Impact stories

Introduction

The Turing works with a range of academic and industry partners on diverse projects. As a relatively new institution, many of our exciting, challenging projects are ongoing, but some are already demonstrating far-reaching real-world impact across sectors. Below is a selection of stories demonstrating the diversity of our research output.

Impact stories

Introduction

Streamlining jet engine design and manufacture

Algorithms developed at the Turing will allow aerospace companies to build engines that waste less fuel and have a lower carbon footprint

A new age of Arctic science discovery

Applying algorithms to the fight against global warming

Working with the NHS in Scotland

Using the latest advances in machine learning and data science to predict emergency hospital admissions in Scotland

Supercharging sustainable development

A bold new approach to development economics, championed by the Turing and the United Nations Development Programme, could boost government-backed sustainable development all over the world

Predicting conflict – a year in advance

Ground-breaking technology developed at The Alan Turing Institute could revolutionise the prediction of global conflict, boosting peacekeeping efforts and saving lives

Putting the AI in air traffic control

The UK’s leading air traffic control provider, NATS, has teamed up with the Turing to explore how the industry can evolve with machine learning

Dynamic forecasting with British Airways

Predicting demand for flights is central to airline business strategy, and enormously complex. In collaboration with British Airways, the Turing has developed novel machine learning techniques to tackle the challenge.

Transforming medicine through AI-enabled healthcare

World-leading expertise in healthcare-focused machine learning combined with the world’s largest, high-quality cancer data collection service could lead to a quantum leap in personalised medicine

Changing the culture of data science

The Turing Way is an online handbook – and global community – dedicated to fostering gold-standard reproducible research. It’s a cultural movement with the potential to transform data science

The next step in precision agriculture

Growing enough food for the world’s burgeoning population may require smart farms that can sense their crop conditions and feed themselves on demand, and Turing researchers are developing the technology they’ll need

A machine learning revolution in disaster response

Co-designing computing

High-performance computing (HPC) environments such as data centres can be ill-equipped to deal with data science tasks, so Turing researchers collaborated with Intel to co-design better architecture for their HPC systems

Towards a greener grid

Turing researchers and doctoral students contributed to a new solar forecasting system for National Grid, which is 33% more accurate at day-ahead forecasts, aiding in more efficient balancing of supply and demand and lowering consumer costs

Augmenting clinical decision-making

Working with the Cystic Fibrosis Trust, Turing researchers have been developing machine learning methods that could dramatically improve the accuracy of clinical assessments of people with cystic fibrosis

Understanding urban air quality

Turing researchers are working with the Greater London Authority to use machine learning and statistical methodology to better analyse air pollution sensor data, design better policy interventions, and improve urban quality of life

Bridging the gap between physical and digital

The Turing's data-centric engineering programme and its collaborators are unlocking insights into the world-first 3D printed steel bridge, using innovative data science techniques and 'digital twin' technology

A right to explanation

Advice from Turing researchers, urging the need for individuals to have a legally binding right to have automated decisions made about them explained, is helping shape how the new EU data protection regulations will be implemented

Fairer algorithm-led decisions

Turing researchers from diverse fields have produced a new way of approaching fairness in algorithm-led decisions, by looking at the causes of certain factors that can often result in biased decision-making