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Design in an Age of Automation

Three reasons to be cheerful about the age of automation, and why designers should champion human strengths in collaborative partnerships with AI.

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Design in an Age of Automation

  1. 1. Design in an age of automation Kevin McCullagh DMI Bonn 11 April 2018
  2. 2. ‘intellectuals hate progress...
  3. 3. ‘intellectuals hate progress [and] intellectuals who call themselves “progressive” really hate progress.’ Steve Pinker, Enlightenment Now: The Case for Reason, Science, Humanism, and Progress, 2018
  4. 4. ‘There certainly will be job disruption. Because what’s going to happen is robots will be able to do everything better than us. ... I mean all of us’. Elon Musk, National Governors Association, 16 July 2017
  5. 5. 1. Mass unemployment
  6. 6. 2. Heightened inequality U.S. Job Polarization Employment Change From Pre-Recession Peak (Millions) Source: BLS, Oregon Office of Economic Analysis 6 4 2 0 -2 -4 -6 -8 2007 2010 2013 2016 High-Wage Low-Wage Middle-Wage
  7. 7. 3. Corporate concentration
  8. 8. Automation anxiety
  9. 9. Technology Humanities Economics
  10. 10. TECH
  11. 11. Technology is only one force acting on the future Ford, FX Atmos concept, 1954
  12. 12. Technology Humanities Economics
  13. 13. ‘Consider thou what the invention could do to my poor subjects. It would assuredly bring them to ruin by depriving them of employment, thus making them beggars’ Elizabeth I, on refusing to patent a knitting machine invented by William Lee
  14. 14. Three reasons to be cheerful
  15. 15. Automation tends to raise productivity, prosperity and employment 1
  16. 16. Why hasn’t 200 years of automation wiped out most of the jobs?
  17. 17. Automation Productivity Prosperity GDP per capita in England since 1270 Adjusted for inflation and measured in British Pounds in 2013 prices (000s) 1270 1400 1500 1600 1700 1800 1900 2016 Source: GDP in England (using BoE 2017), OurWorldInData.org/economic-growth 30 25 20 15 10 5 0
  18. 18. ‘Productivity isn't everything – but in the long run it's almost everything’ Paul Krugman, Nobel prize winning Economist
  19. 19. Source: McKinsey Global Institute analysis 2017
  20. 20. 98%
  21. 21. Automation tends to eliminate tasks
  22. 22. Automation tends to eliminate tasks and create more jobs
  23. 23. Automation often makes work more rewarding 2
  24. 24. Bank tellers vs. ATM machines Fulltime-equivalent bank tellers and installed ATM machines in the US Tellers/ATMs(1000s) 500 400 300 200 100 0 1970 1980 1990 2000 2010 Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox ATMs
  25. 25. Bank tellers vs. ATM machines Fulltime-equivalent bank tellers and installed ATM machines in the US Tellers/ATMs(1000s) 500 400 300 200 100 0 1970 1980 1990 2000 2010 Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox Fulltime equivalent workers ATMs
  26. 26. New tech generally reshapes jobs, rather than replaces them.
  27. 27. New tech generally reshapes jobs, rather than replaces them. They take on the mundane tasks,
  28. 28. New tech generally reshapes jobs, rather than replaces them. They take on the mundane tasks, as humans tend to move onto more complex – and often more meaningful – work.
  29. 29. Pessimists overestimate machines, and underestimate humans 3
  30. 30. Less than 8% of Toyota’s production line is automated
  31. 31. ‘Machines are good for repetitive things, but they can’t improve their own efficiency or the quality of their work. Only people can.’ President of Toyota Manufacturing Plant, Kentucky
  32. 32. Automation – is expensive – is highly inflexible – creates quality problems Gorlech and Wessel
  33. 33. The Singularity ‘By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
  34. 34. Narrow Artificial Intelligence General Artificial Intelligence
  35. 35. Moravec’s paradox Hard easy
  36. 36. Moravec’s paradox Easy hard
  37. 37. ‘We can know more than we can tell...’ Michael Polanyi, 1966
  38. 38. Human intelligence Artificial intelligence≠
  39. 39. [the human mind is] ‘a machine for jumping to conclusions’. Daniel Kahneman, ‘Thinking, Fast and Slow’, 2012
  40. 40. [I aim to make] ‘machines slightly more intelligent — or slightly less dumb.’ John Giannandrea, Head of AI, Apple
  41. 41. J. C. R. Licklider
  42. 42. ‘[people] will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations.
  43. 43. ‘Men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. ‘Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions. . .
  44. 44. ‘The symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them…’ J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
  45. 45. Adobe Sensei
  46. 46. Human strengths Computer strengths Brains Brawn Inspiration Repetition Making judgements Following rules Sense making Data recall Empathy Analysis Most work is made up of...
  47. 47. Human strengths Computer strengths Brains Brawn Inspiration Repetition Making judgements Following rules Sense making Data recall Empathy Analysis Design
  48. 48. Human strengths Computer strengths Brains Brawn Inspiration Repetition Making judgements Following rules Sense making Data recall Empathy Analysis Some Design
  49. 49. ‘The real danger ... is not machines that are more intelligent than we are ... The real danger is basically clueless machines being ceded authority far beyond their competence.’ Daniel Dennett, ‘The Singularity—an Urban Legend’, Edge
  50. 50. Champion human strengths in an age of automation
  51. 51. We join the dots www.plan.london @kevinmccull

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