"lead reservationist"

 Under federal law, employers are required to pay time-and-a-half overtime to salaried workers after 40 hours if they make about $35,500 or less.Credit...Scott McIntyre for The New York Times

A couple of years ago, yours truly wrote blurbs about Middle Management, the modern-day equivalent of purgatory as one has to constantly prove one's worth to upper management in order to hold onto one's job, a situation becoming all the more tenuous with the advent of AI. With this being said, the artful use of language and the skill set of assigning management status to workers who are not managers pays off as management jobs don't include overtime pay as one gets paid by salary, not by hour. 

Dr. Cohen and his co-authors estimate that the practice of mislabeling workers as managers to deny them overtime, which often relies on dubious-sounding titles like “lead reservationist” and “food cart manager,” cost the workers about $4 billion per year, or more than $3,000 per mislabeled employee.

And the practice appears to be on the rise: Dr. Cohen said the number of jobs with dubious-sounding managerial titles grew over the period he and his co-authors studied.

Experts say the denial of overtime pay is part of a broader strategy to drive down labor costs in recent decades by staffing stores with as few workers as possible. If a worker calls in sick, or more customers turn up than expected, the misclassified manager is often asked to perform the duties of a rank-and-file worker without additional cost to the employer.

When companies are financially strapped or in low-wage industries like retail and fast food, they are more likely to misclassify regular workers as managers, a recent report found.
Credit...Max Whittaker for The New York Times

The fix is in.

Again ... it's all about the money.

Middle management ...

Metropolis, the Fritz Lang masterpiece, depicts a futuristic urban dystopia and follows the attempts of Freder, the wealthy son of the city master, and Maria, a saintly figure to the workers, to overcome the vast gulf separating the classes in their city and bring the workers together with Joh Fredersen, the city master. Fast forward from 1927 to 2020 and a new, automated dystopia looms in an excellent The Verge essay titled How Hard Will Robots Make Us Work?

To whit ...

The robots are watching over hotel housekeepers, telling them which room to clean and tracking how quickly they do it. They’re managing software developers, monitoring their clicks and scrolls and docking their pay if they work too slowly. They’re listening to call center workers, telling them what to say, how to say it, and keeping them constantly, maximally busy. While we’ve been watching the horizon for the self-driving trucks, perpetually five years away, the robots arrived in the form of the supervisor, the foreman, the middle manager.

These automated systems can detect inefficiencies that a human manager never would — a moment’s downtime between calls, a habit of lingering at the coffee machine after finishing a task, a new route that, if all goes perfectly, could get a few more packages delivered in a day. But for workers, what look like inefficiencies to an algorithm were their last reserves of respite and autonomy, and as these little breaks and minor freedoms get optimized out, their jobs are becoming more intense, stressful, and dangerous. Over the last several months, I’ve spoken with more than 20 workers in six countries. For many of them, their greatest fear isn’t that robots might come for their jobs: it’s that robots have already become their boss.

It gets better.

The worker who used Cogito, for instance, had only a minute to fill out insurance forms between calls and only 30 minutes per month for bathroom breaks and personal time, so she handled call after call from people dealing with terminal illnesses, dying relatives, miscarriages, and other traumatic events, each of which she was supposed to complete in fewer than 12 minutes, for 10 hours a day. “It makes you feel numb,” she said. Other workers spoke of chronic anxiety and insomnia, the result of days spent having emotionally raw conversations while, in the words of one worker, “your computer is standing over your shoulder and arbitrarily deciding whether you get to keep your job or not.” This form of burnout has become so common the industry has a name for it: “empathy fatigue.” Cogito, in an ebook explaining the reason for its AI, likens call center workers to trauma nurses desensitized over the course of their shift, noting that the quality of representatives’ work declines after 25 calls. The solution, the company writes, is to use AI to deliver “empathy at scale.”

Read the essay in its entirety to see why Metropolis was prescient to a fault.

@ the crossroads ...

Metropolis - The year depicted in the film, 2026

We are Metropolis, where tech rules whether we like it or not. AI is now starting to do the How many monkeys are required to write Hamlet? theorem as code is the next big thing for AI to incorporate at the expense of coders but we already know that, right?

To whit ...

Over two weeks and nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the AI model agents reportedly produced a 100,000-line Rust-based compiler capable of building a bootable Linux 6.9 kernel on x86, ARM, and RISC-V architectures.

Carlini, a research scientist on Anthropic’s Safeguards team who previously spent seven years at Google Brain and DeepMind, used a new feature launched with Claude Opus 4.6 called “agent teams.” In practice, each Claude instance ran inside its own Docker container, cloning a shared Git repository, claiming tasks by writing lock files, then pushing completed code back upstream. No orchestration agent directed traffic. Each instance independently identified whatever problem seemed most obvious to work on next and started solving it. When merge conflicts arose, the AI model instances resolved them on their own.

 

From art & video to law to ... middle management, the push to automate to enable big business to cut costs no matter the cost is in full swing even though the process to do so has been marginal at best as 95% of all ai installs to improve business practices fail.  Question, if you lay off people en mass, will the fired be able to buy your stuff? I thought so.

Galloping horse, animated using photos
by Muybridge (1887)

In looking at this animation depicted as a set of stills, the question to ask is, at any given time,
are all the feet of the horse off the ground? 

Muybridge's The Horse in Motion, 1878

This is where we are at regarding AI.

Riffing with Gemini ... with small edits

2026: The Year the Shadow Caught Up

In 1927, Fritz Lang’s Metropolis premiered, projecting a dystopian fever dream onto a specific date: 2026. Today, we are no longer watching the film; we are inhabiting the set. We live in the verticality Lang envisioned, where the distance between the "Thinkers" in the towers and the "Hands" in the depths is measured in the silent extraction of our digital sovereignty.

The machines have become invisible—submerged in "Nuclear" data centers that churn through the planet's integrity to maintain a "Hollow" surface. We are at the crossroads of the Moloch, where technology is an end in itself, demanding the sacrifice of the human pulse for the sake of efficiency.

As we meander through this centennial echo, the question isn't whether the tech works, but whether the Mediator is present. If the head and the hands remain disconnected, the city doesn't just divide—it de-renders. It's time to look at the shadows on the snow and take back our sovereignty before it's too late.

And so it goes -  K. Vonnegut

Shadows - 11:00PM

AI - A Primer

Out of darkness ...

What’s past is prologue - The Tempest

A journey of a thousand miles begins with a single step. - Lao Tzu

artificial intelligence (abbreviation: AI) noun

the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.—The New Oxford American Dictionary, Third Edition

The quest for AI begins … 

1834: Ada Lovelace, the first programmer, meets Charles Babbage, inventor of The Analytical Engine, the first digital computer. Upon seeing the first section of the machine, Lovelace not only realizes the system is capable of performing original calculations but also can process other types of symbols like musical notes. Said hardware was never completed in Babbage’s lifetime as the engineer was not paid upfront but it was completed later on and worked as advertised. Lovelace programmed using Jacquard Loom punchcards. 

1936: Alan Turing invents both the algorithm a finite sequence of rigorous instructions used in computation and the Turing machine, the first universal computer, an abstract device able to manipulate symbols on a strip of tape according to a table of rules defined by Turing’s algorithm Despite the system’s simplicity, it could and can run any computer algorithm known to man if given enough time. 

In the same year, Turing invents AI via the Imitation Game … “I PROPOSE to consider the question, ‘Can machines think?’ … The new form of the problem can be described in terms of a game which we call the ‘imitation game’. It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. 

The Kicker … “We now ask the question, ‘What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, ‘Can machines think’?”  Note: The Imitation Game becomes the Turing Test.

1945: John Von Neumann invents the modern computer consisting of … 

    • A processing unit with both an arithmetic logic unit and processor registers

    • A control unit that includes an instruction register and a program counter

    • Memory that stores data and instructions

    • External mass storage and 

    • Input and output mechanisms[1][2]

Note: Turing did the same thing but his design never took off. Von Neumann gave him credit. 

1947: The transistor, a semiconductor, invented by Bardeen, Shockley and Brattain, becomes the processing unit used by every digital system in the world.

Machine language, driven by the transistor, is binary, consisting of 0s & 1s. This ultimate simplicity, turning on & off like a light switch, gives rise to the ultimate complexity as 0s and 1s can be stored, processed and distributed without limitation. Additionally, through abstraction (computer operating system/programming language/software program), said bits can represent colors, sounds, graphics or any other content able to run on top of the aforementioned 0s & 1s, entities processed by every known computer in the world. 

1962: The internet emerges, courtesy the Department of Defense, enabling computers to communicate on a distributed network. TCP/IP (Transmission Control Protocol/Information Protocol).

1966: ELIZA, the first chatbot “therapist”, emerges from MIT, the first program to invoke the Turing Test. People thought the clever scripting app was sentient.

1966: The concept of an analog neural net was envisioned but without prerequisite compute power, nothing of note happened.

1966: AI, trying to emulate the functions of the brain by traditional means, goes nowhere.

1965-78: IBM’s System/360, the first general-purpose computer, changes business.

1969: Thompson & Richie create Unix, the first networked-centric OS.

1981: the IBM PC, changes the world.

Truly powerful machines arrive thanks to Moore's law whereby the number of transistors in a dense integrated circuit (IC) doubles about every two years.

1989: The World Wide Web, courtesy CERN and Berner Turner’s Lee, becomes the information appliance able to run on the internet using web browsers to access websites and other entities able to be found using URLs, Uniform Resource Locators. Software becomes web-centric.

1990: the search for web content begins. 

1991: Linux, the open-source variant of Unix, becomes the defacto OS for the Internet. As of 2022, 96.4% of all internet servers run on Linux.

1994: Web Crawler enables one to find any word on any webpage. 

1996: the first search algorithm used to determine page rankings via hyperlinks arrives.

A simple illustration of the Pagerank algorithm. The percentage shows the perceived importance,
and the arrows represent hyperlinks.

1997: IBM’s Deep Blue beats world chess champion Garry Kasparov in six games.

1998: Google enters the search arena using similar tech with the added benefit of selling search terms.

1998:  Google becomes the search engine king. This iterative algorithm ranks web pages based on the number and PageRank of other websites and pages that link there, on the premise that good or desirable pages are linked to more than others. 

AI goes prime time.

2010: Deep Mind, a pioneering British AI Company, is bought by Alphabet/Google in 2015.

2011: IBM’s Watson beats Jennings and Rutter in Jeopardy.

2016: Deep Mind’s AlphaGo solves Go. 

2020: Deep Mind’s AlphaZero defeats other AIs in Chess, Go and shoji.

2021: Deep Mind’s AlphaFold begins to solve how proteins fold. Said app’s open source.

2022: Deep Mind’s MUZero master games without knowing their rules.

        2022: “Time to Edit,” the time required by the world’s highest-performing professional translators
        to check and correct MT-suggested translations is one second. Current AI = 2 seconds.”

All Deep Mind apps use neural nets, the analog front end, enabling programs to input massive amounts of data needed to train the AI in order for the system to master the discipline in question. 

A neural net -  The input, based on a query and encoded in analog, samples video, images, sound etc., etc.. as needs warrant. Any hits passing a designated threshold is sent (output) to the digital part of the system, ready to be analyzed and acted upon by the entity conducting the search.
Analog measures, digital counts.

The real power of AI centers on analog as it’s ideally suited to deal with the vagaries of reality while digital, a fragile process requiring absolute precision in order to function, has the prerequisite ability to analyze and act upon whatever analog output the neural net generates.

ChatGPT, Dall.e 2, GPT3 & Bard use neural nets in their architecture and employ similar text input to conduct searches but the generated response to the query is specific to the AI variant in question with Open AI’s Chat GPT writing out text, DALL-E 2 creating 3D photorealistic images and GPT3 producing code. Google’s Bard’s equivalent to ChatGPT. Note: Only Google’s Bard is currently connected to the net. Note II, Google’s also developing variants similar to Open AI’s. Addendum: ChatGPT-4 is twice as powerful as 3.

2023: It’s tulip time with AI with Microsoft kicking in 10 billion to Open AI as its technology poses the first serious threat to Google’s core business. Hedge funders jump in on the fun as well.

The Question …

Is AI sentient? At this point in time, no, but does it matter as the implications of the tech in 2023 are already becoming rather profound, ranging from students using ChatGPT to write college essays to scientists using the app to search for scientific information relevant to their areas of expertise but there’s more. In essence, AI’s beginning to impact all disciplines requiring thought.

A Faustian bargain or There Ain’t No Such Thing As A Free Lunch.

The good … Just a tiny list … AI’s beginning to unravel the complexities of cancer. It’s revolutionizing man’s view of the universe and it’s paving the way to making drugs safer and more effective. AI’s beneficial impact on renewables will change how we produce energy on planet earth. This benefit also applies to all things related to medical.

Conversely, by 2025, 90% of all online content will be synthetic so who do you trust? 

2023: Middle Management Blues … “That said, the fact that a full quarter of those respondents said they've already replaced workers with AI — and with 93 percent saying they plan to expand use of AI.

Future layoffs are on the horizon, too. An ominous 63 percent of business leaders believe that integrating ChatGPT will either "definitely" or "probably" lead to culling their human workforce.

So far, 66 percent of the companies employing ChatGPT use it to write code, 58 percent for copywriting and content creation, 57 percent for customer support, and 52 percent for summarizing meetings and documents, the survey found.

The business leaders are easily impressed, too, with 55 percent saying ChatGPT's quality of work is “excellent".

There is no certitude

When going to the store, the parking lot’s full but one knows a parking place will eventually open up, we just don’t know WHEN. :) Probabilities rule. Quantum Mechanics, physics of the very small. Note: Without quantum mechanics, computers would not exist.

Who controls the past controls the future, who controls the present controls the past.—1984. 

Everything is theoretically impossible, until it is done. Robert A. Heinlein

AI’s the first open-ended tech created by man. 

Tech has no morality. It depends on who’s using the tech in question. 

In order for AI to do real-time searches, it requires software writing software able to conduct real-time searches in the real world in real-time, which means … We don’t know how AI works because human programmers cannot write code in real-time to conduct real-time searches using evolving genetic algorithms to improve said search in real-time in any way, shape or fashion. 

What happens if the software decides to do the search in order to evolve? 

1968: HAL 9000/2001

2000: Why the future doesn’t need us - Bill Joy “What we do suggest is that the human race might easily permit itself to drift into a position of such dependence on the machines that it would have no practical choice but to accept all of the machines' decisions. As society and the problems that face it become more and more complex and machines become more and more intelligent, people will let machines make more of their decisions for them, simply because machine-made decisions will bring better results than man-made ones. Eventually a stage may be reached at which the decisions necessary to keep the system running will be so complex that human beings will be incapable of making them intelligently. At that stage the machines will be in effective control. People won't be able to just turn the machines off, because they will be so dependent on them that turning them off would amount to suicide.” It's already happening.

2015: “Out of control AI will not kill us believes Microsoft Research chief. Prof Stephen Hawking tells the BBC that such machines could ‘spell the end of the human race’”.

2015: “Looking ahead, many researchers are pursuing 'AGI', general AI which can perform as well as or better than humans at a wide range of cognitive tasks. Once AI systems can themselves design smarter systems, we may hit an 'intelligence explosion', very quickly leaving humanity behind. This could eradicate poverty or war; it could also eradicate us.

That risk comes not from AI's potential malevolence or consciousness, but from its competence - in other words, not from how it feels, but what it does. Humans could, for instance, lose control of a high-performing system programmed to do something destructive, with devastating impact. And even if an AI is programmed to do something beneficial, it could still develop a destructive method to achieve that goal.

AI doesn't need consciousness to pursue its goals, any more than heat-seeking missiles do. Equally, the danger is not from robots, per se, but from intelligence itself, which requires nothing more than an internet connection to do us incalculable harm.” 

2017: The Dark Secret at the Heart of AI “No one really knows how the most advanced algorithms do what they do. That could be a problem.”

When resources get scarce, bots become all too human.

2017: “When apples were abundant, the two agents were happy to collect their fruit without targeting each other. But in more scarce scenarios with fewer apples around, the agents became more aggressive. The researchers also found that the greater the “cognitive capacity” of the agent, the more frequently they attacked their opponent. This makes sense, as in this scenario attacking an opponent is more complex behavior and so requires greater intelligence.”

2019: The rise of the machines … “US Navy moves toward unleashing killer robot ships on the world’s oceans.

2021: “Superintelligence cannot be contained:

‘Machines take me by surprise with great frequency.  This is largely because I do not do sufficient calculation to decide what to expect them to do.’” Alan Turing (1950)  Journal of Artificial Intelligence Research

2022: “Last March, a group of researchers made headlines by revealing that they had developed an artificial-intelligence (AI) tool that could invent potential new chemical weapons. What’s more, it could do so at an incredible speed: It took only 6 hours for the AI tool to suggest 40,000 of them. The most worrying part of the story, however, was how easy it was to develop that AI tool.” 

The implications of AI are too important to ignore. People tend to think of AI as a static thing, not as an actual set of millions of interconnected things, an ever-evolving entity living in a distributed environment where duplication, modification and updating of real-time code work at speeds far beyond the kin of man. With this in mind, an excellent article by Henry Kissenger titled How the Enlightenment Ends, connects AI to the Enlightenment at deep level, a piece needing to be read by everyone concerned about how AI will impact society as we move further into the 21st century.

Tech never sleeps.

“Question everything.” - Einstein

Robert E. Moran - CEO, Digital Constructs Inc. 

This is a web-centric version of a piece written by yours truly for a local pub. :)

For profit HC does not work

Yours truly can honestly say that said rube knows a lot about HC & ER in America having designed code that, based on input from docs who know their craft, solves the problem of HC by reuniting billing to services rendered but ... this "wonderful" blurb is not about that but rather about how Wall Street, along with the insurance companies, has screwed over us citizens for years by charging outrageous fees for HC that boggles the mind. Ask any good doc if they like the system. Epithets will be the reply. What's ironic about all of this is the simple fact they make the money for these bastards while treating patients in need. This is NOT RIGHT as seen by words from a powerful Raw Story piece titled Here’s what Wall Street doesn’t want you to know about its grip on emergency rooms.

To whit ...

Doctor Ling Min is the first emergency room doctor to be fired for going public with his concerns about poor hospital emergency room safety practices and shortages of medical supplies and protective gear for health workers.

He won’t be the last.

Like many hospitals in the US, PeaceHealth St. Joseph Medical Center in Bellingham Washington, where Ling Min worked for the past 17 years as an emergency room doctor, has outsourced the management and staffing of its emergency room. So, Min works on-site at the hospital’s ER, but he is employed by a physician staffing firm that runs the ER. These staffing firms are often behind the surprise medical bills for ER services that patients receive after their insurance company has paid the hospital and doctors, but not the excessive out-of-network charges billed by these outside staffing firms.

About a third of hospital emergency rooms are staffed by doctors on the payrolls of two physician staffing companies—TeamHealth and Envision Health—owned by Wall Street investment firms. Envision Healthcare employs 69,000 healthcare workers nationwide while TeamHealth employs 20,000. Private equity firm Blackstone Group owns TeamHealth, Kravis Kohlberg Roberts (KKR) owns Envision.

Care of the sick is not the mission of these companies; their mission is to make outsized profits for the private equity firms and its investors. Overcharging patients and insurance companies for providing urgent and desperately needed emergency medical care is bad enough. But it is unconscionable to muzzle doctors who speak out to advocate for the health of their patients and co-workers during the global pandemic that is rapidly spreading across the US.

Yet, that is what Blackstone-owned TeamHealth just did. Why would an experienced ER doctor be fired in the middle of a pandemic? One clue may be that Blackstone’s CEO, Stephen A. Schwarzman, is part of President Trump’s inner circle. He may not want to risk that relationship by allowing TeamHealth’s doctors to inform the public about Washington’s mishandling of the allocation of supplies and protective gear. The president might conclude that TeamHealth doctors didn’t appreciate him enough, and where would that leave Schwartzman?

Something is rotten in the state of Denmark - Hamlet