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Short and Long of Data Driven Innovation

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"'Tis true. There's magic in the Web: The Short and the Long of Co-Creation, Web Science, and Data Driven Innovation". Keynote for the DATA-DRIVEN INNOVATION WORKSHOP 2016 collocated with ACM Web Science 2016, Hannover, Germany, Sunday 22 May 2016

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Short and Long of Data Driven Innovation

  1. 1. David De Roure @dder 'Tis true. There's magic in the Web: The Short and the Long of Co-Creation, Web Science, and Data Driven Innovation DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE
  2. 2. Co-creation flourishes in the Web ecosystem, where the affordances of the digital bring a new scale of citizen participation and a new empowerment of the ingenious human. As we enter the era of the Internet of Things we anticipate new opportunities to realise the economic value of data. But what will happen when data supply massively outstrips demand, and as innovative data use is inevitably coupled with increasing automation? This talk will take a long view of co-creation, Web Science, and data driven innovation (roughly from 1600 to 2050).
  3. 3. 1.  The Long View. Is open innovation really something new, or are we just undoing the damage of the last century? I will demonstrate that co-creation (and even social machines) have been with us for at least 400 years (I’ll kick off in 1600s with Shakespeare and Royal Society…) which will put the Web in context and I’ll talk about mode 1, 2, 3 research.
  4. 4. Pip Willcox
  5. 5. Pip Willcox
  6. 6. Pip Willcox
  7. 7. Pip Willcox
  8. 8. Pip Willcox
  9. 9. http://firstfolio.bodleian.ox.ac.uk/
  10. 10. https://blogs.bodleian.ox.ac.uk/digital/2016/04/23/introducing-the-iiif-first-folio/
  11. 11. Pip Willcox
  12. 12. https://en.wikipedia.org/wiki/Mode_2
  13. 13. 2.  Web Science and the digital economy of the Internet of Things. A glance forward. I’ll focus on realising the value of data in IoT through a Web Science lens, and I'll raise the empowerment of machines as well as humans (AI, computational creativity, …)
  14. 14. http://zubitubi.blogspot.co.uk/2016/03/school-funny-picture-prank-memories.html
  15. 15. More people More machines HPC Conven5onal Computa5on Social Networks Science 2.0 Internet of Things Big Data AI Computa5onal Linguis5cs, Musicology, Social Science, Humani5es Ci5zen Science Volunteer Compu5ng SKA Visualiza5on Bio Data sharing Machine Learning Scien5fic Compu5ng Cybersecurity Data Driven Innova5on
  16. 16. https://twitter.com/CR_UK/status/446223117841494016/ Some people's smartphones had autocorrected the word "BEAT" to instead read "BEAR". "Thank you for choosing an adorable polar bear," the reply from the WWF said. "We will call you today to set up your adoption." http://www.bbc.com/news/technology-26723457
  17. 17. Social Media Triangle social media data and analytics social media for engagement with research social media as a subject of research Sam McGregor
  18. 18. New Forms of Data ▶ Internet data, derived from social media and other online interactions (including data gathered by connected people and devices, eg mobile devices, wearable technology, Internet of Things) ▶ Tracking data, monitoring the movement of people and objects (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc) ▶ Satellite and aerial imagery (eg Google Earth, Landsat, infrared, radar mapping etc) http://www.oecd.org/sti/sci-tech/new-data-for- understanding-the-human-condition.htm
  19. 19. A rehearsal for the future ▶  The Internet of Things describes a world in which everyday objects are connected to a network so that data can be shared ▶  But it is really as much about people as the inanimate object ▶  It is impossible to anticipate all the social changes that could be created by connecting billions of devices https://www.gov.uk/government/publications/internet-of-things-blackett-review
  20. 20. There is no such thing as the Internet of Things There is no such thing as a closed system Humans are creative and subversive The Rise of the Bots A Swarm of Drones Accidents happen (in the lab, bin) Holding machines to account Software vulnerability Where are the throttle points? @dder
  21. 21. PETRAS Privacy, Ethics, Trust, Reliability, Acceptability, and Security for the Internet of Things •  The fusion of the cyber, physical and human elements •  Scale: from 1mm3 devices to large infrastructure systems •  Managing devices throughout their (decades long) lifetimes •  New and evolving threat landscape •  Continue to operate when partially compromised The Challenges are numerous •  Safety vs Security •  Security vs Efficiency •  Hardening vs Adaptive Response Tradeoffs
  22. 22. The Macroscope
  23. 23. Observer of one social machine Observers using third party observatory Observer of multiple social machines Human participants in Social Machine Human participants in multiple Social Machines Observer of Social Machine infrastructure 1 4 2 3 5 6 SM SM SM Social Machine Observing Social Machines 7 @dder De Roure, D., Hooper, C., Page, K., Tarte, S., and Willcox, P. 2015. Observing Social Machines Part 2: How to Observe? ACM Web Science
  24. 24. Based on: Bakshy, E. and Wilensky, U. (2007). NetLogo Team Assembly model. http://ccl.northwestern.edu/ netlogo/models/TeamAssembly. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Using netlogo: Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. What happens to our ecosystem with increasing data supply?
  25. 25. A computationally-enabled sense-making network of expertise, data, software, models and narratives Big Data, in a Big Data Centre
  26. 26. Human Digital Physical Triangle human digital physical social media IoT automation and scale
  27. 27. 3.  Future history. When we look back on this period, what will we see?  Is this where we made the decisions that led to a utopian future, or the origins of dystopia?  How did web science inform this moment?
  28. 28. http://www.sciencebusiness.net/OurReports/ReportDetail.aspx?ReportId=87
  29. 29. It is 2050, and Europe and its knowledge economy are competitive. Clusters of well-funded, internationally renowned universities are thriving in many of Europe’s important and growing cities, in strong partnerships with regional institutions… Meanwhile, automation and data-intensive science have changed the nature of doing research. We have moved from open science to radical open access: all kinds of new actors are rushing into the research game, especially in astronomy, ecology, climate and other fields that attract strong public interest. Europe’s research infrastructures are the new cathedrals of this science: Open to all, supported by all.
  30. 30. It is 2050, and Europe is a victim of megatrends beyond its control. Automation and globalisation have triggered mass unemployment, social exclusion, discontent. Service bots, machine learning, ubiquitous sensing – what’s left for the humans to do? Inequality is higher than ever; new creative jobs are constantly evolving from new technologies, but they are only for the skilled few. …the top-cited scientists are in hot demand – often hired by multinationals in a kind of perpetual ‘consultancy without borders.’ These companies, on which public labs and universities rely for major funding, get early access to the real discoveries and use their influence to steer the remaining public funds towards their projects… Europe’s economic base has hollowed out, and the few innovators its universities produce quickly move abroad.
  31. 31. 1.  The Long View. Is mode 2 really something new, or are we just undoing the damage of the last two centuries? Co-creation (and social machines) have been with us forever. We need a new definition of the paradigm shift. 2.  Web Science and the digital economy of the Internet of Things. Web Science is poistioned to co- create the tools and methods to understand data driven innovation. We need to address ethics of data use in the face of increasing supply, and the ethics of automation. 3.  Future history. When we look back on this period, what shall we see?  Is this where we made the decisions that led to a utopian future, or the origins of dystopia?  How does Web Science inform this moment? Is it a discipline or a political campaign?
  32. 32. David De Roure david.deroure@oerc.ox.ac.uk Thanks to Pip Willcox, Christine Borgman, Emil Lupu, Sam McGregor, Vonu Thakuriah. http://www.slideshare.net/davidderoure/short-and-long-of-data-driven-innovation

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