Precarity and Artificial Intelligence: The Foundations of Modern AI

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  Today's post, however, will begin to explore a particular emerging impact to employment for everyone, whether formally educated or not.  That impact is the impact of machine artificial intelligence on the future of work.

As I mentioned in a previous post, 

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers...This reduction of the total number of laborers can be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work? ...

Today we'll start trying to answer the questions stated above.   And at the outset, I must state clearly that I am not an AI expert, although my technical education has exposed me in a rudimentary way to many of the concepts that will be mentioned in our discussion of AI.

First, let's paint a picture.  One of the original motives for trying to invent intelligent machines was the desire for machines that would reliably do the kind of mundane tasks that humans find to be distasteful or unpleasantly difficult.  This desire actually has a very long history, but was popularized in the science fiction of the mid-20th century.  Think of a kid from the 1950's or 1960's who wished he had a robot that could vacuum the living room carpet or take out the garbage or do homework or shovel snow out of the driveway so that the kid could play without being bothered by parents demanding that the kid himself do the tasks listed above.  What kind of "brain" would the robot need in order to know what tasks needed to be performed and when would that brain know the tasks had been performed to acceptable standards?

The question of the kind of "brain" required was solved by the invention of the first programmable all-electronic digital computer during World War Two.  This computer was itself an evolution of principles implemented in previous mechanical and electromechanical computers.  Once engineers developed digital computers with onboard memory storage, these computers became capable of automation of tasks that had been formerly automated by more crude mechanical and electromechanical means such as relays.  Thus the 1950's saw the emergence of digital control systems for automation of chemical processes at refineries; the 1950's and 1960's saw the emergence of computer-assisted or computer-based navigation for ships, aircraft, missiles, and spacecraft; and the late 1960's saw the emergence of programmable logic controllers (PLC's) for automation of factory processes at industrial assembly plants.

The digital electronic automation systems that have been developed from the 1950's onward have thus formed a key component of the development of modern machine AI.  But another key component consists of the principles by which these systems achieve their particular objectives.  These principles are the principles of mathematical optimization, and they also have a rather long history.

Mathematical optimization is the collection of techniques and methods for finding the maximum or minimum value of a mathematical function of one or more independent variables.  Some of the earliest methods of mathematical optimization were based on calculus.  More complex methods include such things as numerical methods for solving nonlinear differential equations.  These methods were only possible to implement easily once electronic digital computers became available.

The first step in optimizing real-world problems consists of turning a real-world problem into a mathematical abstraction consisting of a function of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  Then optimization techniques are used to find the desired maximum or minimum value of the function.  To put it another way,

"Optimization is the act of obtaining the best result under given circumstances.  In design, construction, and maintenance of any engineering system, engineers have to take (sic) many...decisions.  The ultimate goal of all such decisions is either to minimize the effort required or to maximize the desired benefit.  Since the effort required or the benefit desired in any practical situation can be expressed as a function of certain decision variables, optimization can be defined as the process of finding the conditions that give the maximum or minimum value of a function."  - Engineering Optimization: Theory and Practice, Rao, John Wiley and Sons, Inc., 2009

The function to be optimized is called the objective function.  When we optimize the objective function, we are also interested in finding those values of the independent variables which produce the desired function maximum or minimum value.  These values represent the amount of various inputs required to get the desired optimum output from a situation represented by the objective function.  

A simple case of optimization would be figuring out how to catch up with a separate moving object in the shortest amount of time if you started from an arbitrary starting position.  To use optimization techniques, you'd turn this problem into an objective function and then use calculus to find the minimum value of the objective function.  Since the velocity and acceleration are the independent variables of interest, you'd want to know the precise values of these (in both magnitude and direction) which would minimize the value of the objective function.  Note that for simple trajectories or paths of only two dimensions, adult humans tend to be able to do this automatically and intuitively - but young kids, not so much.  Try playing tag with a five or six-year-old kid, and you will see what I mean.  The kid won't be able to grasp your acceleration from observing you, so he will run to where he sees you are at the moment he sees you instead of anticipating where you'll end up.  Of course, once kids get to the age of ten or so, they're more than likely to catch you in any game of tag if you yourself are very much older than 30!

The easiest AI problems are those that can most easily be turned into mathematically precise objective functions with only one output variable.  Examples of such problems include the following: reliably hitting a target with a missile, winning a board game in the smallest number of moves, traveling reliably between planets, simple linear regression, regulating the speed or rate of industrial or chemical processes, and control of HVAC and power systems in buildings in order to optimize interior climate, lighting, and comfort.

Harder AI problems include machine vision (including recognizing a human face or an animal), natural language processing, construction of buildings, navigating physically complex unstructured and random environments (such as forests), and optimization of problems with multiple objectives requiring multiple objective functions to model.  The machine vision and natural language processing problems are harder because they require the use of logistical regression functions as objective functions, and in order to accurately assign the appropriate "weights" to each of the variables of these objective functions, the AI which implements these functions needs massive amounts of training data.  However, these and other harder problems are now being solved with increasing effectiveness through technologies such as deep learning and other advanced techniques of machine learning.  It is in the tackling of these harder problems that AI has the greatest potential to disrupt the future of work, especially of cognitively demanding work that formerly only humans could do.  In order to assess the potential magnitude and likelihood of this disruption, we will need to examine the following factors:

• The current state of the art of machine learning
• The current state of the art of designing objective functions
• And the current state of the art of multi-objective mathematical optimization.

I'll try tackling these questions in the next post in this series.

Precarity, Late Capitalism, And Artificial Intelligence: Pinocchio's Mischief

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  The two most recent previous posts in this series discussed the fact that there are now more college graduates being produced in our society than there are jobs into which to plug those graduates.  The most recent post discussed why this is the case.  As I wrote last week, 

"...the decline in opportunities for college graduates (along with everyone else) is correlated with the rise in the concentration of economic power in the hands of an ever-shrinking elite.  In fact, I will go even farther and assert that the decline in stable employment for college graduates (even those with technical professional degrees) is a direct outcome of the concentration of economic power at the top of society.

Consider the fact that as of 2015, "America's 20 wealthiest people - a group that could fit comfortably in one single Gulfstream G650 luxury jet - now own more wealth than the bottom half of the American population combined..."  These people therefore have an enormous amount of economic and political clout.  And they have used (and continue to use) that clout in order to turn the American economy into a machine whose sole function is to make them as rich as possible.  The increase in precarity, the casualization of increasing types of employment, and the increasing use of task automation and artificial intelligence are typical of the strategies which these wealthy and powerful people have deployed in order to maximize the wealth they can extract from the American economy while minimizing the amount of wealth they give to the rest of us.  The aggressive expansion of the "gig" economy is another such strategy..."

A basic strategic aim in capitalism is that business owners should maximize profit.  A basic tactic for the achievement of this aim is to maximize profit per unit of goods sold by lowering the cost of production for each unit of goods sold.  Lowering costs can be achieved by attacking the cost of materials, capital machinery, energy, and labor.  In the limit, at the extreme of optimization, this leads to extremely flimsy goods sold for extremely high prices, goods that are produced by extremely poor laborers.

The labor part of this tactic is what we have been discussing in our consideration of precarity.  By making employment casual and temporary, with no fixed covenant between businesses and laborers and no benefits (other than a wage) granted to laborers, businesses have succeeded in driving down the cost of labor.  As mentioned in last week's post, that pressure on labor costs has reached even technical professions requiring a baccalaureate degree or above.  This is leading to an increasingly unsustainable situation in which, for instance, you might spend more than $40,000 to earn a four-year engineering degree - only to find yourself working for an engineering temp agency after graduation!

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers.  If you're the CEO of a large company, the progression of this tactic can be sketched as follows: First, destroy any expectation of stable employment or decent wages among your labor pool.  Then, reduce the actual number of laborers you use.  This reduction of the total number of laborers can occur by a number of means (including working employees to death by giving each employee the amount of work that should normally be handled by two or three such employees).  It can of course also be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work?
• What can AI do and not do?
• What countries are at the forefront of AI deployment in their societies?
• How will AI capabilities likely evolve over the next few decades?
• What effects might AI have on human life and human societies over the next few decades?
• How will AI affect the world of work over the next few decades?

The next few posts in this series will attempt to tackle these questions.  I must warn you that what you'll get in those posts are merely my guesses at an answer.  However, because I want the guesses to be educated guesses, I'm going to need to do some research.  So these guesses might be slow in coming.

Precarity, American-Style: Introduction

Every child had a pretty good shot

To get at least as far as their old man got

But something happened on the way to that place

They threw an American flag in our face

- Billy Joel, Allentown, 1982

This post is a continuation of my series of posts on the spread of economic precarity among the majority of the world's population.  The opening posts in this series explored precarity as a global phenomenon and characteristic of economies in both the developed world and the developing world which are dominated by rampant free-market capitalism.  Subsequent posts described how precarity came to characterize even those societies which withdrew from global capitalism for several decades in the 20th century but which returned to the capitalist fold during the last decade of the 20th century.  It is now time to consider the case of a nation which has been dominated by the "free market" arrangements created by its wealthiest citizens from its founding to the present day - namely, the United States.   It is time especially to examine the late-stage outworkings of those arrangements.  Those outworkings can be summed up as the spread of precarity throughout the United States.  We have looked at other peoples' messes.  It's time to look at our own.

A quick review: the first post in this series defined precarity as "the state of being uncertain or likely to get worse," and, "a situation in which someone's job or career is always in danger of being lost."  That first post also said,

The Journal of Cultural Anthropology describes precarity as ". . . an emerging abandonment that pushes us away from a livable life . . . [It is] the politically induced condition in which certain populations suffer from failing social and economic networks . . . becoming differentially exposed to injury, violence, and death."  The University of Georgia has an article on its "Neoliberalism Guide for Educators" webpage which describes precarity in concrete human terms, starting with the questions "Have you ever or do you currently live paycheck to paycheck?  Do you work 40 hours a week or more and still can't afford rent?"

The University of Georgia webpage cited above describes how precarity was not always a feature of modern American life.  That page describes how under President Lyndon Johnson, the United States began to construct a social safety net that actually worked to bring economic advancement to all Americans and not just the rich.  The page also describes how wealthy business interests organized from the 1970's onward to begin to unravel that safety net in order to protect and expand their dominance over the American economy.  Today's post will explore how the resulting increase in precarity spread throughout the United States.  We will see that, to use a word picture, many of those who thought that they would make out all right by being friends of wolves wound up becoming lamb chops themselves.

Let's consider first a paper titled, "Changes in Precarious Employment in the United States: A Longitudinal Analysis" (Oddo, et al, Scandinavian Journal of Work, Environment and Health, December 2020).  This paper introduces the term "precarious employment" (or PE, as the authors abbreviate it) and describes it as "the accumulation of multiple unfavorable facets of employment quality."   In plain words, PE is the accumulation of those factors in a person's job which make the job more like Hell than Heaven.  The paper measured PE in terms of the following seven variables:

1. Material rewards - that is, the relative adequacy or inadequacy of wages
2. Work time arrangements - that is, how long a person has to work as well as how much control the worker has over his or her own schedule
3. Stability - that is, whether the job has a stable employment contract or whether it is unstable (as in temporary, seasonal, contracted limited-time, etc.)
4. Workers' rights - that is, whether the employer offers benefits such as health insurance or retirement, as well as protection of workers from exploitation
5. Collective organization or empowerment - that is, whether employees are helped or hindered in their attempts to organize themselves for collective bargaining power
6. Interpersonal relations - that is, whether workers have to deal with toxic bosses or bosses who don't allow the workers to control how they do their jobs
7. Training and employability opportunities - that is, whether there is room for growth and advancement in the job

A high score in each of these variables indicated a workplace that provided healthy, long-term employment.  A low score in these variables indicated a workplace of precarious employment.  The authors of the paper measured changes in these variables over time for various populations and geographic regions in the United States from 1988 to 2016.  They then used this data to construct a precarious employment score (PES) for each group and region.  The authors found, unsurprisingly, that the overall PES for the United States increased during that period, rising from 2.96 in 1988 to 3.43 in 2016.  They also found, unsurprisingly, that the PES increased among historically oppressed groups in the United States, such as African-Americans, Latinos, women, and people without advanced education.  However, there were some rather surprising findings as well.  Men showed an 11 percent increase in precarious employment during this time, as compared to a 6 percent increase for women.  Precarious employment also increased significantly among those Americans with bachelors or advanced degrees.  Most surprising of all was the large increase in precarious employment among workers with higher wages.  

As far as regional variations, the American South showed the highest percentage of precarious employment and highest increase in PES.  This is significant because of the way in which state governments in the South have turned their states into attractive places for foreign manufacturers to set up plants - particularly automotive plants.  Yet this strategy has not translated to increased prosperity for the majority of people living in these states.  This may have to do especially with the fact that, starting from the 1970's onward, automotive plants in the United States have increasingly relied on the use of temporary staff to meet just-in-time production targets, as noted in an interview given by Professor Timothy Minchin of La Trobe University in 2021.  Professor Minchin has also written a book about the activities of foreign-owned automakers in the United States.

It is interesting that the beginning and growth of widespread precarity in the United States coincided with the terms of Republican presidents from Nixon onward.  This is especially significant when we examine the presidency of Donald Trump.  The Trump administration used to boast that it had achieved nearly full employment for Americans.  However, what actually happened was that there was a huge expansion of precarious, low-wage work under Trump, whose labor policies classified huge numbers of gig and temporary workers as "independent contractors" without rights to health or retirement benefits or other job protections.  Note that the term "gig worker" includes not only those highly-skilled, college-educated people who can craft a somewhat sustainable life out of present-day flexible, short-term economic arrangements.  It also includes the many, many people who were tricked into working low-skilled menial jobs without benefits for chump change wages, first by FedEx in the 1970's and 1980's, then by Uber, Lyft, Airbnb, and Door Dash, among others in more recent years.  As a result of the expansion of such arrangements, the United States has the highest percentage of low-paid, precarious workers among any OECD economy, as noted by Kathleen Thelen in her paper titled, "The American Precariat: U.S. Capitalism in Comparative Perspective."  At present, around 25 percent of the American workforce falls into the category of low-paid precarious workers.

These facts and considerations are quite naturally a big concern to me, because I am an African-American, and I have had to live my life watching many of my people suffer the malignancy and oppression of American society.  I have also experienced some of that malignancy and oppression myself.  This experience has taught me that the oppression of even one person threatens the peace of everyone who is not yet oppressed, or, to put it another way, allowing injustice against one leads eventually to injustice against all.  This is why I can't look the other way when I see others being oppressed, even when they are not of my "tribe".  But I think that what has blinded the eyes of many white Americans (especially among the privileged) over the last few decades has been the appeals to naked self-interest which have been pitched to them by the masters of the American economy.  So I'd like to return to a theme set forth in the opening paragraphs of this post, namely a description of how precarity has begun to bite even the members of the formerly "middle-class" or "upper middle-class" in recent years.  

Consider therefore a 2019 book by Alyssa Quart titled, Squeezed: Why Our Families Can't Afford America.  I am in the middle of this book right now, and have learned some surprising things, as well as being reminded of things I already knew.  One of the surprising things is how many degreed working female professionals suffer discrimination and career damage simply from the act of having a baby.  Another thing is the link between increasing overall precarity and the increase in costs of child care for working parents.  (Child care and other forms of paid personal care represent one of the fastest-growing segments of the U.S. economy, by the way, although personal care workers receive the lowest wages.)  Yet another thing is how people who once considered themselves well-off find their neighborhoods becoming hostile to them as those neighborhoods are taken over by the super-wealthy - a surprising case of how gentrification hurts even those who thought they were insulated from its effects by being part of the dominant culture.  Case in point: a game between two high school varsity baseball teams.  One was "decently middle-class" while the other consisted of kids from a wealthier nearby suburb.  Each group was cheering their own team, but the wealthier kids - and their parents - began to taunt the kids from the other team by chanting "Lower Average Income! Lower Average Income!" and "Can't your parents afford to feed you?  Can we call Child Protective Services?"  Quart's book also describes the shock and surprise which many formerly smug professionals are experiencing as they witness the dwindling of the value of a college degree in the United States.  We will consider these things in a future post, by the way, as this phenomenon is not just limited to the United States.  As noted in a previous post, Chinese degree-holders are finding out that there are fewer jobs than graduates in the Chinese economy.

As we explore the theme of precarity in the United States, we will consider the mechanisms by which precarity began to appear from the 1970's onward.  We will see, rather surprisingly, the link between these mechanisms and the increasing difficulties faced by small businesses and would-be entrepreneurs over the last four decades.  We will also ask what is to be done about the precarious situation many of us now face.  Here's a hint: I don't believe the answer lies solely in electoral politics or issuing policy recommendations.  While these things can be important, they are not enough by themselves, since they leave the locus of power outside of those communities which are suffering the most from precarity.

Here I speak as a Black man who has studied strategic nonviolent resistance and who has experienced cognitive liberation of the kind which moves me to challenge existing situations of oppression.  My study of precarity in the United States has shown me just how dire the African-American situation is.  But my attempts over the last six or seven years to organize my brothers and sisters for collective action have left me rather frustrated.  So I'd like to ask, How will we begin to change our situation?

First, let's say straight up that passivity and magical thinking will not help at all.  I am thinking of some of the people I have tried to organize and how they have told me that I should join an organization that helps people instead of bothering them.  But my answer to that is to quote from The Little Red Hen.  Others have seen what I was asking them to do and have said in essence, "Well, I don't have the time or resources to help organize my people where I live.  But I know that there are places where we do see Black doctors and lawyers and bankers - I think I'll move there!"  My answer (especially to those who tell me that such a magical place exists in the American South) is to say that there is no "Big Rock Candy Mountain" that they can run away to, and that they are lying to themselves in order to avoid the sacrifices, hard work and potential risks of collective action.

The inescapable reality is that the only thing that will reliably alter our situation is our choice to begin to organize ourselves for collective action.  As Maciej Bartkowski said in his book Recovering Nonviolent History, 

"The guilt of falling into . . . predatory hands . . . [lies] in the oppressed society and, thus, the solution and liberation need to come from that society transformed through its work, education, and civility.  Victims and the seemingly disempowered are thus their own liberators as long as they pursue self-organization, self-attainment, and development of their communities."

Or, to quote from Alex Soojung Kim-Pang,

"Collective action is the most powerful form of self-care."  (Emphasis added.)