Computational simulations allow us to make predictions about how biological molecules interact with (stick to) each other, and how these interactions, if they go wrong, can lead to disease. If this is understood then there is the potential to design new drugs that prevent this unwanted interaction between the protein molecules, and so treat the disease. This approach has great potential in areas as diverse as cancer therapy and new antibiotics. The problem is that the computer simulations needed for this type of study are enormous - typically they require access to the world's largest supercomputers. However, new research has shown how the same type of simulation study can be accomplished by spreading the work over very large numbers of smaller computers which may be spread all around the world. Such computer facilities - "the cloud" - are already incredibly important in fields ranging from business to social media, but the idea hasn't yet really made an impact in computational medical science. Our aim is to help this happen. Building on years of previous experience developing computer software to help biological scientists and chemists easily use supercomputers for their research, we will develop a toolkit for "cloud-based computational chemistry". This will make it possible for far more researchers, all round the world, to do the same sort of cutting-edge medical research that until now was only possible for those groups who could access a supercomputer. We will test the power of this new facility by using it to study two particular diseases - cancer and antibiotic resistance. In both cases we will build on the research experience and interests of our industrial partner, the pharmaceutical company UCB Celltech. This ensures that, should we get some promising results, the theoretical predictions can quickly be tested in the lab, and if they hold up, taken forward into the development of new drugs for these key health problems.

The ecosystem of compute devices is highly connected, and likely to become even more so as the internet-of-things concept is realized. There is a single underlying global protocol for communication which enables all connected devices to interact, i.e. internet protocol (IP). In this project, we will create a corresponding single underlying global protocol for computation. This will enable wireless sensors, smartphones, laptops, servers and cloud data centres to co-operate on what is conceptually a single task, i.e. an AnyScale app. A user might run an AnyScale app on her smartphone, then when the battery is running low, or wireless connectivity becomes available, the app may shift its computation to a cloud server automatically. This kind of runtime decision making and taking is made possible by the AnyScale framework, which uses a cost/benefit model and machine learning techniques to drive its behaviour. When the app is running on the phone, it cannot do very complex calculations or use too much memory. However in a powerful server, the computations can be much larger and complicated. The AnyScale app will behave in an appropriate way based on where it is running. In this project, we will create the tools, techniques and technology to enable software developers to create and deploy AnyScale apps. Our first case study will be to design a movement controller app, that allows a biped robot with realistic humanoid limbs to 'walk' over various kinds of terrain. This is a complex computational task - generally beyond the power of embedded chips inside robotic limbs. Our AnyScale controller will offload computation to computers on-board the robot, or wirelessly to nearby servers or cloud-based systems. This is an ideal scenario for robotic exploration, e.g. of nuclear disaster sites.

Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the 'elephant in the room' is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems. We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory. The project will test the core ideas in a number of areas, including personalisation of hearing aids, analysis of cancer data, and adapting the computing resources for a major bank.

The vision of this CDT is to enhance society's resilience to changes in our environment through the development of Environmental Intelligence (EI): using the integration of data from multiple inter-related sources and Artificial Intelligence (AI) to provide evidence for informed decision-making, increase our understanding of environmental challenges and provide information that is required by individuals, policy-makers, institutions and businesses. Many of the most important problems we face today are related to the environment. Climate change, healthy oceans, water security, clean air, biodiversity loss, and resilience to extreme events all play a crucial role in determining our health, wealth, safety and future development. The UN's 2030 Agenda for Sustainable Development calls for a plan of action for people, planet and prosperity, aiming to take the bold and transformative steps that are urgently needed to shift the world onto a sustainable and resilient path. Developing a clear understanding of the challenges and identifying potential solutions, both for ourselves and our planet, requires high quality, accessible, timely and reliable data to support informed decision making. Beyond the quantification of the need for change and tracking developments, EI has another important role to play in facilitating change through integration of cutting edge AI technology in energy, water, transport, agricultural and other environmentally-related systems and by empowering individuals, organisations and businesses through the provision of personalized information that will support behavioural change. Students will receive training in the range of skills they will require to become leaders in EI: (i) the computational skills required to analyse data from a wide variety of sources; (ii) environmental domain-specific expertise; (iii) an understanding of governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders. The training programme has been designed to be applicable to students with a diverse range of backgrounds and experiences. Graduates of the CDT will be equipped with the skills they need to become tomorrow's leaders in identifying and addressing interlinked, social, economic and environmental risks. Having highly trained individuals with a wide range of expertise, together with the skills to communicate with a diverse range of stakeholders and communities, will have far reaching impact across a wide number of sectors. Traditionally, PhD students trained in the technical aspects of AI have been distinct from those trained in policy and business implementation. This CDT will break that mould by integrating students with a diverse range of backgrounds and interests and providing them with the training, in conjunction with external partners, that will ensure that they are well versed in both cutting edge methodology and on the ground policy and business implementation. The University of Exeter's expertise in inter- and trans-disciplinary environmental, climate, sustainability, circular economy and health research makes it uniquely placed to lead an inter-disciplinary CDT that will pioneer the use of AI in understanding the complex interactions between the environment, climate, natural ecosystems, human social and economic systems, and health. Students will benefit from the CDTs strong relationships with its external partners, including the Met Office. Many of these partners are employers of doctoral graduates in AI and see an increasing need for employees with skills from across multiple disciplines. Their involvement in the planning and ongoing management of the CDT will ensure that, in this rapidly changing domain, the CDT delivers leading-edge research that will enable partners and others to participate effectively in EI and lead to optimal employment opportunities for its graduates.

Society is seeing enormous growth in the development and implementation of autonomous systems, which can offer significant benefits to citizens, communities, and businesses. The potential for improvements in societal wellbeing is substantial. However, this positive potential is balanced by a similar potential for societal harm through contingent effects such as the environmental footprint of autonomous systems, systemic disadvantage for some socio-economic groups, and entrenchment of digital divides. The rollout of autonomous systems must therefore be addressed with responsibilities to society in mind. This must include engaging in dialogue with society and with those affected, trying to anticipate challenges before they occur, and responding to them. One such anticipated challenge is the effect of change on autonomous systems. Autonomous systems are not designed to be deployed in conditions of perfect stasis, as they are unlikely to encounter such conditions in real-world environments. They are frequently designed for changing environments, like public roads, and may also be designed to change themselves over time, for instance by means of learning capabilities. Not only that, but these changes in deployed systems and in their operating conditions are also likely to take place against a shifting contextual background of societal alteration (e.g. other technologies, 'black swan' events, or simply the day-to-day operation of communities). The effects of such change, on the systems themselves, on the environments within which they are operating, and on the humans with which they engage, must be considered as part of a responsible innovation approach. The RAILS project brings together a team from UCL and the Universities of York, Leeds and Oxford, from multiple disciplines, with the aim of engaging with the challenges associated with the long-term operation of autonomous systems and the effects of change on these systems. In particular, we will explore how the notion of responsibility is affected by (i) open-ended dynamic environments - situations that change over time, and (ii) lifelong-learning systems - i.e. systems that are designed to adapt themselves to their circumstances and 'learn' over time. The RAILS project will focus on such independent long-term autonomous systems in different applications. These will include (i) autonomous vehicles and (ii) autonomous robot systems such as unmanned aerial vehicles (drones). RAILS will look at social and legal contexts, as well as technical requirements, in order to assess whether and how these systems can be designed, developed, and operated in a way that they are responsible, accountable, and trustworthy. The overall aim of the RAILS project is to bring together responsible development principles with governance mechanisms and technical understanding to create new understandings of how autonomous systems can adapt to change, how they can be deployed in a responsible and trustworthy way, and how such deployment can be framed by governance to ensure accountability and flexibility.