Redesigning design in an age of automation Kevin McCullagh Leaders of Change 05 December 2018
‘ 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
Automation anxiety
‘ 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
1 There are many reasons to be cheerful about Automation
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There are many reasons to be cheerful about Automation
Most jobs are best tackled with a mix of human and machine strengths
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2
3
There are many reasons to be 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...
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2
3
There are many reasons to be 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
Three reasons to be cheerful
1 Automation tends to raise prosperity and employment
Automation
Productivity
Prosperity
GDP per capita in England since 1270 Adjusted for inflation and measured in British Pounds in 2013 prices (000s) 30
25
20
15
10
5
0
1270
1400
1500
Source: GDP in England (using BoE 2017), OurWorldInData.org/economic-growth
1600
1700
1800
1900
2016
‘ Productivity isn't everything – but in the long run it's almost everything’ Paul Krugman, Nobel prize winning Economist
Dismally low productivity growth Annual productivity growth
United States Europe 8%
World War I
Great Depression
World War II
Great Recession
6%
4%
2%
0%
1870
1880
1890
1900
-2%
McKinsey Global Institute: Solving the productivity puzzle; Brookings Institution
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
2018
Source: McKinsey Global Institute analysis 2017
Source: McKinsey Global Institute analysis 2017
98%
Automation tends to eliminate tasks
Automation tends to eliminate tasks and create more jobs
Labour market is in constant state of churn Annual job creation and destruction rates (US)
20%
18%
16%
14%
12%
10% 1980
1982
1984
US Census Bureau’s Dynamics Statistics, 2015
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2 Automation often makes work more rewarding
Bank tellers vs. ATM machines Fulltime-equivalent bank tellers and installed ATM machines in the US 500
Tellers/ATMs (1000s)
400
300
200 ATMs 100
0
1970
1980
1990
2000
2010
Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox
Bank tellers vs. ATM machines Fulltime-equivalent bank tellers and installed ATM machines in the US 500
Tellers/ATMs (1000s)
400
300 Fulltime equivalent workers 200 ATMs 100
0
1970
1980
1990
2000
2010
Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox
New technology generally reshapes jobs, rather than replaces them.
New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks,
New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks, as humans tend to move onto more complex – and often more meaningful – work.
3 Pessimists overestimate machines, and underestimate humans
Less than 8% of Toyota’s production line is automated
‘ 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
Automation – is expensive – is highly inflexible – c reates quality problems Gorlech and Wessel
‘ By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
‘ By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
The Singularity
Narrow Artificial Intelligence
General Artificial Intelligence
Narrow Artificial Intelligence
General Artificial Intelligence
Moravec’s paradox
Hard easy
Moravec’s paradox
Easy hard
‘ We can know more than we can tell...’ Michael Polanyi, 1966
Human intelligence
≠
Artificial intelligence
[I aim to make] ‘machines slightly more intelligent — or slightly less dumb.’ John Giannandrea, Head of AI, Apple
‘ 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
Overtrust noun The growing tendency of humans to place unwarranted trust in, and defer to, automated technology
J. C. R. Licklider
‘ [people] will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations.
‘ C Momputing en will setmachines the goals,will formulate do the routinizable hypotheses, work determine that must be to prepare the way thedone criteria, and perform the for insights evaluations. and decisions. . .
‘ The symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them…’ J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
Most work is made up of...
Human strengths
Machine strengths
Low data situations
High data situations
Most work is made up of...
Human strengths
Machine strengths
Judgement
Following rules
Most work is made up of...
Human strengths
Machine strengths
Judgement Empathy
Following rules Analysis
Most work is made up of...
Human strengths
Machine strengths
Judgement Empathy Creativity
Following rules Analysis Speed
Most work is made up of...
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation
Following rules Analysis Speed Accuracy
Most work is made up of...
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation Leadership
Following rules Analysis Speed Accuracy Repetition
Most work is made up of...
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation Leadership Mode shifting
Following rules Analysis Speed Accuracy Repetition Always on
Headline 30pt
Human Machine Interlace
5 types of collaboration
Assigned
Assigned
–C ertain tasks in a human workflow are outsourced to a machine. –T he machine completes the task unaided, with varying levels of instruction.
5 types of collaboration
Assigned
Supervised
Supervised – Decision making processes are automated, but under a human eye. –T his mode requires the machine to be aware of and communicate risks and unknowns to human users.
5 types of collaboration
Assigned
Supervised
Coexistent
Coexistent
– We will increasingly live and work alongside intelligent machines, sharing the same spaces, but focusing on separate taskflows. – Machines in these scenarios must be able to effectively negotiate shared space and anticipate human intent.
5 types of collaboration
Assigned
Supervised
Coexistent
Assistive
Assistive 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
– 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.
5 types of collaboration
Assigned
Supervised
Coexistent
Assistive
Symbiotic
Symbiotic –T his emerging mode of collaboration is a highly interactive and reciprocal. –P eople input strategic hypotheses and the machine suggests tactical options.
Job Destruction
Job Re-design
Humans vs machines as rivals
Humans + machines as allies
Be there
89
5 Be there Remote connection and care
Professional human service
Devices
Digital service
Be there
93
Be there Remote connection and care for the elderly
Remote
Family
Professional human service
Enable more involvement with elderly parents and help them live independently for longer. (+ guilt relief)
Face to face
Wearable Sensor
Parent
Alerts
Personal Sensor
Combat loneliness, promote physical activity, and provide cost-effective care.
Management TV hub Devices
Home Sensor
Business collaboration Electronics
Innovation type Product Service Business
Digital service
Analytics
Communicate
Reimagining work
New
Human + Machine capabilities
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation Leadership Mode shifting
Following rules Analysis Speed Accuracy Repetition
New
Human + Machine capabilities
Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
Facilitating automation
– Training – Explaining – Sustaining
Machine strengths Following rules Analysis Speed Accuracy Repetition
New
Human + Machine capabilities
Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
Facilitating automation
– Training – Explaining – Sustaining
Human augmentation
– Amplifying – Interacting – Embodying
Machine strengths Following rules Analysis Speed Accuracy Repetition
Facilitating automation Laying the groundwork for effective automation – Training – Explaining – Sustaining
Facilitating automation
Training
Teaching machines how to perform tasks or act more human Job titles – Automation design anthropologist – Data hygienist – Empathy trainer – Personality trainer – Worldview trainer – Interaction modeller Activities
Mellisa Cefkin, AV design anthropologist, Nissan
– Identifying relevant data – Cleaning data – Tagging data – Having machine observe decision making – Improving machine language – Defining and developing brand AI personality
Facilitating automation
Explaining
Untangling machine decision making and translating to 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
Ensuring proper use of AI, overcoming setbacks, stakeholder management, maintaining momentum Job titles
Facilitating automation
Sustaining ‘Saftey green’ cobots for GM
– 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
Human augmentation Machines giving people superpowers – Amplifying – Interacting – Embodying
AI enhances the effectiveness of human activities and decision making Activities – Automate repetitive and low-level tasks – Prioritise options – Identify anomalies and trends
Human augmentation
Amplifying
AI agents with advanced voice-driven interfaces facilitate interactions between people at scale Activities
Human augmentation
Interacting SEB Aida chatbot
– Answer customer support FAQs, and hand-on hard questions to humans – Accelerate customer understanding based on context – Enable natural language querying
AI combines with sensors and actuators to allow robots to safely and effectively physically augment human workers Activities
Human augmentation
Embodying Cobots at BMW
– Navigate around humans – Extend sight, hearing and touch – Assist with precise, repetitive and physically arduous work
BMW researchers found that human-robot interactions in their car plants were 85% more productive than either humans or robots on their own.
Human augmentation
Embodying Cobots at BMW
Redesigning design
Design and tech careers are forecast to be among the winners
Skills
Hours worked in 2016 (billions)
Change in hours worked by 2030 (%)
Hours worked in 2016 (billions)
Change in hours worked by 2030 (%)
Physical and manual
90
-11
113
-16
Basic cognitive
53
-14
62
-17
Higher cognitive
62
+09
78
Social and emotional
52
+26
67
+22
Technological
31
+60
90
+52
McKinsey, 2018
n g i s De
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation Leadership Mode shifting
Following rules Analysis Speed Accuracy Repetition
e m o S n g i s De
Human strengths
Machine strengths
Judgement Empathy Creativity Improvisation Leadership Mode shifting
Following rules Analysis Speed Accuracy Repetition
Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
Augmented creativity
Machine strengths Following rules Analysis Speed Accuracy Repetition
Redesigning design with AI
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AI identifies new data patterns and connections
AI uncovers new insights from existing consumer or user insight data
AI created design options within predefined constraints
AI accelerates and democratises prototyping
AI accelerates iteration and unlocks new creative possibilities
6
7
8
9
10
AI lowers the analysis load
AI optimises parameters
AI enables new levels of personalisation
AI facilitates more effective collaboration
AI streamlines hiring process
Discover
Test
Level of sophistication
Empathise
Optimise
Generate
Customise
Prototype
Collaborate
Refine
Hire
1
Discover AI identifies new data patterns and connections
Yossarian
2
Empathise AI uncovers new insights from existing consumer or user insight data
Crimson hexagon
3
Generate AI created design options within predefined constraints
Autodesk Dreamcatcher
4
Prototype AI accelerates and democratises prototyping
Aimybox
5
Refine AI accelerates iteration and unlocks new creative possibilities
Adobe Sensei
6
Test AI lowers the analysis load
Descript
7
Optimise AI optimises parameters
Nike Superfly elite spikes
8
Customise AI enables new levels of personalisation
Stitch Fix
9
Collaborate AI facilitates more effective collaboration
Marcel
10 Hire
AI streamlines hiring process
Arya
The Human-Machine Interlace should make work more human
Champion human strengths in an age of automation
We join the dots www.plan.london @kevinmccull