Replacing and Extending Human Work
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The webinar discusses the impact of automation on human work, exploring economic principles, opportunities for automation, and the co-evolution of humans with technology. It highlights the distinction between labor and capital as production factors, the role of machine learning, and prevailing views regarding job replacement versus task extension through technology. Additionally, it touches on futurist Ray Kurzweil's prediction of advanced artificial intelligence by 2045, suggesting potential implications for workforce education.
Replacing and Extending Human Work
- 1. WEBINAR SPONSORED BY THE UNIVERSITY COUNCIL ON WORKFORCE AND HUMAN RESOURCE EDUCATION 6 APRIL 2016
- 2. Emerging role of automation in replacing and extending human work
- 4. A technique of making an apparatus, a process, or a system operate without direct control by a person (i.e., automatically).
- 5. Automatically controlled operation of an apparatus, a process, or a system by mechanical or electronic devices that take the place of human labor.
- 10. I will talk at you for a while; you will enter comments/questions in ”chat” window At intervals, I will respond to your comments/questions When the end is near, I will respond to your general comments/questions
- 11. Some economic principles that affect the replacement and extension of human work. Frameworks for analyzing automation opportunities. Is co–evolution of humans with technology a possibility?
- 13. Material production of goods and services occurs using a mix, a “recipe,” of factors of production: • Land • Capital – financial and tangible assets. • Labor – human time/effort exchanged for wages.
- 14. • Minimize costs. • Maximize returns on investment. • All factors are costs to be minimized. ∴There is no imperative to create jobs, which are viewed under this ethic as a cost.
- 15. ∴There is no imperative to create jobs, which are just a cost. In this view, it is not the lack of invention that sets boundaries for economic growth, but rather powerful interests in promoting the technological status quo.
- 16. • are substitutes The mix of the amounts of capital and labor in production is altered based on their relative costs (“either-or”). • are complements Sometimes capital and labor are used together beneficially (“and”).
- 17. • are substitutes Possible for labor to be replaced by capital when the relative price of labor increases and capital is more efficient. • are complements Possible for the productivity of labor to be extended by capital.
- 19. “We can know more than we can tell.” “The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from the knowledge of its physiology.” Polayni, M. (1966). The tacit dimension. NewYork: Doubleday.
- 20. • explicit Formalized and codified. Sometimes referred to as “knowing what.” Fairly easy to identify, store, and retrieve. • tacit Intuitive, hard-to-define knowledge that is largely experience based. Sometimes referred to as “knowing how.” Context dependent and personal in nature. Hard to communicate and deeply rooted in action, commitment, and involvement.
- 21. We tacitly know a lot about the way the world works. Yet, we are not able to explicitly describe this knowledge.
- 22. Source: Autor, D., Levy, F., & Murnane, R. H. (2003).The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333.
- 23. Source: Autor, D., Levy, F., & Murnane, R. H. (2003).The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333. Autor et al. Implications from Polanyi’s Dilemma Tasks from the quotidian to the sublime cannot currently be computerized because we don’t know the rules. At the economic level, though, tasks that cannot be computerized often are complemented by technology.
- 24. • work redesign Engineering the environment to make workflow more predictable. Examples: assembly lines, rail tracks, paved roads, warehouses, ports. • machine learning Use of code that applies inductive reasoning so that machines can learn from statistical patterns rather than use explicitly programmed instructions. Exposure…training…reinforcement.
- 25. • Massive amounts of structured and unstructured information are being collected…more than conventional analytics can handle. • This information represents varied experience from which patterns, and trends can be derived.
- 26. Machine learning examples BRETT, the Berkeley Robot for the Elimination of TediousTasks, can fold a towel. But it takes a long time — best is, it’s not your time.
- 27. Machine learning examples SLAM Simultaneous localization and mapping. Mapping an unknown environment while keeping track of the mapping agent.
- 28. Machine learning examples Facial Recognition Given a digital photo album of many hundreds of digital photographs, identify those photos that include a given person.
- 29. An automated system available today does some of the work. Little physical contact or manipulation necessary. Content transmission is a key part of the job. Content analysis is a key part of the job. Ability to understand and analyze data is key. The task can be simulated or performed virtually.
- 30. Consistency of performance is critical to the role. Content creation is based on data and analysis. Well–defined formal rules dictate work performance. Davenport,T. H. (2015, April 8).The knowledge work jobs of 2024. Wall Street Journal bog posting.
- 32. Many analysts believe that substitution of labor for capital will replace jobs with machines and will de-skill human work.
- 33. Many analysts believe that substitution of labor for capital will replace jobs with machines and will de-skill human work. Others say that complementary technology will extend the human work remaining.
- 34. Kurzweil’s Law of Accelerating Returns The growth of nonbiological computer processing power is exponential rather than linear; and its tempo shows no sign of slackening.
- 35. Ray Kurzweil, innovator and futurist, predicts that around 2045 artificial intelligence will emerge capable of self-improvement and autonomous building of smarter machines.
- 36. Ray Kurzweil, innovator and futurist, predicts that around 2045 artificial intelligence will emerge capable of self-improvement and autonomous building of smarter machines. THEN Our intelligence is canned, digitized, and uploaded into a less perishable substrate. Distinction between biological and nonbiological entities ceases. Some have dubbed: this the point of “fusion.” Others: the point of “replacement.”
- 37. What is the endgame for Workforce and Human Resource Education?
- 38. A link to a video recording available on Google The slide deck available on SlideShare http://goo.gl/Y2kOLV https://goo.gl/V9LQt7
- 39. Registered participants receive invitations to complete an assessment of this webinar. • If not attending, suggestions about timing, focus, structure, and technology for UCWHRE webinars. • If attending, reactions to webinar and suggestions.
- 40. WEBINAR SPONSORED BY THE UNIVERSITY COUNCIL ON WORKFORCE AND HUMAN RESOURCE EDUCATION 6 APRIL 2016