With the availability of more and more data, increased computing power, and easier access to tools, there’s never been a better time to tap into the world of machine learning (ML). But it’s also a topic which is not without controversy.
This episode of Perspectives* LIVE explored how we crack the machine learning matrix for public services:
Time stamps below the video indicate the start of each presentation or discussion panel
Part One: Entering the matrix - exploring the opportunities presented by machine learning and its potential to drive positive change
- 02:17: Timandra Harkness, Author of ‘Big Data’, science writer and co-host of Radio 4’s ‘FutureProofing’
- 17:19: Giuseppe Sollazzo, Deputy Director, Head of AI Skunkworks & Deployment, NHS AI Lab, NHSX
- 28:23: Speakers' Q&A session
Part Two: Revelations - looking to the future, a number of global innovative start-ups with the aim to inspire you about where we could go
- 37:05: Dr Arun Arumugam, Head of Sales, Astrosat
- 40:05: Priya Prakash, Founder, Design for Social Change
- 43:10: Geoff McGimpsey, Head of Marketing, Analytics Engines
Part Three: Reloaded - bringing all the elements together an expert panel give new insights and practical advice on how to maximise ML’s value
47:01: Discussion with:
- Eamonn O’Neill, Director ART-AI Centre, University of Bath
- Patrick Dawson, CIO, Paradigm Housing
- Harold de Neef, Group Director - Cloud and Innovation, Civica
- Dan Sandhu, CEO, Sparx Learning
More information:
- Designed to inform and inspire, Perspectives* from Civica explores how emerging technologies can help us build more innovative public services. Download your copy of volume 3: Machine learning reloaded
- Timandra Harkness's slides
- Giuseppe Sollazzo's slides
- Learn more about Arun Arumugam's example on their ‘Isolation Plus’ project with the European Space Agency which used ML to identify hidden vulnerable communities
- Can citizen insights aid better decision-making to improve public infrastructure in cities? mentioned by Priya Prakash
- Reducing four days’ work to a matter of minutes - Analytics Engines case study
