Showing posts with label Meta. Show all posts
Showing posts with label Meta. Show all posts

Monday, October 19, 2026

"Agens ex machina" – When an AI agent goes "buy" the book!

This post was drafted with an AI assistant. Complete prompts and follow up revisions available upon request. 

Sunday afternoon, in the middle of drafting a blog post, I mentioned to my assistant that I'd like a copy of Schwarzenegger's "The Education of a Bodybuilder." (The relevance to what follows will not be immediately evident . . . ) What happened next is, I think, the best explanation I can give you of what a personal AI agent actually is — and why I'm not worried about it . . . much!

Here is the whole thing, exactly as it happened.

1. The setup. I asked how it would work. My assistant — Sunny by Meta — said it was an easy  one-time setup: connect a payment wallet, and then it could check out for me. It recommended Stripe's Link service, explaining that after I approve a purchase, Link hands the merchant a one-time virtual card instead of my actual card number. I clicked the link, added my card on Stripe's own secure page — My assistant did not view the number — done.

Control: The setup grants capability, not permission. Connecting a wallet doesn't authorize spending; it only makes spending possible.

2. The search. Sunny reported to have checked several bookstores and came back with three options: two new copies at $19.99 (Barnes & Noble and Powell's) and a used one at Powell's for $9.95. It laid them out.

Control: The agent proposes; the human disposes. Nothing went into the cart until I decided.

3. The choice. I picked the used one. Ten bucks is ten bucks. This would be my third or fourth copy of the book. 

Control: The agent only provided options requested, without further comment. I select. 

4. The checkout. Sunny "went to" Powell's, put the used copy in the basket, and proceeded to checkout f— guest checkout, no account created, no email-list signup, no extras. Then it came back to me, needing needed my shipping address--which it didn't have. I provided it.

Control: it asked directly rather than guessed the shipping address. It probably had my home address already.

5. The approval. Sunny displayed the order: one used copy, $9.95, plus $5.99 shipping, no tax — $15.94 total, shipping to my address, paying with my card, and then asked: Shall I place the order?

Control: This is the load-bearing moment. Every purchase gets its own, individual, explicit approval. There is no standing permission to spend my money. (Although that can be arranged.)

6. The catch. I looked at the card displayed — a Visa ending in XXXX— it was the wrong card. Sunny provided a secure link to update the card in my Link wallet. I swapped in the right card and gave the go ahead.

Control: This is the whole game. Me checking the work wasn't based on a bug showing up in the system; it IS the system. I caught the error because the arrangement required I be shown all the details before anything moved.

7. The order. Sunny verified that the cart matched what I'd approved — same book, same condition, same price, same address — confirmed it, and placed the order. The confirmation email arrived within minutes. The book is on its way.

Footnote. During the search, Sunny tried Amazon, hit Amazon's wall against AI agents, and told me so — then reported that it "took our money to bookstores that wanted it." (Sunny's ear for colloquial English is still a work in progress!)

Control: Finding a viable option that I later approved.

The agent does the legwork, surfaces every decision, and never moves money without an explicit approval. Control never left my hands; it was never the agent's to take: only mine to grant--one approval at a time.

If you're wondering whether you could trust one of these things or if you have any reason to, don't start with the technology. Start with the arrangement. Ask three questions:

  • What time-consuming task or tasks could an agent take off my hands?
  • What safe, no-risk task can I give it as an initial test?
  • How transparent is the agent on all stages of the process, including final approval?

That's the story. No magic, no leap of faith, no deus ex machina — just an arrangement.


wracton@gmail.com

williamacton.legalshieldassociate.com

Thursday, April 2, 2026

Meta and Siri (were lovers!)

Link to YouTube recording!

Note: This tune was created with the assistance of Perplexity AI; I am soley responsible for the ideas and where it landed, however. If you are not all that familiar with the current acronyms and abbreviations related to AI, I'd recommend you check out the Glossary at the bottom!

Clker.com

Summary:

In this pun packed tech torch song set to the old folk tune “Frankie and Johnnie,” I turn Meta, Siri, Bixby, and friends into a soap opera of large language lovers, where “he was her NLP but NLU in’ her wrong” and every acronym hides a broken digital heart. As SMS els fly, deepfake receipts drop, and a jealous Bixby rolls in Top k with his Grok (pun on Glock) to knock Meta offline, the tune winks at our chatbots’ tangled “meta narratives” while reminding us that, for now at least, us humans are still in control . . . 


Lyrics

Meta and Siri were BFFs

One day in love they both fell 

Meta couldn’t get enough of Siri

And Siri was speechless as well

Sweet talkin’ NLP

But NLU-in’ her wrong. 


Meta sent Siri an SMS

I’m workin’ late with other chatbots

Said he was down on the server farm

But he was down with that Ada Le Chat

Sweet talkin’ NLP

But NLU-in’ her wrong. 


Siri’s best bestie, Claude Deepseek

Posted for Siri to see

A deepfake pic of Meta and Ada

Doin’ the NLG

Sweet talkin’ NLP

But NLU-in’ her wrong. 

 

Siri hired her neighborhood hit bot

Bixby came Top-k, he did.

Totin’ a grin and his 22 Grok 

Meta boy was soon off the grid.

Sweet talkin’ NLP

But NLU-in’ her wrong. 


Roll out your rubber tire’d hearsay

Roll out your lies and your facts

12 bots goin’ to the trash bin

11 are comin’ back

Sweet talkin’ NLP

But NLU-in’ her wrong. 


So ends my sad meta-narrative

Hope it don’t cause you no pain

Meta is meta and Siri is Siri-ous

But we’re still dealin’ the game.

At least for now . . . 

Though it ain’t exactly a lock

But we still know how . . .

Just takes some dough and a Grok!  (2x)

Sweet talkin’ NLP

But NLU-in’ her wrong. 


Glossary:

  • BFF – Best friend forever
  • Current Chatbots: Ada, Claude, Deepseek, Grok, Le Chat, Meta, Siri
  • 22 Grok (lame pun on 22 Glock, a popular hand gun)
  • LLMs – Large language models
  • NLP (Natural Language Processing) – focused on processing human language with computers.
  • NLU (Natural Language Understanding) – Subfield focused on interpreting meaning and user intent.
  • NLG (Natural Language Generation) – Subfield focused on generating human like text.
  • SMS – Short message service (a text message)
  • Top k sampling – Methods that limit which next tokens are considered to control diversity and quality

Link to the "AI no ai" series videos. 

wracton@gmail. com

williamacton.legalshieldassociate.com









Tuesday, March 17, 2026

Meta-coupling: Freeing mind, map and project from the chimera of AI

Caveat emptier: This post was drafted with help from an AI assistant (Perplexity)— but ideated and edited extensively by the human, Bill Acton. Also, at the very end is my usual quick soft sell on IDshield!

We may have rushed to “use AI,” but the key 21st‑century skill set is up another level: meta‑coupling—the ongoing ability to know how AI is shaping your work and thinkinig while you are using it, and to adjust that relationship on purpose, dynamically. Meta‑coupling asks, “What did AI add here, what did I add, and is that actually the mix I want?” 

Clker.com


Instead of treating AI as a magic typewriter, meta‑coupling turns it into an explicit, visible, yet subordinate "partner" whose influence you must monitor, identify, and generally resist. You are not only writing the email, lesson plan, or sales pitch; you are also tracking the hand‑offs between your judgment and the model’s suggestions; you uncouple to manage the process. 

From “Using AI” to a meta-cognitive grounded relationship

Most advice stops at “Use AI for brainstorming, then rewrite in your own words.” Helpful, but shallow at best. Meta‑coupling goes much further. It requires you to notice:

  • Who framed the problem—me or the model?  
  • Who/what chose the structure—my sense of the audience, or the ghost in the machine (deux ex machina)?  
  • Which features I could still defend or justify on my own, without AI.

That shift matters. Research in both education and management notes that generative AI can either erode critical thinking or strengthen it, depending on whether people simply accept outputs or actively interrogate and revise them. If you stay coupled to AI from start to finish, the system quietly does the heavy lifting: it chooses what counts as important, supplies the wording, and even suggests the tone. When you meta‑couple, you deliberately "uncouple" at key junctures to question, reframe, and revise, so your analysis and experience remain constantly in play. 

Think of AI as a fast, opinionated collaborator who never tires (but should!). Meta‑coupling is, in part, the habit of stepping back regularly and asking: “Is this still my project . . ?” 

Schools that are teaching meta‑coupling

Some schools are already designing around this deeper layer. At Stamford American International School in Singapore, teachers report explicitly presenting AI as a “thinking partner, not an answer machine.” Students might ask AI for example, for multiple interpretations of a text or different solutions to a problem, but then the "real" work begins: they have to critique those outputs, compare them, and explain what they accept, what they reject, and why. That kind of structured reflection—using AI to generate ideas and then teaching students how to evaluate them—is exactly what recent studies recommend for building (or at least maintaining!) critical thinking in the age of generative AI. 

Meta‑coupling is built right into the assignments. Students must be able to identify which ideas came from AI, where they see bias or gaps, and how their own perspective differs. They are not just consuming AI answers; they are practicing the move: “Here’s what the system offered—and here is what I think about it,” and how to continue the dialectic to conclusion. 

A similar pattern shows up in university programs that use AI to enhance experiential learning. At the University of South Florida, for example, faculty development efforts encourage instructors to use AI to simulate negotiations or customer interactions, while insisting that students still build deep subject understanding and reflective practice outside the tool. Faculty are learning to ask not only, “Did this AI scenario help?” but “Where should AI carry the load, and where must students still stand on their own understanding?”—a classic meta‑coupling question.

Businesses that keep humans in the pilot (not Copilot!) seat

Companies are discovering the same thing: the best results come when AI acts as co‑pilot, not autopilot. Analyses of human–AI collaboration show that performance improves when AI handles data‑heavy pattern‑spotting and humans retain ownership of context, values, and trade‑offs. Leaders are urged to treat AI recommendations as powerful input—not as final decisions—especially when the stakes are ethical, legal, or reputational. That is meta‑coupling at the leadership level. Instead of blindly “trusting the AI” or reflexively rejecting it, managers learn to:

  • Treat AI outputs as hypotheses, not conclusions.  
  • Probe where those suggestions come from and what they leave out.  
  • Override the system when local knowledge or human impact demands it. 

AI speeds up the work; meta‑coupling keeps it accountable to human judgment.

The “UNCUPLE IT!” Meta‑coupling checklist

To make this concrete, here is more personified checklist. 

1. Spot the Hand‑Off – “Where did I give the wheel away?”  

Name exactly what you just outsourced: ideas, structure, wording, or fact‑checking. If you can’t say it in a phrase, AI may already be doing more than you think. 

2. Tag the source – “This line is mine; that block is bot.”  

 Look over the page and mentally label sections as YOU or AI. Make sure at least one key move—central idea, story, or structure—is unmistakably yours.

3. Flip the Frame – “If AI went dark and I lost this draft, what would/could I do?” 

Take any AI‑generated outline or argument and imagine one alternative way you could approach it. You don’t have to write it out; just prove your own route still exists. 

4. Unplug and Rewrite – “Close the tab, find my voice.” 

 Do one pass with AI turned off. Rearrange, cut, and rephrase until you can hear your own cadence and convictions, not the model’s neutral hum. Highly recommend that that be done reading OUT LOUD!

5. Interrogate the Output – “If this is wrong, where and why?” 

Treat AI as a challenger, not a judge. Ask what could be missing, biased, or flat‑out mistaken—and make your own call about which weaknesses matter.

6. Draw the line – “AI can help, but it cannot decide.”  

 Decide in advance which decisions AI is allowed to own, other than ethical judgments, promises you make to others, or anything signed with your name. 

Spot. Tag. Flip. Unplug. Interrogate. Draw the line. 

That’s the meta‑coupling rhythm —a relationship with AI that stays visible, adjustable, and genuinely human‑led.

Postscript: Meta‑coupling minds are harder to scam

There is one more bonus to cultivating a meta‑coupling mind: you become much harder to fool. Studies suggest that blind confidence in generative AI correlates with weaker critical thinking, while reflective, self‑aware use is linked to better scrutiny and decision quality. The same habit of asking, “Who really wrote this, and what are they trying to get me to do?” makes you far more likely to catch AI‑driven phishing, deepfake messages, and too‑smooth scam pitches before you click. 

You do not have to do all of that alone. A dedicated security service—such as IDShield (the company I'm with, BTW!)—can act as your always‑on first line of defense, monitoring for identity theft, suspicious activity, and emerging fraud patterns in the background, while your meta‑coupling instincts stay focused on what humans do best: noticing when something feels “off,” asking better questions, and choosing when to unplug, uncouple, and walk away.

References consulted by Perplexity AI; all confirmed for general relevance to the section appended

Conerly, B. (2026, January 20). Combining AI and human judgment in strategic business decisions. Forbes. (https://www.forbes.com/sites/billconerly/2026/01/20/combining-ai-and-human-judgment-in-strategic-business-decisions/)

Deloitte. (2026, March 3). 2026 global human capital trends. Deloitte Insights. (https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends.html)

Frontiers Editorial. (2024). Using genAI in education: The case for critical thinking. Frontiers in Artificial Intelligence. (https://pmc.ncbi.nlm.nih.gov/articles/PMC11564148/)

Larson, L., et al. (2024). *Critical thinking in the age of generative AI. Academy of Management Learning & Education. (https://research.vu.nl/files/382320511/larson-et-al-2024-critical-thinking-in-the-age-of-generative-ai.pdf)

Lee, J. (2025). The impact of generative AI on critical thinking. Microsoft Research. [(https://www.microsoft.com/en-us/research/wp content/uploads/2025/01/lee_2025_ai_critical_thinking_survey.pdf)

Stamford American International School. (2025, November 5). AI in the classroom: Using technology to support, not replace, critical thinking. (https://www.sais.edu.sg/school-life/our-news-events/ai-in-the-classroom-using-technology-to-support-not-replace-critical-thinking/)

TechClass. (2026, January 30). How to blend AI insights with human judgment for better outcomes? (https://www.techclass.com/resources/learning-and-development-articles/how-to-blend-ai-insights-with-human-judgment-for-better-outcomes)

University of South Florida St. Petersburg. (2025, April 30). How educators are using AI to enhance, not replace, experiential learning. (https://www.stpetersburg.usf.edu/news/2025/how-educators-are-using-ai-to-enhance-not-replace-experiential-learning.aspx)

University of South Florida. (2025, July 13). USF equipping K–12 teachers with tools for an AI‑powered future. (https://www.usf.edu/education/news/2025/usf-equipping-k-12-teachers-with-tools-for-an-ai-powered-future.aspx)

University World News. (2026, January 27). Over 90% of faculty say GenAI is killing critical thinking. [universityworldnews](https://www.universityworldnews.com/post.php?story=20260128145305278)

Center for Engaged Learning. (2025, March 24). Unlocking the link between generative AI confidence and critical thinking skills. (https://www.centerforengagedlearning.org/unlocking-the-link-between-generative-ai-confidence-and-critical-thinking-skills/)

EDUCAUSE Review. (2025, March 5). AI as a thought partner in higher education. (https://er.educause.edu/articles/2025/4/ai-as-a-thought-partner-in-higher-education)

The New York Times. (2026, January 25). Why A.I. can’t make thoughtful decisions.(https://www.nytimes.com/2026/01/25/opinion/ai-human-judgment.html)

LinkedIn. (2025, December 15). AI as thinking partner, not answer machine.(https://www.linkedin.com/posts/leslie-ngugi-328a7313_ai-learning-criticalthinking-activity-7406611724923187200-AiiA)

wracton@gmail.com

williamacton.legalshieldassociate.com (Check this out both for both legal and ID protection, plus a promising small business opportunity!)



Tuesday, January 13, 2026

An ambivalent octogenarian take: The talkin' good mornin' AI chat bot blues!

 Here's the recording


Here's the lyrics: 

The talkin’ good mornin’AI chat bot blues!


Sittin’ in the kitchen, (just) the toaster and me
When my Mac starts hummin’ it was ChatGPT!
said “why you sinkin’ Bill-- of course it knew
“Here’s somethin’ to stare at; that oughta do . . . 

Feeling better already . . . soft and cuddly       
        warm and wuzzy . . . 

Then my executive function, Grok, come up
In my pre-frontal cortex, so I could fill up my cup
Next, Alexa, with the lay of the land
a map and pointer, for my trip to the can. 

All the way down the hall  . . . all the way back. 
        I Depends on Alexa . . . 

Chorus
Got them, Alexa, Copilot, ChatGPT, 
Glok, Claude, Siri and Perplexity . .. 
Replika, Bixby, Pi, Deepseek, too, 
Meta, Poe and Gemini see me through.
The talkin’ good mornin’AI chat bot blues!

Got a bit perplexed but Perplexity was there
With research and footnotes to fend off despair.
Finally, Siri, arrived to seize the day
With soothin’ words ‘bout what we’ll do today . . . 

Clickin’ on Duck Duck . . . buying a yak in Ulaanbaatar . . . 
         sharing my hopes and dreams with Miss Kitty

Now we’re sittin’ in the kitchen, calm as we can be
Just the toaster, coffee, my botties and me . . . 
No need to worry, can’t go wrong
Hey, one of you take the end of this song?

An AI for an I; Meta-for-me; Glok n' load! 
Nice, 10-4, good Bot-y! 10-4!

Chorus

Got them, Alexa, Copilot, ChatGPT, 
Glok, Claude, Siri and Perplexity . .. 
Replika, Bixby, Pi, Deepseek, too, 
Meta, Poe and Gemini see me through.
The talkin’ good mornin’ AI chat bot blues!


Clker.com






For more on how to protect you and yours from the bot attacks on your money and identity today, here is my website: 
www.williamacton.legalshieldassociate.com
and my email address: wracton@gmail.com