Serendipity35

I have referenced microlearning in earlier posts, but I want to say more about how microlearning works effectively with andragogy (adult learning theory), which differs from the more commonly heard pedagogy (children).

Microlearning provides the flexible format and focused content that perfectly complements the goal-oriented, self-directed nature of the adult learner. (Not that children don't want their learning to be self-directed, but they are less capable of doing that on their own.) Andragogy principles are strengthened by microlearning's ability to combat the forgetting curve. Microlearning often incorporates spaced repetition through short, periodic knowledge checks or quizzes. By revisiting core concepts in brief intervals, the information is reinforced, helping to move the content from short-term to long-term memory, which is vital for busy adult learners who may not have dedicated study time.

Adult learners, by definition, value autonomy and prefer to be self-directed in their education. So, microlearning modules are typically accessed on demand via mobile devices or learning platforms. Much of that learning occurs outside of traditional learning spaces. This allows adults to choose what they need to learn and when it fits into their busy personal and professional schedules, fully supporting their desire to take control of their learning path.

Adults are motivated to learn when the content is immediately relevant and can be applied to solve a real-life problem or job-related task. Each microlearning module is intentionally designed to focus on one specific learning objective. That might be "how to change the blade on a lawn mower," but also  'how to execute X function in the software." This problem-centered focus provides just-in-time training, ensuring the information is practical, immediately useful, and valuable for their current role.

Adults are most ready to learn when they encounter a specific need or challenge in their work or life.

Younger learners are more likely to accept the "authority" of the teacher that something needs to be learned at this time, even if they don't see a need for it themselves. It's not that younger learners don't sometimes do the same kind of "just in time," self-motivated learning. They might search for a video on how to do something when starting a task. But this is more likely to occur with older learners.

Adult learners have accumulated a wealth of experience and are often battling time shortages. They need efficient learning that builds on what they already know. Microlearning usually respects the adult's time by eliminating filler and focusing only on the "need-to-know" core information. 

AI chatbots are certainly the latest form of just-in-time microlearning that is being used outside classrooms. Its use is not unlike someone earlier looking for a help video on YouTube, but it is incredibly fast and personalized.  

You may have heard the advice to speakers to open with a joke, so here we go.
A bunch of scientists created a huge machine capable of complex calculations and called it UNIVAC. Eager to test their invention, they asked it, “Is there a God?”The vacuum tubes hummed, and the tape spools spun for several minutes. Finally, the machine spat out a little card, on which was written, “THERE IS NOW.”

That's an old joke, but it seems fresh in this "Intelligence Age" of artificial intelligence and fears of a singularity. In this time of AI and having a computer in the palm of our hand, it is interesting to consider what was happening in tech history back in 1951. That was when the Remington Rand Corporation signed a contract to deliver the first UNIVAC computer to the U.S. Census Bureau.

UNIVAC I (which stands for Universal Automatic Computer) took up 350 square feet of floor space — about the size of a one-car garage — and was the first American commercial computer. It was designed for the rapid and relatively simple arithmetic calculation of numbers needed by businesses, rather than the complex calculations required by the sciences. It was intended to compete against IBM’s punch card-reading computers, but UNIVAC read magnetic tapes, not punch cards, so a special “card to tape converter” had to be designed.

Though the government contract was signed and a ceremony held on March 31, the computer wasn’t actually delivered until the following December. There was only one UNIVAC I, and Remington Rand wanted to use it for demonstration purposes. They asked for and received time to build a second computer.

The government was the first big customer of the UNIVACs, with subsequent models going to the Air Force, the Army Map Service, the Atomic Energy Commission, and the Navy.

The computer first came to the notice of the general public in 1952, when CBS used one to predict the outcome of the presidential election. UNIVAC correctly picked Eisenhower and predicted his electoral count within 1 percent, but the network didn’t release the results until after the election was called, so as not to affect the outcome.

The first commercial sale was to General Electric, for their Appliance Division, followed soon after by the Metropolitan Life Insurance Company, in 1954.

There were 46 UNIVAC I’s built and delivered, in all.

If you’ve found yourself both fascinated and/or unsettled by the accelerating pace of artificial intelligence, THE AI DOC; OR HOW I BECAME AN APOCALOPTIMIST offers one way to lean into that tension rather than avoid it.

This week it was my film for the Film Matinee Club with Montclair Film. Our discussion after viewing the film was "spirited." Artificial intelligence certainly pushes people's intellectual and emotional buttons.

Directed by Daniel Roher and Charlie Tyrell and hosted by Roher. It is about making a documentary, and it is about AI, and it is also a personal narrative centered on Roher’s own fears about the future, especially as he and his wife contemplate having their first child in an AI-driven world.

Across all sources, the documentary’s expert roster includes top AI CEOs, pioneering researchers, alignment and ethics leaders, and public intellectuals. The film intentionally spans “doomers,” “optimists,” and “apocaloptimists,” giving a wide-angle view of the AI debate.

Roher becomes an Apocal (as in apocalypse) optimist because (spoiler alert) as he learns more and understands AI's capabilities, he begins to see more positive possibilities. And yet the answer to whether AI will cause the end of us or make our lives very much improved is still an open question. Even the experts don't know no matter what side they take on the AI debate.

The film very deliberately does not settle on a single answer about the future of AI. The film’s whole structure is built around the tension between optimism and existential risk, and it ends by embracing that unresolved state rather than resolving it./p>

Nate Silver is an American statistician, author, and professional poker player who transformed the landscape of political and sports analysis through probabilistic modeling. Silver first gained prominence in the early 2000s by developing PECOTA, a system for forecasting Major League Baseball player performance. He soon applied these "sabermetric" techniques to politics, founding the influential site FiveThirtyEight in 2008. He famously cemented his reputation by correctly predicting the presidential winner in 49 states that year. Following his departure from ABC News in 2023, Silver returned to his independent roots by launching the Silver Bulletin on Substack. As of 2026, he remains a central figure in election forecasting, providing real-time data modeling for the current midterm cycle. His work has shifted toward a broader exploration of risk; his 2024 book, On the Edge: The Art of Risking Everything, examines the high-stakes world of professional gambling, crypto, and venture capital.

Here is a recent post of his about social media, focusing on the evolution (devolution) of Twitter to X.

The content that gets “engagement” on Twitter is mostly complete crap

And yet, while Facebook is now almost completely irrelevant to the political discourse, that isn’t quite true for Twitter. Google search traffic in the U.S. for the precise term “twitter” is down quite a lot, but that’s not fair to X because the platform now has a new name. Broader traffic for search topics related to Twitter/X is also down, by more than half relative to the peak in late 2012. But the recent decline has been more gradual: about 20 percent as compared to two years ago. That seems to track with other third-party data showing a slow-but-steady decline in Twitter engagement, though nobody can be quite sure since X is no longer a public company.

It’s not hard to notice that Twitter has become extremely right-leaning. But I’d argue there’s an equally important trend: the top accounts are of incredibly low quality. Elon, with the algorithmic boost he built in for himself, is at the eye of the storm, of course. But “Catturd” literally gets far more engagement than the New York Times, for instance.

Here's the graphic Silver made using Claude AI to show what's hot on X this year.