Research
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Machine Learning
Over 200 years ago the Luddites marched into factories to smash up the machines that were taking their jobs. During the first industrial revolution, they rebelled against the ‘fraudulent and deceitful’ use of machinery to get around standard labour practices.
As the fourth industrial revolution gets into full swing, the threat to people’s jobs and livelihoods now comes from AI, robots, smart tech and autonomous cars. But tech is now an accepted part of everyday life. To better understand our attitude towards tech today, we’re looking to the past, as part of a major £multi-million project.
We’re part of a diverse team that’s using pioneering new methods to track how new tech brought about cultural and social change after the industrial revolution. Together we’re gathering data to provide a context for debates about the fourth industrial revolution. Emma Griffin, Professor of Modern History here at UEA is one of two UK historians to be part of this ambitious project that’s set to be one of the UK’s biggest humanities and science research initiatives.
Backed by UKRI’s Strategic Priorities Fund, ‘Living with Machines’ is a five-year project that embraces a radical approach to collaboration. It brings together data scientists and software engineers from The Alan Turing Institute with curators from the British Library and computational linguists, digital humanities scholars, historians and researchers from different universities.
We’re creating innovative new tools and software to analyse digitised historical collections at scale for the very first time. We’re talking censuses, millions of old newspaper pages and birth, marriage and death registrations. This will help us to look at changing attitudes to tech and put the lives of ordinary people centre stage rather than the privileged perspectives of decision-makers and public commentators.
Our Perspectives
“This project is an innovative interdisciplinary collaboration, harnessing the skills of data scientists, as well as humanities scholars. As one of two historians on the team, it’s my role to help select historical data to work with and to shape the research questions. Most of the sources that the project will work with are well known to historians. However, most historians don’t have the time to work with very large datasets, so my role involves explaining to scientists how historians would like to work with particular records, and why it’s important to us.”
– Professor Emma Griffin
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Team Goals
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Missing Millions
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Machine Learning
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