1.
Design in an
age of automation
Kevin McCullagh
DMI Bonn
11 April 2018

2.
‘intellectuals hate progress...

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.
‘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.
1.
Mass
unemployment

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.
3.
Corporate
concentration

8.
Automation
anxiety

9.
Technology
Humanities
Economics

10.
TECH

11.
Technology is only one
force acting on the future
Ford, FX Atmos concept, 1954

12.
Technology
Humanities
Economics

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.
Three
reasons
to be cheerful

15.
Automation tends
to raise productivity,
prosperity and
employment
1

16.
Why hasn’t 200 years of automation
wiped out most of the jobs?

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.
‘Productivity isn't everything
– but in the long run
it's almost everything’
Paul Krugman, Nobel prize winning Economist

19.
Source: McKinsey Global Institute analysis 2017

20.
98%

21.
Automation tends to
eliminate tasks

22.
Automation tends to
eliminate tasks and
create more jobs

23.
Automation often
makes work more
rewarding
2

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.
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.
New tech generally
reshapes jobs, rather than
replaces them.

27.
New tech generally
reshapes jobs, rather than
replaces them. They take
on the mundane tasks,

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.
Pessimists
overestimate
machines, and
underestimate
humans
3

30.
Less than 8%
of Toyota’s
production line
is automated

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.
Automation
– is expensive
– is highly inflexible
– creates quality
problems
Gorlech and Wessel

33.
The Singularity
‘By 2029,
computers
will have
human-level
intelligence.’
Raymond Kurzweil, SXSW interview 2017

34.
Narrow
Artificial
Intelligence
General
Artificial
Intelligence

35.
Moravec’s paradox
Hard easy

36.
Moravec’s paradox
Easy hard

37.
‘We can
know more
than we
can tell...’
Michael Polanyi, 1966

38.
Human
intelligence
Artificial
intelligence≠

39.
[the human mind is]
‘a machine for
jumping to
conclusions’.
Daniel Kahneman, ‘Thinking, Fast and Slow’, 2012

40.
[I aim to make]
‘machines
slightly more
intelligent —
or slightly
less dumb.’
John Giannandrea, Head of AI, Apple

41.
J. C. R. Licklider

42.
‘[people] will set the goals,
formulate the hypotheses,
determine the criteria, and
perform the evaluations.

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.
‘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.
Adobe Sensei

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.
Human
strengths
Computer
strengths
Brains Brawn
Inspiration Repetition
Making judgements Following rules
Sense making Data recall
Empathy Analysis
Design

48.
Human
strengths
Computer
strengths
Brains Brawn
Inspiration Repetition
Making judgements Following rules
Sense making Data recall
Empathy Analysis
Some
Design

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.
Champion
human strengths
in an age of
automation

51.
We join the dots
www.plan.london
@kevinmccull