Scalable Online Machine Learning

The project relates to extending the state-of-the-art to enable machine learning to fully capitalise on the information present in never-ending data streams. The additional data that arrives over time contains information that should facilitate improved machine learning. Not using this information gives rise to consistent yet surprising errors: this typically occurs when the training data is small relative to the algorithm's empirical experience. Concept drift can also occur: the passage of time also provides scope for the phenomena that give rise to the data to change. The result of concept drift is that, even if the phenomena of interest do not change, because the statistical environment changes, the performance of the machine learning is prone to degrading. Furthermore, since the quantity of historic data is ever growing, given finite data storage and computational resources, innovative techniques are needed to summarise the information present in data and currently pertinent without requiring all the raw data ever received to be stored.

The proposed solution involves three novel components. First, to reduce the storage and computation that would otherwise be required, the pertinent data received up to the current time will be summarised in an adaptive tree-based data structure. This definition of this data structure will build on previous work on Approximate Bayesian Computation and involve approximating the information present in the raw data with summaries. Second, to ensure concept drift is catered for, these summaries will explicitly relate to the time-derivatives of the parameters that the machine learning is attempting to estimate. Finally, to maximise performance, previous related work involving variational inference, which will be extended to consider the aforementioned data structures, will also be adapted to consider numerical Bayesian inference.

The approach will be applied to real-world datasets involving combinations of: near-constant parameters for which concept drift is not relevant (e.g. related to rare events of interest); parameters that fluctuate smoothly over long timescales (e.g. diffusive spread of memes); sudden shifts in concepts (e.g. new memes appearing). Such datasets are anticipated to involve large and continually growing text corpuses (e.g. social media)

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.