Machine learning, a form of AI, automatically learns solutions to problems from data. This includes healthcare, where AI can detect patterns associated with diseases and health conditions by studying healthcare records and other data.
For healthcare applications, the AI activity will build on the multi-million-pound Christabel Pankhurst Institute for Health technology, which aims to capitalise on the University’s strengths in digital health, AI, and advanced materials and develop innovative products and services for the healthcare sector. In turn, this will drive business growth and employment as well as boost the long-term health benefits of the city region.
As part of this new AI driven approach, The University of Manchester has also received a share of £4.4 million research funding from UKRI, in addition to contributions from the partners and the university totalling over £10 million.
Limitations of AI
Artificial intelligence is limited by the fact that human intervention is needed to set appropriate objectives and rewards to tell AI systems which outcomes are desired. This is difficult when the goal is only partially known, as is the case at the beginning of scientific research.
In drug design, for instance, the most advanced current tools are able to generate candidate molecules if we can specify a precise objective function for them. However, that is difficult for humans to do – and if our specification is not perfect, the system will produce unwanted results. Researchers say that this is where the new approaches will help.
The potential for AI in research and complex decision-making is still relatively untapped. Now, Professor Kaski aims to develop new ways for machine learning systems to help humans in the process of designing experiments and interpreting what results mean, before deciding what to measure next, and to finally reach trustworthy solutions to problems. In lung cancer personalised medicine, for example, to maximise effectiveness of radiotherapy for a new patient while minimising side effects, doctors need to combine their expertise and what can be learned from measurements from earlier patients.
Professor Kaski said: “This is where AI can help, but we need new kinds of AI assistants which can learn to work well with humans and complement their skills. That requires new fundamental AI research, and I am glad Manchester has recognised this opportunity and is considerably strengthening its AI research.”
This new approach will be applied to three challenges: diagnosis and treatment decision-making in personalised medicine; the guidance of scientific experiments in synthetic biology and drug design; and the design and use of digital twins to design physical systems and processes.
Digital twins, a virtual representation of a complex objects or systems, can be built for patients for personalised medicine, but also for physical systems, such as complex buildings, a farm and even a city. With the twins it is possible to plan changes to roads, for instance, and anticipate effects on traffic and air quality.
An AI centre of excellence will be established at The University of Manchester, in collaboration with the Turing Institute and a number of partners from the industry and healthcare sector, and with strong connections to the networks of best national and international AI researchers.