UK to publish civil service ‘innovation map’
The UK’s Government Digital Service (GDS) has created a tool to map and record innovative use of technology across the country’s civil service bodies.
GDS director general Kevin Cunnington announced the new “innovation map” during a presentation at the Public Sector Show in London late last month, during a speech on the next steps for the UK’s eGovernment agenda.
The map tracks and records “where government is using interesting technologies like biometrics, machine learning, augmented reality, the internet of things and distributed ledgers,” Kevin Cunnington said at the event, as reported by Computer Weekly.
The route to innovation
GDS began manually recording the data last year, before progressing to supervised machine learning to document innovation. The automated process uses an algorithm to locate and record the relevant data, and means the map’s owners can capture masses of information rapidly.
Cunnington told the audience: “We have a map of all the innovation going on across government, and it is all automated. Every ALB [arm’s-length body], innovation fund or catapult has been captured using machine learning, studying [things like] intranets, and that map is automated internally.”
The team hopes to promote collaboration between civil service departments looking for digital solutions, and intends to publish the tool on GOV.UK. A spokesperson from the Cabinet Office said GDS has been working on an “early prototype” of the dataset, but couldn’t confirm exactly when it will be released. “The Government is committed to transparency and open data, and will publish this data in due course,” they said.
Robot librarians
GDS has also been using machine learning to organise all the data on the pan-government website, GOV.UK. In a blog post last week Neil Williams, head of GOV.UK, explained that his team has had to categorise and tag more than half a million individual pages on the site.
After beginning manually, they taught an algorithm to recognise patterns in the data and were then able to complete the mammoth task using the automated system.
He wrote: “By using supervised machine learning, we’ve been able to tag most of the content on GOV.UK to a subject-based taxonomy in just 6 months. If we’d done this manually, it would have taken years at best. At worst, we might never have been able to do it.”
About Natalie Leal
Natalie is a freelance journalist whose work has been published by The Sun Online, The Guardian, Novara Media, Positive News, and Welfare Weekly, among others. She also writes reports and case studies on global business trends for behavioural insights agency, Canvas8. Prior to working as a journalist Natalie worked for the public sector in social services for several years. She switched careers in 2013 after winning a fully funded NCTJ in a national writing competition. She holds a Masters degree in social anthropology from Sussex University where she specialised in processes of social change and international conflict and reconciliation processes.
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