The Royal Society and IORMA : The impact of machine learning (ML) on UK business and competitiveness.

Barbara Walker
Director of Innovation and Government Relations


I was invited by the Royal Society to a one day workshop held on 19 January 2016 to gather evidence on the impact of machine learning (ML) on UK business and competitiveness.

This is a subject of keen interest to me as IORMA’s director of innovation and government relations, as machine learning is such a powerful technology in exploiting big data, of huge value now and in the future to businesses in consumer commerce.

artificial-intelligenceThe Royal Society is keen in this project to explore how ML is created, how it can be used, and the impact on people - as consumers, as seekers after the best jobs and as citizens interested in how technology can transform society.

In addition we debated in the workshop how machine learning can make its optimum contribution to UK competitiveness, keeping this country at the forefront of ML expertise.

Also taking part in the evidence session were data scientists from Ocado, dunnhumby and BT Research, as well as from senior people at some of the UK’s top universities. So this gathering provided a rich exchange of experience and ideas on ML’s current use and its future potential, including adverse aspects – might intelligent machines take over and run those parts of our lives that we prefer to keep to ourselves?

In discussion I drew on instances of how IORMA Associates are using machine learning, and also some interests and concerns that they share at our private roundtables. IBM and SAP, both on our Board, are leading developers, and other associates are keen potential contributors to knowledge sharing.

In particular, IORMA businesses have a significant interest in working across the traditional sector boundaries to connect FMCG with retail travel and transport, brands and advertising, leisure and hospitality – globally.

Consumer commerce has a rich fund of data about customers as groups and individuals, including their attitudes to data privacy and price sensitivity. This could be of huge value to the public services that are looking most closely at use of ML – healthcare and education.

We also discussed the public’s attitudes to ML and other advanced technologies -including robotics, Internet of Things and virtual reality. I gave my view that people are more excited than fearful about these advances, and that any public campaign to raise awareness of machine learning may well have to give way to raising people’s consciousness of cyber security and the need to protect their own data privacy.

These issues look set to hold top priority in the minds of public policy makers.

Machine Learning is a powerful technology that allows machines to learn from data and to self-improve.

It is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and many apps on our phones, such as voice recognition.


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