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.

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.

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.

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.

The development of future real-world technologies will be dependent on our ability to understand and harness the underlying principles of living systems, and to direct communication between biological parts and man-made materials. Recent advances in DNA synthesis, sequencing and ultra-sensitive analytical techniques amongst others, have reignited interest in extending the repertoire of functional materials by interfacing them with components derived from biology, blurring the boundary between the living and non-living world. These bio-hybrid systems hold great promise for use in a range of application areas including, for example, the sensing of toxins or pollutants in our environment, diagnosing life-threatening ilnesses in humans and animals, or delivering drugs to specific locations within patients bodies to treat a range of diseases, e.g. cancer. During this project we propose to develop innovative manufacturing methods to enable the reliable and scaleable production of evolvable bio-hybrid systems that possess the inherent ability to sense and repair damage, so-called 'immortal' products. This will ultimately lead to the development of products and devices that can continue to function without needing repair or replacement over the course of their life. For example, imagine a mobile phone that can self-repair its own screen after being dropped, or a circuit board in a laptop computer that can repair itself after being short-circuited. The outputs of this project have the potential to provide solutions to some of our greatest societal challenges and by doing so to reinvigorate the UK manufacturing industry by establishing it as a world leader in the production of self-healing systems. We propose to focus our efforts on three specific application areas. These are: 1. Electrochemical energy devices, e.g. fuel cells and batteries that are needed to power our everyday lives, from mobile phones to electric cars. 2. Consumer electronics, which underpin many of the core technologies that we encounter and use on a day-to-day basis, e.g. computers or televisions. 3. Safety critical systems that are used in the nuclear industry and deep sea technologies, e.g. deep sea cables that can withstand many years of use without needing to be replaced.