Understanding history with new eyes

About Us

Living with Machines is both a research project, and a bold proposal for a new research paradigm. In this ground-breaking partnership between The Alan Turing Institute, the British Library, and the Universities of Cambridge, East Anglia, Exeter, and London (QMUL), historians, data scientists, geographers, computational linguists, and curators have been brought together to examine the human impact of industrial revolution.

This programme, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund,  is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London.

Project Reference: AH/S01179X/1

First industrial revolution

It is widely recognised that Britain was the birthplace of the world’s first industrial revolution, yet there is still much to learn about the human, social, and cultural consequences of this historical moment. Focussing on the long nineteenth century (c.1780-1918), the Living with Machines project aims to harness the combined power of massive digitised historical collections and computational analytical tools to examine the ways in which technology altered the very 
fabric of human existence on a hitherto unprecedented scale.

The work on the project is organised around five ‘Labs’ that explore different historical and methodological questions and approaches. The Labs are: Language, Space and Time, Sources, Communities, and Integration, Infrastructure and Interfaces (3I). These Labs are supported by the work of the Data Acquisition and Wrangling group.

"Living with Machines represents a hugely exciting and innovative development in arts and humanities research. The collaboration between historians and data scientists, exploiting the remarkable growth of digital archives, will open up dramatic new perspectives on the well-known story of the industrial revolution and the history of society’s relationship with machines and technology since the eighteenth century."
Professor Roey Sweet, Director of Partnerships and Engagement at the Arts and Humanities Research Council

The mechanisation of work practices

The central theme – the mechanisation of work practices – speaks directly to present debates about how society can accommodate the revolutionary consequences of AI and robotics. To understand the fraught co-existence of human and machine, this project contends that we need research methods that combine technological innovation and human expertise.


Living with Machines aims to:

  • Generate new historical perspectives on the effects of the mechanisation of labour on the lives of ordinary people during the long nineteenth century.
  • Support the wider academic and cultural heritage sector in using digital methods to answer historical questions.
  • Create new tools and code that can be reused and built upon in future projects.
  • Develop new computational techniques for working with historical research questions.
  • Enrich the British Library’s data holdings for the benefit of all
  • Advance public awareness of how digital research in the humanities can enhance understanding of history.

About our Labs

The Sources Lab engages with issues of representativeness, genre balance and bias in the source base, and will develop new methods for exposing, assessing and compensating for historical biases and absent voices. Initial work includes examining how the British newspaper ‘landscape’ has evolved in the 19th century, analysing the various geographical, political and economic transformations that marked the evolution of the Victorian press, and what voices are and are not present in that corpus.

The Language Lab is exploring the social and cultural impact of the Industrial Revolution as reported in newspapers and other types of textual sources. Their initial research task asks how machines were represented in specific relation to causation. In other words, to what extent were machines imagined as autonomous agents, able to drive change? They are collaborating to create gold standard datasets and a lexicon of machinery, and develop methods both for geo-referencing mentions of places and for lexicon expansion and the assessment of the impact of bad OCR in historical texts.

The Space and Time Lab is interested in how the changes that occurred during the industrial revolution altered the lived experiences of ordinary people. This involves collaboration between historians and others to clean and use census and cartographic data on a hitherto unmanageable scale in order to address specific questions about the human experience of industrialisation. Their first research task is testing image analysis and computer vision methods on digital images of nineteenth-century British Ordnance Survey (OS) maps.

The Communities Lab leads on outreach and engagement with the public(s), and with academic disciplines. It seeks to contribute to the emerging field of Human Computation by collaborating with other Labs to create systems that integrate crowdsourcing and public engagement activities with data science processes. Initially, they are setting up a crowdsourcing project to collect historical newspaper articles about workplace accidents involving machinery, with information about any named individuals, organisations, locations, equipment etc., feeding into other Labs.

The 3I (Integration, Infrastructure and Interfaces) Lab addresses the challenge of integrating the project’s interdisciplinary research agenda by developing a shared infrastructure. This infrastructure supports the exchange of information between the different labs, enabling outputs generated in one lab (in the form of enhanced or enriched datasets) to feed into the others as inputs, thereby unlocking new analyses. Their initial work focusses on establishing a common data model. This shared data model is essential for the integration of research activity and to support queries across heterogeneous data sources such as newspaper articles, census records and OS maps.

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