Slide deck used during Spring 2016 Webinar of the University Council for Workforce and Human Resources Education presented by David Passmore (Penn State

1. WEBINAR SPONSORED BY THE UNIVERSITY COUNCIL
ON WORKFORCE AND HUMAN RESOURCE EDUCATION
6 APRIL 2016

2. Emerging role of automation in replacing and extending human work

4. A technique of making an
apparatus, a process, or a system
operate without direct control by
a person (i.e., automatically).

5. Automatically controlled
operation of an apparatus, a
process, or a system by
mechanical or electronic devices
that take the place of human
labor.

9. Stick withTheory

10.  I will talk at you for a while; you will enter
comments/questions in ”chat” window
 At intervals, I will respond to your
comments/questions
 When the end is near, I will respond to
your general comments/questions

11.  Some economic principles that affect the
replacement and extension of human work.
 Frameworks for analyzing automation
opportunities.
 Is co–evolution of humans with technology a
possibility?

13. Material production of goods and
services occurs using a mix, a “recipe,”
of factors of production:
• Land
• Capital – financial and tangible assets.
• Labor – human time/effort
exchanged for wages.

14. • Minimize costs.
• Maximize returns on investment.
• All factors are costs to be minimized.
∴There is no imperative to create
jobs, which are viewed under this
ethic as a cost.

15. ∴There is no imperative to create
jobs, which are just a cost.
In this view, it is not the lack of
invention that sets boundaries for
economic growth, but rather powerful
interests in promoting the
technological status quo.

16. • are substitutes
The mix of the amounts of capital and
labor in production is altered based on
their relative costs (“either-or”).
• are complements
Sometimes capital and labor
are used together beneficially
(“and”).

17. • are substitutes
Possible for labor to be replaced by
capital when the relative price of labor
increases and capital is more efficient.
• are complements
Possible for the productivity of
labor to be extended by
capital.

19. “We can know more than we can tell.”
“The skill of a driver cannot be
replaced by a thorough schooling in
the theory of the motorcar; the
knowledge I have of my own body
differs altogether from the
knowledge of its physiology.”
Polayni, M. (1966). The tacit dimension. NewYork: Doubleday.

20. • explicit
Formalized and codified. Sometimes referred to as
“knowing what.” Fairly easy to identify, store, and
retrieve.
• tacit
Intuitive, hard-to-define knowledge that is largely
experience based. Sometimes referred to as
“knowing how.” Context dependent and personal
in nature. Hard to communicate and deeply rooted
in action, commitment, and involvement.

21. We tacitly know a lot about the
way the world works.
Yet, we are not able to explicitly
describe this knowledge.

22. Source: Autor, D., Levy, F., & Murnane, R. H. (2003).The skill content of recent technological change:
An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333.

23. Source: Autor, D., Levy, F., & Murnane, R. H. (2003).The skill content of recent technological change:
An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333.
Autor et al. Implications from Polanyi’s Dilemma
Tasks from the quotidian to the sublime cannot
currently be computerized because we don’t
know the rules.
At the economic level, though, tasks that
cannot be computerized often are
complemented by technology.

24. • work redesign
Engineering the environment to make workflow
more predictable. Examples: assembly lines, rail
tracks, paved roads, warehouses, ports.
• machine learning
Use of code that applies inductive reasoning so
that machines can learn from statistical patterns
rather than use explicitly programmed
instructions. Exposure…training…reinforcement.

25. • Massive amounts of structured and
unstructured information are being
collected…more than conventional
analytics can handle.
• This information represents varied
experience from which patterns, and
trends can be derived.

26. Machine learning
examples
BRETT, the
Berkeley Robot
for the
Elimination of
TediousTasks,
can fold a towel.
But it takes a
long time — best
is, it’s not your
time.

27. Machine learning
examples
SLAM
Simultaneous
localization and
mapping.
Mapping an unknown
environment while
keeping track of the
mapping agent.

28. Machine learning
examples
Facial
Recognition
Given a digital photo
album of many
hundreds of digital
photographs, identify
those photos that
include a given person.

29.  An automated system available today does
some of the work.
 Little physical contact or manipulation
necessary.
 Content transmission is a key part of the job.
 Content analysis is a key part of the job.
 Ability to understand and analyze data is key.
 The task can be simulated or performed
virtually.

30.  Consistency of performance is critical to the
role.
 Content creation is based on data and
analysis.
 Well–defined formal rules dictate work
performance.
Davenport,T. H. (2015, April 8).The knowledge work jobs of
2024. Wall Street Journal bog posting.

32. Many analysts believe that substitution of
labor for capital will replace jobs with
machines and will de-skill human work.

33. Many analysts believe that substitution of
labor for capital will replace jobs with
machines and will de-skill human work.
Others say that complementary technology
will extend the human work remaining.

34. Kurzweil’s Law of
Accelerating Returns
The growth of
nonbiological
computer
processing power
is exponential
rather than linear;
and its tempo
shows no sign of
slackening.

35. Ray Kurzweil, innovator and futurist, predicts that
around 2045 artificial intelligence will emerge capable
of self-improvement and autonomous building of
smarter machines.

36. Ray Kurzweil, innovator and futurist, predicts that
around 2045 artificial intelligence will emerge capable
of self-improvement and autonomous building of
smarter machines.
THEN
Our intelligence is canned, digitized, and uploaded
into a less perishable substrate.
Distinction between biological and nonbiological
entities ceases.
Some have dubbed: this the point of “fusion.” Others:
the point of “replacement.”

37. What is the endgame for
Workforce and
Human Resource Education?

38.  A link to a video
recording available
on Google
 The slide deck
available on
SlideShare
http://goo.gl/Y2kOLV
https://goo.gl/V9LQt7

39.  Registered participants
receive invitations to
complete an
assessment of this
webinar.
• If not attending,
suggestions about
timing, focus, structure,
and technology for
UCWHRE webinars.
• If attending, reactions to
webinar and suggestions.

40. WEBINAR SPONSORED BY THE UNIVERSITY COUNCIL
ON WORKFORCE AND HUMAN RESOURCE EDUCATION
6 APRIL 2016