Machine learning, AI and cognitive computing

Developing new and improved algorithms

Data is the world’s newest natural resource

Machine learning, AI and cognitive computing have the potential to revolutionise the way in which computing affects our everyday lives. At a time when we are producing more data than ever before, it becomes essential to develop new and improved algorithms for decreasing the time to insight from these exponentially growing data sources.

Much of the data produced outside of the social media footprint is produced by industry, and developing capabilities which can allow industry to reap the benefits of advanced machine learning, AI and cognitive computing methods is well aligned with the UK Digital Strategy, and the recent report on Growing the AI industry in the UK.

Our approach

In our research team, we look to achieve this by answering three fundamental questions:

Seeing

Seeing

How does AI adapt the way it views the world through data?

Thinking

Thinking

How does AI make decisions, and balance risk and reward when searching for new solutions?

Dreaming

Dreaming

How does AI take what it knows, and uses it to construct new — as yet unseen — suggestions?

In order to achieve this, we work on three main technologies:

Deep learning: The construction of neural networks to build end-to-end learning machines capable of taking raw, unprocessed data, building representations, and using these representations to infer potential target values.

Bayesian optimization: A black-box optimization strategy which uses a probabilistic model to balance explore and exploit in complex search problems. One example of this is constructing the architectures of complex deep learning machines.

Reinforcement learning: Coupling deep learning with traditional reinforcement learning techniques (known as Deep-Q, or double Deep-Q learning) to teach agents how to respond to situations, or environments in an optimal way.

Case study

Cognitive treatment plant

The cognitive treatment plant project aims to increase the efficiency of the water treatment process through the use of an artificial intelligence (AI) technique known as deep reinforcement learning. We teach the AI to minimise energy usage and disposal cost, while maintaining high quality effluent and minimise the number of regulatory violations. By exposing the AI to a wide variety of conditions, through a simulator we aim to ensure the robustness and stability of the control system. Common problems for operating a wastewater plant efficiently are that sensors do not always accurately measure the process; there are uncertainty in loads and volume of influents (which are dependent on weather and domestic/industrial usages).

As a result, wastewater plants are often run in a risk-averse way. For example the plant is operated as if a storm were coming, so more oxygen is pumped into the aerated tanks to ensure that the wastewater is treated properly even under adverse conditions. This means that there might be opportunities for smart pumping scheduling or reduced pumping to lower energy/­electricity/­operational costs based on using weather forecasts and a thorough analysis of which amount of pumping should be sufficient.

We have implemented AI on a controller that controls the pumping rate into an aerated tank. While in operation, the controller learns how different setpoints can lead to different influent qualities and energy costs. It then continuously learns and determines how to optimise the setpoints under different states of the tank so that it can minimise the operational costs while still ensuring that the effluents satisfy the treatment standard.

Molecular discovery

Using Deep Learning to Design the Molecules of Tomorrow

Using Deep Learning to Design the Molecules of Tomorrow

Watch video

Ask the experts

Edward Pyzer-Knapp

Edward Pyzer-Knapp

Lan Hoang

Lan Hoang