Presented at the Heart of North Texas Conference on November 1, 2019. The Conference is an outreach program of the Northeast Leadership Forum.

1. www.wrksolutions.com 1.888.469.JOBS (5627)
Workforce Solutions is an equal opportunity employer/program. Auxiliary aids and services are available upon request to individuals with disabilities.
(Please request reasonable accommodations 48 hours in advance.) Relay Texas: 1.800.735.2989 (TDD) 1.800.735.2988 (voice) or 711
Automation and Artificial
Intelligence:
The Impacts on Occupations and
Industries in the Dallas-Fort
Worth Metroplex
www.wrksolutions.com 1.888.469.JOBS (5627)
Workforce Solutions is an equal opportunity employer/program. Auxiliary aids and services are available upon request to individuals with disabilities.
(Please request reasonable accommodations a minimum of two business days in advance.) Relay Texas: 1.800.735.2989 (TDD) 1.800.735.2988 (voice) or 711
Gulf Coast Workforce Board

2. Gulf Coast Workforce Board
2
Texas Workforce
Development
Areas
WDA ID Name
1 Panhandle
2 South Plains
3 North Texas
4 North Central Texas
5 Tarrant County
6 Greater Dallas
7 Northeast Texas
8 East Texas
9 West Central Texas
10 Borderplex
11 Permian Basin
12 Concho Valley
13 Heart of Texas
14 Capital Area
15 Rural Capital Area
16 Brazos Valley
17 Deep East Texas
18 Southeast Texas
19 Golden Crescent
20 Alamo
21 South Texas
22 Coastal Bend
23
Lower Rio Grande
Valley
24 Cameron
25 Texoma
26 Central Texas
27 Middle Rio Grande
28 Gulf Coast
1
2 3
4
5 6
7
89
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
North Central
Tarrant
County
Dallas County
Gulf Coast

3. Topics
• About the Gulf Coast Workforce/Workforce Solutions
• Demythologizing Artificial Intelligence
• The Oxford Predictive Model on Chance of Job Automation
• Progress in Eliminating Technological Bottlenecks to Automation
• Advanced Risk Assessment of Job Automation Using BLS/TWC
Employment Projections
Gulf Coast Workforce Board
3

4. Demythologizing Artificial
Intelligence
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4

5. Generative Adversarial Network Photorealistic
Imitations
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5

6. AI Case Study: #1
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7. 7

8. 8

9. 9

10. 10

11. Gulf Coast Workforce Board
11
What went wrong?

12. What Went Wrong?
Source: malletsdarker / imgur
Gulf Coast Workforce Board
12

13. 4 Likely Explanations
• Too few examples for algorithm to learn from
• Fundamental shift in distance of focal point from 1st and 2nd
photos to 3rd
• Introduction of new elements (i.e. people) in 3rd photo not
present in other 2
• Algorithm interpreted dark ski suit as tree shadows (note the
omission of the person on the left)
Gulf Coast Workforce Board
13

14. Role of Human Judgement in AI and Automation
Input
Data
Algorithm
Output/
Predictio
ns
Role of Human Judgement
Decide appropriate
inputs based on
domain knowledge
Choose appropriate
algorithm
Determine reward
function
Evaluate for
consistency with
original goals
Assess cost of false
positives
Source: adapted from Ahmed Elgammal AI Art Creative Process Schematic – www.artsy.net “What the Art World Is Failing to Grasp about Christie’s AI Portrait Coup”
Source: Ajay Agrawal, Joshua Gans, Avi Goldfarb – www.hbr.org “How AI Will Change the Way We Make Decisions”
14
Gulf Coast Workforce Board

15. AI Case Study: #2
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15

16. AI Headlines
Gulf Coast Workforce Board
16

17. Facebook Chatbots Bob and Alice
coats have zero to me to me to me to me to me to me to
me to me to
Alic
e
Bob
i can i i everything else . . . . . . . .
. . . . . .
you i everything else . . . . . . . . .
. . . . . coats have a coat to me to me to me to me to me
to me to mei i can i i i everything else . . . . . . . .
. . . . . . coats have a coat to me to me to me to me to me
to me to mei . . . . . . . . . . . . . . . . . . .
coats have zero to me to me to me to me to me to me
to me to me toyou i i i i i everything else . . . . . . .
. . . . . . . coats have 0 to me to me to me to me to me to me to
me to me toyou i i i everything else . . . . . . . .
. . . . . . coats have zero to me to me to me to me to me to me to
me to me to
Gulf Coast Workforce Board
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18. Gulf Coast Workforce Board
18
What went wrong?

19. Facebook Chatbots Bob and Alice
book hat coat total
coun
t
valu
e
coun
t
valu
e
coun
t
valu
e
coun
t
valu
e
Alice 3 1 2 1 1 5 6 10
Bob 3 0 2 0 1 10 6 10
Reward function designed to maximize total value,
not preserve human natural language syntax hence
“secret language”
Gulf Coast Workforce Board
19

20. Assessing the Impact of
Automation/AI on the Metroplex
Workforce
Gulf Coast Workforce Board
20

21. Oxford University Predictive Model
• Oxford University researchers Frey and Osborne publish 2013
study “The Future of Employment: How Susceptible Are Jobs to
Computerisation?”
• Study was NOT a prediction of future job losses but how
exposed existing jobs are to AI and automation
• News outlets misrepresented findings by stating “47% of jobs in
the U.S. will be displaced by automation”
21
Gulf Coast Workforce Board

22. Oxford University Predictive Model
• The speed and extent of adoption of AI-based technologies will
vary significantly by industry, occupation, specific employer,
location, regulations, costs, labor shortages, etc.
• The research assumes that an occupation is homogenous;
occupational variation e.g. truck drivers of consumer goods vs.
hazardous chemicals will likely be affected by AI in different
ways with different timelines for adoption
• Oxford model cannot account for new competitors’ disruption
of traditional business models e.g. Amazon vis-à-vis retail,
Uber vis-à-vis taxis
22
Gulf Coast Workforce Board

23. Technological Bottlenecks Limiting Automation
Creative
Tasks
● Originality
● Fine Arts
Perception
Manipulation
Tasks
● Finger Dexterity
● Manual
Dexterity
● Cramped
Workspace
Social
Tasks
23
Gulf Coast Workforce Board
● Social
Perceptiveness
● Negotiation
● Persuasion
● Care-giving

24. Dallas-Fort Worth Metroplex + 3
Palo Pinto Parker
Wise Denton Collin
Hunt
Tarrant Dallas
Rockwall
Erath
Hood
Somervell
Johnson Ellis
Navarro
Kaufman
Gulf Coast Workforce Board
24

25. Metroplex + 3 Employment Chance of Automation
36%
16%
48%
Low (< 30%) Moderate (30% to 70%) High (> 70%)
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26. Metroplex + 3 Employment Chance of Automation
by Occupational Typical Education Required
14%
22%
27%
36%
37%
48%
70%
98%
98%
2%
7%
18%
16%
34%
33%
23%
1%
0%
84%
71%
55%
48%
29%
19%
7%
0%
1%
-20% 0% 20% 40% 60% 80% 100% 120%
No formal credential
Some college no degree
HS diploma
Total, All Occupations
Postsecondary nondegree
Associate's
Bachelor's
Doctoral or professional
Master's
Low (< 30%) Moderate (30% to 70%) High (> 70%)
Gulf Coast Workforce Board
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27. 36%
54%
78%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
High Digital Skills Medium Digital Skills Low Digital Skills
Metroplex + 3 Employment Chance of Automation
by Digital Skills Required
Low AutomationRisk (< 30% chance)
Moderate AutomationRisk (30% to 70% chance)
HighAutomationRisk (> 70% chance)
Source Methodologies: Metropolitan Policy Program at Brookings, Mark Muro “Digitalization and the
American workforce”
Oxford University, Carl Frey & Michael Osborne “The Future of Employment: How susceptible are jobs Gulf Coast Workforce Board
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28. Metroplex + 3 Employment Chance of Automation
by Social Interaction Required
Occupation Family Median Wages
Avg. Chance of
Automation
Community & Social Service $50,000
14%
Management $107,000
Education, Training, & Library $48,000
Healthcare Practitioners & Technical $60,000
Arts, Design, Entertainment, Sports, &
Media $48,000
Healthcare Support $31,000
Personal Care & Service $22,000
Legal $107,000
49%
Architecture & Engineering $89,000
Protective Service $65,000
Sales & Related $40,000
Business & Financial Operations $44,000
Life, Physical, & Social Science $69,000
Computer & Mathematical $90,000
Construction & Extraction $46,000
Installation, Maintenance, & Repair $40,000
Production $30,000
81%
Office & Administrative Support $38,000
Transportation & Material Moving $22,000
Food Preparation & Serving Related $32,000
Farming, Fishing, & Forestry $22,000
Building & Grounds Cleaning &
Maintenance $26,000
Relatively
High Degree
of Social
Interaction
Required
Relatively
Low Degree
of Social
Interaction
Required
Low Chance of
Automation
(< 30%)
Moderate
Chance of
Automation
(30% to 70%)
High Chance of
Automation
(> 70%)
Gulf Coast Workforce Board
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29. Industry Chance of Automation: Example #1
NAICS Industry
Industry Avg.
Chance of
Automation
Share of
Regional
Employment
Targeted
Industry
4841 General Freight Trucking 82% 0.7% Yes
Automation
Chance
Occupation
Occupation %
Chance of
Automation
% Share of
Industry
Employment
HSHG
Occupation
1st-Line Spvrs. of Trans. & Material-Moving Machine & Vehicle Op. 14.1 2.6
General & OperationsMgrs. 6.7 1.2
Trans., Storage, & DistributionMgrs. 6.0 0.8
1st-Line Spvrs. of Office & Admin. Support Workers 6.3 0.6
1st-Line Spvrs. of Helpers, Laborers, & Material Movers, Hand 19.6 0.5
Bus& Truck Mechanics& Diesel Engine Specialists 62.7 3.8 ▲
Dispatchers, Ex. Police, Fire, & Ambulance 63.9 3.2
SalesRepresentatives, Services, All Other 61.7 1.2
Accountants& Auditors 65.1 0.3 ▲
Production, Planning, & Expediting Clerks 68.1 0.2 ▲
Heavy & Tractor-Trailer Truck Drivers 91.2 61.4 ▲
Laborers& Freight, Stock, & Material Movers, Hand 91.2 5.4
Office Clerks, General 90.6 3.2
Light Truck or Delivery ServicesDrivers 93.0 2.8
Industrial Truck & Tractor Oprs. 89.2 1.6
High
> 70%
Low
< 30%
Moderate
30% to 70%
8%
9%
82%
Gulf Coast Region: Share of General
Freight Trucking Employment by Chance of
Automation
Low Moderate High
Gulf Coast Workforce Board
29

30. Progress in Reducing
the Technological
Bottlenecks to
Automation
Gulf Coast Workforce Board
30

31. Technological Bottlenecks Limiting Automation
Creative
Tasks
● Originality
● Fine Arts
Perception
Manipulation
Tasks
● Finger Dexterity
● Manual
Dexterity
● Cramped
Workspace
Social
Tasks
31
Gulf Coast Workforce Board
● Social
Perceptiveness
● Negotiation
● Persuasion
● Care-giving

32. Creative Tasks: AI-Generated Art & Music
Source (Left): The Verge – “How three French students used borrowed code to put the first AI portrait in Christie’s” October 23, 2018
Source (Center): The Guardian – “New Rembrandt to Be Unveiled in Amsterdam”
Source (Right): Quartz – “The first pop song ever written by artificial intelligence is pretty good, actually” September 24, 2016
Gulf Coast Workforce Board
32

33. Perception & Manipulation: High Dexterity Robotic
Hands
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33
Source (left): NBC News – “Amazon Just Patented a Package-Packer Bot to Add to Its Fleet of 45,000 Robots” January 27, 2017
Source (right): Engadet – “Fingertip sensor lets robots 'see' what they're touching” September 19, 2014
• Judges:
• Hardness of object to determine gripping
power needed
• Shape of object using 3-D mapping
• Orientation of object in robotic hand

34. Social Tasks: Amazon Marketplace vs. Retail Group
-Amazon replaces retail supply chain managers with data scientists-
Source: Bloomberg – “Amazon’s Clever Machines Are Moving from the Warehouse to Headquarters” June 13, 2018
Gulf Coast Workforce Board
34

35. Advanced Risk Assessment of Job
Automation Using
BLS/TWC Employment Projections
Gulf Coast Workforce Board
35

36. Long-term Employment Projections
• Produced by BLS for the U.S. and localized by Texas Workforce Commission for
the 28 workforce development areas across state
• Current state and local projections cover 10-year timeframe of 2016 to 2026 with
new projections released every two years for next 10-year period
• Projects 10-year net and percent change in employment for 238 industries and
646 occupations across Metroplex + 3 area
• Used by workforce boards to prioritize strategic investments in critical
occupations
• Assumes broad social and demographic trends will continue and economy will
operate at full capacity in final year i.e. does not try to anticipate changes in the
business cycle
• Is a straight line projection, not a dynamic forecast
• Does not account for automation risk only technological change which may
increase productivity and potentially decrease demand for workers
36
Gulf Coast Workforce Board

37. Metroplex + 3 Employment Projections 2016-2026
37
Gulf Coast Workforce Board
Projected Job Growth
723,000
3.7 to 4.4 million jobs
19.5%
(vs. U.S. 7.4% and TX 16.6%)

38. Automation Workforce Impacts vs. Employment
Projections
38
Gulf Coast Workforce Board
At-risk for
accelerated
obsolescence
(e.g. cashiers, office clerks,
secretaries)
Stable or
Growing + Job
Duty
Transformation
(e.g. fast food prep &
serving, CSRs,
automotive mechanics)
Decline due to
non-automation
related factors
(e.g. CEOs, exec. secretaries,
correctional officers and
jailers)
Stable or
Growing +
Minimal Impact
from
Automation/AI
(e.g. nurses, teachers,
gen. mgrs.)
High Automation Risk
Low Automation Risk
Projected
Job Gains
Projecte
d
Job
Losses New jobs
(by definition
will initially be
fast-growing
and have low
automation risk)
*Oxford Model automation risk predictions are based ability of current technology to replace specific job tasks,
not the potential for competitors to disrupt traditional business models e.g. Amazon vis-à-vis retail.

39. Gulf Coast Workforce Board
39
Good News!

40. Automation Workforce Impacts vs. Projections
40
Gulf Coast Workforce Board
13% 3%
39%
42%
2%
United States
1% 1%
54%
43%
1%
Gulf Coast
1% 0.4%
55%
42%
1% Metroplex + 3
At-risk for Accelerated Obsolescence
Decline Due to Non-automationRelated Factors
Stable or Growing + Job Duty Transformation
Stable or Growing + Minimal Impact fromAutomation/ AI
Unclassified

41. Thank You!
Parker A. Harvey
Principal Economist
Gulf Coast Workforce Board/Workforce
Solutions
713-993-2462
parker.harvey@wrksolutions.com
wrksolutions.com/reportcard

42. Gulf Coast Workforce Board
42
BONUS SLIDES

43. Metroplex + 3 Employment Chance of Automation
by Industry Sector
36% 16% 48%
0% 20% 40% 60% 80% 100%
Accommodation and Food Services
Construction
Manufacturing
Information
Mining, Quarrying, and Oil and Gas Extraction
Agriculture, Forestry, Fishing and Hunting
Total, All Employment
Other Services (except Public Administration)
Retail Trade
Public Administration
Educational Services
Low (< 30%) Moderate (30% to 70%) High (> 70%)
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43

44. Metroplex + 3 Employment Median Wages of
Occupations
by Chance of Automation
$61,000
$55,000
$33,000
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Low (< 30%) Moderate (30% to 70%) High (> 70%)
Median Wages
All Occupations:
$39,000
Gulf Coast Workforce Board
44

45. Technological Bottlenecks Limiting Automation
Finger
Dexterity
Manual
(Hand)
Dexterity
Cramped
Work
Space
Originalit
y
Fine Arts
Social
Perceptiv
e-ness
Negotiatio
n
Persuasio
n
Assisting
& Caring
for
Others
Perception
&
Manipulati
on Tasks
Creative
Tasks
Social
Tasks
Bottlene
ck
O*NET Variables
Source: O*NET and Carl Frey and Michael Osborne – “The Future of Employment: How Susceptible Are Jobs to Computerisation? September 17, 2013
Gulf Coast Workforce Board

46. Industry Chance of Automation: Example #4
NAICS Industry
Industry Avg.
Chance of
Automation
Share of
Regional
Employment
Targeted
Industry
3251 BasicChemical Manufacturing 62% 0.7% Yes
Automation
Chance
Occupation
Occupation %
Chance of
Automation
% Share of
Industry
Employment
HSHG
Occupation
Chemical Engineers 25.8 5.9 ▲
1st-Line Spvrs. of Production& Opr. Workers 6.8 4.4
General & OperationsMgrs. 6.7 1.6
Industrial ProductionMgrs. 1.7 1.4
SalesRep., Wholesale & Manufacturing, Ex. Tech. & ScientificProducts 27.7 1.1
Chemical Technicians 66.1 3.6
Chemists 57.6 3.2
Industrial Machinery Mechanics 66.7 2.6 ▲
Industrial Engineers 48.6 1.2 ▲
Production, Planning, & Expediting Clerks 68.1 1.0 ▲
Chemical Equipment Oprs. & Tenders 83.5 19.5
Chemical Plant & SystemOprs. 80.8 8.9 ▲
Maintenance & Repair Workers, General 86.2 3.2
Heavy & Tractor-Trailer Truck Drivers 91.2 3.2 ▲
Mixing & Blending Machine Setters, Oprs., & Tenders 89.8 2.0
High
> 70%
Low
< 30%
Moderate
30% to 70%
27%
18%
56%
Gulf Coast Region: Share of Basic
Chemical Manufacturing Employment by
Chance of Automation
Low Moderate High
Gulf Coast Workforce Board
46

47. Industry Chance of Automation: Example #2
NAICS Industry
Industry Avg.
Chance of
Automation
Share of
Regional
Employment
Targeted
Industry
6221 General Medical and Surgical Hospitals 31% 3.0% Yes
Automation
Chance
Occupation
Occupation %
Chance of
Automation
% Share of
Industry
Employment
HSHG
Occupation
Registered Nurses 4.9 29.5 ▲
RadiologicTechnologists 11.0 2.2 ▲
Medical & HealthServicesMgrs. 7.6 2.0
Medical Assistants 10.0 1.9
Respiratory Therapists 6.8 1.9 ▲
Nursing Assistants 48.5 6.1
Medical & Clinical Laboratory Technicians 69.3 1.2 ▲
Pharmacy Technicians 63.4 1.1
Orderlies 54.3 0.8
Receptionists& InformationClerks 66.1 0.6
Medical Secretaries 82.7 5.5
Maids& Housekeeping Cleaners 94.4 1.8
Office Clerks, General 90.6 1.7
Medical Records& HealthInformationTechnicians 94.0 1.2 ▲
Customer Service Representatives 75.5 1.0
High
> 70%
Low
< 30%
Moderate
30% to 70%
65%
13%
22%
Gulf Coast Region: Share of General
Medical and Surgical Hospitals
Employment by Chance of Automation
Low Moderate High
Gulf Coast Workforce Board
47

48. Industry Chance of Automation: Example #3
NAICS Industry
Industry Avg.
Chance of
Automation
Share of
Regional
Employment
Targeted
Industry
4411 Automobile Dealers 57% 0.9% Yes
Automation
Chance
Occupation
Occupation %
Chance of
Automation
% Share of
Industry
Employment
HSHG
Occupation
Retail Salespersons 27.4 23.1
1st-Line Spvrs. of Retail SalesWorkers 5.6 3.5
1st-Line Spvrs. of Mechanics, Install, & Repair 5.7 2.6
General & OperationsMgrs. 6.7 2.4
SalesMgrs. 2.7 2.2
Automotive Service Technicians& Mechanics 68.4 17.0
Receptionists& InformationClerks 66.1 1.4
SalesRepresentatives, Services, All Other 61.7 1.2
Accountants& Auditors 65.1 0.7 ▲
Bus& Truck Mechanics& Diesel Engine Specialists 62.7 0.5 ▲
Cleanersof Vehicles& Equipment 79.0 7.6
Office Clerks, General 90.6 4.6
Counter & Rental Clerks 82.9 3.2
Automotive Body & Related Repairers 83.0 2.6
Customer Service Representatives 75.5 2.4
High
> 70%
Low
< 30%
Moderate
30% to 70%
38%
22%
40%
Gulf Coast Region: Share of Automobile
Dealers Employment by Chance of
Automation
Low Moderate High
Gulf Coast Workforce Board
48