Despite being far from having reached 'artificial general intelligence' - the broad and deep capability for a machine to comprehend our surroundings - progress has been made in the last few years towards a more specialised AI: the ability to effectively address well-defined, specific goals in a given environment, which is the kind of task-oriented intelligence that is part of many human jobs. Much of this progress has been enabled by deep reinforcement learning (DRL), one of the most promising and fast-growing areas within machine learning. In DRL, an autonomous decision maker - the "agent" - learns how to make optimal decisions that will eventually lead to reaching a final goal. DRL holds the promise of enabling autonomous systems to learn large repertoires of collaborative and adaptive behavioural skills without human intervention, with application in a range of settings from simple games to industrial process automation to modelling human learning and cognition. Many real-world applications are characterised by the interplay of multiple decision-makers that operate in the same shared-resources environment and need to accomplish goals cooperatively. For instance, some of the most advanced industrial multi-agent systems in the world today are assembly lines and warehouse management systems. Whether the agents are robots, autonomous vehicles or clinical decision-makers, there is a strong desire for and increasing commercial interest in these systems: they are attractive because they can operate on their own in the world, alongside humans, under realistic constraints (e.g. guided by only partial information and with limited communication bandwidth). This research programme will extend the DRL methodology to systems comprising of many interacting agents that must cooperatively achieve a common goal: multi-agent DRL, or MADRL.

Data science and artificial intelligence will transform the way in which we live and work, creating new opportunities and challenges to which we must respond. Some of the greatest opportunities lie in the field of human health, where data science can help us to predict and diagnose disease, determine the effectiveness of existing treatments, and improve the quality and affordability of care. The Oxford EPSRC CDT in Health Data Science will provide training in: - core data science principles and techniques, drawing upon expertise in computer science, statistics, and engineering - the interpretation and analysis of different kinds of health data, drawing upon expertise in genomics, imaging, and sensors - the methodology and practice of health data research, drawing upon expertise in population health, epidemiology, and research ethics The training will be provided by academics from five university departments, working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision. The CDT will be based in the Oxford Big Data Institute (BDI), a hub for multi-disciplinary research at the heart of the University's medical campus. A large area on the lower ground floor of the BDI building will be allocated to the CDT. This area will be refurbished to provide study space for the students, and dedicated teaching space for classes, workshops, group exercises, and presentations. Oxford University Hospitals NHS Foundation Trust (OUH), one of the largest teaching hospitals in the UK, will provide access to real-world clinical and laboratory data for training and research purposes. OUH will provide also access to expertise in clinical informatics and data governance, from a practical NHS perspective. This will help students to develop a deep understanding of health data and the mechanisms of healthcare delivery. Industrial partners - healthcare technology and pharmaceutical companies - will contribute to the training in other ways: helping to develop research proposals; participating in data challenges and workshops; and offering placements and internships. This will help students to develop a deep understanding of how scientific research can be translated into business innovation and value. The Ethox Centre, also based within the BDI building, will provide training in research ethics at every stage of the programme, and the EPSRC ORBIT team will provide training in responsible research and innovation. Ethics and research responsibility are central to health data science, and the CDT will aim to play a leading role in developing and demonstrating ethical, responsible research practices. The CDT will work closely with national initiatives in data science and health data research, including the ATI and HDR UK. Through these initiatives, students will be able to interact with researchers from a wide network of collaborating organisations, including students from other CDTs. There will also be opportunities for student exchanges with international partners, including the Berlin Big Data Centre. Students graduating from the CDT will be able to understand and explore complex health datasets, helping others to ask questions of the data, and to interpret the results. They will be able to develop the new algorithms, methods, and tools that are required. They will be able to create explanatory and predictive models for disease, helping to inform treatment decisions and health policy. The emphasis upon 'team science' and multi-disciplinary working will help to ensure that our students have a lasting, positive impact beyond their own work, delivering value for the organisations that they join and for the whole health data science community.

Lattice Field Theory (LFT) provides the tools to study the fundamental forces of nature using numerical simulations. The traditional realm of application of LFT has been Quantum Chromodynamics (QCD), the theory describing the strong nuclear force within the Standard Model (SM) of particle physics. These calculations now include electromagnetic effects and achieve sub percent accuracy. Other applications span a wide range of topics, from theories beyond the Standard Model, to low-dimensional strongly coupled fermionic models, to new cosmological paradigms. At the core of this scientific endeavour lies the ability to perform sophisticated and demanding numerical simulations. The Exascale era of High Performance Computing therefore looks like a time of great opportunities. The UK LFT community has been at the forefront of the field for more than three decades and has developed a broad portfolio of research areas, with synergetic connections to High-Performance Computing, leading to significant progress in algorithms and code performance. Highlights of successes include: influencing the design of new hardware (Blue Gene systems); developing algorithms (Hybrid Monte Carlo) that are used widely by many other communities; maximising the benefits from new technologies (lattice QCD practitioners were amongst the first users of new platforms, including GPUs for scientific computing); applying LFT techniques to new problems in Artificial Intelligence. The research programme in LFT, and its impact, can be expanded in a transformative way with the advent of pre-Exascale and Exascale systems, but only if key challenges are addressed. As the number of floating point operations per second increases, the communications between computing nodes are lagging behind, and this imbalance will severely affect future LFT simulations across the board. These challenges are common to all LFT codebases, and more generally to other communities that are large users of HPC resources. The bottlenecks on new architectures need to be carefully identified, and software that minimises the communications must be designed in order to make the best usage of forthcoming large computers. As we are entering an era of heterogeneous architectures, the design of new software must clearly isolate the algorithmic progress from the details of the implementation on disparate hardware, so that our software can be deployed efficiently on forthcoming machines with limited effort. The goal of the EXA-LAT project is to develop a common set of best practices, KPIs and figures of merit that can be used by the whole LFT community in the near future and will inform the design and procurement of future systems. Besides the participation of the LFT community, numerous vendors and computing centres have joined the project, together with scholars from 'neighbouring' disciplines. Thereby we aim to create a national and international focal point that will foster the activity of scholars, industrial partners and Research Sotfware Engineers (RSEs). This synergetic environment will host training events for academics, RSEs and students, which will contribute to the creation of a skilled work force immersed in a network that comprises the leading vendors in the subject. EXA-LAT will set the foundations for a long-term effort by the LFT community to fully benefit of Exascale facilities and transfer some of the skills that characterise our scientific work to a wider group of users across disciplines.

How can we trust autonomous computer-based systems? Autonomous means "independent and having the power to make your own decisions". This proposal tackles the issue of trusting autonomous systems (AS) by building: experience of regulatory structure and practice, notions of cause, responsibility and liability, and tools to create evidence of trustworthiness into modern development practice. Modern development practice includes continuous integration and continuous delivery. These practices allow continuous gathering of operational experience, its amplification through the use of simulators, and the folding of that experience into development decisions. This, combined with notions of anticipatory regulation and incremental trust building form the basis for new practice in the development of autonomous systems where regulation, systems, and evidence of dependable behaviour co-evolve incrementally to support our trust in systems. This proposal is in consortium with a multi-disciplinary team from Edinburgh, Heriot-Watt, Glasgow, KCL, Nottingham and Sussex, bringing together computer science and AI specialists, legal scholars, AI ethicists, as well as experts in science and technology studies and design ethnography. Together, we present a novel software engineering and governance methodology that includes: 1) New frameworks that help bridge gaps between legal and ethical principles (including emerging questions around privacy, fairness, accountability and transparency) and an autonomous systems design process that entails rapid iterations driven by emerging technologies (including, e.g. machine learning in-the-loop decision making systems) 2) New tools for an ecosystem of regulators, developers and trusted third parties to address not only functionality or correctness (the focus of many other Nodes) but also questions of how systems fail, and how one can manage evidence associated with this to facilitate better governance. 3) Evidence base from full-cycle case studies of taking AS through regulatory processes, as experienced by our partners, to facilitate policy discussion regarding reflexive regulation practices.

The Peta-5 proposal from the University of Cambridge brings together 15 world-leading HPC system and application experts from 10 different institutions to lead the creation of a breakthrough HPC and data analytics capability that will deliver significant National impact to the UK research, industry and health sectors. Peta-5 aims to make a significant contribution towards the establishment and sustainability of a new EPSRC Tier 2 HPC network. The Cambridge Tier 2 Centre working in collaboration with other Tier 1, Tier 2 and Tier 3 stakeholders aims to form a coherent, coordinated and productive National e-Infrastructure (Ne-I) ecosystem. This greatly strengthened computational research support capability will enable a significant increase in computational and data centric research outputs, driving growth in both academic research discovery and the wider UK knowledge economy. The Peta-5 system will be one of the largest heterogeneous data intensive HPC systems available to EPSRC research in the UK. In order to create the critical mass in terms of system capability and capacity needed to make an impact at National level Cambridge have pooled funding and equipment resources from the University, STFC DiRAC and this EPSRC Tier 2 proposal to create a total capital equipment value of £11.5M; the request to EPSRC is £5M. The University will guarantee to cover all operational costs of the system for 4 years from the service start date, with the option to run for a fifth year to be discussed. Cambridge will ensure that 80% of the EPSRC funded element of Peta-5 is deployed on EPSRC research projects, with 65% of the EPSRC funded element of Peta-5 being made available to any UK EPSRC funded project free of charge by use of a light weight resource allocation committee, 15% going to Cambridge EPSRC research and 20% being sold to UK industry to drive the UK knowledge economy. The Peta-5 system will be the most capable HPC system in operation in the UK when it enters service in May 2017. In total Peta-5 will provide 3 petaflops (PF) of sustained performance derived from 3 heterogeneous compute elements, 1PF Intel X86, 1PF Intel KNL and 1PF NIVIDIA Pascal GPU (Peta-1) connected via a Pb/s HPC fabric (Peta-2) to an extreme I/O solid state storage pool (Peta-3), a petascale data analytics (Machine Learning + Hadoop) pool (Peta-4) and a large 15 PB tiered storage solution (Peta-5), all under a single execution environment. This creates a new HPC capability in the UK specifically designed to meet the requirements of both affordable petascale simulation and data intensive workloads combined with complex data analytics. It is the combination of these features which unlocks a new generation of computational science research. The core science justification for the Peta-5 service is based on three broad science themes: Materials Science and Computational Chemistry; Computational Engineering and Smart Cities; Health Informatics. These themes were chosen as they represent significant EPSRC research areas, which demonstrate large benefit from the data intensive HPC capability of Peta-5. The service will clearly be valuable for many other areas of heterogeneous computing and Data Intensive science. Hence a fourth horizontal thematic of "Heterogeneous - Data Intensive Science" is included. Initial theme allocation in the RAC will be: Materials 30%, Engineering 30%, Health, 20%, Heterogeneous - Data Intensive 20%. The Peta-5 facility will drive research discovery and impact at national level, creating the largest and most cost effective petascale HPC resource in the UK, bringing petascale simulation within the reach of a wide range of research projects and UK companies. Also Peta-5 is the first UK HPC system specifically designed for large scale machine learning and data analytics, combining the areas of HPC and Big Data, promising to unlock both knowledge and economic benefit from the Big Data revolution.