Singapore

The UKRI CDT in Artificial Intelligence (AI) for Healthcare will be the world's leading centre for PhD training of the next-generation innovators in AI applied to Healthcare. There is a unique role for AI in healthcare by providing more accurate decisions faster while reducing cost and suffering across society. AI in healthcare needs and drives current AI research avenues such as interpretable AI, privacy-preserving learning, trust in AI, data-efficient learning and safety in autonomy. These are key due to the immediate impact on life and health for users depending on AI for healthcare support. Healthcare applications require many AI specialists that can apply their skills in this heavily regulated domain. To address this need, we propose to train in total 90+ PhD students including 16 clinical PhD Fellows in five cohorts of 18+ PhDs, which will establish a new generation of cognitively diverse AI researchers with backgrounds ranging from computer science, psychology to design engineering and clinical medicine. The CDT focus areas arise from our early engagement in AI research and collaboration with clinicians, partnered technology companies and patient organisations, reflecting the healthcare areas of the UK industrial strategy. The Centre is grouped into 4 complementary healthcare themes and 4 cross-cutting AI expertise streams. The 4 healthcare themes are: (1) Productivity in Care: making healthcare provision more efficient and effective by increasing the productivity of doctors and nurses; (2) Diagnostics & Monitoring: developing AI-based diagnostics & monitoring that can detect disease earlier and monitor health with more precision; (3) Decision support systems: AI-based decision support systems that will support e.g. freeing up doctors' time to focus on the patient or can accelerate the development of novels drugs and treatments and empowering patients to be active agents within the decision-making by explaining, and (4) Biomedical discovery: driven by AI that accelerates drug discovery and linking genome, microbiome and environment data to discover novel disease mechanisms and treatment pathways. The themes are linked by 4 cross-cutting AI expertise streams: a. Perceptual AI technology enables to perceive, structure, and recognise from sensory data clinically relevant information. b. Cognitive AI technology mimics the reasoning, i.e. cognitive process, of healthcare specialists. c. Assistive AI technology supports clinicians with decision making as well as patients directly d. Underpinning AI technologies are driving factors for clinical and patient-focused AI innovations and will be enabling AI methodologies to operate beyond the currently possible. Our unique cohorts will benefit from an integrated training program and co-creation process with industry and patient organisations. PhD training is split into three phases that provide underpinning skill training (Foundation phase), research training (Research Phase) and finally drive PhD impact (Impact phase). During the Impact phase, the students will either (1) commercialising their research through a mentored start-up route (incubator partners), (2) deploying their technology in a clinical trial (two NIHR biomedical research centre (BRC) partners), or (3) testing their work in person through an NHS honorary contract (three NHS trusts as partners). Bespoke training will be created, such as AI bias & ethics, security, trust, inclusivity, differential privacy, transparency, accessibility and usability, service design, global inclusivity, healthcare treatments, clinical statistics and data regulation, Healthcare technology regulation, and technology commercialisation. We offer an exit Strategy (month 9-12) through a master's degree. The centre will place special emphasis on research that explores diversity in AI for healthcare research, including services to underserved communities and minority-specific care requirements.

The greatest advance in magnetic technology in the last 20 years has been the development of "nanomagnetic" devices, magnetic systems with dimensions as small as ten billionths of a metre. The most common examples of this are found in computer hard-disk drives, where both the storage media and the sensors used to read data back are nanomagnetic in nature. The prevalence of modern personal computers means that the vast majority of homes and businesses in the United Kingdom, and indeed in much of the developed world, are now in some way dependent on nanomagnetic technology. Many other nanomagnetic devices are also being developed including magnetic memory devices, magnetic logic devices, microwave resonators, devices for medical diagnostics and magnetic sensors. These new technologies have the potential to be faster, cheaper and more efficient than their existing counterparts. For example, non-volatile magnetic memory chips will allow personal computers to be booted up into the exact state they were in prior to being shut down, removing the necessity of leaving systems switched on over extended periods. Similarly, magnetic bio-chips will soon allow complex medical tests to be performed at the doctor's surgery rather than in a laboratory, and at a faction of the price. In nanomagnetic systems understanding the effect of finite temperature is of critical importance, as thermal effects introduce disorder making it impossible to predict exactly how a device will behave. In hard-disks thermal excitations can cause data to be lost by reversing the individual "bits" that make up a file. This phenomenon is the primary factor that restricts the capacity of modern hard-disks. In other technologies the randomising effects of thermal perturbations make devices unreliable by making it impossible to predict the exact state a device will be in before and after an external operation is performed. Again, this lack of reliability is a leading factor in preventing new nanomagnetic technologies, and the social and environmental benefits they will bring, being available on the high street. Despite the huge technological importance of these "stochastic" effects they are poorly understood with most studies considering them only in a phenomenological or empirical fashion. To be able to understand and accurately predict stochastic behaviour in magnetic systems it is necessary to have a thorough knowledge of two parameters: the energy barrier, which determines how strongly a system is confined to a given state; and the attempt frequency, which determines how often thermal excitations try to alter the configuration of a system. Unfortunately neither of these parameters are accessible by standard measurement techniques, and hence they are neither well understood, nor characterised. In this fellowship I will use time, frequency and temperature resolved measurements, coupled with new numerical modelling techniques, to directly measure both attempt frequencies and energy barriers across a broad range of technologically relevant magnetic systems. These will include those for use in new hard-disk technologies, memory devices, information processing systems, novel sensors and microwave resonators. In doing this I will create the first comprehensive framework with which to a) understand, b) predict and c) mitigate the effects of stochastic behaviour in nanomagnetic devices. This will allow researchers and technologists to, at last, quantitatively predict how thermal perturbations will affect nanomagnetic devices, and understand how the problems they introduce can be overcome. There is currently an explosion of interest in developing new nanomagnetic technologies in both academia and in industry. This fellowship will be critical to ensuring that progress is not inhibited by a lack of understanding of stochastic magnetic behaviour, and that the great potential of nanomagnetic technology is brought to the high street.

SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.

SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.