TWhile reviewing my prior reflections to help me prepare for this final paper, I noticed that Vygotsky was at the top of my mind when we started to explore Creativity during module one. He returned during the second-to-last lesson about cognitive mechanisms of Intelligence in which we focused on scaffolding as well as the zone of proximal development. His ideas seem the most natural and appropriate to well-developed learning environments that improve students’ self-efficacy by keeping both parties of the educational experience – student and teacher – investing in appropriate levels of engagement. This fits very well with Wallas’s model of creativity as well as Palincsar’s elements of cognitive instruction in which intentional learning requires the teacher to view all students as capable of the classwork, and most importantly, communicate consistently as they work together. This class has enabled me to expand prior knowledge from a summer class about Learning, Cognition and Assessment with additional connections that clarify for me the learning relationship between creativity and intelligence. As the lecture notes stated, “Helping students learn intentionally… will empower them to become independent learners.”

I was pleasantly shocked while preparing my extra credit assignment when I encountered several achievement tests used to measure the impact of chess training on math, reading, and meta-cognitive skills. First, I was shocked because I had stopped feeling dismissive of the tests as ‘just another IQ estimate’ because of the time we used to deep dive into the intelligence tests at the midpoint of this semester. Second, I was shocked because as I looked up the tests online, they reminded me of the Digit Span module in the WCIS test as well as the Sequential Processing and Achievement subtests in the K-ABC test, and the section in Binet’s test for Quantitative Reasoning came to mind as I read more about the SPM for math assessment. It felt good to be comfortable with the content in these tests and it felt good to read them and more quickly understand what they meant, so I spent less time on that deep dive. The case studies, the scholarly article review, and the extra credit assignment provided so many opportunities to learn about the scientific study of psychology and that changed my perception of intelligence testing in general. Additionally, the exposure to the reports in the case studies helped me develop a format to organize the information in the scholarly article and the extra credit projects, prioritizing incisive brevity in the summary, being clear upfront about the mechanisms used by the study and their weaknesses, then leading with the successful results because they were most interesting to my audience.

A significant difference between creativity and intelligence that became more apparent to me while working on the material for this class will be useful as I continue my education here at UMN. Externalization is a foundational aspect of creativity – both little-c and big-C – because an idea requires more than the mind of the beholder to be creative. On the other hand, the evidence about Intelligence implies such a biogenetic causation that sharing probably is not required. Sharing is a huge part of creativity – in individualistic and collectivist societies alike despite each culture’s drastically different response to it. Intelligence is a state of being that may be recognized and measured with a test, by successfully completing a task at work or in school, or by a count of patents. That last measure is where it overlaps with Creativity but the difference is significant. While most of the course material about Intelligence can be categorized as one or both of two of Cattell’s five factors, fluid and crystallized intelligence, the multi-factorial generalized intelligence is the mechanism that overlaps with creativity and can be seen in measurements such as patents. At Amazon, where I worked for so long, patents have been an important aspect of career development at the highest levels. Many of the senior executives have at least one and some of the top leaders have twenty or more. Working as we did during the beginning of retail e-commerce era, patent ideas were a bit like fish in the barrel, but those executives have the extreme fluid intelligence (internally, named “intuition”), and a broad scope of crystallized knowledge, and they use both creatively to transfer ideas between domains and show off their consistently good judgement through high velocity decision-making every day.

Finally, I am thankful for Ritchie’s debunking of “learning styles” in the opening pages of his book because it provided good reasons for the skepticism I have always felt about the concept. Later, as we learned about more modern approaches to intelligence, the differences in intelligence that were caricatured by “learning styles” developed into the modern, more reasonable approach of multi-factor intelligence, for example Sternberg’s triarchic intelligence which I most certainly prefer. My goal is to work in the public high school system where I will encounter many different types of students and parents with different educational backgrounds. After this class, I feel more prepared to apply what I have learned in this course about scaffolding, fluid and crystallized intelligence, and the importance of externalizing creativity to my classroom, lesson plans, and assessment strategies. 

Title: The U.S. Congress Should Expand the TAA Program to Include Technology

Specific Purpose: Explain the technological unemployment exigency and show that income replacement through the TAA Program is the best solution.

​Central Idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.


INTRODUCTION

1. Attention-getter: You might think we cannot automate creativity, but you would be wrong.  Algorithms have been beating the world’s chess and Go champions for years and they are proving to be better than American service workers, too.
2. Demonstrate audience significance: Kelly (2020) Forbes: You probably know one the 60 million people that the World Economic Forum counted as unemployed because an algorithm does their job more efficiently at almost no cost to the business, and you can pass legislation to protect American workers like the US Congress did in the 1960s when facing the same problem but from International Trade.
3. Enhance your ethos: In just over a decade of work at Amazon.com, my algorithms have automated the jobs of tens of thousands of human workers and each time we implemented the change successfully, we earned tax breaks from the government that we invested into new automation for the remaining jobs on our customer service and finance teams.
4. State the central idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.
5. Preview: I will briefly recap the problem caused by technological unemployment before discussing the best solution and a counter argument, and finish by talking about the security and well-being of the nation.

The loss of jobs to technology has a very long history that until recently was good for human workers.

What is Workplace Automation?

1. In 2021, many people don’t realize that Automation has been happening for thousands of years.
   1. Aristotle (2012) Duke Classics, in Politics, written in the 4th century BC, “for if every instrument at command or from a preconception of its master’s will could accomplish its work, obeying or anticipating the will of others… the shuttle would then weave and the lyre play of itself.”
   2. Tech can replicate valuable ideas and processes at very low cost.
   3. This creates convenience for society and wealth for owners of the tech, but it also diminishes the demand for many kinds of labor.
2. The historical fear of jobs loss to tech is called the “Luddite Fallacy.”
   1. In the 19th century Luddites tried to protect their livelihoods by smashing looms, typesetters, and other industrial machines.
   2. They thought that destroying the machines would destroy the threat of tech replacing them, but they thought wrong.
   3. Tech unemployment has been happening for three-thousand years, but it is only recently that human workers lose more jobs than automation creates.
3. Technology advances have tended to favor improved working conditions for humans, productivity gains, and net increases in new jobs, until the last 34 years.
   1. The danger technology presents to the social well-being of society has been growing for over a century.
   2. But according to Dizikes (2020) MIT News: Prior to 1987, jobs lost to automation were replaced by 2% more jobs, but after 1987 jobs lost to automation destroyed 6% of new opportunities.

Understanding why this change has occurred is very important; the game of Go and the Uber Effect are two good examples that will help us understand ‘the why’…

B. Algorithms are more than a replacement for a process because they are becoming more creative than the programmers who make them, and more efficient than the human workers they were designed to enhance.

1. My background in creative writing and poetry gave me a significant advantage when competing with scientists and programmers for jobs to work on projects that required us to build automation.
   1. For me, the writing process is about realizing when a mistake was a creative, positive way of saying something, or seeing the world.
   2. I applied that same approach to building automation.
   3. When many people thought a computer program had failed, I found ways to use what we learned from our mistakes to make the next version even better, even when that was not what we originally intended.
2. Algorithms have been beating the world’s chess and Go champions for years because they are more efficient tacticians, but even more so because they can discover and make extremely creative moves.
   1. About 5 years ago, AlphaGo was the first algorithm to beat a Go grandmaster and it’s move #37 in the 1st game has been widely discussed in machine learning research and blogs.
   2. International observers of the matches noted that AlphaGo’s moves were so original that they intimidated his human opponent; maybe most intimidating when it seemed to be making a mistake.
   3. We’ll never know if move 37 was a software error, a mistake in an otherwise perfect game, or the remarkable insight of a machine unrestricted by human convention, but it won the game.
3. The Uber Effect is a clear example of how an algorithm intended to be a more convenient taxi experience destroyed a more than century-old industry, the NYC cabbies. Goldstein (2018) Forbes:
   1. In 2014 the market was 8% rideshare, 55% rental and 37% taxi and NYC Taxi medallions were worth $1.2M.
   2. In 2018, the Uber effect rocketed rideshare from 8% to 70%, rentals were cut in half but still retained 23%, but most importantly - cabbies were left with only 6% of total rides, an 80% decline.
   3. Even worse, in those 4 years, the value of taxi medallions dropped 90% to just $120,000; I’ll return to this topic later when I discuss the practicality of the solution.

Automation of hailing a ride by Uber and Lyft eliminated the business for 9 out of 10 New York City cabbies proving that Algorithms can be as dangerous as manufacturing jobs moving overseas, yet this is a problem that can be solved much like it has been solved before.

C. The Proposal

1. I am here to advocate for a lost wage replacement insurance program for American Workers.
   1. With great foresight for the coming globalization of business, the US Congress in the 1960s created the TAA program (an acronym for Trade Adjustment Assistance) to protect American Workers who would lose their jobs to lower cost labor markets overseas.
      1. Key terms of the program include full income replacement for two years via direct payments, relocation allowances so workers can move to employment opportunities, and case management services such as career counseling and referrals to support services for food security or childcare.
   2. Stettner (2018) Century Foundation detailed how to expand the TAA program to include jobs lost to automation.
      1. It is easier to add one new eligibility requirement to an already successful unemployment insurance program than it is to build and fund an entirely new program.
   3. Stettner (2018) Century Foundation: “Expand TAA to Include Technology: The Extra T in TAA… This would be a single program in terms of benefits provided but with added qualification rules for technology on top of the current rules for trade… to respond to the challenges of job loss due to technology.”
2. Some who oppose targeted support for unemployment caused by tech cite the TAA programs flaws but it is important to note that the program has been reauthorized three times by both parties in the last 70 years, as recently as 2017 along with a few changes to expand benefits.
   1. The program enabled legislators to grow international trade.
3. Despite evidence that automation destroys 6% of jobs when it once created new opportunities, some ideological groups deny the problem and won’t accept any cost in response to this cause of unemployment.
   1. Cass (2018) National Review: Orren Cass wrote an article in 2016 for the National Review that was republished on many different websites about why he thinks income replacement is a bad idea, stating that it “would only entrench our misconceptions about the relationship between the individual and the state.”
      1. But America already established a relationship between the individual and he States in the form of existing unemployment programs.
   2. He thinks that worker protection programs eliminate an “essential role as the way to earn a living, work would instead be an activity one engaged in by choice, for enjoyment, or to afford nicer things.”
      1. We need to protect all workers from hardships created by jobs lost to tech, and work is so much more than a means to afford nicer things.

The cost of doing nothing is huge and will cause suffering that leads to hasty action by local & national governments.

D. We should understand the impact of expanding the TAA to include Tech jobs.

1. Returning to the Uber example, the New York City cabbies just ended their hunger strike a few weeks ago and the cost of the government support program will be $140,000 per cabbie!
   1. Just 5K cabbies in NYC will cost the local and federal government $750M to account for wages lost to algorithms.
   2. Imagine the cost to support the more than 60M workers who are projected to lose their jobs to automation in the next ten years.
      1. Using the cabbie bailout cost, a bailout for 60M would cost $12 trillion.
      2. We can’t fund that much, but the Trade Adjustment Assistance program because it has proven to be successful.
2. It is easier to add one new eligibility requirement to an already successful unemployment insurance program than it is to build and fund an entirely new program from scratch.
   1. The TAA program proved that it is more effective to deploy capital directly to the worker, instead of through a complex job training or placement program that is managed at a federal level.
   2. Federal legislators struggle to understand what will work best at local levels and should rely on individuals to determine the best path forward and States manage unemployment system already.
3. Reflects our ideals of the power of the American individual as a targeted response to unemployment caused by automation.
   1. Individualism – Americans can figure out how to best use the income replacement dollars.
   2. TAA provides relocation services and direct case management for each employed worker so they can make the best decision based on their family’s circumstances.
      1. Atkinson (2018) ITIF: “Rather than slow down technological disruption… policymakers should focus on doing significantly more to help those who are displaced transition successfully into new jobs and occupations.”
      2. Same as Trade job loss; job training programs are not as effective due to the location of new jobs and the existing skills of the workers who lost their jobs to technology.

In conclusion, let's review and reiterate the main ideas and suggestions we reviewed today during this presentation.

CONCLUSION

1. Review of main points: Technology has encroached on our jobs for millennia, but only recently has it taken more than it gave back. Millions of workers have been affected and millions more will be affected and lump sum, knee-jerk reactions will cost huge pots of money. 70 years ago, the same problem existed for international trade, but the Trade Adjustment Assistance program has allowed the US to invest in Trade knowing that workers would be protected. We should add one requirement for Technology to it so that the program protects workers from jobs lost to automation, too. The problem is equally real.
2.  Reinforcement of Central Idea: Algorithms are destroying American jobs for the first time in history and the US Congress should expand an existing, successful income replacement program to help workers.
3. Final idea: The foresight of the US Congress in the 1960s prepared the United States to manage unemployment caused by trade policies and the cost of labor overseas. We should have that same foresight today and take action to rename the TAA program as the TTAA program because technology is now just as dangerous to American jobs as international trade.

References

• Aristotle - translated by William Ellis (2012) The Politics of Aristotle. A Treatise on Government.  North Carolina: Duke Classics.
• Atkinson, R. D. (2018, February 20). How to reform worker-training and adjustment policies for an era of technological change. How to Reform Worker-Training and Adjustment Policies for an Era of Technological Change. Retrieved November 16, 2021, from https://itif.org/publications/2018/02/20/technological-innovation-employment-and-workforce-adjustment-policies.
• Cass, O. (2017, March 21). Why a universal basic income is a terrible idea. Manhattan Institute. Retrieved November 15, 2021, from https://www.manhattan-institute.org/html/why-universal-basic-income-terrible-idea-8984.html.
• Dizikes, P. (2020, May 5). Study finds stronger links between automation and inequality. MIT News | Massachusetts Institute of Technology. Retrieved November 16, 2021, from https://news.mit.edu/2020/study-inks-automation-inequality-0506.
• Goldstein, M. (2021, June 30). Dislocation and its discontents: Ride-sharing's impact on the taxi industry. Forbes. Retrieved November 16, 2021, from https://www.forbes.com/sites/michaelgoldstein/2018/06/08/uber-lyft-taxi-drivers/.
• Kelly, J. (2020, October 27). U.S. lost over 60 million jobs-now robots, Tech and artificial intelligence will take millions more. Forbes. Retrieved November 16, 2021, from https://www.forbes.com/sites/jackkelly/2020/10/27/us-lost-over-60-million-jobs-now-robots-tech-and-artificial-intelligence-will-take-millions-more/?sh=e02a4501a525.
• Stettner, A. (2018, April 26). Mounting a response to technological unemployment. The Century Foundation. Retrieved November 16, 2021, from https://tcf.org/content/report/mounting-response-technological-unemployment/. 

Module 7 – Teaching and the components of intelligence

1. Perkins Survey (1995): Feuerstein’s IE; deBono’s CoRT; Lipman’s Philos.4kids & Project Intel’
2. Palincsar; and Stone and Reid – improving thinking skills for LD students
3. Expertise – Perkins, Mackintosh; Domain-specific's and General Intelligence's role in expertise
4. Training to develop Cognitive Complexity
5. Brief Survey of findings regarding intelligence research

This was my favorite module for several reasons. First, I am starting to feel acquainted with the major theories of intelligence and more experienced with how they overlap and how they contrast. I read the Ritchie book on vacation last August and was totally unfamiliar with the concepts and references, but it was not until this Module’s reading that encountering the names of education enrichment programs and Luria in Chapter 5, as well the Bell Curve in Chapter 6 had meaning greater than the scope of the passage. Two weeks ago in a discussion that was focused on a single intelligence model in Anthropology class, I compared Sternberg’s triarchic approach to intelligence and the factor analytic approaches. Even though that extra context digressed the discussion further than needed for an evolutionary evaluation of differences between the Homo species and Hominins, it felt good to be comfortable with the material.

​The introduction to Instrumental Enrichment (IE) will be useful when creating lesson and assessment plans as an English teacher. Vygotsky’s approach is already my preference, but the four features of IE are excellent complements for teaching programs designed to promote self-efficacy and positive feedback loops with my students. The zone of proximal development is expanded by IE’s more challenge-focused requirements, to strengthen areas of weakness through information input, elaboration, and engaging assumptions in output through positive self-reflection, as well as through finding a starting point that requires the least prior knowledge. I imagine that scaffolding could be perceived as coddling, or not providing sufficient challenge to students, and these features of IE provide an effective counterpoint. This approach was deepened by the information presented about Cognitive Instruction and Intentional Learning, notably prolepsis and reflective abstraction. One of the test questions for this module reinforced the importance of purposeful goals that are transparent to the student. This is also an important feature of managing at scale, for example, when my direct reports were hundreds or even many thousands of miles away in another country. Actionability of a goal can be directly observed as it is created when all parties engage in a reasonable discussion of the intent of the goal.

When this module turned to a review of expertise, it provided a chance for me to return to ideas I had considered during the study of creativity, notably how eminence was most often the most objective factor used to identify judges of levels of creativity. In this module, expertise is the synthesis of reflective and experiential intelligence much like fluid and crystallized intelligence creates a measure of general intelligence. As a manager of departments about which I knew only the basics and for which I had had no formal training, I can attest that domain-specific knowledge is a strong predictor of professional competence, something sought when investing long hours trying to find the right candidate for a job. Fluid intelligence and the ability of someone to reflect on their domain knowledge so that it can be more easily applied to new scenarios is also a major factor because real-world problems always have plenty of ambiguity.

Finally, I thoroughly enjoyed the statistics and factoids presented in the summary at the end of the lecture notes. Except for the seventh point, they were meaningful reminders and additional context about prior lessons. The correlation data was most meaningful to me because it provides sound basis for the belief that intelligence is most strongly influenced by genetic factors. The data in the sixth point succinctly states the case in favor of genetics.