#323 Turning Point and a Really Bad-Hair Week for the Donald

28 Monday Jan 2019

Posted by Jordan Abel in Economics, Gov't Policy, Possible Solutions, Societal Issues

Readers: this blog is set in the future (sometime after the year 2020). Each entry assumes there has been a 5th revolution in the US — the Revenge Revolution. More about the Revenge Revolution and author, How the 5th US Revolution Begins and About the Author. Many entries are formatted as conversations. Occasionally I do a “sense check” about the likelihood of a Revenge Revolution. Entry #318 is the most recent “sense check.” One more note — sometimes I write about another topic that does not quite fit the theme of the blog. Those comments are available on the page titled “JRD Thoughts and Comments.”

Regular readers know I try to avoid getting hung up on daily/weekly events. The purpose of this blog is to analyze if and how long-term patterns might contribute to a post-2020 revolution in the US, aka the Revenge Revolution. However, events this past week seem to be beyond the usual “Beltway noise.” The week’s events could help change the trajectory of politics in Washington. “C’mon,” you say. “A heady week but not that heady.” I think that heady. Read on.

Key events during the week: (i) Michael Cohen, Trump’s long-time lawyer and now convicted felon, proposed delaying his volunteer testimony before the House Oversight Committee. One day after the announced delay, the House Intelligence Committee issued a summons for Cohen to appear; (ii) Roger Stone, long-time associate of Trump’s and known dirty trickster for approaching 50 years, was arrested on a number of charges related to the 2016 presidential campaign. More charges are expected; (iii) Trump and Senate Majority Leader Mitch McConnell caved and agreed to end the government shutdown of more than 35 days; (iv) Rudy Giuliani’s continued to disclose suspicious events previously denied. In addition to Rudy’s babbling, an article in the New York Times indicated one or more members of the Trump campaign met with the Russians at least 100 times before inauguration.

A fifth, less publicized event, was the appearance of Paul Manafort, Trump’s 2016 campaign chair, at a hearing to determine if an additional 10 years should be added to Manafort’ s existing prison sentence. The prosecution charged that Manafort intentionally withheld relevant information about sharing data with the Russians. The judge delayed the decision.

The events, if considered individually are interesting, but not necessarily significant. When combined the events, at least in my view, represent a major shift in power in Washington. The federal government is no longer controlled by the Bully-in-the-Oval-Office as Republicans in the House and Senate cowered. Power in Washington took a turn back toward the people.

As a result of pressure from constituents and some Republican senators, Mitch McConnell came out of hiding and convinced Trump that he should agree to reopen the government. The House and Senate then passed bills and Trump signed…with no guarantee there would be a wall at the southern border. While the agreement to open is limited to three weeks…and despite Trump’s huffing and puffing and threats to blow the house down if he doesn’t get his wall…pressure from constituents likely will prevent another shutdown

How do these events tie together? The key seems to be Republicans in the Senate are starting to show some backbone. Until a few weeks ago, Trump had free reign to do whatever he wanted. He felt immune from impeachment because of a Republican controlled House and Senate. Well, no more and no doubt this week Trump felt the noose tighten around his neck.

Start with Cohen. When testifying Cohen has nothing to lose and everything to gain by telling all. There’s a chance Mueller et al will again recommend a shorter sentence. Based on comments from several members of the committee, as much of Cohen’s testimony as possible will be made public.

Next we have Roger Stone. While defiant after his arrest and claiming loyalty to Trump, Stone may end up behaving like many of Trump associates already indicted as a result of the Muller probe – and flip. When the reality of a likely long prison sentence sets in – and for Stone it effectively could be a life sentence according to several former Federal prosecutors – Stone may drop the “never-tell-on-Trump” boast and decide saving the Donald is less important than trying to save his personal life.

The arrest of Stone leaves but a few people in Trump’s inner-circle not indicted. Steve Bannon may be next on Mueller’s list. Bannon will be quickly discredited by Trump, providing incentive for Bannon to flip – if he hasn’t already. The group remaining to be indicted is all in Trump’s family. Junior and Jared look like shoe-in’s for an indictment. And the odds for Ivana are better than 50:50.

So, who in the family might flip? If you’re a Trump family member, covering up for the Donald is high risk. When all the dust settles, especially after investigations and prosecutions by the Southern District of NY, the State of NY, and the IRS…along with a likely plethora of civil suits by condo owners, contractors, etc…there likely won’t be any money left in the Trump piggy bank.

So, if I’m a family member, let me consider my alternatives. If I refuse to cooperate with Muller, I go to jail and when a get out, I’ll probably get little or no money. Or, I can cooperate with Mueller et al and maybe avoid jail time. Mmm, which one should I choose?

As far as Trump, he’s acting like most bullies. When confronted with a tough opponent, the bluster goes away and the bully caves. And for Trump-the-Bully, his nemesis is not some physical tough guy. His nemesis is an older woman (by a few years) who’s raised five (5) kids. The past few weeks Nancy Pelosi treated Trump the same way she probably treated one of her kids, when a two-year old and throwing a tantrum. She hung tough and the kid folded.

What may be the final mental straw for Trump, however, the adoring, brain-washed base of supporters is shrinking. The 35-day government shutdown started shedding light on how Trump was willing to screw over the working class to save face over a physical wall. Many people now understand a Trump-style wall would do significant harm to the environment and offer little protection against illegal immigration and shipments of illegal drugs. Based on reports I’ve read, 85-90% of the illicit drugs enter the US through monitored ports of entry.

While the information from Mueller probe could…and probably should…lead to impeachment proceedings, Trump also faces another hurdle that seems to trump (pun intended) most presidents seeking re-election – “It’s the economy, stupid.”

The US has experienced an exceptionally long period of economic growth. The growth started under president Obama following efforts through the Federal Reserve to kick-start the economy following the financial crisis at the end of the Bush Administration. While the current economy appears strong based on certain indicators – unemployment rate and some increases in wages, e.g. – there are many soft spots.

The problem facing the Trump Administration is how to counter an economic downturn. Normally, monetary policy is the first step – the Federal Reserve lowers interest rates to stimulate borrowing and investment. This option is almost off the table since interest rates remain near historic lows. Fiscal policy follows with an increase in government spending (and the deficit) for such high-employment projects as road building/repairs. Oops, the deficit is climbing while the economy is strong so this is a limited option. Why is the deficit climbing? Because of the Trump tax cut. (I’ll save a longer discussion on the economy for another entry.)

There is a solution. While the solution is an enigma to current Trump Republicans, I have a feeling that over the next few years many of these Republicans will support the solution. What can be done? Raise taxes and redistribute income through a number of different means. And no, the approach isn’t classic socialism. The approach is called Keynesian economics. If you don’t think it works and is necessary for a stable society, then ask your parents or grandparents to tell family stories about what life was like during the Great Depression. If no one in the family has stories, then there are lots of books and movies. I’m not suggesting we’re headed for another Great Depression but there are few options left for countering a recession. (And, FYI, higher taxes did not slow growth in the 1990’s under president Clinton or in the 1950’s under Eisenhower.)

So, what does the Donald do? In the face of all the problems, he resigns to avoid being indicted. Remember, the Donald is a bully. Bullies cut and run when faced with a difficult situation. Even if he doesn’t resign, he doesn’t seek reelection…maybe because his bone spurs start to act up.

What about the Revenge Revolution? Still going to happen or will these events prevent it? Still going to happen. There’s a group that supported Trump that still feels screwed. First they felt screwed by the establishment and now screwed by Trump. If the Democrats in the House can begin to pass legislation that will help mitigate some of the inequities, real and perceived, then there will be significant pressure on the Senate to support the legislation. Such legislation will help mitigate the intensity of the Revenge Revolution.

The Revenge Revolution will be more cultural, although expect some bloodshed. For reference think of the cultural changes in the 1960’s and early 1970’s. We’re going to see another sea-change in society. Lots of issues to address – managing contributions to climate change, reducing income inequity, improving public education for all ages, implementing universal medical care, and more. To get an idea of the changes ahead, all we have to do is look at the mix of incoming members of the House. That group is more like America’s future and that group is going to force the 5^th US revolution and societal changes.

#322 Artificial Intelligence Applied at the Micro Level – Personality Profiles

21 Monday Jan 2019

Posted by Jordan Abel in Personal Stories, Societal Issues, Tech Tsunami

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Occasionally I do a “sense check” about the likelihood of a Revenge Revolution. Entry #318 is the most recent “sense check.” One more note — sometimes I write about another topic that does not quite fit the theme of the blog. Those comments are available on the page titled “JRD Thoughts and Comments.”

This entry is part of a series is about the coming “Technology Tsunami.” The series addresses what might be involved and some suggestions for mitigating and even capitalizing on the opportunity. Entries #321 and #322 are intended to describe AI in more understandable terms, using personal experiences as examples.

The examples in Entries #321 and #322 are “early stage” uses of AI and selected to demonstrate: (i) applications that are easy to understand: (ii) AI-based applications have been around for a number of years; (iii) how AI can be used to increase the effectiveness of “gut-feel” profiling.

Entry #321 addresses how artificial intelligence (AI) could be helpful in certain business decisions – e.g., introducing new products and setting production schedules. Much of Entry #321 discusses an AI application to create clusters of people with certain tendencies — i.e., “birds of a feather flock together.” A cluster includes people more likely to buy a specific type or brand of product. The entry also discusses how over the years the size of clusters has shrunk from zip codes to neighborhoods to households.

Yet, even as the size of clusters has decreased over time, the focus has been on behavior of the group without regard to say behavior of person X or person Y. For reference, think of ads in Facebook or Google…or efforts to sway voters. All those efforts focus on behavior of groups, not individuals. In the bluntness of terms, the advertisers do not care about you as a person as long as their message persuades a certain percentage of the group.

Even though social media platforms and on-line retailers have lots of data about your purchases, the ads are still a game of percentages. Think of these efforts as macro-economics – focus on the behavior of groups and not individuals.

What about behavior of individuals? What about micro-group behavior. When focusing on the behavior of a specifically identified individual, can AI programs be useful…or harmful? The short answer is “Yes” to both.

As I noted a few entries ago and as a reminder, these write-ups are designed for general discussion and not an academic journal or graduate thesis at a university. So please read the entries accordingly. If you cannot let go of your academic bent, then stop reading and go do something else. You can rest assured the data are credible and the approach sound.

Stating the obvious – to have a successful relationship in business or personal life, the relationship must be positive. A positive personal relationship in business does not need to extend to personal life. In fact, one can argue that it is better to keep business and personal relationships separate.

So how does one develop a positive relationship? A simple first step is trying to understand what makes the other person tick. How does he or she approach issues? How does he or she interact with other people? How does he or she determine what’s important?

At the end of Entry #321, there was a lead-in to this entry. In the lead-in I noted that, in general, women seemed much better than men at understanding what’s important/unimportant to another person. With age, many men begin to realize they’ve been “manipulated” by women for many years. If you’re a man…and don’t believe women have “manipulated” you…at some point you will probably realize what’s been happening for many years. Just accept the fact and move on. Just so there is no misunderstanding, most of the “manipulation” I’ve experienced has been positive.

So how do we better understand someone else? Can AI-based programs help?

An AI-based program that I’ve found extremely useful in helping me understand others is Myers-Briggs. A person’s Myers-Briggs personality profile is developed by the respondent answering a number of seemingly simple, but quite insightful questions. Based on my understanding, the answers are then subjected to a series of regressions, which create a personality profile consisting of four (4) categories, or general attributes. The degree or amount of a category trait is noted on a continuum.

For example, one category describes an individual’s preference to be around other people. At one end of the continuum is someone who absolutely loves to be around others (and dislikes being alone) – an “Extrovert.” At the other end of the continuum is someone who strongly prefers being alone and finds being around others discomforting at best – “Introvert.”

The continuum has a mid-point. Those on the say left side of the mid-point are labeled “E” for extrovert. Those on the right side of the mid-point are labeled “I” for introvert. The scale is not binary but relative so some people are more introverted/extroverted than others. While all category scales are relative, in some categories people tend to fall toward one of the extremes. General categories are:

How people interact with others – Extrovert: Introvert
How people gather information – Sensing (more analytical approach); Intuitive (more abstract approach)
How people make decisions – Thinking (fact-based, analytical): Feeling (more emotion based decisions)
How people tend to deal with the outside world — Judging (prefer structure and firm decisions); Perceiving (more open and flexible environment)

An individual’s profile is described by using one of the pair of underlined letters noted above. For example, one person’s profile might be INTP; another’s profile might be ESFJ. (If you want to learn more about Myers-Briggs and/or see what your profile is, lots of information on the web. Good start is https://www.verywellmind.com/the-myers-briggs-type-indicator-2795583. More on the history at the Myers-Briggs Foundation.)

If my experience is representative, one’s profile can change a bit over time or in different situations. For example, in assignments where I’ve been responsible for “blank-sheet-of-paper” kind of projects, I’ve tended to view topics/problems as a set of possibilities. In assignments where I’ve been trying to provide more structure and discipline to organizations, my profile leaned more toward yes/no decisions.

How does one use Myers-Briggs profiles in real-world? A couple of examples.

#1. 1980’s, Buick Motor Division, GM. Soon after being introduced to Myers-Briggs, another manager left and I inherited his department. While I was familiar with most of the members of the staff, I had never been responsible for direct assignments to those staff members.

One staff member had undergraduate and graduate degrees from Ivy League schools. After completing an assignment the person presented a report with recommendations that were about 180^o from what I expected.

My first thought was how someone that well educated could have missed the mark so much. While going through the recommendations we were trying to figure out what went wrong. Rather than pointing fingers, the other person asked, “By the way, what’s your (Myers-Briggs) profile?”

When we compared profiles the answer to what went wrong became clearer. In that job, I was prone to paint the general picture for an assignment and not provide much detail. I was especially careful with this person given the educational background. Too much detail, or so I thought, would be an insult to the person’s intelligence.

When I conveyed my concern about too much detail as an insult, the response was, “Oh, no, I like detail.” Then the person proposed the following solution. When discussing an assignment, I would continue to provide detail until she (which you probably guessed by now) raised her hand, which meant, “I’ve got it. Stop.” We implemented the hand-raising system and it worked wonderfully.

#2. 2015, Energy company based in Houston. In the intervening 25+ years from Example #1, I’d been involved with a range of differing and challenging assignments – large companies, research organizations and start-ups. The Houston assignment was in an industry where I was familiar with the end product but not the production process.

The management team had extensive experience on the field-operations side but needed someone to help set up the financial structure and reporting systems to help the business operate without a large overhead staff. After a few weeks of learning the very basics, I suggested everyone on the management team complete a Myers-Briggs profile. To give you an idea of what I didn’t know about the industry, have you ever known a petrophysicist, let alone know what one does? Well, neither did I. But check YouTube. There’s a video titled “Petrophysics for Dummies”…and it’s very informative.

As usual, some members of the group supported the idea of comparing personality profiles, others grumbled but went along and a few refused. The CEO was probably the most supportive.

As a reminder, we you start comparing personality profiles with others, remember a different profile does not make one person superior to the other. The profile points out differences in the categories described earlier, not skill levels.

When the CEO and I compared profiles, there were marked differences in a couple of key areas. Understanding those differences helped me frame and propose solutions in a way consistent with his profile. While I continued to approach and solve problems in a way I was most comfortable, I understood that to be more effective when presenting to him, I needed to frame the recommendations in a way consistent with his profile. It worked.

These are but two examples of using Myers-Briggs. I have many others. Why Myers Briggs? Aren’t there other approaches to creating a personality profile? Yes. I used Myers-Briggs because it was the first approach I learned and one with the widest range of personal examples.

Is there a downside of knowing an individual’s profile? Yes. “Manipulation” can be either positive or negative. A widely discussed example how profiling a specific individual might be used negatively is Donald Trump. The question raised by many, “Has Donald Trump been manipulated by the Russians as well as some conservative media talking heads?” Whether one leans left or right politically, president Trump’s favorable behavior toward the Russians seems at odds with 70+ years of the post-WWII relationship between Washington and Moscow. Let’s hope the Mueller investigation makes the issue more clear.

But we should not think that Trump is the only person subject to manipulation. Over time, all of us may be targeted individually. As AI programs become more sophisticated and as people convey more answers to personality-profile like questions on their social media posts and/or continue to buy more goods on-line through say Amazon, it will become easier for AI-programs to migrate from targeting a certain percentage of a group to targeting specific individuals.

Minimizing the influence of such targeting will require considerable diligence on everyone’s part. More ideas developing such an approach in an upcoming entry.

Back to personality profiles. If you’ve never completed a Myers-Briggs (or similar) personality profile…or if it’s been a few years…I suggest you get on the web and complete one (see links earlier in this entry). If nothing else, comparing profiles is great cocktail conversation. But I think you’ll find your profile far more useful.

As far as the next AI-related blog entry? Not sure. I need to do some research before deciding. Thanks for your time.

#321 Using AI for Profiling – Birds of a Feather Flock Together

13 Sunday Jan 2019

Posted by Jordan Abel in Causes of the Revolution, Societal Issues, Tech Tsunami

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This series is about the coming “Technology Tsunami.” The series addresses what might be involved and some suggestions for mitigating and even capitalizing on the opportunity. Entries #321 and #322 are intended to describe AI in more understandable terms, using personal experiences as examples.

A widespread use of AI today is what is called “profiling.” Ever notice after you’ve searched something on Google, an ad appears for the product? How does the computer know?

This entry discusses how AI was used to create “profiles” and how those profiles were used in a commercial application. The examples in this entry and the next (Entry #322) are “early stage” and intentionally selected to demonstrate: (i) applications that are easy to understand: (ii) AI-based applications have been around for a number of years; (iii) how AI can be used to increase the effectiveness of “gut-feel” profiling.

The concept of profiling is simple. Profiles are based on the assumption that “birds of a feather flock together.” That is, people with similar profiles have similar behavior.

Of course, not everyone in the flock, or profile group, behaves the same way. But to the user, profiling is not about individuals but about probabilities of member in the group. What percentage of the people in the profile group will behave a certain way? The goal is to create a group, or profile, where there is a high likelihood that members will have a specific desired behavior.

Profiling is not a new idea. Profiles existed for eons before being formalized with computer programs. Further, virtually everyone creates profiles. Most all of us put strangers into categories based on such factors as geographic location, appearance – skin color, hair color, hair style, clothing, etc. – age, education and a host of other criteria. Think back to someone you met, then after you got to know the person much better, said to yourself, “Gee, that person is a lot different from I first imagined.”

As for this entry, the first example seems rather crude by today’s standards. At the time the profiling technique described was considered “state-of-the-art.” Remember an abacus was considered state-of-the-art when introduced.

The time period for this entry is the mid-1980’s, at Buick Motor Division of General Motors, where I’m director of marketing. As described previously, Buick has used AI-programs to improve the accuracy of its sales forecast and to start allowing dealers more discretion when ordering cars. (Reading Entry #320 will provide more context.)

The next logical step to try to continue building market share was helping dealers refine how to order the appropriate number and mix/models of cars. For example, dealers in the Northeast knew smaller cars were preferred, but which ones were likely to sell more rapidly in a dealer’s particular sales area? Same problem with dealers throughout the country.

I do not remember who or how the introduction was made – could have been one of the “crazy phone calls” the staff often accused me of taking – but Buick was introduced to a company called Claritas. At the time Claritas had combined zip code and general demographic data. The results were “clustered” into 40 groups, or profiles. Each profile had general buying information for products ranging from food to wine to vehicles to many other items. Claritas also assigned a descriptive and memorable to each group. Some examples of names of group – “Pools & Patios,” “Furs & Station Wagons,” “Hard Scrabble,” “Down-Home Gentry,” “Blue Blood Estates,” etc.

As I recall Buick was the first auto company to use the Claritas profiling. We introduced the concept at the annual dealer announcement meeting. And then not much happened for several months. Finally I got a call from a dealer who purchased a store in Florida that had gone bankrupt and was in the process of converting to a Buick store.

The call went something like this, “You remember that program you told us about at the announcement meeting? I’ve forgotten the name of the program but do you think it might help me order the cars more effectively for this new store?” We asked for the zip codes he thought most likely to consider shopping buy at his store. Based on the zip codes we suggested a mix of cars he should consider ordering.

About six months later, my wife and I were hosts on an incentive trip for dealers. During cocktail hour one night, the dealer said, “I owe you a drink. You’ve made me a ton of money.” As he told the story, the profiling program had been a major contributor to helping him turn what had been an unprofitable dealership into one that was very profitable. And, yes, I let him buy me a drink…even though drinks were already paid for.

He told his success story to many other Buick dealers and the use of the program became more widespread. What seems like standard marketing procedure now was anything but standard then.

Within a couple of years starting to work with Claritas, Buick developed a variation of an existing car that designed to appeal to a very small audience. Because introducing such a “niche” car in the traditional way would be too expensive – major national tv and print campaign that could eat all the potential profits – we decided on a targeted campaign using the information from Claritas. What was the result of promoting the niche car to selected profiles – “Pools & Patios,” “Blue Blood Estates” and a few others? A very successful, and profitable, introduction.

What’s the status of profiling today? Profiling has migrated from projecting buying patterns based on zip code (5 digit) to neighborhood profiles (9-digit zip codes) to profiles by families to profiles for individuals within the same household. The same philosophy applies – birds of a feather flock together. However, the flock is no longer defined by geography but by attitudes and behavior gathered from information on search engines, websites, on-line purchases and social media platforms. Profiling is still about probabilities – and not individuals – even though the clusters can include specific information about individuals within the group.

What does this migration portend for the future? One of the unintended consequences of profiling seems to be the diminished value of small geographic social groups. When one had more face-to-face interaction with neighbors, it was difficult to simply walk away from people with different opinions. While you might not always agree with your neighbor, one at least tried to be civil because that neighbor would be there the next morning and you might need to rely on him or her for something. Amazon, on-line buying, delivery services, etc. have reduced the reliance for many activities. No longer does longer does one even need to talk face-to-face with neighbors. One can replace face-to-face chats by going on-line and finding a chat room of like-minded people, thereby avoiding having to listen to the neighbor with whom one might disagree.

In the future are we going to continue only to seek others in our profile and therefore become more isolated? Maybe for a few more years…then I’m hopeful the tide will turn. The underlying premise of my blog (www.usrevolution5.com): the US is headed for a 5^th revolution sometime after 2020. I’ve labeled revolution as the Revenge Revolution. One societal change that I think will result is a return to neighborhoods. Some groups and communities have maintained active neighborhoods, but far too few. What I’m hoping evolves from the Revenge Revolution is a sense of cohesion among neighbors.

Yes, post Revenge Revolution you’ll still be able to use your smart phone and order on-line. At the same time, people will become more aware of and concerned about others, especially those in their neighborhoods. Maybe naively, this awareness will help neighborhoods begin to have the feel more like the 1950’s – not quite like Wally and the Beaver but a lot closer than today. And, no, in my view fences do not make good neighbors.

(In the next entry, a discussion about how AI-developed personality profiles can be extremely useful in dealing with others. Of course, women have used this approach for centuries. Men are still in the learning phase.)

#320 Personal Experience Developing AI and Implications for Skills and Employment

08 Tuesday Jan 2019

Posted by Jordan Abel in Education Issues, Gov't Policy, Societal Issues, Tech Tsunami

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Occasionally I do a “sense check” about the likelihood of a Revenge Revolution. Entry #318 is the most recent “sense check.” One more note — sometimes I write about another topic that does not quite fit the theme of the blog. Those comments are available on the page titled “JRD Thoughts and Comments” as well as “Tech Tsunami”, which has more articles about how technology might affect US…and add a dimension to the Revenge Revolution.

Background to Technology Tsunami series focuses on how implementation of technology may change the family earnings structure in the US.

In “Technology Tsunami” (Entry #319) I noted that with the increased use of artificial intelligence, many current workers will need to increase skills in order to remain employed. But just what is artificial intelligence? And how can it be used? To make AI more concrete and less abstract, thought it might be interesting to allocate the next couple of entries to describing some personal experience developing AI and what happened as a result.today

First, let’s go back to define just what constitutes artificial intelligence, or AI? (Readers, please keep in mind this is not an article for an academic journal. The article is aimed at trying to help the general populace understand more about what AI is and how it might affect the workforce.)

The term “artificial intelligence,” which was first used in the 1950’s, seems to be applied to an ever-increasing range of computer-based applications. Much of AI we hear about today has been developed by applying to very large data bases sophisticated multiple regressions (regressions look for an association between one action/word and another). The algorithms that result become the foundation for software to support an AI application. What has expanded the use of AI is the availability of very large databases and much more computing power. However, as demonstrated by this example, a useful and effective AI program can be developed without an overly large database and/or staff.

A question associated with AI, “When AI is implemented, will people be replaced?” Yes, but people have always been replaced with the introduction of new technology. Farm hands were replaced by tractors and mechanical harvesting equipment. The printing press replaced scribes. The telegraph replaced the Pony Express. Trains replaced stage coaches. Cars replaced buggies…and endless other examples.

In the current wave of AI, the jobs that seem most vulnerable in the near-term are ones that involve repetition. Jobs where running a software program or using robot could perform most or all of the task. Such jobs might be assembling parts, loading/unloading shelves, providing certain types of information (clerks, including law clerks could be replaced by a more sophisticated Siri, for example), completing forms or completing some basic analysis (proof reading, financial analysis, etc.), steering vehicles and similar jobs.

The list of vulnerable jobs is quite lengthy and includes a considerable number of white-collar positions. For example, when General Motors announced in fall 2018 the intent to close five plants in the US/Canada, more white-collar workers were affected than assembly workers.

OK, how about a real-world example. In 1980…yes, that was many moons ago…I transferred to headquarters of Buick Division of General Motors. One of the staffs I managed was responsible for forecasting sales – short and long-term. The short-term forecast – 180 days – was used to set production schedules at assembly plants and suppliers.

When I arrived, the accuracy of the forecast was abysmal. Even though Buick had been in business about 75 years, it was not uncommon for forecast sales for the current month to miss actual sales by 30-40%…sometimes 50%. Such a variance made it extremely difficult to manage inventory. The forecast/actual discrepancies also caused frustrations with Buick dealers because arrival dates for cars ordered varied widely from the original schedule, which in turn frustrated customers.

To increase the accuracy of the forecast, we developed an application of AI. The AI-based forecast consisted of three key estimates: (i) industry sales; (ii) mix of sales by category – % small cars, % mid-size cars, % full-size, % SUV’s, etc. – within the industry; (iii) Buick % share within the general categories.

Unlike today, at the time most assembly plants were limited to a few models with little variation in size. Further, changing the production mix at an assembly plant could be time consuming and costly.

Buick’s solution to this dilemma (and common in the industry) was to “force” the dealers to take the mix of cars produced. Further, there was little recognition of differences in consumer preference by region of the country. Dealers in New England, where smaller cars were preferred, would end up with mix of small/large cars very similar to the dealers in say Texas, where larger cars were preferred. “Encouraging” dealers to take the production mix required the field staff to spend considerable time with the dealer and often involved some type of costly incentive – free financing, extra cash per car, etc. Dealers would then have to try to steer customers to these “unwanted” cars.

The solution to fixing the problem was conceptually simple: (i) a more accurate forecast; (ii) allowing dealers to order what cars they wanted. Improving the accuracy of the forecast was the critical first step. Doing so required building a math model that would predict more accurately upcoming changes in demand.

Previous sales forecasts had been based on changes in the rate of actual sales. Basing the forecast on “lagging indicators” – sales the past few months – is akin to trying to drive a car by looking only in the rearview mirror. Doing so reduces one’s speed and increases the chance of making a serious error. The previous method of forecasting was always “catching up” to changes in demand rather than being ahead of the curve.

Developing the AI model was remarkably easy – or so it seems now. We ran regressions of historical sales data for the industry as well as Buick. Fortunately, the auto companies had been reporting monthly sales for many years, so the data base was credible. The results of the regressions yielded useful, seasonal patterns. We also analyzed the shift in mix of sales over time. This helped determine if sales of smaller cars were increasing faster or slower than say mid-size or luxury cars. Another task was estimating how many people were switching from cars to what were then early-version SUV’s.

Finally, we had to determine Buick’s likely share of each category. At the time the overall car market was shifting to smaller cars. While Buick had competitive smaller car entries, it was more successful in larger cars. The effect of the shift in consumer preference was profound. Even though in a given month Buick could gain in market share in every major industry category compared to the previous year, that same month could show Buick’s overall share had declined compared to a year ago. That phenomenon was always fun to try to explain. “Yes, we gained market share in every category…but, no we lost market share overall.”

Within about one year of starting the AI model, the US industry experienced a major economic downturn and vehicle sales took a nosedive. The AI model helped Buick management begin to make more informed decisions about setting production schedules and marketing plans. With the implementation of the AI-model, the accuracy of the forecast improved markedly. Rather than a variance of 30-40% between actual and forecast for a given month, the variance fell to less than 5%. The improvement helped smooth production schedules, reduce short-term layoffs and/or overtime at Buick and suppliers and made lead-times for deliveries to dealers much more accurate.

The increased forecast accuracy allowed Buick to migrate to what is called a “free-expression” forecast and production schedule. Dealers were allowed much more freedom to order the number and model of cars they wanted.

The decision to migrate to “free-expression” forecast/production caused great angst among staff members tied to the old “dealers-will-order-what-we-tell-them” system. In the end, however, most everyone became a convert because the overall production volume and mix were about what the dealers wanted.

Other benefits of the AI forecast model? The field staff was able to spend more time helping dealers with marketing programs, working on customer satisfaction and finding ways to improve profitability. The dealers then started to order more cars from Buick because the turnover rate improved. In the three-year period following implementation of the AI model, Buick increased market share more than any other manufacturer, domestic or foreign. While not all the gain in market share can be attributed to the AI model, the number of new products Buick introduced during the same period was limited, so most of the gain in market share came from “non-product” activities.

What happened to employment? Buick reduced the number of field offices from 26 to 20. Buick also started a call center to increase contact with dealers located outside urban areas. The non-urban dealers still received some personal visits, but less frequently.

Use of AI also changed the skills required of the office staff. To be effective in the new environment, staff members needed more skills in math, statistics, economics and marketing. If today’s computing power were available then, we could have cut the staff in half, possibly more. Even skills of and the number of senior managers would have been affected. At the retirement party of a key sales executive, who’d grown up in the days of gut-feel and seat-of-the-pants forecasts, the retiring executive told me – after several drinks – “I never understood what you were talking about, but I trusted you.” I appreciated the compliment but was a bit taken aback by the admission.

Does this example help us look ahead for what might happen when more AI is implemented? I think so. What did this rather simple application of artificial intelligence help Buick accomplish?

+ Increased sales
+ Increased market share
+ Increased profits
+ Increased customer satisfaction (dealer and buyer)
– Reduced employment
– Higher skills required of employees

If you’re a shareholder and/or your compensation is tied to profits, you will view the results of implementing the AI program as positive. If you’re an employee whose job was eliminated and/or you were unable to learn the additional skills required, you will view the AI program as negative. The inherent conflict between perspectives, unless we quickly start to manage more effectively, will likely be another contributing factor to the Revenge Revolution.

(In the next entry, another real-world example of using AI – an early application of consumer profiling. While the profiling was not as sophisticated as done today by Google, Facebook, Amazon and many others, the effort allowed Buick to spend marketing dollars more effectively. We’ll also address why it is important that the output of AI programs is understood and trusted. )

#319 Technology Tsunami Headed toward US Shores

01 Tuesday Jan 2019

Posted by Jordan Abel in Causes of the Revolution, Economics, Gov't Policy, Possible Solutions, Tech Tsunami

≈ 4 Comments

Background to Technology Tsunami Series. Thought it might be worthwhile to take a break from all the craziness in Washington and discuss other issues that likely will contribute to the Revenge Revolution. A key issue that seems to be getting less attention than it deserves….maybe because of all the noise emanating from the Trump White House…is how the implementation of technology will change the family earnings structure in the US.

We’ve seen some of the changes already, with the reduction in manufacturing jobs and the stagnation of wages for a large segment of the population. In my view the changes so far are just a small taste of what is to come. The next several blog entries…and I don’t know how many at this point…will focus on what I’m labeling the coming “technology tsunami.” The first of the entries, which follows, is a bit long but tries to set the stage.

There are already numerous early warning signs of the tsunami. An example – the announcement by General Motors in November 2018 of its intent to close five (5) plants in North America. Another warning sign is a story in the New York Times about a robotic arm playing the piano. While a robot playing a piano may seem like a bit of a novelty, think about the implications. The more dexterous robots become, the more robots can perform tasks of people who are highly skilled. Robots in warehouses and welding or painting in cars/trucks are commonplace. Those tasks are fairly straight forward compared to cooking or performing surgery or a host of other tasks.

As noted in this entry, over the centuries societies have coped with implementation of new technologies. Some societies have adopted new technologies and succeeded; others did not adopt new technologies and fell behind. An example — in 1910, GDP per capita in Argentina was about 80% of US GDP per capita. By 2010, 100 years later, GDP per capita in Argentina had fallen to about 30% of US GDP per capita.

Adopting successfully is very difficult. There are a couple of interesting books about adopting new technologies that we’ll discuss in a later entry. For now let’s get started. As you read, keep in mind how the disruption caused by adopting new technologies might compound societal problems currently facing the US. Numerous factors point to another revolution in the US – the technology tsunami could accelerate the Revenge Revolution and make it worse. And, yes, Mrs. Lincoln, enjoy the play.

Entry Begins

After General Motors announced plans to close five (5) plants in North America (November 2018), I was asked by several friends and colleagues for my opinion of the merits of the decision. While I had no inside information, based on my experience at GM and additional analysis, I concluded GM made the correct decision and should be congratulated.

To explain my logic in more detail, I wrote a couple of informal articles and published links on Facebook. The articles included the term “technology tsunami,” which I thought might help explain some of GM’s rationale for closing the plants…and why GM’s decision might portend what’s ahead for other companies. (GM had additional reasons for the closings. Links to articles on Tech Tsunami page.)

Reaction to the term “technology tsunami” seemed to beg for more explanation. So, here goes. I selected the term “technology tsunami” because the characteristics of a tsunami seemed to be a good proxy for how the wave of artificial intelligence (AI), increased use of robots, implementation of the blockchain, and other technologies will affect employment in the US. The effect will not be limited to the manufacturing and some service sectors but include many white-collar professionals (GM, for example, laid off more salaried white-collar staff, than hourly manufacturing workers.)

First let’s look at the sequence of a tsunami. The start is often an earthquake or volcanic eruption deep in the ocean. The energy from that quake is transferred in the form of a series of powerful ocean waves. In the open ocean, the change in the wave pattern caused by the earthquake is not necessarily apparent. To the naked eye, tsunami waves appear relatively normal.

The strength of the waves becomes more apparent as waves move closer to shore. As the waves start to come ashore, the waves are compressed. The more gradual the slope of the shoreline, the more compression.

And there is not just one wave that is compressed and hits the shore, but a series of waves. The waves are powerful and of such height that virtually everything at or near the shoreline is completely destroyed. The waves continue inland, causing significant damage. A tsunami usually is more powerful and destructive than the surge associated with a hurricane.

An usual characteristic of a tsunami is how it affects the waterline preceding its arrival. As the tsunami gets closer to shore, the water at shore’s edge recedes. The shoreline looks as if there is an exaggerated low tide. This phenomenon might last several minutes. Then, the waterline changes quickly and drastically as repeated high and powerful waves come ashore, destroying virtually everything.

With that picture in mind, let’s examine how a technology tsunami might affect employment in the US. In my view, the earthquake has already occurred that will cause the technology tsunami. The energy from that quake has been transferred to form of a series of large and destructive waves. And those waves are headed toward the US shore. Warning signs of the tsunami are becoming more evident at the shoreline as the waterline has begun receding.

The US shoreline is filled with people. Many at the shore still work in manufacturing and service industries. However, few at the shore seem to understand the implication of the receding water line and even fewer take action to avoid the pending disaster. As the waves roll closer to shore, the beach remains filled with people.

In the next few moments – for this analysis consider next “few moments” as next “few years” – the pending disaster becomes apparent. The waterline begins moving ashore rapidly as the first of a series of giant waves becomes visible. The people at the shore – those with limited education and skills – try to escape, but it is too late and waves overwhelm them.

The powerful waves continue inland, destroying many long-standing structures, once thought invincible. Much is lost and chaos ensues for those who survive.

Am I overreacting to the potential impact of a technology tsunami? Is a technology tsunami even possible? Or, as a couple of people have suggested, am I being like “Chicken Little”?

My concern about a technology tsunami has less to do with whether AI will become smarter than humans and more to do with the potential impact on the stability of society. How many lower-skilled, semi-skilled and even skilled blue and white-collar jobs will technology replace?

Trying to stop implementation of technology is foolhardy. Depending on when such a stop-technology approach was implemented, today we might be travelling by horse and buggy and living without electricity, telephones, tv/radio, computers, internet, etc.

And yes, I agree that societies have survived major technology disruptions in the past. But transitions to new technologies have rarely, if ever, been smooth. Even worse, countries that did not transition to new technologies became also-rans.

During the technology tsunami, what is likely to happen to societal stability in the US? How will people react who are replaced by technology? As middle-class jobs continue to be eliminated…and many new jobs are at lower pay, if available at all…will people sit idly by? (When formulating your answer don’t be misled by the unemployment rates in recent months. Look at constant-dollar median incomes over time compared to GDP per capital. Income has not kept up with productivity. Also significant wealth has transferred toward the very top. The longer-term trend is a much smaller middle class with less wealth accumulation.)

If a technology tsunami seems possible, then what are we…societal we…doing to prevent a likely social upheaval that follows the tsunami? As best I can tell, we are doing nothing of substance. Policies of the Trump Administration seem to be focused on preventing adoption and even overturning technology rather than planning how to manage the transition.

In a way, the logic for why we should prepare for a technology tsunami is similar to the logic of why we should make efforts to prevent further global warming. Who’s right about the cause of accelerated global warming does not matter. If global-warming deniers are correct and man has contributed virtually nothing to global warming, the consequences are the same…and the consequences are not good. By doing something, then there’s a chance to reduce the negative effects.

Since we have a good idea of the effect of a technology tsunami, how do we start preparing? Maybe the first step should be to look at the 1930’s. In response to widespread unemployment (at least 25%), reduced net worth among most families, and no clear prospect for an economic turnaround, FDR and Congress implemented programs to create jobs. Creating jobs had a twofold effect: (i) putting money into people’s pockets so they could begin buying again; (ii) allowing families to regain self-respect.

One can argue about the efficacy of specific New Deal programs. However, there should be little argument that these programs helped bring stability back to US society.

Part of the New Deal not often discussed is the effort to increase participation in public education. During the 1930’s, many grammar and high schools were built and students encouraged to complete high school.

The efforts resulted in a sharp increase in the percentage of the population graduating from high school. The increase in percent graduating from high school continued until the 1970’s when the rates plateaued.[1]

Emphasis on education continued after WWII with the GI Bill of Rights and then with availability of low-cost loans encouraging more students from lower- and middle-income families to attend college.

The lesson of these programs for today? Existing and emerging technologies require more math/analytical skills to utilize capabilities of the technologies. With the need for more math/analytic skills…and the risk of becoming an also-ran country by not adopting the technologies…what actions do we take? How does US society get more people educated, especially those on the shore unaware of the pending technology tsunami?

Following are some ideas. You’ll likely look at the list and say, “What’s so innovative about the list? I’ve heard these ideas before.” And, you’re right. The ideas are not new…but you know what? We’re not implementing them, and in some cases we seem to be regressing.

The list is intended to start the discussion:

Help society understand that expenses for public education are investments, not merely costs. Investments may take time to payback but result in a benefit that spans generations.
Increase pay for…and respect for teachers. Make the qualifications and salaries for teachers competitive with, and possibly slightly above, the private sector.
Reinstitute more technical training in high schools. Almost everyone agrees not everyone is suited for college. Not attending college does not mean one does not have valuable skills. Far from it. The public schools should provide everyone an opportunity for training in how to use, leverage and maintain technology skills. At one time “technical training” was common in high schools. Time for it to return.
Make loans for college affordable with a provision to “earn-out” the loan over a reasonable period. Unlike today, make compliance for the earn-out provision easy to understand and execute. Provide assistance to the participant – not everyone is an expert at filling out government paperwork. Encourage people to become teachers. Don’t discourage them with onerous penalties for slight mistakes in completing paperwork.
Cut back, or eliminate private charter schools. Yes, all organizations need fixing over time. Public education is no exception. But charter schools are not necessary to fix problems in public education. Charter schools destroy the very foundation of public education…and operate with far less accountability. The trend toward charters needs to stop and charters eliminated.
Create meaningful education programs for older workers. The claim by some that “I’m too old to learn” is an excuse, not a reason. My experience has been many older people are embarrassed to ask for help. When assistance is framed the right way, it is rare that someone turns down the opportunity to learn. We…again societal we…need to be flexible in how we approach teaching students, whether the student prospect is in grammar school or a grandparent.
Implement meaningful education programs and works-skills programs in prisons. Incarceration is incredibly expensive. While different studies include different amounts for overhead and other costs, the least amount of cost per year to incarcerate someone is roughly the same as tuition, room and board at a state university. In many studies, the cost is multiples higher than tuition, room and board. Incarceration without rehabilitation is wasted money. Educating prisoners and having prisoners do meaningful work while incarcerated seems to be “common sense.”

How do we implement some of these ideas? More in the next article. Stay tuned.

[1] 120 Years of American Education: a Statistical Portrait, US Department of Education, 1993.

Links to articles re GM Plant Closings

usrevolution5

~ USA Headed for a 5th Revolution! Why?

Monthly Archives: January 2019

#323 Turning Point and a Really Bad-Hair Week for the Donald

#322 Artificial Intelligence Applied at the Micro Level – Personality Profiles

#321 Using AI for Profiling – Birds of a Feather Flock Together

#320 Personal Experience Developing AI and Implications for Skills and Employment

#319 Technology Tsunami Headed toward US Shores