We rely on our senses to interact with the world around us. Do we actually need to travel to be somewhere to experience it fully? This project will develop a virtual cocoon through which people can interact naturally with the world without actually travelling or being put in that particular, potentially dangerous, real situation. All five senses will be stimulated to provide a rich sensory real virtuality experience. A key feature behind this real virtuality project is the attention to be paid to the degree of naturalness perceived by the user in the virtual world. The virtual cocoon will revolutionise the way in which we do business by providing low-cost, high confidence, high quality multi-sensory knowledge directly to your current location. This will significantly change, for example, purchasing via the internet because you could smell the flowers, feel the fabric of a dress, try out a sofa for comfort, examine products in any desired lighting condition, etc, all before you buy them, and with the confidence that the purchasing experience is the same as if you were there in the shop examining the product using all your appropriate senses. The virtual cocoon could even be linked to, for example Google Earth, to enable you to investigate the ambiance of a restaurant on the other side of the world when you are planning your trip.Furthermore with a virtual cocoon, you could visit an African game park as a family, even if some members are distributed around the world; select your desired listening position at a concert in the Albert Hall; design comfort levels and sensory aesthetics for new buildings or refurbishments; explore hypotheses about site utilisation such as the perception of prehistoric cave art as it was being painted; examine a patient in a remote location from a local GP's consulting room; visit ancient Rome during History or Latin lessons; be trained as a pilot to land in brown out conditions in the desert; gain experience as a driver in rare, but highly dangerous conditions; or mock up digitally organisational workflow incorporating environmental as well as locational attributes. What will people make of virtual cocoons and how will they change tomorrow's society? We cannot yet say what the exact nature of the virtual cocoon will be to achieve widespread adoption and economic success, but we do assume it will need to be low-cost, easy to interface with and highly portable, ideally something you can keep in your pocket. These issues will also be thought through in substantial details via series of activities whcih will bring together leading academics and industry. There will also be a number of opportunities for the general public to join in this thinking through open public debates lead by experts who will put opposing views to stimulate discussion..

We will improve patient health and medical research by maximising the use of vast amounts of human data being generated in the NHS. But there are two obstacles: (i) inter-related clinical and research datasets are dispersed across numerous computer systems making them hard to integrate; (ii) there is a serious shortage of computational expertise as applied to clinical research. As part of the UK's healthcare strategy to overcome these limitations, we have assembled a world-class consortium of institutions and scientists, including UCL Partners (containing NHS Trusts treating >6 million patients), Francis Crick Institute, Sanger Institute and European Bioinformatics Institute. Close links with the NHS (through Farr and Genomics England) will allow information exchange for health and disease progression. We have also engaged leading companies like GSK and Intel. We will use the MRC funds for two purposes: 1. Create a powerful eMedLab data centre. We will build a computer cluster that allows us to store, integrate and analyse genetic, patient and electronic health records. By co-locating in a single centre, we eliminate delays and security risks that occur when information is transmitted. Research Technologists supplied by the partners will install and maintain the infrastructure and software environment. 2. Expand scientific and technical expertise in UK Medical Bioinformatics through a Research & Training Academy. Basic and clinical scientists, and bioinformaticians will be trained to perform world-leading computational biomedical science. We will train in the whole range of skills involved in medical bioinformatics research with taught courses, seminars, workshops and informal discussion. To coordinate research activities across partners, we will establish Academy Labs, which are flexible, semi-overlapping groupings of academic and industrial researchers to share insights and plan activities in areas of common analytical challenges. The Academy will provide a mechanism for information and skills exchange across the traditional boundaries of disease types. These will enable existing projects in 3 disease domains in which we have unique strengths: rare diseases, cardiovascular diseases and cancer. Rare: We house 31/70 Nationally Commissioned Highly Specialised Services; ~0.5M of the 6M of our patients have a rare disease, including >50% of those treated at Great Ormond Street Hospital. >200 research teams generate large quantities of genetic, imaging (eg, 3D facial reconstructions), and clinical information (eg, patient records). Cardiovascular: We also lead genomic, imaging, and health informatics programmes in cardiovascular disease with contributions to projects like UK10k project and host multiple national cardiovascular registries through the National Institute for Cardiovascular Outcomes Research. These are linked to primary and hospital clinical care records through Farr@UCLP with current cohort sizes of ~2M people. Cancer: We also have particular clinical expertise in some of the most difficult to treat cancer types and we host major international data resources. These include individuals recruited to the TRACERx study of lung cancer, 8,500 women with abnormal cervical smears in whom methylation patterns of the HPV16 genome predict progression to high-grade precursor disease, and one of the largest sarcoma biobanks in the world. Ultimately, this bid will allow us to use new computational approaches to (i) link patient records and research data in order to understand the pathogenesis of disease, (ii) use genomic, imaging and clinical data to identify diagnostic, prognostic and predictive biomarkers to guide therapy, predict outcome and increase recruitment to clinical trials based on stratified populations and (iii) translate new IP by engagement with the pharmaceutical industry.

Probabilistic AI involves the embedding of probability models, probabilistic reasoning and measures of uncertainty within AI methods. The ProbAI hub will develop a world leading, diverse and UK-wide research programme in probabilistic AI, that will develop the next generation of mathematically-rigorous, scalable and uncertainty-aware AI algorithms. It will have far-reaching impact across many aspects of AI, including: (1) The sudden and rapid growth of AI systems has led to a new impetus for businesses, governments and creators of AI tools to understand and convey the inherent uncertainties in their systems. A probabilistic approach to AI provides a framework to represent and manipulate uncertainty about models and predictions and already plays a central role in scientific data analysis, robotics and cognitive science. The consequential impact arising from from such developments has the potential to be wide-ranging and substantial: from utilising a probabilistic approach for effective resource allocation (healthcare), prioritisation of actions (infrastructure planning), pattern recognition (cyber security) and the development of robust strategies to mitigate risks (finance). (2) It is possible to gain important theoretical insights into AI models and algorithms through studying their, often probabilistic, limiting behaviour in different asymptotic scenarios. Such results can help with understanding why AI methods work, and how best to choose appropriate architectures - with the potential to substantially reduce the computational cost and carbon footprint of AI. (3) Recent breakthroughs in generative models are based on simulating stochastic processes. There is huge potential to both use these ideas to help develop efficient and scalable probabilistic AI methods more generally; and also to improve and extend current generative models. The latter may lead to more computationally efficient and robust methods, to generative models that use different stochastic processes and are suitable for different types of data, or to novel approaches that can give a level of certainty to the output of a generative model. (4) Models from AI are increasingly being used as emulators. For example, fitting a deep neural network to realisations of a complex computer model for the weather, can lead to more efficient approaches to forecasting the weather. However, in most applications for such methods to be used reliably requires that the emulators report a measure of uncertainty -- so the user can know when the output can be trusted. Also, building on recent generalisations of Bayes updates gives new approaches to incorporate known physical constraints and other structure into these neural network emulators, leading to more robust methods that generalise better outside the training sampler and that have fewer parameters and are easier to fit. Developing these new, practical, general-purpose probabilistic AI methods requires overcoming substantial challenges, and at their heart many of these challenges are mathematical. The hub will unify a fragmented community with interests in Probabilistic AI and bring together UK researchers across the breadth of Applied Mathematics, Computer Science, Probability and Statistics. The hub will promote the area of probabilistic AI widely, encouraging and facilitating cross-disciplinary mathematics research in AI, and has substantial flexibility to fund the involvement of researchers from across the breadth of the UK during its lifetime. ProbAI will draw on and benefit from the well-established world-leading strength in areas relevant to probabilistic AI across different areas of Mathematics and Computer Science, with the aim of making the UK the world-leader in probabilistic AI.

Over the next few decades, quantum computing (QC) will transform the way we design new materials, plan complex logistics and solve a wide range of problems that conventional computers cannot address. The Hub for Quantum Computing via Integrated and Interconnected Implementations (QCI3) brings together >50 investigators across 20 universities to address key challenges, and deliver applications across diverse areas of engineering and science. We will work with 27 industrial partners, the National Quantum Computing Centre, the National Physical Laboratory, academia, regulators, Government and the wider community to achieve our goals. The Hub will focus on where collaborative academic research can make transformative progress across three interconnected themes: (T1) developing integrated quantum computers, (T2) connecting quantum computers, and (T3) developing applications for them. Objectives for each are outlined below. (T1) Developing integrated quantum computing systems, with a goal of creating quantum processors that will show real utility for specific problem examples. Objectives: OB1.1: Demonstrate quantum advantage in analogue platforms with neutral atoms and photons OB1.2: Make neutral atom quantum simulation platforms available in the cloud OB1.3: Develop new applications for these and other near-term systems (T2) A key challenge of building the million qubit machines of the future is that of 'wiring' together the quantum processors that will create such a machine. The Hub will develop technologies that help achieve this and develop models to understand how such machines will scale. Objectives : OB2.1: Develop interconnect technologies for quantum processors OB2.2: Demonstrate blind computing and multi-component networks with trapped ion quantum computers OB2.3: Demonstrate transduction and networking of superconducting processors (T3) Developing applications in science and engineering, including materials design, chemistry and fluid dynamics. Objectives: OB3.1: Develop new methods for materials and chemical system modelling and design, fluid dynamics, and quantum machine learning OB3.2: Identify the nearest routes to quantum advantage for these application areas OB3.3: Develop implementations of these algorithms on T1 and T2 Hardware These will be supported by work in overarching tools (T4) that can be used across the themes of the Hub, including error correction, digital twins, verification and software stack optimisation. Skills and training Hub partners will work with end-users, our students and researchers, and partners across the UK National Quantum Technologies Programme (UKNQTP) to ensure members of the Hub have the skills they need. Specific objectives include: Provide training in innovation, commercialisation and IP, Equality, Diversity and Inclusion and Responsible Research and Innovation (RRI) to Hub partners Provide reports and training to end-users, working in partnership with the NQCC and others Continue to provide advocacy and advice to policy makers, through work in such areas as RRI Exploitation and Engagement: The Hub will build on the strong engagement activities of the UK programme, further developing the technology pipeline. We will play a key role in strengthening and expanding the UK ecosystem through events, networking and education. Specific goals are to: Broaden the partnership of the Hub, bringing new academic, government and industrial partners into the Hub network Contribute to regulation and governance through programmes of work in standards and RRI, and close collaboration with UKNQTP partners Support the generation and protection of intellectual property within the Hub, and its exploitation Develop Hub and cross-Hub outreach initiatives, working with the RRI team, to help ensure the potential of quantum computing for societal benefit can be realised

Microbes continually evolve antibiotic-resistant strains despite the best efforts of biomedical scientists to combat them. This is taking us towards a future where routine operations and infections become high-risk, and where we cannot produce sufficient food globally (70% of antibiotics in the USA are used in animals for food production). A new strategy is needed to combat Antimicrobial Resistance (AMR). This network will take world-leading Engineering and Physical Science (EPS) researchers and introduce them into a new Network for Antimicrobial Resistance Action (NAMRA). In a series of structured events, they will share their expertise with clinicians from the NHS, with biomedical scientists, and researchers from Health and Life Sciences. These people can tell the EPS researchers about the AMR problems that need tackling, and how any solutions must be designed to work in a real-world environment for use by healthcare workers, farmers, industry and the workplace. To help in this, the Network also includes leading researchers from Social and Human Sciences who can explain how AMR solutions must fit in with human behaviour, with Geographers who are experts in how distribution of the waters supply, and how livestock practices, affect AMR; with experts in the Legal and Ethical issues in developing new solutions to AMR for use in the wider world; and with experts in Business who research how supply chain issues affect AMR. The EPS researchers have developed many world leading technologies, from the award-winning StarStream cleaning product, to surfaces that keep clean by mimicking shark skin. Such technologies were developed for other sectors (defence, nuclear etc.) and it is vital that such expertise be translated into the fight against AMR. Within NAMRA, the inventors can access the experts who understand AMR, and access laboratories and clinics to test the step-changing solutions they collaboratively identify. In return, world-leading work by current AMR researchers can be enhanced through NAMRA contacts to: -engineer solutions; -shape them for ready adoption by healthcare workers and others; -set out the behavioural, ethical and legal framework for their adoption; and -develop the business solutions so that, rather than staying on the laboratory bench, step-changing technologies can be fashioned into products that are available across the UK, and beyond. The project begins with a 'Start-up' conference for attendees to share expertise and identify possible collaborators. Break-out sessions facilitate collaborative bids for NAMRA to fund 3-6 month projects to explore new ideas. One year in, a 'Community expansion' conference reviews the success of the collaborations to date, plans new collaborations, and invites AMR workers from across the UK, and representatives from NHS, Gov and local Gov, to discuss progress. We will hold monthly meetings on particular AMR topics for smaller sub-groups within NAMRA. We will develop a Cognitive Computing facility to identify the knowledge gaps and possible fruitful areas of collaboration, working alongside the Steering Committee which performs its own assessment. We will also work hard to ensure that, after the two years of funding for NAMRA expires, we can sustain the network. Measures to do this include offering support, training and guidance: -to ensure that interdisciplinary researchers do not 'fall into the cracks' between disciplines when publishing or applying for grants; -to team-build an exhibit for public display on AMR, covering such issues as handwashing, biofilms and the use of antibiotics; -identify and apply for sources of funding to continue their collaborations (incl. peer-reviewing proposals); -to communicate their work to the public, via websites, school visits, Science Fairs; -to the next generation of leaders in AMR. The project ends with a 'Way Ahead' conference to ensure the good work continues after this funding ceases.