Precarity and Artificial Intelligence: What "HAL" Might Do To "Dave's" Future

Note: the title of this post is a nod to an old, rather slow sci-fi movie with a mind-blowing ending that avoided cheesiness while actually being ahead of its time in many ways... 

This post is a continuation of my series of posts on economic precarity.  As I mentioned in my most recent post in this series, we have been exploring the impact of machine artificial intelligence (AI) on the future of  work, whether that work requires advanced education or not.  But perhaps it might be good to start with a more basic preliminary question: what might be the impact of AI on human life in general?  Of course, the answer to that question is dependent on two factors, namely, the kinds of predictions that are being made concerning the development of AI, and the likelihood of those predictions coming true.  Over the last several years, prognosticators have predicted massive disruptions to human life resulting from the massive and rapid development of AI capabilities.  The tone of these predictions has varied between the optimistic and the dystopian.  Let's limit ourselves to the optimistic for now and ask whether we would want to live in a world in which the most optimistic predictions came true.

One of the more optimistic points of view can be found in a book published in 2021 titled, AI 2041:

Ten Visions for Our Future, by Kai-Fu Lee and Chen Qiufan.  Dr. Kai-Fu Lee holds a PhD from Carnegie Mellon University and has founded or led a number of tech companies as well as doing extensive research and writing in the field of artificial intelligence.  Chen Qiufan is Chinese science fiction writer who formerly worked for tech companies Google and Baidu before launching into a full-time creative career.  AI 2041 is a multifaceted picture of Kai-Fu Lee's predictions of the evolution of AI capabilities from now to the year 2041, combined with Chen Qiufan's short stories portraying fictional settings in which each of these predictions comes true.  Among the things which Dr. Lee believes we are most likely to encounter are the following:

• The use of deep learning and big data paired with social media to guide customers of financial products into decisions and lifestyles which have the least risk of adverse outcomes and the greatest chance of net benefit as calculated by an AI objective function.  (See the story "The Golden Elephant.")
• The use of natural language processing and GPT as tools for creating customized virtual "teachers" for children.  (See the story "Twin Sparrows.")
• The use of AI tools for the rapid analysis of pathogens and the rapid development of drugs for emerging new diseases, as well as the use of automation in management of epidemics and pandemics.  (See the story "Contactless Love.")
• The displacement of skilled manual laborers by AI, and the use of AI to create virtual solutions for this displacement which return some sense of purpose to workers who have lost their jobs.  (See the story "The Job Savior.")
• The ways people cope with the likely displacements and disruptions which will be experienced by societies in which having one's basic needs met becomes decoupled from having to work to earn a living. (See the story "Dreaming of Plenitude.") 

Note that I have listed only five of the ten possible scenarios sketched by Dr. Lee.  However, these five are most relevant to the topic of today's post.  It is already becoming possible to design AI-powered virtual "life coaches" to guide people in their life decisions.  (In fact, if you really want to let your bloody smartphone tell you how to run your life, you can find apps here, here, and here for starters.)  However, when using these apps, one must remember that at their heart they are simply machines for optimizing objective functions which have been designed by humans and which have been tuned by massive amounts of human-supplied training data.  Thus these "coaches" will be only as smart (or as stupid) as the mass of humanity.  And they can be made to encode and enshrine human prejudices, an outcome which is especially likely whenever decisions involving money or social power are involved.  This is illustrated in the story "The Golden Elephant."  (For a harder-edged, more pessimistic view of this sort of AI application, please check out the short story "The Perfect Match" by Ken Liu.)

The use of AI tools in medicine for discovery of pathogen structure and rapid drug development is a fine example of the emerging use of machine implementation of multi-objective function optimization.  I truly have nothing but praise for this sort of application, as it has saved countless lives in the last half decade.  For instance, this sort of technology was instrumental in the rapid development of safe and effective COVID vaccines.  However, when we get to the use of AI to replace the kind of skilled labor that has historically depended on the development of human cognitive capabilities, I think we're headed for trouble.  Consider the case of teaching children, for instance, as exemplified by Chen Qiufan's short story "Twin Sparrows."  Teaching in modern First World societies has evolved into the delivery of a standardized curriculum by means of standardized methods to children, and the evaluation of the learning of these children by means of standardized tests.  

Now I know a little about teaching children, as I volunteered for a few years to be an after-school math coach.  And I can tell you that teaching arithmetic to one or a few children requires more than just knowing arithmetic.  It also involves emotional intelligence and the skill of careful observation as well as a certain amount of case-by-case creativity.  We must ask whether these things can be captured by an AI application that has been "optimized" to maximize learning.  How does one measure things like student engagement?  For instance, do we write some polynomial regression function in which one of the terms stands for whether the kid's pupils are dilated, another term stands for whether a kid's eyes are open and looking at the teacher or whether they're closed, another term captures whether a kid is sitting quietly or throwing a fit, etc.?  And what happens when we move beyond a standard curriculum?  How, for instance, do you make an AI "virtual" art teacher?

I won't attempt to answer these questions here, although I will mention that China has already begun to deploy AI in primary school education, as noted in the 2020 Nesta article titled, "The Future of the Classroom? China’s experience of AI in education" and the 2019 article "Artificial intelligence and education in China," which is unfortunately behind a Taylor and Francis paywall.  It will be interesting to see comprehensive, multi-year studies which document whether the use of AI in education is actually living up to its promise.  

But let's say that the deployment of AI in education really does turn out to be effective.  What happens to the human teachers in such a case?  Kai-Fu Lee says in AI 2041 that teachers will still be needed to be confronters, coaches, and comforters.  In fact, this seems to be a rather stock answer given whenever the potential massive occupational disruptions promised by the widespread deployment of AI are mentioned.  We are told that when jobs that formerly required powers of observation, quick assessment, logical reasoning, computational or motor skills, or memorization are taken over by AI, the newly-displaced workers can be retrained as "compassionate caregivers."  But it might be good to confront the fact that the widespread deployment of AI under an optimistic scenario would certainly mean the de-skilling of large numbers of people.  What possibly unforeseen effects would this de-skilling have on the displaced workers even if they were retrained as "compassionate caregivers?"

Consider, for instance, what might happen to London cab drivers if they were replaced by self-driving taxis.  To become a London taxi driver, a person must memorize a huge amount of London metro local geography, then pass a special test administered by the British government.  (From what I hear, you can't cheat on the test by using a GPS!)  All that memorization (especially visual memorization of London streets and intersections) induces strong development of key regions of the brains of aspiring London taxi drivers.  If this challenge is taken away from a London cabbie, he or she will lose that brain development.  Consider also the personnel who comprise flight crews of airliners.  Up to the 1960's, one of the positions on the flight deck of an airliner was the navigator.  But the navigator position was eliminated by autopilots.  So flight crews shrank from four to three people.  But then, further advances in automation eliminated the position of flight engineer.  So now flight crews consist of only two people.  What development was lost in the brains of the navigators when they were replaced by machines?  (What navigational feats are humans capable of when those humans are pushed to their cognitive limits?  Consider for instance how the peoples of Oceania learned to sail between their islands reliably and successfully without needing maps or a compass.)

AI has eliminated not only aircraft navigators and flight engineers, but an increasing number of other degreed professionals including medical radiologists, as well as receptionists, telephone operators, fast-food cooks, waiters, and waitresses.  AI "expert systems" are threatening the jobs of an increasing number of skilled, educated technical professionals, as noted here and here, for instance.  An increasing number of news stories are documenting the ongoing erosion of human labor markets by AI.  It must be asked what will happen to people whose jobs required the development of hard cognitive skills when those skills are replaced by AI.  Preliminary answers to that question are not encouraging.  For instance, the British Journal of Medicine published a 2018 article titled, "Intellectual engagement and cognitive ability in later life (the “use it or lose it” conjecture): longitudinal, prospective study," in which the authors concluded that lifelong intellectual engagement helps to prevent cognitive decline later in life.  There is also a 2017 article published in the Swiss Medical Weekly whose authors concluded that "low education and cognitive inactivity constitute major risk factors for dementia."  In other words, by ceding to AI the hard cognitive challenges which have traditionally been the hallmark of many kinds of paying work, we may well be at risk of turning ourselves into a society of de-skilled idiots.

Ahh, but there's more.  Let's consider the obvious fact that when AI takes over a job, one or more humans is thrown out of work.  Let's consider the response of various politicians to this fact.  For instance, let's consider the rhetoric spouted by crooks like Donald Trump and other Republican Party politicians (as well as their millions of adoring fans) in the run-up to the 2016 election.  Let's also consider the "scholarly" articles, ethnographic studies and books such as Hillbilly Elegy which sought to "explain" the Trump phenomenon.  One of the key assertions of the Trump crowd in 2016 was that the reason why the white American working class was becoming increasingly poor was the threat posed by immigrants (especially dark-skinned immigrants) taking jobs away from "real" Americans.  Thus America needed to build walls - made both of barbed wire and cement, and of policies and legislation -  in order to keep the great unwashed from stealing what "rightfully" belongs to America.  In other words, one of the biggest drivers of the growth of Trumpism was the loss of jobs and income among the white American working class.  But if concern about job losses was really so bloody important to the architects of Trumpism, why is it that they did not utter a single word in protest against the threat to jobs posed by the deployment of AI?  Why is it that NO ONE in the Rethuglican Party nowadays has anything bad (or even cautionary) to say about the use of AI by American businesses?  The silence of the Rethuglicans regarding the disruptions of AI can be explained quite simply.  AI helps business owners increase profits while reducing labor costs.  Thus AI helps the rich get richer.  Also, Trumpism is not and never was about bringing jobs back to the "working class".  It was rather always an expression of collective narcissism.  Thus all the talk about jobs, like all the rest of the rhetoric of the American Right, was and is utter crap.

To be sure, we do need to start having urgent conversations, both locally and on a wider scale, regarding the deployment of machine artificial intelligence in society.  Such conversations need to ask what AI can reasonably be expected to be able to do, as well as asking whether we really need machines to do what AI is promised to do.  If we decide that it is actually in our best interest to continue the massive development and deployment of AI, we need to figure out how to do this in such a way that we maximize the benefits of AI while minimizing our exposure to the potential downsides and negative externalities of AI.  Lastly, we need to start asking whether it might make sense to establish a basic universal income and other social structures which allow the people in our societies to develop their full human potential even in an era of the expanding use of AI.

Precarity and Artificial Intelligence: A Four-Wheeled Reason to be Skeptical about AI Optimism

The most recent post in my series on economic precarity hinted that the wildly optimistic claims of what artificial intelligence can do or is about to be able to do may be a bit overblown.  A case in point just surfaced this week: the Tesla Corporation (and its CEO Elon Musk in particular) are now being investigated by Federal prosecutors about claims made by Musk that Tesla's "self-driving car" AI technology has actually produced cars that drive themselves without any human input.  It seems this claim is not quite true, as "hundreds of crashes and dozens of fatalities" have proven over the last few years.  Musk may soon find himself the target of State-sponsored vengeance - a vengeance carried out by human prosecutors, plaintiffs, judges, and juries instead of robots.  They may optimize their "objective function" to return a guilty verdict.  Could this be the start of a rocky road for Musk ... ?

The "Principled" Boneheads

Today I ran into some members and organizers of the protests against Israeli violence in Gaza.  I had known about the war between Israel and Hamas, and had heard of the extremely disproportionate response of Israel, but I had not been personally involved in any protest action.  And on a certain level, today was no different for me in that my contact with the protesters was entirely a chance encounter that came about because we just happened to be in the same place at the same time.  During my encounter I saw that the protesters had printed a bunch of flyers urging voters in the Democratic primary to write "Uncommitted" in the ballot choice for the Presidency of the United States.

Now I can quite understand public outrage among many people in the United States over Israel's actions in this present war.  I can also understand why many people would characterize those actions as attempted genocide.  The brunt of Israel's violence has fallen on poor Palestinian civilians, especially women and children, and Israel has caused many tens of thousands of casualties among these.  Let me say right now that although I am a Christian and I believe Israel is God's earthly people, I most emphatically do not believe that God has created Israel to be a special pet who gets to trash the other peoples of the earth.  There is no nation on earth that has a right to make itself great by oppressing the powerless.  Therefore to the extent that I can, I am committed to such things as researching the country of origin of the things that are offered to me for sale, so that I can boycott those products which are made by nations that oppress.  That includes boycotting Israel.

However, when protesters in this country begin to urge withdrawing support from the Democratic party as a means of pressuring the Biden administration to withdraw military aid from Israel, I am reminded of how Russian operatives and propaganda organs managed to weaken and depress the Democratic vote in 2014 and 2016 by promoting "principled" spokespersons who pointed out to us all the weaknesses of Barack Obama and of Hillary Clinton.  I am also reminded of how the candidacy of Bernie Sanders weakened the candidacy of Hillary Clinton in 2016.  (Although Hillary won the 2016 popular vote by 2.7 million, evidently this was not enough of a margin to prevent Donald Trump from capturing the White House.  Go figure!)  I am also reminded of how operatives from the Right used mouthpieces such as Umair Haque in 2020 to try to do the same thing to Biden.

Such things as this make me wonder what it is that some of the present protesters really want.  If they really want to put the powerless into a better position to resist the predations of the powerful, they should consider what will happen to the powerless in the United States in the event of a wave of Republican victories in the November elections.  Among the things we are all likely to receive from such victories are the following:

• The continued erosion of the rights of women and dark-skinned ethnic minorities in the United States
• The continued concentration of wealth and corruption among the richest Americans
• The continued impoverishment of the poor and the continued expansion of economic precarity in the United States
• The continued expansion of fascism and the continued development of an American police state
• The continued erosion of American democracy
• The continued expansion of Russian power (including the possible loss of Ukraine to Russia)
• Oh, and by the way: the Republicans will also continue to support Israeli militancy, in case no one noticed.

Given these possible outcomes, why are the antiwar protesters trying to tamper with the 2024 American elections?  I can think of only two possible reasons.  First, they may be incredibly stupid in their idealism.  It is far too easy to make an emotional, yet senseless response to an evil situation.  Then, when one's emotional response turns out to have evil consequences, the person who made the response can try to comfort himself by claiming that he is paying the price of martyrdom.  To such people I say, please go to school and enroll in a crash course in strategic thinking.  Then think of some other way to put irresistible pressure (including economic pressure) on the powers that be in this country to force them to withdraw military support for Israel without engaging in actions that endanger the 2024 U.S. elections.  Just to be clear: I am all for pressuring the powers that be to withdraw military support for Israel as long as Israel continues to attempt genocide against the Palestinian people.

But maybe the attempt to tamper with the 2024 elections is itself an example of fine strategic thinking - although the strategy in question has an actual aim that is very different from the aim which its creators claim they want to see.  In that case, maybe some of the antiwar protesters are themselves being disingenuous.  

Precarity and Artificial Intelligence: Review of Objective Functions, and A Contrarian Perspective

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.  However, the most recent previous post in this series began to explore the impact of machine artificial intelligence (AI) on the future of all work, whether that work requires advanced education or not.  

In my my most recent post in this series, I wrote that the greatest potential for the disruption of the future of work through machine AI lies in the development of machine AI tools that can tackle the increasingly complex  tasks that are normally associated with human (or at least highly developed animal) intelligence.  Such tasks 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.  Today I'd like to amend that statement by saying that there is another potentially massive disruptive impact of machine AI on the future of work, namely, the ways in which the wide deployment of machine AI in a society might condition and change the humans in that society.  I'll have more to say on that subject in another post.  What I'd like to do right now is to take another look at mathematical objective functions and their place in the implementation of machine AI.  But even before that, let's review the two main types of applications we are talking about when we talk about "machine AI".  Also, let me warn you that today's post will move rather deep into geek territory.  I'll try to have some mercy.

As I mentioned in the most recent post in this series, AI applications can be broken down into two main categories.  The first category consists of the automation of repetitive or mundane tasks or processes in order to ensure that these processes take place at the proper rate and speed and thus yield the appropriate steady-state or final outcome.  This sort of AI has been around for a very long time and first came into existence in entirely mechanical systems such as steam engines with mechanical governors that regulated the speed of the engines.  An example of a mid-20th century electromechanical control system is the autopilot with mechanical gyroscopes and accelerometers and simple electronic computers which was invented to guide airliners and early guided missiles during long-distance flights.  Other early electronic examples include the programmable logic controllers (PLC's) which were developed in the 1960's to regulate assembly line processes in industrial plants.  In his 2022 paper titled "The two kinds of artificial intelligence, or how not to confuse objects and subjects," Cambridge Professor Alan F. Blackwell characterizes these systems as servomechanisms, which Merriam-Webster defines as "an automatic device for controlling large amounts of power by means of very small amounts of power and automatically correcting the performance of a mechanism" and which Blackwell himself defines as "...any kind of device that 'observes' the world in some way, 'acts' on the world, and can 'decide' to 'behave' in different ways as determined by what it observes."  

The construction (and hence performance) of servomechanisms can be increasingly complex as the number and type of processes regulated by the servomechanisms increases, but that does not mean that the servomechanisms possess any real native intelligence.  Consider, for instance, a very simple servomechanism such as the thermostat from a heating system in a house built during the 1950's.  Such a thermostat would regulate the timing and duration of the burning of a fuel in a heating furnace, and would most likely consist of a simple switch with a movable switch contact and a stationary contact with the movable contact attached to a bimetallic strip.  Because the shape of the bimetallic strip is regulated by the temperature of the air, when the air temperature drops, the movable switch contact eventually touches the stationary contact, closing the switch and turning on the flow of fuel to the furnace.  We can say that the thermostat "decides" when the furnace turns on or off, but that's all this thermostat can "decide" to do.  You certainly wouldn't want to rely on the thermostat to help you decide what movie to watch with your spouse on a weekend!  Servomechanisms are what Blackwell calls "objective AI" which "measures the world, acts according to some mathematical principles, and may indeed be very complex, even unpredictable, but there is no point at which it needs to be considered a subjective intelligent agent."  In other words, all a servomechanism can do is to mechanically or electronically regulate a physical process on the basis of process measurements provided to the controller by means of physical sensors that sense a process variable.  It can't think like humans do.

The second type of AI is designed to make value judgments about the world in order to predict how the world (or some small subset of the world) will evolve.  In the most optimistic cases, this AI uses these value judgments to generate the most appropriate response to the world which is supposedly evolving according to prediction.  But is this really a native intelligence created by humans, yet now embodied in a machine and existing independently of humans?  A possible answer to that question can be found in another paper written by Blackwell and published in 2019, titled, "Objective functions: (In)humanity and inequity in artificial intelligence."

The value judgments and predictions made by the second type of AI are made by means of objective functions. These objective functions are mathematical abstractions consisting of functions of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  If the purpose of the objective function is to predict the numerical value of an outcome based on historical values of independent input variables, then optimizing the function means making sure that for a given set of historical inputs, the objective function yields an output value that is as close as possible to the historical outcome associated with the particular historical inputs. This ensures that for any set of future possible inputs, the objective function will accurately predict the value of the output.  Two levels of objective functions are needed: the first level, which makes a guess of the value of an output based on certain values of inputs, then a second supervisory level which evaluates how close each guess is to a set of historical output values based on corresponding sets of input values.  The output of this second supervisory objective function is used to adjust the weights (in the case of a polynomial function, the coefficients) of the primary objective function in order to produce better guesses of the output value. 

Objective functions are mathematical expressions; hence, the second type of AI is a primarily mathematical problem which just happens to be solved by means of digital computers.  This also includes the implementation of multi-objective optimization, which is really just another mathematical problem even though it is implemented by machines.  Thus, the second type of AI is really just another expression of human intelligence.  This is seen not only in the development of the objective functions themselves, but also in the training of the supervisory objective function to recognize how close the output of the primary objective function is to the a value that actually reflects reality.  This training takes place by several means, including supervised learning (in which humans label all the training data), and partially-supervised and unsupervised learning (in which the training data is out there, but instead of it being labeled, a human still has to create the algorithms by which the machine processes the training data).  

An example that illustrates what we have been considering is the development of large language models (LLM's) such as ChatGPT which predict text strings based on inputs by a human being.  A very, very, very simple model for these AI implementations is that they consist of objective functions that guess the probability of the next word, phrase, sentence, or paragraph in a string on the basis of what a human has typed into an interface.  These AI implementations must be trained using data input by human beings so that they can calibrate their objective functions to reduce the likelihood of wrong guesses.  Cases like these lead scientists such as Alan Blackwell to conclude that the second type of AI is not really a separate "intelligence" per se, but rather the embodiment and disguising of what is actually human intelligence, reflected back to humans through the intermediary of machines.  The calibration of the objective functions of these AI deployments (or, if you will, the training of this AI) is performed by you every time you type a text message on your smartphone.  For instance, you start by typing "Hello Jo [the phone suggests "Jo", "John", "Joe", but the person you're texting is actually named "Joshiro", so as you type, your phone keeps making wrong guesses like "Josh", "Joshua", and "Josh's" but you keep typing until you've finished "Joshiro"]. You continue with "I'm at the gym right now, but I forgot my judo white belt [the phone guesses almost everything even though you misspelled "at" as "st" and the phone auto-corrected.  However, the phone chokes when you start typing "judo" so you have to manually type that yourself].  You finish with "Can you grab it out of my closet?"  The next time you text anyone whose first name starts with the letters "Jo", your phone will be "trained" to think you are texting Joshiro about something related to judo - or more accurately, the generative LLM in your phone will have determined that there is a statistically higher likelihood that your message will contain the words "Joshiro" and "judo".  Your phone's LLM is thus "trained" every time you correct one of its wrong guesses when you type a text.

Of course, the longer the predicted phrase or sentence or paragraph the AI is supposed to return, the more training data is required.  The boast of the developers of large language models and other similar AI implementations is that given enough training data and a sufficiently complex statistical objective function, they can develop AI that can accurately return the correct response to any human input.  This unfortunately leads to an unavoidable conclusion of the second type of AI: the assumption that the universe, reality, and life itself are all deterministic (thus there is no free will in the universe).  Why? Because the kind of intelligence that can accurately predict how the universe and everything in it will evolve and thus generate the most appropriate local response to the moment-by-moment evolution of your particular corner of the universe can always be modeled by an appropriately elaborate statistical objective function trained on an appropriately huge set of training data.  In other words, given enough data, a statistical objective function can be derived which accurately predicts that your spouse's sneeze at the dinner table tonight will provoke an argument which ends with you sleeping on the couch tomorrow night, and that this will lead to the invention of a new technology later in the week that causes the stock market of a certain country to crash, with the result that a baby will cry on a dark and stormy night five days from now, and that this cry will enable a computer to predict all the words that will be in the novel you sit down to write a week from today...  This is my rather facetious illustration of the "generative AI" of chatGPT and similar inventions.  The fallacy of determinism is that life, the Universe, and reality itself are full of phenomena and problems that can't easily be modeled by mathematics.  Scientists call them "wicked problems."  Thus the claims made about the second kind of AI may be overblown - especially as long as the implementation of this second kind of AI remains primarily dependent on the construction and optimization of appropriately complex statistical objective functions.

Yet it can't be denied that the second type of AI is causing some profound changes to the world in which we live, and even the first type of AI - the implementation of servomechanisms, or the science of cybernetics - has had a profound effect.  The effects of both types of AI to date - especially on the world of work - will be the subject of the next post in this series.

P.S. Although I am a technical professional with a baccalaureate and a master's degree in a STEM discipline, I am most definitely NOT an AI expert.  Feel free to take what I have written with a grain of salt - YMMV. 

P.P.S. For more commentary on ChatGPT and other LLM's, feel free to check out a 2021 paper titled, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Emily Bender and others.  The authors of this paper were Google engineers who were fired by Google for saying things that their bosses didn't want to hear regarding LLM's.   Oh, the potential dangers of writing things that give people in power a case of heartburn...

 

Random Sunday Ramblings, Part 2

As readers can guess, I'm still in the midst of a busy period, and am trying hard to tame my schedule.  So today's post will be short.  But I want to mention that I just finished listening to the latest installment of the podcast "In God's Name: An Unseen Cult."  This latest episode dealt with the abuse perpetrated against women by the fringe group known as the Assemblies of George Geftakys.  It is well known that the Assemblies largely disintegrated after the revelations of the horrible domestic violence perpetrated by George Geftakys' son David and the fact that George and his lieutenants covered up this abuse for over two decades.  But the latest episode of the "In God's Name" podcast also sheds light on a number of cases of domestic abuse among rank-and-file members of the Assemblies that did not at first attract notice because they blended into the toxic cultural miasma of these groups.

It's interesting to note how the Bible was used to justify this toxic culture, as well as to note how extreme even some of these rank-and-file cases became.  I can imagine how much of an aversion to the Bible many women might now have after suffering the long-term experience of a group such as this.  I am not a woman but a man, and yet as a Black man I can tell you that over the last few years I too have struggled at times with the same aversion.  The aversion is triggered by the experience of encountering prominent people behind pulpits who waved Bibles in our faces and said (in so many words, even though they would deny that this is actually what they were saying) "THIS BOOK SAYS that God has given US the RIGHT and the MISSION and the MANDATE to MAKE OURSELVES GREAT by trashing everyone we can get our hands on!  Therefore it is the WILL OF GOD for you who are not of our tribe to willingly submit yourselves to us so that we can turn you into our toilet paper, our vomit buckets, our spittoons, our diapers, our doormats on which we can wipe our feet, our beasts of burden, our crates full of china dishes that we can fling against a wall whenever we need to get our angries out, our furniture that we can kick, our clay pigeons, our punching bags!!!"

Regardless of what white evangelicals might say, this has been the message of white American evangelicalism for far too long.  And my, how prominent white evangelicals (and some nonwhite evangelicals who have drunk the same Kool-Aid) have perverted and bastardized Scripture to justify their message and their treatment of the rest of us.  Eventually, however, even the most passive of victims gets tired of being treated like a clay pigeon.  Which is why I'm not attending church today and haven't attended a church since March 2020.  I still read the Bible, as can be seen from the posts I've written for this blog over the last six or seven years.  But I'm figuring things out for myself.  And I have abandoned treating other people like clay pigeons.  I am now ashamed of the times in which I was guilty of dishing out such treatment to others, especially women.

One last note: as has been mentioned frequently, the Assemblies are by no means the only misogynistic conservative evangelical group to appear in American culture.  According to a recently discovered source, Grace Community Church in Southern California has been obstructing the efforts of women in its congregation to obtain protection from abusive husbands.  Grace Community Church is pastored by John MacArthur, a man who has publicly denied that the Bible contains any mandate for social justice toward the poor and those who are not of the dominant culture.  MacArthur seems to be yet another evangelical doofus who has perverted the Gospel of good news for the poor into a message of dinner time for the rich and powerful.

Introducing a New Podcast - "In God's Name: An Unseen Cult"

Today's post will be short.  I still owe a continuation of my series of posts on precarity.  I'm in So. Cal. right now helping an elderly family member with cognitive decline issues.  Perhaps on the plane ride home I can finish the post on frontiers on artificial intelligence...

But I do want to let readers know about an upcoming new podcast series focused on the experience some of us (including myself) had in the evangelical fringe cult of the Assemblies of George Geftakys.  The podcast is being produced by someone who was born into the cult and who left as a young child along with her family just before the Assemblies collapsed.  In recent years she has applied her university education to analyzing our cult experience and shedding light on the implications of that experience.  The name of the podcast is "In God's Name: An Unseen Cult" and the first episode will be out later this month.  

This podcast is one of several podcasts dealing with evangelical/Protestant cults and groups with cultic tendencies which I have discovered over the last few weeks.  To those former members of the Geftakys cult whose primary focus has been on the Geftakys cult experience, I would just point out that many of the things we encountered there - erasure of personal boundaries, hyper-competitiveness in seeking "ministry" positions, forced communal living, long meetings, excessive busy-ness, and child abuse - have by now spread far and wide throughout mainstream evangelicalism.  Thus there has been a multiplication of  podcasts and related books essays, and news articles which examine such groups as YWAM (Youth With A Mission), Teen Mania (now defunct, and similar to YWAM in its tactics and the trauma it caused), the continuing menace of cultic front groups on college campuses, the proliferation of teachings on child rearing that encourage child abuse (such as the books by Michael and Debi Pearl, J. Richard Fugate, James Dobson, and Gary Ezzo), the continuing menace and harm caused by Dominionism, the prevalence of sexual and domestic violence in evangelical churches, and the excesses of the American "troubled teen industry" - an "industry" which is for the most part extremely lacking in governmental regulation and oversight.

That such groups and phenomena are associated so strongly with conservative white American evangelicalism/Protestantism is not surprising.  These groups and their associated phenomena of asserting abusive control over those whose power of resistance has been taken away are a symptom of a larger problem within American society.  That these groups, teachings, and leaders are proliferating now is a sign of the insecurity of the dominant culture and of its willingness to hold onto its dominant position at any cost.  It is vital to understand how this expression of white American evangelicalism is affecting the broader society, and how both this expression and the larger society will evolve as reality continues to impose limits on the unrestrained exercise of American evangelical power.