Context Social intelligence is an important aspect of human cognition making us capable of dealing with others' attitudes, intentions, feelings, personality, and expectations. Correspondingly, Artificial Social Intelligence is the area of Artificial Intelligence (AI) that aims at endowing machines with such social intelligence, i.e., with the ability to interact with their users in the same way as people interact with one another. While being driven by technological needs, Artificial Social Intelligence is an inherently interdisciplinary field that revolves around humans as much as it revolves around the building of machines. As a result, the proposed Centre for Doctoral Training (CD) is based on the collaboration between different experts in human behaviour - students will be trained by specialists ranging in expertise from neural, physiological, cognitive and psychological processes to verbal/nonverbal societal communication - and experts in AI methodologies. They will gain expertise and skills that will range from the synthesis of human/societal interactive behaviour to the distillation of knowledge from sensors and data. - Aims and objectives The goal of the CDT is to train the next generation of experts in Artificial Social Intelligence, young researchers and practitioners well versed not only in AI, but also in a range of fields spanning from Psychology/Social Science and Neuroscience to Human-Computer Interaction and Data Science. These different disciplines will come together to train the cohort in: a. Identifying principles and laws underlying social interactions between users and agents; b. Developing technological approaches that allow artificial agents to act as believable partners in social interactions involving human users; c. Integrating artificial agents into the wider technological infrastructures; d. Investigating human responses to artificial agents in a naturalistic, real-world social settings. Academic involvement will be in the form of the provision of courses across the topics listed above, advanced workshops and direct supervision in cutting edge research that is not necessarily (yet) part of the industrial workflow. - Applications and Benefits The proposed training approach will be in tight collaboration and co-creation with our industrial partners as the aim is to provide the students with the best of both the academic and industry worlds. Industrial involvement will be in the form of co-design and co-supervision of the PhD project as well as placements, usually over a period of 3 months. This will allow the students to co-create innovation through the PhD proposal and the development of specific, real-world industry problems. Such a tight interaction with industry will also be of advantage to the UK economy that will benefit from a pool of talent trained not only from a scientific and technological point of view, but also in terms of professional skills and experience necessary to operate in highly technological companies. Students will further benefit from wider social sciences training through the proposed partnership with the Scottish Graduate School for Social Sciences (SGSSS), a ESRC funded Doctoral Training Partnership (DTP) with a track record for excellence in Teaching, Cohort training and Knowledge Exchange and Impact. The outlined training model will inform AI approaches with the findings on human behaviour and, vice versa, AI technologies will be used to better understand and model human behaviour. Last, but not least, the emphasis on ethics and social issues is of great societal importance as AI-driven technologies play an increasingly important role in sensitive settings such as healthcare, assistance, education, law enforcement, etc.

1) To create the next generation of Natural Language Processing experts, stimulating the growth of NLP in the public and private sectors domestically and internationally. A pool of NLP talent will provide incentives for (existing) companies to expand their operations in the UK and lead to start-ups and new products. 2) To deliver a programme which will have a transformative effect on the students that we train and on the field as a whole, developing future leaders and producing cutting-edge research in both methodology and applications. 3) To give students a firm grounding in the challenge of working with language in a computational setting and its relevance to critical engineering and scientific problems in our modern world. The Centre will also train them in the key programming, engineering, and machine learning skills necessary to solve NLP problems. 4) To attract students from a broad range of backgrounds, including computer science, AI, maths and statistics, linguistics, cognitive science, and psychology and provide an interdisciplinary cohort training approach. The latter involves taught courses, hands-on laboratory projects, research-skills training, and cohort-based activities such as specialist seminars, workshops, and meetups. 5) To train students with awareness of user design, ethics and responsible research in order to design systems that improve user statisfaction, treat users fairly, and increase the uptake of NLP technology across cultures, social groups and languages.

The worldwide software market, estimated at $250 billion per annum, faces a disruptive challenge unprecedented since its inception: for performance and energy reasons, parallelism and heterogeneity now pervade every layer of the computing systems infrastructure, from the internals of commodity processors (manycore), through small scale systems (GPGPUs and other accelerators) and on to globally distributed systems (web, cloud). This pervasive parallelism renders the hierarchies, interfaces and methodologies of the sequential era unviable. Heterogeneous parallel hardware requires new methods of compilation for new programming languages supported by new system development strategies. Parallel systems, from nano to global, create difficult new challenges for modelling, simulation, testing and verification. This poses a set of urgent interconnected problems of enormous significance, impacting and disrupting all research and industrial sectors which rely upon computing technology. Our CDT will generate a stream of more than 50 experts, prepared to address these challenges by taking up key roles in academic and industrial research and development labs, working to shape the future of the industry. The research resources and industrial connections available to our CDT make us uniquely well placed within the UK to deliver on these aspirations. The "pervasive parallelism challenge" is to undertake the fundamental research and design required to transform methods and practice across all levels of the ICT infrastructure, in order to exploit these new technological opportunities. Doing so will allow us to raise the management of heterogeneous concurrency and parallelism from a niche activity in the care of experts, to a regularised component of the mainstream. This requires a steady flow of highly educated, highly skilled practitioners, with the ability to relate to opportunities at every level and to communicate effectively with specialists in related areas. These highly skilled graduates must not only have deep expertise in their own specialisms, but crucially, an awareness of relationships to the surrounding computational system. The need for fundamental work on heterogeneous parallelism is globally recognised by diverse interest groups. In the USA, reports undertaken by the Computing Community Consortium and the National Research Council recognise the paradigm shift needed for this technology to be incorporated into research and industry alike. Both these reports were used as fundamental arguments in initiating the call for proposals by the National Science Foundation (NSF) on Exploiting Parallelism and Scalability, in the context of the NSF's Advanced Computing Infrastructure: Vision and Strategic Plan which calls for fundamental research to answer the question of "how to enable the computational systems that will support emerging applications without the benefit of near-perfect performance scaling from hardware improvements." Similarly, the European Union has identified the need for new models of parallelism as part of its Digital Agenda. Under the agenda goals of Cloud Computing and Software and Services, parallelism plays a crucial role and the Commission asserts the need for a deeper understanding and new models of parallel computation that will enable future technology. Given the UK's global leadership status it is imperative that similar questions be posed and answered here.

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.

AP4L is a 3-year program of interdisciplinary research, centring on the online privacy & vulnerability challenges that people face when going through major life transitions. Our central goal is to develop privacy-by-design technologies to protect & empower people during these transitions. Our work is driven by a narrative that will be familiar to most people. Life often "just happens", leading people to overlook their core privacy and online safety needs. For instance, somebody undergoing cancer treatment may be less likely to finesse their privacy setting on social media when discussing the topic. Similarly, an individual undergoing gender transition may be unaware of how their online activities in the past may shape the treatment into the future. This project will build the scientific and theoretical foundations to explore these challenges, as well as design and evaluate three core innovations that will address the identified challenges. AP4L will introduce a step-change, making online safety and privacy as painless and seamless as possible during life transitions To ensure a breadth of understanding, we will apply these concepts to four very different transitions through a series of carefully designed co-creation activities, devised as part of a stakeholder workshop held in Oct'21. These are relationship breakdowns; LBGT+ transitions or transitioning gender; entering/ leaving employment in the Armed Forces; and developing a serious illness or becoming terminally ill. Such transitions can significantly change privacy considerations in unanticipated or counter-intuitive ways. For example, previously enabled location-sharing with a partner may lead to stalking after a breakup; 'coming out' may need careful management across diverse audiences (e.g - friends, grandparents) on social media. We will study these transitions, following a creative security approach, bringing together interdisciplinary expertise in Computer Science, Law, Business, Psychology and Criminology. We will systematise this knowledge, and develop fundamental models of the nature of transitions and their interplay with online lives. These models will inform the development of a suite of technologies and solutions that will help people navigate significant life transitions through adaptive, personalised privacy-enhanced interventions that meet the needs of each individual and bolster their resilience, autonomy, competence and connection. The suite will comprise: (1) "Risk Playgrounds", which will build resilience by helping users to explore potentially risky interactions of life transitions with privacy settings across their digital footprint in safe ways (2) "Transition Guardians", which will provide real-time protection for users during life transitions. (3) "Security Bubbles", which will promote connection by bringing people together who can help each other (or who need to work together) during one person's life transition, whilst providing additional guarantees to safeguard everyone involved. In achieving this vision, and as evidenced by £686K of in-kind contributions, we will work with 26 core partners spanning legal enforcement agencies (e.g., Surrey Police), tech companies (e.g., Facebook, IBM), support networks (e.g., LGBT Foundation, Revenge Porn Helpline) and associated organisations (e.g., Ofcom, Mastercard, BBC). Impact will be delivered through various activities including a specially commissioned BBC series on online life transitions to share knowledge with the public; use of the outputs of our projects by companies & social platforms (e.g., by incorporating into their products, & by designing their products to take into consideration the findings of our project) & targeted workshops to enable knowledge exchange with partners & stakeholders.