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![[I aim to make]
‘machines
slightly more
intelligent —
or slightly
less dumb.’
John Giannandrea, Head of AI, Apple](https://image.slidesharecdn.com/redesigningworkinanageofautomation-181205110728/75/Redesigning-work-in-an-age-of-automation-48-2048.jpg)
![‘[people] will set the goals,
formulate the hypotheses,
determine the criteria, and
perform the evaluations.](https://image.slidesharecdn.com/redesigningworkinanageofautomation-181205110728/75/Redesigning-work-in-an-age-of-automation-52-2048.jpg)
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Redesigning work in an age of automation
The document discusses how automation and AI will change the nature of work. It argues that most jobs will be redesigned to take advantage of both machine strengths like speed and accuracy as well as human strengths like creativity and judgment. New roles will emerge that facilitate the implementation of automation or use AI to augment human capabilities. Overall, automation tends to raise prosperity by taking over routine tasks while leaving more complex problems for humans. The document outlines different types of human-machine collaboration and how design careers in particular may be transformed by pairing creative human skills with AI's ability to analyze large datasets and generate new ideas.
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Redesigning work in an age of automation
- 1. Redesigning work in anage of automation Kevin McCullagh Leaders of Change 05 December 2018
- 3. ‘There certainly willbe 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.
- 7. ‘Consider thou whatthe 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
- 8. There many reasons tobe cheerful about Automation 1
- 9. There many reasons tobe cheerful about Automation Most jobs are best tackled with a mix of human and machine strengths 1 2
- 10. There many reasons tobe cheerful about Automation Most jobs are best tackled with a mix of human and machine strengths Most jobs will be redesigned to take advantage of automation... 1 2 3
- 11. There many reasons tobe cheerful about Automation Most jobs are best tackled with a mix of human and machine strengths Most jobs will be redesigned to take advantage of automation... including design 1 2 3
- 12.
- 13. Automation tends to raiseprosperity and employment 1
- 14. Automation Productivity Prosperity GDPper 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
- 15. ‘Productivity isn't everything –but in the long run it's almost everything’ Paul Krugman, Nobel prize winning Economist
- 16. Dismally low productivitygrowth -2% 0% 2% 4% 6% 8% World War I World War II Great Depression Great Recession McKinsey Global Institute: Solving the productivity puzzle; Brookings Institution United States Europe Great Recession Annual productivity growth 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2018
- 17. Source: McKinsey GlobalInstitute analysis 2017
- 18. Source: McKinsey GlobalInstitute analysis 2017
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- 22.
- 23. Automation tends to eliminatetasks and create more jobs
- 24. Labour market isin constant state of churn US Census Bureau’s Dynamics Statistics, 2015 Annual job creation and destruction rates (US) 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 10% 12% 14% 16% 18% 20%
- 25. Automation often makes work morerewarding 2
- 27. 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
- 28. 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
- 30. New technology generally reshapesjobs, rather than replaces them.
- 31. New technology generally reshapesjobs, rather than replaces them. It takes on the mundane tasks,
- 32. New technology generally reshapesjobs, rather than replaces them. It takes on the mundane tasks, as humans tend to move onto more complex – and often more meaningful – work.
- 33.
- 35. Less than 8% ofToyota’s production line is automated
- 36. ‘Machines are good forrepetitive things, but they can’t improve their own efficiency or the quality of their work. Only people can.’ President of Toyota Manufacturing Plant, Kentucky
- 37. Automation – is expensive –is highly inflexible – creates quality problems Gorlech and Wessel
- 40. ‘By 2029, computers will have human-level intelligence.’ RaymondKurzweil, SXSW interview 2017
- 41. The Singularity ‘By 2029, computers willhave human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
- 42.
- 43.
- 44.
- 45.
- 46. ‘We can know more thanwe can tell...’ Michael Polanyi, 1966
- 47.
- 48. [I aim tomake] ‘machines slightly more intelligent — or slightly less dumb.’ John Giannandrea, Head of AI, Apple
- 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. Overtrust noun The growing tendency of humansto place unwarranted trust in, and defer to, automated technology
- 51. J. C. R.Licklider
- 52. ‘[people] will setthe goals, formulate the hypotheses, determine the criteria, and perform the evaluations.
- 53. ‘Men will setthe 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. . .
- 54. ‘The symbiotic partnershipwill perform intellectual operations much more effectively than man alone can perform them…’ J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
- 55. Most work is made up of... Machine strengths Highdata situations Human strengths Low data situations
- 56. Most work is made up of... Machine strengths Followingrules Human strengths Judgement
- 57. Most work is made up of... Machine strengths Followingrules Analysis Human strengths Judgement Empathy
- 58. Most work is made up of... Machine strengths Followingrules Analysis Speed Human strengths Judgement Empathy Creativity
- 59. Most work is made up of... Machine strengths Followingrules Analysis Speed Accuracy Human strengths Judgement Empathy Creativity Improvisation
- 60. Most work is made up of... Machine strengths Followingrules Analysis Speed Accuracy Repetition Human strengths Judgement Empathy Creativity Improvisation Leadership
- 61. Most work is made up of... Machine strengths Followingrules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
- 62.
- 63.
- 64. Assigned – Certain tasksin a human workflow are outsourced to a machine. – The machine completes the task unaided, with varying levels of instruction.
- 65. 5 types of collaboration AssignedSupervised
- 66. Supervised – Decision making processesare automated, but under a human eye. – This mode requires the machine to be aware of and communicate risks and unknowns to human users.
- 67. 5 types of collaboration AssignedSupervised Coexistent
- 68. Coexistent – We will increasingly liveand 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.
- 69. 5 types of collaboration AssignedSupervised Coexistent Assistive
- 70. Source: Jaguar LandRover 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 – 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.
- 71. Assigned Supervised CoexistentAssistive Symbiotic 5 types of collaboration
- 72. Symbiotic – This emerging modeof collaboration is a highly interactive and reciprocal. – People input strategic hypotheses and the machine suggests tactical options.
- 73. Job Destruction Humans vs machines asrivals Job Re-design Humans + machines as allies
- 74.
- 75. New Human + Machine capabilities Machine strengths Followingrules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
- 76. Facilitating automation – Training – Explaining –Sustaining Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting New Human + Machine capabilities
- 77. Facilitating automation – Training – Explaining –Sustaining Human augmentation – Amplifying – Interacting – Embodying Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting New Human + Machine capabilities
- 78. Facilitating automation Laying the groundwork foreffective automation – Training – Explaining – Sustaining
- 79. Facilitating automation Training Teaching machines how toperform tasks or act more human Job titles – Automation design anthropologist – Data hygienist – Empathy trainer – Personality trainer – Worldview trainer – Interaction modeller Activities – Identifying relevant data – Cleaning data – Tagging data – Having machine observe decision making – Improving machine language – Defining and developing brand AI personalityMellisa Cefkin, AV design anthropologist, Nissan
- 80. Facilitating automation Explaining Untangling machine decision making and translatingto stake-holders Job titles – Algorithm forensics analyst – Transparency analyst – Explainability strategist Activities – Test, observe and explain algorithms – Make sense of machine outputs – Explain outputs to stakeholders IBM AI OpenScale
- 81. Facilitating automation Sustaining Ensuring proper useof AI, overcoming setbacks, stakeholder management, maintaining momentum Job titles – Context designers – AI safety engineers – Ethics compliance managers – Automation ethicists – Robot maintenance technicians Activities – Commercial, ethical and legal policy maker – Ensuring data and output quality – Thinking critically and holistically about AI performance and impact ‘Saftey green’ cobots for GM
- 82. Human augmentation Machines giving people superpowers –Amplifying – Interacting – Embodying
- 83. Human augmentation Amplifying AI enhances the effectiveness ofhuman activities and decision making Activities – Automate repetitive and low-level tasks – Prioritise options – Identify anomalies and trends
- 84. Human augmentation Interacting AI agentswith advanced voice-driven interfaces facilitate interactions between people at scale Activities – Answer customer support FAQs, and hand-on hard questions to humans – Accelerate customer understanding based on context – Enable natural language querying SEB Aida chatbot
- 85. Human augmentation Embodying AI combines with sensorsand actuators to allow robots to safely and effectively physically augment human workers Activities – Navigate around humans – Extend sight, hearing and touch – Assist with precise, repetitive and physically arduous work Cobots at BMW
- 86. Human augmentation Embodying BMW researchers foundthat human-robot interactions in their car plants were 85% more productive than either humans or robots on their own. Cobots at BMW
- 87.
- 88. Design and techcareers are forecast to be among the winners McKinsey, 2018 Skills Hours worked in 2016 (billions) Change in hours worked by 2030 (%) Change in hours worked by 2030 (%) Hours worked in 2016 (billions) Physical and manual Basic cognitive Higher cognitive Social and emotional Technological 90 53 62 52 31 113 62 78 67 90 -11 -16 -14 -17 +09 +07 +26 +22 +60 +52
- 89.
- 90.
- 91.
- 92. Redesigning design with AI Levelof sophistication 2Empathise AI uncovers new insights from existing consumer or user insight data 7Optimise AI optimises parameters 1Discover AI identifies new data patterns and connections 6Test AI lowers the analysis load 3Generate AI created design options within predefined constraints 8Customise AI enables new levels of personalisation 4Prototype AI accelerates and democratises prototyping 9Collaborate AI facilitates more effective collaboration 5Refine AI accelerates iteration and unlocks new creative possibilities 10Hire AI streamlines hiring process
- 93. 1Discover AI identifies newdata patterns and connections Yossarian
- 94. 2Empathise AI uncovers newinsights from existing consumer or user insight data Crimson hexagon
- 95. 3Generate AI created designoptions within predefined constraints Autodesk Dreamcatcher
- 96. 4Prototype AI accelerates and democratisesprototyping Aimybox
- 97. 5Refine AI accelerates iteration andunlocks new creative possibilities Adobe Sensei
- 98. 6Test AI lowers theanalysis load Descript
- 99. 7Optimise AI optimises parameters NikeSuperfly elite spikes
- 100. 8Customise AI enables newlevels of personalisation Stitch Fix
- 101. 9Collaborate AI facilitates moreeffective collaboration Marcel
- 102. 10Hire AI streamlines hiringprocess Arya
- 103. The Human-Machine Interlace shouldmake work more human
- 104. Champion human strengths in anage of automation
- 106. We join thedots www.plan.london @kevinmccull