Evil Robots and Intelligent Computers

Transcription

1 Evil Robots and Intelligent Computers A Practical Introduction to the Impact of Automation and Artificial Intelligence on Workforce Planning Fulton Derrick Edwards President and Chief Technology Officer AGS Data Systems / G*STARS NCWE Annual Conference October 25, 2017 Collect. Analyze. Achieve.

2 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

3 Take Away from Today 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

4 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 task even if humans can t explain the task Impacts information processing and remote social interaction

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

6 The Future of Employment: How Susceptible Are Jobs To Computerization? Top 25 Bottom 25 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 Rank Probability Occupation 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

7 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

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

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

10 View at the Industry Level, but Analyze at the Occupation Level

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

12 Geography Is a Poor 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 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

15 Traditional Forecasting Tools 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 disruptions

16 Curriculum Gap Analysis You must look at your curriculum at the level of individual careers and not at the industry level Ask how key economic forecasting models account for automation Focus on developing curriculum for creating, managing, or supporting automation jobs

17 Famous Quote Every curriculum should be reviewed and revised to account for the impact and opportunity of automation. - Me

18 Resources at GSTARS.com/ncwe2017

19 How Did We Do? 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 Questions?

20 Thank You! Derrick Edwards Please rate this session! Visit on your phone, click on Session Survey, then select this workshop from the bottom of the dropdown list