Bayesian inference is a process which allows us to extract information from data. The process uses prior knowledge articulated as statistical models for the data. We are focused on developing a transformational solution to Data Science problems that can be posed as such Bayesian inference tasks. An existing family of algorithms, called Markov chain Monte Carlo (MCMC) algorithms, offer a family of solutions that offer impressive accuracy but demand significant computational load. For a significant subset of the users of Data Science that we interact with, while the accuracy offered by MCMC is recognised as potentially transformational, the computational load is just too great for MCMC to be a practical alternative to existing approaches. These users include academics working in science (e.g., Physics, Chemistry, Biology and the social sciences) as well as government and industry (e.g., in the pharmaceutical, defence and manufacturing sectors). The problem is then how to make the accuracy offered by MCMC accessible at a fraction of the computational cost. The solution we propose is based on replacing MCMC with a more recently developed family of algorithms, Sequential Monte Carlo (SMC) samplers. While MCMC, at its heart, manipulates a single sampling process, SMC samplers are an inherently population-based algorithm that manipulates a population of samples. This makes SMC samplers well suited to the task of being implemented in a way that exploits parallel computational resources. It is therefore possible to use emerging hardware (e.g., Graphics Processor Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Intel's Xeon Phis as well as High Performance Computing (HPC) clusters) to make SMC samplers run faster. Indeed, our recent work (which has had to remove some algorithmic bottlenecks before making the progress we have achieved) has shown that SMC samplers can offer accuracy similar to MCMC but with implementations that are better suited to such emerging hardware. The benefits of using an SMC sampler in place of MCMC go beyond those made possible by simply posing a (tough) parallel computing challenge. The parameters of an MCMC algorithm necessarily differ from those related to a SMC sampler. These differences offer opportunities for SMC samplers to be developed in directions that are not possible with MCMC. For example, SMC samplers, in contrast to MCMC algorithms, can be configured to exploit a memory of their historic behaviour and can be designed to smoothly transition between problems. It seems likely that by exploiting such opportunities, we will generate SMC samplers that can outperform MCMC even more than is possible by using parallelised implementations alone. Our interactions with users, our experience of parallelising SMC samplers and the preliminary results we have obtained when comparing SMC samplers and MCMC make us excited about the potential that SMC samplers offer as a "New Approach for Data Science". Our current work has only begun to explore the potential offered by SMC samplers. We perceive significant benefit could result from a larger programme of work that helps us understand the extent to which users will benefit from replacing MCMC with SMC samplers. We propose a programme of work that combines a focus on users' problems with a systematic investigation into the opportunities offered by SMC samplers. Our strategy for achieving impact comprises multiple tactics. Specifically, we will: use identified users to act as "evangelists" in each of their domains; work with our hardware-oriented partners to produce high-performance reference implementations; engage with the developer team for Stan (the most widely-used generic MCMC implementation); work with the Industrial Mathematics Knowledge Transfer Network and the Alan Turing Institute to engage with both users and other algorithmic developers.

The digitalisation of manufacturing is a key enabler in the UK Government drive to raise the level of industrial productivity to match and exceed leading competitors. Whilst basic digital technology applications in manufacturing are not new, there are two key trends that predicate the timeliness of the proposed research: (1) manufacturing organisations are increasingly seeing information as a key strategic addition to their product offerings; (2) major innovations in computer science, control and informatics have created new opportunities for major breakthroughs in manufacturing. One of the critical challenges is how to support the digital manufacturing transformation of SMEs and introduce new methods of production that take into account the latest control, communication and AI technologies in a sector characterised by limited capital investment and research potential. Whilst there is significant body of knowledge in this area it is mostly focused on relatively expensive solutions which are often unaffordable to SMEs? This project will therefore address a common concern that recent developments in digital manufacturing are unlikely to accessible by SMEs owing to the associated capital cost of upgrading industrial computing and communication environments. The project proposes a radically different approach to the digital evolution of a manufacturing operation by focussing predominantly on non industrial solutions to industrial automation and information challenges. It will seek to exploit very low cost commercially available technologies for mobile computing, sensing, AI and tackle the challenges associated with integrating these safely and securely into a small scale manufacturing environment. As well as conventional research activities, the project will more radically involve student hackathons as a means of stimulating low cost software development, will use an in-house technology transfer organisation to access SME organisations, and engage directly with the High Value Manufacturing catapult demonstration network as a means of reaching the maximum number of potential users. Stretch targets for the programme include the introduction of low cost product tracking, exploiting emerging industrial IoT platforms and AI-based flexible control using commercially available AI and voice recognition development environments. The project will supplement the traditional research and development approaches with some innovative implementation development activities in which (i) undergraduate and graduate students in both engineering and computer science and integrated via a series of hackathons and software and hardware development competitions (ii) a series of workshops will be targeted at local start up and SME IT communities to engage them directly in the development of applications and products (iii) by working directly with technology transfer organisations to ensure that not only the final message but also the starting rationale for the work fully engages the SME manufacturing community.