The Impact of AI and Automation on Curriculum Development

Transcription

1 The Impact of AI and Automation on Curriculum Development Derrick Edwards President and Chief Technology Officer / G*STARS NCWE Annual Conference September 26, 2018

2 Take Away from Today Working definitions: robotics, automation, AI, Block Chain, etc. Framework for describing subjectivity to Automation Discussion of types of career pathways at risk, not at risk, and changing The larger context of technological change and the Fourth Industrial Revolution Outline for evaluating curricula relative to these topics

3 Resources at GSTARS.com/ncwe2018

4 A Lot of Noise 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 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

6 Careers Being Impacted Highly Cited The Future of Employment: How Susceptible Are Jobs To Computerization? Carl Benedikt Frey and Michael A. Osborne Oxford University,

7 The Future of Employment: How Susceptible Are Jobs To Computerization? Top 25 Bottom 25 Rank Probability Occupation Rank Probability Occupation Telemarketers Title Examiners, Abstractors, and Searchers Sewers, Hand Mathematical Technicians Insurance Underwriters Watch Repairers Cargo and Freight Agents Tax Preparers Photographic Process Workers and Processing Machine Operators New Accounts Clerks Library Technicians Data Entry Keyers Timing Device Assemblers and Adjusters Insurance Claims and Policy Processing Clerks Brokerage Clerks Order Clerks Loan Officers Insurance Appraisers, Auto Damage Umpires, Referees, and Other Sports Officials Tellers Etchers and Engravers Packaging and Filling Machine Operators and Tenders Procurement Clerks Shipping, Receiving, and Traffic Clerks Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic Recreational Therapists First-Line Supervisors of Mechanics, Installers, and Repairers Emergency Management Directors Mental Health and Substance Abuse Social Workers Audiologists Occupational Therapists Orthotists and Prosthetists Healthcare Social Workers Oral and Maxillofacial Surgeons First-Line Supervisors of Fire Fighting and Prevention Workers Dietitians and Nutritionists Lodging Managers Choreographers Sales Engineers Physicians and Surgeons Instructional Coordinators Psychologists, All Other First-Line Supervisors of Police and Detectives Dentists, General Elementary School Teachers, Except Special Education Medical Scientists, Except Epidemiologists Education Administrators, Elementary and Secondary School Podiatrists Clinical, Counseling, and School Psychologists Mental Health Counselors

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

9 Careers Being Impacted Let s talk through a few examples Agriculture Transportation Middle Management Construction Health Care

10 Careers Being Impacted Derrick, don t forget to show the videos

11 Wages Rates Are a Weak Predictor Source: O*NET 2014 and McKinsey & Company Analysis ss-functions/digital-mckinsey/ourinsights/four-fundamentals-ofworkplace-automation

12 Geography Is No Predictor Source: Joshua Wright, EMSI, 2014 Low-Skill Jobs Are Booming, But They re at Greatest Risk for Automation 31/low-skill-jobs-are-booming-but-theyre-atgreater-risk-for-automation/

13 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

14 How Do We Predict Source: McKinsey & Company ness-functions/digitalmckinsey/our-insights/wheremachines-could-replace-humansand-where-they-cant-yet

15 View at the Industry Level Source: McKinsey & Company ness-functions/digitalmckinsey/our-insights/wheremachines-could-replace-humansand-where-they-cant-yet

16 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

17 Questions and discussion?

18 Traditional Forecasting Tools Issue 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

19 Curriculum Analysis Considerations Construct a simplified protocol for rating subjectivity to automation Rate each Career Path offered, not just the associated industry Adjust based on specific forecast of the technological change/impact Understand how key economic forecasting models account for automation

20 Analyze at the Occupation Level Each individual course should be reviewed and revised to account for the impact and opportunity of automation.

21 Curriculum Development Considerations Develop curriculum for creating, managing, or supporting automation jobs Acquire (buy/partner/collaborate) content for extremely short cycle-time Develop at least one hybrid model with industry partner extending the internship/apprenticeship model

22 On net some career pathways will disappear, some will be created, but within the 2-year system our primary goals will be learning to forecast the impact of technology on specific careers, drastically shorten curriculum cycle-times, and evolve the structure of our relationship with employers.

23 Complicating Factors Industrial / Corporate Education Portable / Micro Credentials Differentiation Career Changers Curricula Cycle-time

24 Questions and discussion before we move on to the future?

25 Fourth Industrial Revolution First: Steam and mechanization, 1800* Second: Electrification and mass preproduction, 1900* Third: Computerization and electronics, 1975* Fourth: Automation and machine intelligence, 2000* * all dates are ish

26 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

27 Most Disruptive Technologies Block Chain / Smart Contracts Every Industry, greatest digital transformation since the internet itself Automation / Robotics (driven by AI) Every job with a physical component AI / Machine Learning Every job with an analytical component Quantum Computing Most highly derivative, will drive entirely new industries and material sciences

28 Question and Discussion? Working definitions: robotics, automation, and AI Types of career pathways at risk Impact: loss, growth, income divergence Issues with traditional forecasting tools Considering your own curriculum Resources

29 Thank You! Derrick Edwards, President & CTO / G*STARS derrickedwards@gstars.com gstars.com/ncwe2018 Call, write, or connect with me on LinkedIn