The Future of Work: Technology and the Workforce

1. The Future of Work: Technology and the WorkforceHow to Consider the Impact of AI and Automation in the Context of Curriculum Development Derrick Edwards President and Chief Technology Officer AGS Data Systems / G*STARS NCWE New Workforce Professionals Academy April 11-12, 2019

2. Resources at https://GSTARS.com/npwa2019.html

3. What brings me here… President and CTO, AGS Data Systems (18 years) Academic and commercial background in Data Analysis, Commercial Software Development, Machine Learning Involved in workforce data management system (WIA, WIOA) development since 1996, most recently with the develop of grant tracking systems for SWFI, APG, TAAACT, TechHire, Scaling Apprenticeships Chair, University of Wisconsin System Advisory Board for BS in Applied Computing Advisory Board, University of Wisconsin River Falls Masters in Computer Science program. Originator and Co-Principal Investigator, University of Wisconsin System, Bridgeway Scholars Program (hybrid academic / industry model for STEM based education) Instructor, Data Analysis and Ethics, NCWE New Workforce Professionals Academy

4. A Lot of Noise About Robotics and Automation “13 Jobs That Robots, AI, And Automation Won’t Steal” – Forbes “3 Reasons You Won’t Mind When AI Replaces Half of All Jobs” – Inc. “AI Will Put 10 Million Jobs at High Risk” – CBInsights “10 Jobs That AI Will Replace” – Hubspot “Robots taking jobs in five year is BS, GE CEO says” – CNBC “Robots Have been Taking American Jobs, Study Says” – US News “Automation Taking Jobs” – C-Span

5. Take Away from Today Working definitions for robotics, automation, AI, etc. Technological change and the Fourth Industrial Revolution Career pathways at risk, not at risk, and changing Framework for describing subjectivity to Automation Outline for evaluating curricula relative to these topics

6. Definitions Automation Any machine that performs a job with reduced levels of human interaction Most impactful on physically repetitive or predictable work Robotics ( = automation ) Subset of Automation, where manipulation and mobility are involved Most impactful on complex repetition and social interaction Artificial Intelligence (AI) and Machine Learning (ML) Allows computer to learn a task even if humans can’t explain the task Impacts information processing and remote social interaction

7. Robotics in Construction, Video Links Brick Laying Robot One Brick Laying Robot Two Timber Building Robot Drywalling Robot Atlas Robot 2016 Atlas Robot 2018

8. Fourth Industrial Revolution First: Steam and mechanization, 1800* Second: Electrification and mass preproduction, 1900* Third: Computerization and electronics, 1975* Fourth: Automation and machine intelligence, 2010* Fifth: Gene editing and quantum computing, ? * all dates are “ish”

9. Fourth Industrial Revolution Will come all at once, everywhere Will, interestingly, have a positive, relative, impact on production costs for advanced economies May drive adoption of the Universal Basic Income

10. Most Impactful Technologies Block Chain / Smart Contracts Every Industry, greatest digital transformation since the internet itself AI / Machine Learning Every job with an analytical component Automation / Robotics Every job with a physical component Quantum Computing Will drive entirely new industries and material sciences Gene Editing Technology such as CRISPR allows of the editing of humans (see, He Juinkui and CCR5 gene) IOT Coupled with Big Data Signals the end of privacy as we have always understood it

11. Careers Being Negatively Impacted Predictable and Repetitive Actions Garment Manufacturing Brick Laying Dental Lab Technician Information Collection and Analysis Tax Preparation Insurance Underwriting Financial Planning Limited Scope Human Interaction Customer Service Home Health Care

12. Five Types of Impact Job Growth – working with automation (e.g. CNC Operators) Job Loss – due to automation (e.g. Automotive Welders) Economic Growth without job growth – warehouse robotics Income Divergence – warehouse humans Demand Cascade – top down pressure on existing jobs

13. Traditional Forecasting Tools Have Issues Key indicators with historical correlations Census and surveys of employers Industry-specific growth estimate research “A rise in [ fill_in_the_blank ] growth equals an increase in job growth” Models have a hard time seeing disruptive forces

14. Analyzing Susceptibility to Automation “Highly Cited…” The Future of Employment: How Susceptible Are Jobs To Computerization? Carl Benedikt Frey and Michael A. Osborne Oxford University, 2013 http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

15. The Future of Employment: How Susceptible Are Jobs To Computerization? Top 25 Bottom 25

16. Wages Rates Are a Weak Predictor Source: O*NET 2014 and McKinsey & Company Analysis https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/four-fundamentals-of-workplace-automation

17. Geography Is No Predictor Source: Joshua Wright, EMSI, 2014 “Low-Skill Jobs Are Booming, But They’re at Greatest Risk for Automation” http://www.economicmodeling.com/2014/10/31/low-skill-jobs-are-booming-but-theyre-at-greater-risk-for-automation/

18. Susceptibility to the Impact of Automation Source: McKinsey & Company https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

19. View at the Industry Level Source: McKinsey & Company https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

20. It’s Ultimately About the Cost “Technology can provide better efficiency and quality with menial tasks, as Webster noted. But employers are less likely to invest in that technology if there aren’t a high volume of workers to replace, or if it’s more expensive than really cheap labor.” JOSHUA WRIGHT, EMSI, OCTOBER 31, 2014 “Low-Skill Jobs Are Booming, But They’re at Greatest Risk for Automation”

21. Evolving Curriculum Curriculum Analysis Framework Curriculum Development Considerations

22. Curriculum Analysis, Framework Develop a framework that will analyze the curriculum Consider subjectivity to automation Adjust based on economic likelihood of automation Adjust based on specific forecast of the technological development

23. Curriculum Analysis, Considerations Rate each Career Path offered, not just the associated industry Understand how your existing key economic forecasting models account for automation (or not) at the career path level

24. Curriculum Development, Suggestions Develop courses as you always have, but consider… Developing curriculum for creating, managing, or supporting automation jobs, not just the jobs themselves Acquiring (buy/partner/collaborate) content for extremely short cycle-time Developing at least one hybrid model with industry partner extending the internship/apprenticeship model

25. Curriculum Development, Complicating Factors Industrial / Corporate Education Portable / Micro Credentials Differentiation Career Changers Cycle-time

26. “On net…” …some career pathways will disappear, some will be created, but our primary goal will be learning to forecast the impact of technological change on specific careers, drastically shorten curricula development cycle-times, and evolve the structure of our relationship with employers.

27. Questions? Discussion? How can we help?

28. Thank You! Derrick Edwards, President & CTO AGS Data Systems / G*STARS derrickedwards@gstars.com Links to resource materials Call, write, or connect with me on LinkedIn