Aggregating Computers and Experts

Human experts have opinions that are the product of enviable experience and pronounced cognitive biases. Calibrated computer simulations can make impressively precise predictions albeit with questionable accuracy. Given that both experts and computers each answer queries with different unknown biases and accuracies, it is unclear how we make best aggregate the information to assess what we know and what we would gain from further inputs from either a computer or an expert.

Techniques (e.g., Gaussian Processes) exist to use data from either an expert or a computer and to interpolate between previously measured data-points. These techniques rely on some notion of the extent to which fluctuations in input parameters can result in fluctuations in output data. This notion can be captured mathematically in the "kernel". It is possible to use recently-developed distributed numerical Bayesian techniques (Sequential Monte Carlo samplers) to efficiently search the huge space of kernels: these techniques are better able to exploit distributed hardware than pre-existing alternatives (e.g., Markov chain Monte Carlo). This ability to search efficiently is of paramount importance when the kernel also captures the extent to which biases can exist between the output of an expert and a computer.

The problem of aggregating computer outputs and expert judgement is pertinent to Unilever's ability to accelerate the development of new products. Such aggregation would make it possible to: ascertain the utility of requesting additional expert input and/or running additional computer simulation; estimate the biases present; identify the optimal compromise between trusting the experts and relying on the apparent fidelity of the computer simulations and ultimately the level of acceptance and adoption of such simulations to the scientists in their product design activities. Unilever will work with the student to identify and access data pertinent to a specific instance of this challenge as well as to understand how the technology being developed could be deployed in the context of the formulation of new products.

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.