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The Frontier for the Future WorkforceReginald Brothers, Ph.D.
Ashley Gierlach, M.A.
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Near & Far Term
Educational Requirements
Implications for Future Workforce
Future VisionHistorical
Global Context
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Ford Factory 1927
Ford Factory 2000
Tesla Factory 2017
I. Historical Global Context: Industrial Revolutions
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A Fourth Industrial Revolution:
World Economic Forum
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Near & Far Term
Educational Requirements
Implications for Future Workforce
Future VisionHistorical
Global Context
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II. A Future Vision: Global Dimensions and Emerging Tech
CloudComputing
Accelerating Pace of Technology
Artificial Intelligence/ Machine Learning
Expanding Global Knowledge Base
Technological Convergence
Disruptive Business Models
Black Swan Syndrome
Distributed Collaboration
Industry overtakes Government in
applied R&F spending
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II. A Future Vision: Complex Interrelationships
Policy
Technology
Law
Ethics
Innovation
Market
• Science• Brain –> neuroscience AI• Mathematics -> math formalism -> software
assurance• CS/Material Science -> AI chips• Mathematics+CS+life sciences = Omics• Behavoral Sciences • Quantum +CS = Quantum Computation • What is the ‘Science-Vector’?
• Technology• AI- thinking tech• AR/VR – Visualization tech• Robotics – doing tech• Energy sources/storage• Engineered biology • 3D Manufacturing• Distributed identity platforms
• Policy/Law/Ethics• Intersection with science and tech• Insufficient frameworks
• Speed and agility overall theme
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Agility, Speed and efficiency is essential
User-Producer Innovation Wide-Aperture Search
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Near & Far Term
Educational Requirements
Implications for Future Workforce
Future VisionHistorical
Global Context
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III. Implications for the Future Workforce
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III. Implications for the Future Workforce: A case study
Traditional Clothing Manufacturing 3D Knitting Machine
Human labor was historically cheaper than machine labor, but in the context of the current technological innovation we are experiencing, machine labor is beginning to
become cheaper than human labor.
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III. Implications for the Future Workforce: The “Human-Machine Teaming Balance”
Human Workload
Human Decision Making “Workload”
Present Future
Machine Decision Making “Workload”
(Advancements in Autonomous Technology)
Minimum Human Operator Oversight
(Level set by policy,legal and ethical frameworks)
AI-Based Decision Making
Hum
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Time
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III. A vision of the future workforce
Innovation
Centaur trans-disciplinary teams
“Centaur”
2017 Super Bowl
Hardware
Wetware
Software
Innovation
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Near & Far Term
Educational Requirements
Implications for Future Workforce
Future VisionHistorical
Global Context
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IV. Near & Far Term Educational Requirements
Two main priorities:• Diversifying education and credentialing ecosystem: Most experts see a new education and
training ecosystem emerging and expect the education marketplace – especially online learning platforms – to continue to change in an effort to accommodate the widespread needs.
• A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: The human talents that machines and automation may not be able to duplicate should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI. Experts suggest that workers of the future should learn to deeply cultivate and exploit creativity, collaborative activity, abstract and systems thinking, complex communication, and the ability to thrive in diverse environments.
“A key idea emerging from many conversations is that changes in educational and learning environments are necessary to help people stay employable in the labor force of the future”
Pew Research Center
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Five major themes about the future of jobs training in the tech age:
Speed
Pew Research Center
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IV. Near & Far Term Educational Requirements
Historical issues to consider:
1. Lack of adaptability• Designated order of classes: factory model
(antiquated)• Limited recognition of different learning styles
• Learning is a perishable skill• Retraining non-lifetime learners
2. Prejudice3. Fear
Solutions via education:
1. Innovation• Agile curriculum (electronic printing)• Design Thinking
2. Computational thinking• First level: Programming language (second
language)• Second level: Machine compatible thinking and
norms
3. Collaboration• Human-Human Teams• Human-Machine Teams (norms/ social cognitive)
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IV. Near & Far Term Educational Requirements
Speed & Agility – Flow/Zone – Challenging Assumptions
The Key to Success = Innovation