One of the distinguishing characteristics of software systems is that they evolve: new patches are committed to software repositories and new versions are released to users on a continuous basis. Unfortunately, many of these changes bring unexpected failures that break the stability of the system or affect its security, and users face the uncomfortable choice between using an old stable version which misses recent features and bug fixes, and upgrading to a new version which improves the software in certain ways, only to introduce other bugs and security vulnerabilities. In this fellowship, I plan to investigate novel techniques for improving the reliability and security of evolving software, based on the idea of combining the execution of multiple software versions in such a way as to increase the reliability and security of the "multi-version" application and eliminate a large number of common bugs introduced by software updates. This is an ambitious proposal, which presents several challenges spanning the areas of software engineering, computer systems, and security: understanding how software evolves, and particularly the effects of incorrect updates on software evolution; addressing the technical challenges of multi-version execution such as creating an application-level sandboxing environment and devising lightweight record and replay techniques; designing error recovery strategies that effectively combine different software versions; and determining the applicability of multi-version execution to the different types of applications and code changes encountered in practice.

This project is a direct response to significant changes taking place in the domain of computing and the arts. Recent developments in Artificial Intelligence and Machine Learning are leading to a revolution in how music and art is being created by researchers (Broad and Grierson, 2016). However, this technology has not yet been integrated into software aimed at creatives. Due to the complexities of machine learning, and the lack of usable tools, such approaches are only usable by experts. In order to address this, we will create new, user-friendly technologies that enable the lay user - composers as well as amateur musicians - to understand and apply these new computational techniques in their own creative work. The potential for machine learning to support creative activity is increasing at a significant rate, both in terms of creative understanding and potential applications. Emerging work in the field of music and sound generation extends from musical robots to generative apps, and from advanced machine listening to devices that can compose in any given style. By leveraging the internet as a live software ecosystem, the proposed project examines how such technology can best reach artists, and live up to its potential to fundamentally change creative practice in the field. Rather than focussing on the computer as an original creator, we will create platforms where the newest techniques can be used by artists as part of their day-to-day creative practices. Current research in artificial intelligence, and in particular machine learning, have led to an incredible leap forward in the performance of AI systems in areas such as speech and image recognition (Cortana, Siri etc.). Google and others have demonstrated how these approaches can be used for creative purposes, including the generation of speech and music (DeepMinds's WaveNet and Google's Magenta), images (Deep Dream) and game intelligence (DeepMind's AlphaGo). The investigators in this project have been using Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and other approaches to develop intelligent systems that can be used by artists to create sound and music. We are already among the first in the world to create reusable software that can 'listen' to large amounts of sound recordings, and use these as examples to create entirely new recordings at the level of audio. Our systems produce outcomes that out-perform many other previously funded research outputs in these areas. In this three-year project, we will develop and disseminate creative systems that can be used by musicians and artists in the creation of entirely new music and sound. We will show how such approaches can affect the future of other forms of media, such as film and the visual arts. We will do so by developing a creative platform, using the most accessible public forum available: the World Wide Web. We will achieve this through development of a high level live coding language for novice users, with simplified metaphors for the understanding of complex techniques including deep learning. We will also release the machine learning libraries we create for more advanced users who want to use machine learning technology as part of their creative tools. The project will involve end-users throughout, incorporating graduate students, professional artists, and participants in online learning environments. We will disseminate our work early, gaining the essential feedback required to deliver a solid final product and outcome. The efficacy of such techniques has been demonstrated with systems such as Sonic Pi and Ixi Lang, within a research domain already supported by the AHRC through the Live Coding Network (AH/L007266/1), and by EC in the H2020 project, RAPID-MIX. Finally, this research will strongly contribute to dialogues surrounding the future of music and the arts, consolidating the UK's leadership in these fields.

In our everyday life we rarely think about the effects of quantum mechanics, and yet they are constantly around us, determining the properties of every material object in our world. The laws of quantum physics define every property of matter, from the behaviour of individual atoms, to how the atoms bind together to form materials, to the characteristics of these resultant materials. They also determine if and how systems of many interacting particles establish an equilibrium or steady state governed by a handful of statistical laws. Physicists are now able to engineer large, tunable collections of interacting quantum particles, both in quantum computing devices and in ultracold atomic gases and solid-state materials. Often, such systems cannot be described by standard techniques that focus on quantum states that have simple structures. In many cases, the routes by which such systems come to equilibrium involve subtle and surprising features of quantum mechanics, necessitating entirely new ways of thinking, or require substantial extensions of older approaches such as hydrodynamics. Another striking new idea that has emerged recently is that quantum mechanical coherence can be preserved even when many-body systems are far from their lowest-energy state. The word "coherence" here implies that many microscopic objects are acting together in concert. Such behaviour, when it occurs, allows for the effects of quantum physics to be greatly enhanced, but it is usually washed out as systems achieve equilibrium, which can often be described well using classical physics. Finding routes to evade this equilibrium allows for new and unusual physical phenomena with significant potential utility for quantum technology. Yet a third set of new concepts is motivated by the capabilities of the present-day "noisy, intermediate-scale quantum" (NISQ) devices. In contrast to conventional platforms, these offer the possibility of punctuating the time evolution of a many-body system by measurements, and using the results to shape future evolution - a new form of "quantum interactive dynamics", where the scientist is an active participant rather than a passive spectator. Understanding the new states of matter enabled in this setting and the protocols needed to implement them on NISQ processors is an exciting new frontier. We have organised our research into three themes: (1) What are the mechanisms by which quantum systems approach an equilibrium state? We will develop a better understanding of universal aspects of the equilibrium state in quantum many-body systems. We will also seek to understand certain experimental systems, such as cold atomic gases or solid-state materials, that can be studied using hydrodynamic principles and their generalizations. (2) How can quantum many-body systems evade thermalization to access novel non-equilibrium regimes? We will seek to understand how frozen-in randomness and special symmetries can arrest the approach to equilibrium and allow quantum coherence to persist even in highly excited states. (3) What new possibilities are enabled by "quantum interactive dynamics"? We will clarify how the evolution of quantum systems towards or away from equilibrium can be shaped by measurement and feedback. The answers to these questions are likely to be central in harnessing the full power of quantum mechanics to accomplish complex tasks. Understanding the far-from-equilibrium and interactive dynamics of quantum many-particle systems is likely to play a similar role in the development of future quantum computing devices as the quantum theory of solids did in the technological revolutions of the past century. Thus, while our research is mainly academic in nature, we hope that our discoveries will enable technologies needed to address the challenges of the next century.

There have recently been significant leaps in deep reinforcement learning algorithms, with notable successes in games such as Atari arcade games and Go; however, there is still a need to adapt these techniques to be more widely applicable in other domains, such as the life science sector. Identifying regulatory relationships between genes is one of the primary research activities carried out by molecular biologists and geneticists, since learning the structure of gene regulatory networks is critical for many applications, for example understanding the origins of many diseases and how crops respond to their environments. Biologists sequentially conduct experiments that provide information about the gene network structure, but they must operate under strict cost and time limits. This project aims to formulate this experiment design procedure in a reinforcement-learning framework, to ascertain how biologists should prioritise experiments to maximise information about the gene networks, under constraints. The primary deliverable will be a Computer-aided Experimental Design (CoED) software tool to aid researchers in utilising their resources most effectively. This reinforcement-learning framework could also be used to identify the bottlenecks for biomedical research, such as the pricing model or the time-intensity of certain experiments, thereby identifying the most impactful areas for further development in experimental methodology. We will deliver impact by providing consultation services to laboratory supply and service providers, and through our collaboration with our industrial partner Google Brain Genomics. This project primarily aligns with the new approaches to data science and high productivity services through specialised artificial intelligence priority areas of this call.

In digital discrimination, users are treated unfairly, unethically or just differently based on their personal data. Examples include low-income neighborhoods targeted with high-interest loans; women being undervalued by 21% in online marketing; and online ads suggestive of arrest records appearing more often with searches of black-sounding names than white-sounding names. Digital discrimination very often reproduces existing instances of discrimination in the offline world by either inheriting the biases of prior decision makers, or simply reflecting widespread prejudices in society. Digital discrimination may also have an even more perverse result, it may exacerbate existing inequalities by causing less favourable treatment for historically disadvantaged groups, suggesting they actually deserve that treatment. As more and more tasks are delegated to computers, mobile devices, and autonomous systems, digital discrimination is becoming a huge problem. Digital discrimination can be the result of algorithmic biases, i.e., the way in which a particular algorithm has been designed creates discriminatory outcomes, but it also occurs using non-biased algorithms when they are fed or trained with biased data. Research has been conducted on so-called fair algorithms, tackling biased input data, demonstrating learned biases, and measuring relative influence of data attributes, which can quantify and limit the extent of bias introduced by an algorithm or dataset. But, how much bias is too much? That is, what is legal, ethical and/or socially-acceptable? And even more importantly, how do we translate those legal, ethical, or social expectations into automated methods that attest digital discrimination in datasets and algorithms? DADD (Discovering and Attesting Digital Discrimination) is a *novel cross-disciplinary collaboration* to address these open research questions following a continuously-running co-creation process with academic (Computer Science, Digital Humanities, Law and Ethics) and non-academic partners (Google, AI Club), and the general public, including technical and non-technical users. DADD will design ground-breaking methods to certify whether or not datasets and algorithms discriminate by automatically verifying computational non-discrimination norms, which will in turn be formalised based on socio-economic, cultural, legal, and ethical dimensions, creating the new *transdisciplinary field of digital discrimination certification*.