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Human-Machine Interlace

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Not only do we overestimate how easy it is to replace humans, replacing them is often neither desirable nor the best use of AI. A better way to think about the future of AI is interlacing its strengths with those of humans.

Autonomous vehicles are often posed as reducing human interaction with vehicles to a minimum. While they will take more of the cognitive load of driving off humans, in many cases it is more useful to think of a human-machine collaboration.

Published in: Automotive
  • This caused me to reflect on a recent visit to the BMW Mini plant. Someone observed that there seemed so few humans on the bodyshop assembly floor. She asked how many workers were currently employed, compared to before BMW bought the plant and introduced widespread automation. It turns out that nearly 5000 are currently employed, compared to only 2000 previously, thanks to vastly increased capacity and improved quality. This backs-up your assertion that robotics can have significant and satisfying beneficial effects for human workers. We saw evidence of how MINI encourages workers to use their cognitive skills to problem solve and improve robot performance.
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Human-Machine Interlace

  1. 1. Human-Machine Interlace Kevin McCullagh IxDA London 25 July 2018
  2. 2. Product strategy
  3. 3. We tend to overestimate technology and underestimate people 1
  4. 4. We tend to overestimate technology and underestimate people Most jobs are best tackled with a mix of machine and human strengths 1 2
  5. 5. We tend to overestimate technology and underestimate people Most jobs are best tackled with a mix of machine and human strengths Autonomous Vehicles will present a myriad of new UX challenges 1 2 3
  6. 6. ‘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
  7. 7. 98%
  8. 8. Bank tellers vs. ATM machines Full time-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
  9. 9. Bank tellers vs. ATM machines Full time-equivalent bank tellers and installed ATM machines in the US Tellers/ATMs(1000s) 500 400 300 200 100 0 1970 1980 1990 2000 2010 Fulltime equivalent workers ATMs Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox
  10. 10. Less than 8% of Toyota’s production line is automated
  11. 11. ‘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
  12. 12. Automation – is expensive – is highly inflexible – creates quality problems Gorlech and Wessel
  13. 13. New technology generally reshapes jobs, rather than replaces them.
  14. 14. New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks,
  15. 15. New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks, as humans tend to move onto more complex work.
  16. 16. Most jobs are best tackled with a mix of machine and human strengths 2
  17. 17. The Singularity ‘By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
  18. 18. Narrow Artificial Intelligence General Artificial Intelligence
  19. 19. Narrow Artificial Intelligence General Artificial Intelligence
  20. 20. Moravec’s paradox Hard easy
  21. 21. Moravec’s paradox Easy hard
  22. 22. ‘We can know more than we can tell...’ Michael Polanyi, 1966
  23. 23. Human intelligence Artificial intelligence≠
  24. 24. [the human mind is] ‘a machine for jumping to conclusions’. Daniel Kahneman, ‘Thinking, Fast and Slow’, 2012
  25. 25. [I aim to make] ‘machines slightly more intelligent — or slightly less dumb.’ John Giannandrea, Head of AI, Apple
  26. 26. ‘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
  27. 27. J. C. R. Licklider
  28. 28. ‘[people] will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations.
  29. 29. ‘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. . .
  30. 30. ‘The symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them…’ J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
  31. 31. Human strengths Computer strengths Brains Brawn Inspiration Repetition Making judgements Following rules Sense making Data recall Empathy Analysis Most work is made up of...
  32. 32. Human Machine Interlace
  33. 33. 5 types of collaboration Assigned
  34. 34. Assigned – Certain tasks in a human workflow are outsourced to a machine. – The machine completes the task unaided, with varying levels of instruction.
  35. 35. Assigned
  36. 36. 5 types of collaboration Assigned Supervised
  37. 37. Supervised – Decision making processes are automated, but under a human eye. – This mode requires the machine to be aware of and communicate risks and unknowns to human users.
  38. 38. Supervised
  39. 39. 5 types of collaboration Assigned Supervised Coexistent
  40. 40. Coexistent – We will increasingly live and work alongside intelligent machines, sharing the same spaces, but focusing on separate task-flows. – Machines in these scenarios must be able to effectively negotiate shared space and anticipate human intent.
  41. 41. Coexistent
  42. 42. 5 types of collaboration Assigned Supervised Coexistent Assistive
  43. 43. Assistive – Machines that will help us perform tasks faster and better. – They support particular tasks in human workflows, and will excel in discerning human goals and learning their preferences.
  44. 44. Source: Jaguar Land Rover Bike Sense. Seat shoulder taps the rings a bicycle bell if it senses a cyclist near the car and Door handles ‘buzz’ to prevent doors being opened into the path of bikes Assistive
  45. 45. 5 types of collaboration Assigned Supervised Coexistent Assistive Symbiotic
  46. 46. Symbiotic – This emerging mode of collaboration is a highly interactive and reciprocal. – People input strategic hypotheses and the machine suggests tactical options.
  47. 47. Autonomous Vehicles will present a myriad of new UX challenges 3
  48. 48. 2000 2010 2020 2030 2040 Servitisation Mobility as a service 1 4 Automation Autonomous vehicles Electrification Electric vehicles 2 Zonification Urban zoning 3 AVs are just one moving part in the future of mobility
  49. 49. Servitisation
  50. 50. Electrification
  51. 51. Zonification Dynamic kerbspace
  52. 52. Automation
  53. 53. Most ask when Predictions and targets of AV penetration across global markets 2015 2020 2025 2030 2035 2040 2045 2050 Canalys McKinsey UoTA IoEEE Amica Research Thatcham Thatcham Oliver Wyman BCG IMechE100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Tony Seba KPMG Fehr Peers Share of car sales Share of cars on the roads
  54. 54. Headline 30ptWhere?
  55. 55. Headline 30ptWhere?
  56. 56. Headline 30ptWhere?
  57. 57. Headline 30ptNot here!
  58. 58. The tech is not ready Scenarios that GM’s self-driving cars have trouble handling or aren’t being tested broadly in San Francisco Changing lands Tunnels U-turns Construction zones Orange cones on road Pull-over at curb Narrow two-way street Distinguishing between motocycles and bikes Going around cars trying to parallel park Unprotected left turns Crossing solid white lines Go through steam emanating from manholes ‘Zipper merge’ (when two lanes merge into one) Intersections with faint traffic lights ‘Soft’ poles that separate lanes Passing a cyclist Bushes that protrude into lanes Heavy rain Low sun Going around cross-traffic that’s stuck in intersection Source: The Information reporting
  59. 59. Headline 30ptWho?
  60. 60. Headline 30ptWho?
  61. 61. Headline 30ptWho?
  62. 62. Headline 30ptHow?
  63. 63. Headline 30ptHow?
  64. 64. What?
  65. 65. What?
  66. 66. What?
  67. 67. Driving Tacit motor skills Collaborating Conscious decision making
  68. 68. Headline 30pt
  69. 69. Headline 30pt Sociology before technology
  70. 70. Champion human strengths in an age of automation
  71. 71. We join the dots www.plan.london @kevinmccull www.slideshare.net/planstrategic/presentations

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