The current AI paradigm at best reveals correlations between model input and output variables. This falls short of addressing health and healthcare challenges where knowing the causal relationship between interventions and outcomes is necessary and desirable. In addition, biases and vulnerability in AI systems arise, as models may pick up unwanted, spurious correlations from historic data, resulting in the widening of already existing health inequalities. Causal AI is the key to unlock robust, responsible and trustworthy AI and transform challenging tasks such as early prediction, diagnosis and prevention of disease. The Causality in Healthcare AI with Real Data (CHAI) Hub will bring together academia, industry, healthcare, and policy stakeholders to co-create the next-generation of world-leading artificial intelligence solutions that can predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The CHAI Hub will develop novel methods to identify and account for causal relationships in complex data. The Hub will be built by the community for the community, amassing experts and stakeholders from across the UK to 1) push the boundaries of AI innovation; 2) develop cutting-edge solutions that drive desperately needed efficiency in resource-constrained healthcare systems; and 3) cement the UK's standing as a next-gen AI superpower. The data complexity in heterogeneous and distributed environments such as healthcare exacerbates the risks of bias and vulnerability and introduces additional challenges that must be addressed. Modern clinical investigations need to mix structured and unstructured data sources (e.g. patient health records, and medical imaging exams) which current AI cannot integrate effectively. These gaps in current AI technology must be addressed in order to develop algorithms that can help to better understand disease mechanisms, predict outcomes and estimate the effects of treatments. This is important if we want to ensure the safe and responsible use of AI in personalised decision making. Causal AI has the potential to unearth novel insights from observational data, formalise treatment effects, assess outcome likelihood, and estimate 'what-if' scenarios. Incorporating causal principles is critical for delivering on the National AI Strategy to ensure that AI is technically and clinically safe, transparent, fair and explainable. The CHAI Hub will be formed by a founding consortium of powerhouses in AI, healthcare, and data science throughout the UK in a hub-spoke model with geographic reach and diversity. The hub will be based in Edinburgh's Bayes Centre (leveraging world-class expertise in AI, data-driven innovation in health applications, a robust health data ecosystem, entrepreneurship, and translation). Regional spokes will be in Manchester (expertise in both methods and translation of AI through the Institute for Data Science and AI, and Pankhurst Institute), London (hosted at KCL, representing also UCL and Imperial, leveraging London's rapidly growing AI ecosystem) and Exeter (leveraging strengths in philosophy of causal inference and ethics of AI). The hub will develop a UK-wide multidisciplinary network for causal AI. Through extended collaborations with industry, policymakers and other stakeholders, we will expand the hub to deliver next-gen causal AI where it is needed most. We will work together to co-create, moving beyond co-ideation and co-design, to co-implementation, and co-evaluation where appropriate to ensure fit-for-purpose solutions Our programme will be flexible, will embed trusted, responsible innovation and environmental sustainability considerations, will ensure that equality diversity and inclusion principles are reflected through all activities, and will ensure that knowledge generated through CHAI will continue to have real-world impact beyond the initial 60 months.

This CDT will create a cohesive, internationally-leading, cross-domain training and research community fusing algebraic, geometric and quantum methods across Algebra, Geometry and Topology, Mathematical Physics, and their Interfaces. The scientific aim of our CDT is no less than to develop new foundations unifying all three disciplines, and in the process to bolster and future-proof UK capability in mathematics. The breadth of mathematical mastery necessary to achieve these aims, on which our training programme is based, is of the highest international standard, and training students in this area requires both the deep focus and the wide scope which only the resources of a CDT can enable. Our three scientific areas Algebra, Geometry and Quantum Fields are established, flagship, internationally-leading areas of UK mathematical strength. Algebra: quite simply *the* language, and controlling structure, of symbolic computation and symmetry. Geometry: the mathematically rigorous foundations of our human spatial and visual intuition. Quantum Fields: the mathematical incarnation of our quantum physical reality. A hallmark feature of 21st century mathematics is the dramatically increased synergy and inter-dependence between these three fundamental disciplines. Whereas in centuries past mathematics and physics interacted primarily through analysis and calculus, the advent of quantum mechanics posits a fundamentally different, fundamentally algebraic, set of laws for the universe. Geometry enters irrevocably when we pose quantum mechanical laws in the presence of fields, such as the electro-magnetic and gravitational fields, which permeate throughout time and space. A surprising and thrilling discovery of 21st century mathematics has been that the mathematically rigorous study of quantum fields yields some of the most powerful predictive theories within algebra and geometry, even to questions with no a priori physical formulation. These fundamental scientific developments have had a vast and direct impact on our modern world, and on a remarkably short timescale. Algebra, geometry and quantum fields are the driving force behind key developments such as internet search, quantum computation, machine learning, and both classical and quantum cryptography. Society and industry need the students we will train. Our graduates' skills are both fundamentally transferable and widely applicable across many external partnerships and stakeholders. The Deloitte report, commissioned by EPSRC, attributed 2.8M jobs and £200BN of the UK economy to mathematical sciences research, highlighting R&D, computing/tech, public administration, defence, aerospace and pharmaceuticals as economic sectors requiring graduates with advanced mathematical training. Sustainable energy consulting has since emerged as a further industry requiring ever-advanced mathematical sophistication. Crucially a physical and mathematical powerhouse needs to be a diverse powerhouse, yet the traditional structure of training in these areas has inhibited diversity of entrants, both to career academia and to industry. Building on our track record, and equipped with the resources and flexibility only a CDT can provide, we will create a diverse and confident cohort, equipped with the mathematical skillsets needed for our tech-led future to flourish, and able to influence a wide range of people, sectors and institutions.

Machine Learning (ML) already has a dramatic impact on our daily lives. ML developments in large language models and deep generative models cement that further. The recent explosion in ML, however, is built on the back of improved computer systems able to train and generate ever more powerful models. Systems design fundamentally defines ML performance and capability. This is true for Internet-scale ML and artificial intelligence (AI). Yet, more recently, it is especially evident in distributed, efficient, device-oriented, secure, personalised, privacy-preserving ML. UK strength in this fast developing area is dependent on a skilled R\&D workforce. Systems research and ML research are symbiotic. Current innovation in systems research is driven by the ubiquitous need for efficient and reliable ML. ML research, conversely, is steered by deployment capability and the economic and environmental impact of the resulting systems. Furthermore, systems research increasingly relies on ML methods to automate design, and ML research develops such methods. Major gains are made when the development of ML and systems are co-developed and co-optimized. This is relevant across a broad spectrum of industries: in-car systems, medical devices, mobile phones, sensor networks, condition monitoring systems, high-performance compute and high-frequency trading. Yet PhD training that brings together systems and ML is rare; research training is often siloed in the individual sub-disciplines. Instead, we need researchers trained in both fields and experienced in working across them. Hence: The ML Systems CDT will train a new type of student -- the ML-systems researcher. The ML Systems researcher is critically capable in both fields, and has collaborative research experience across the systems-ML stack. An example concretises this. A company is developing and deploying wearable body monitors. Effective models must be learnt on collected data, but data must be privacy preserving and bandwidth minimized. This is then personalised to each individual, adaptable to circumstance while being battery efficient and not connection dependent. To manage such a project requires knowledge of effective data-efficient ML signal analysis methods, designed and optimized for low-power hardware, itself tailored for the purpose through ML optimization methods. Knowledge of personalisation methods and the payoffs of privacy preserving methods vitally complement this. The societal impact, e.g.\ on those who might be obsessive about their medical state must also be considered, and will impact development. This CDT will train individuals with cross-cutting capability in all these components. Students must have broad understanding of different hardware designs, different platforms, different environments, different models, and different goals beyond their immediate research focus. This makes a cohort-based CDT vital. Standard PhD training in ML systems can result in research focus on a single ML technique and a single system. The CDT treats ML Systems as a holistic discipline. Cohort interaction, and integration gives students real experience across multiple systems, approaches and methodologies. Furthermore students will join together to contribute to a unified toolkit for the ML-Systems stack, and make use of others' contributions to that toolkit. On leaving the CDT, our graduates will understand fully where to focus resources to best improve a company's real-world ML development - whether that be at the ML-algorithm level, the hardware level, the compiler, level or even the legal level. They will be able to evaluate work at every level. We expect our graduates to be the leading team managers in real-world cutting-edge company ML.

How can we design technologies that go beyond simply making data publicly accessible, and instead open up data to effective, innovative and potentially transformative public use? There is a broad consensus that the availability of digital data and communication technologies can foster economic and social well-being, as well as business innovation and productivity. Indeed, this has been a key expectation of Open Data policies since the G8 Open Data Charter of 2013. Following Open Knowledge International, the recognised definition of Open Data ensures quality and encourages compatibility between different pools of open material. It is data that anyone can access, use and share: "Knowledge is open if anyone is free to access, use, modify, and share it - subject, at most, to measures that preserve provenance and openness". Citizen empowerment is a principle driven by expectations that new technologies facilitate more responsive governments - access to, and use of, information engenders economic growth as well as creative and social fulfilment. Research in the UK is forward-looking in terms of thinking about Open Data as a public resource for connecting communities and empowering citizens, and creating prosperity. It is hoped that the development of "transformational technologies which connect people, things and data together, in safe, smart, secure, trustworthy, and productive ways" will help foster a data economy for a Connected Nation. Although there are a few examples of excellent practice, Open Data platforms in Scotland are often characterised by an isolated, silo approach to design and implementation. Through initial scoping research, the project team has identified three major problems resulting from this: disjointedness; single-level use design, and inconsistency. Using the everyday social issue of waste management as a case study, "Data Commons Scotland" will prototype an adaptive, learnable Open Data platform with multiple secondary applications and immediate UK-wide implications for Open Data infrastructure, to tackle these problems. In bringing together research expertise in the fields of Human Computer Interaction (HCI), digital learning and data ethics, we will develop participatory design methodologies needed to produce learnable Open Data platforms, underpinned by intentionally designed economic, social and ethical values. Our objectives are to: 1. Design and prototype an Open Source, multi-level Open Data platform for waste management information and community engagement. 2. Develop a learning methodology for participatory design, embedding a recommender system in the platform to support user data literacy. 3. Develop a (co-)design methodology for learning platforms. These objectives address issues relating to at least two Digital Economy Priority Themes: Trust, identity, privacy and security. The project will operate within policy guidelines as set out in the G8 Open Data Charter (2013), the Open Data Strategy for Scotland (2015) and will be fully compliant with ICO guidance on GDPR. This project will take as its baseline principles, a number of the EPSRC's ambitions for innovative research. Amongst these, the project will deliver an Open Data prototype platform that will contribute essential understanding of human interaction with Open Data, in turn contributing to the development of a secure, collaborative, socially-aware Open Data infrastructure. Content creation and consumption. This project will build a prototype to enable the co-creation and exchange of content for social, cultural or business purposes We will explicitly develop, through co-design research and technical standardisation, a platform for curation and distribution of Open Data on waste management. We believe that such inclusive technology will support behaviour change in a number of fields (such as circular economy or Open Energy), fostering collaborative, sustainable environmental awareness through data literacy.

Currently, only 40% of people who could benefit from Hearing Aids (HAs) have them, and most people who have HA devices don't use them often enough. There is social stigma around using visible HAs ('fear of looking old'), they require a lot of conscious effort to concentrate on different sounds and speakers, and only limited use is made of speech enhancement - making the spoken words (which are often the most important aspect of hearing to people) easier to distinguish. It is not enough just to make everything louder! To transform hearing care by 2050, we aim to completely re-think the way HAs are designed. Our transformative approach - for the first time - draws on the cognitive principles of normal hearing. Listeners naturally combine information from both their ears and eyes: we use our eyes to help us hear. We will create "multi-modal" aids which not only amplify sounds but contextually use simultaneously collected information from a range of sensors to improve speech intelligibility. For example, a large amount of information about the words said by a person is conveyed in visual information, in the movements of the speaker's lips, hand gestures, and similar. This is ignored by current commercial HAs and could be fed into the speech enhancement process. We can also use wearable sensors (embedded within the HA itself) to estimate listening effort and its impact on the person, and use this to tell whether the speech enhancement process is actually helping or not. Creating these multi-modal "audio-visual" HAs raises many formidable technical challenges which need to be tackled holistically. Making use of lip movements traditionally requires a video camera filming the speaker, which introduces privacy questions. We can overcome some of these questions by encrypting the data as soon as it is collected, and we will pioneer new approaches for processing and understanding the video data while it stays encrypted. We aim to never access the raw video data, but still to use it as a useful source of information. To complement this, we will also investigate methods for remote lip reading without using a video feed, instead exploring the use of radio signals for remote monitoring. Adding in these new sensors and the processing that is required to make sense of the data produced will place a significant additional power and miniaturization burden on the HA device. We will need to make our sophisticated visual and sound processing algorithms operate with minimum power and minimum delay, and will achieve this by making dedicated hardware implementations, accelerating the key processing steps. In the long term, we aim for all processing to be done in the HA itself - keeping data local to the person for privacy. In the shorter term, some processing will need to be done in the cloud (as it is too power intensive) and we will create new very low latency (<10ms) interfaces to cloud infrastructure to avoid delays between when a word is "seen" being spoken and when it is heard. We also plan to utilize advances in flexible electronics (e-skin) and antenna design to make the overall unit as small, discreet and usable as possible. Participatory design and co-production with HA manufacturers, clinicians and end-users will be central to all of the above, guiding all of the decisions made in terms of design, prioritisation and form factor. Our strong User Group, which includes Sonova, Nokia/Bell Labs, Deaf Scotland and Action on Hearing Loss will serve to maximise the impact of our ambitious research programme. The outcomes of our work will be fully integrated, software and hardware prototypes, that will be clinically evaluated using listening and intelligibility tests with hearing-impaired volunteers in a range of modern noisy reverberant environments. The success of our ambitious vision will be measured in terms of how the fundamental advancements posited by our demonstrator programme will reshape the HA landscape over the next decade.