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.
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Human-Machine Interlace
- 3. We tend to overestimate technology and underestimate people 1
- 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. 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. ‘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
- 8. 98%
- 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 Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox ATMs
- 10. 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
- 12. Less than 8% of Toyota’s production line is automated
- 13. ‘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
- 14. Automation – is expensive – is highly inflexible – creates quality problems Gorlech and Wessel
- 16. New technology generally reshapes jobs, rather than replaces them.
- 17. New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks,
- 18. New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks, as humans tend to move onto more complex work.
- 19. Most jobs are best tackled with a mix of machine and human strengths 2
- 22. The Singularity ‘By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
- 27. ‘We can know more than we can tell...’ Michael Polanyi, 1966
- 29. [the human mind is] ‘a machine for jumping to conclusions’. Daniel Kahneman, ‘Thinking, Fast and Slow’, 2012
- 30. [I aim to make] ‘machines slightly more intelligent — or slightly less dumb.’ John Giannandrea, Head of AI, Apple
- 31. ‘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
- 32. J. C. R. Licklider
- 33. ‘[people] will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations.
- 34. ‘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. . .
- 35. ‘The symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them…’ J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
- 36. Human strengths Computer strengths Brains Brawn Inspiration Repetition Making judgements Following rules Sense making Data recall Empathy Analysis Most work is made up of...
- 39. Assigned – Certain tasks in a human workflow are outsourced to a machine. – The machine completes the task unaided, with varying levels of instruction.
- 40. Assigned
- 41. 5 types of collaboration Assigned Supervised
- 42. 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.
- 43. Supervised
- 44. 5 types of collaboration Assigned Supervised Coexistent
- 45. 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.
- 46. Coexistent
- 47. 5 types of collaboration Assigned Supervised Coexistent Assistive
- 48. 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.
- 49. 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
- 50. 5 types of collaboration Assigned Supervised Coexistent Assistive Symbiotic
- 51. Symbiotic – This emerging mode of collaboration is a highly interactive and reciprocal. – People input strategic hypotheses and the machine suggests tactical options.
- 52. Autonomous Vehicles will present a myriad of new UX challenges 3
- 53. 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
- 54. Servitisation
- 55. Electrification
- 57. Automation
- 58. 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
- 63. 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
- 69. What?
- 70. What?
- 71. What?
- 73. Headline 30pt
- 75. Champion human strengths in an age of automation
- 76. We join the dots www.plan.london @kevinmccull www.slideshare.net/planstrategic/presentations