Kenneth Ronkowitz - Serendipity35

The Canvas Hack

ronkowitz@gmail.com (Kenneth Ronkowitz) — Mon, 11 May 2026 18:30:00 +0000

This month, colleges and universities across the country postponed final exams and due dates for assignments after Canvas, a learning management system used by 41 percent of North American higher ed institutions, temporarily went offline due to a hack. The University of Illinois at Urbana-Champaign postponed “all final exams and assignments, including papers, projects, etc., scheduled for Friday, Saturday, or Sunday,” provost John Coleman wrote to students and employees, and that, for “consistency and clarity,” the postponement affects all classes—even those that don't use Canvas.

Cybercrime group ShinyHunters identified itself as the hackers.

Message that appeared to Canvas users

ShinyHunters first emerged in 2020 and claims to have successfully attacked 91 victims so far. The group is primarily after money, but has also been willing to cause reputational damage to their victims. In 2021, ShinyHunters announced they were selling data stolen from 73 million AT&T customers. ShinyHunters received global attention in 2025 after Google urged 2.5 billion users to tighten their security following a data breach via Salesforce, a customer management platform.

Unlike data breaches where hackers directly break into databases holding valuable information, ShinyHunters – and several other groups – have recently targeted major companies through voice-based social engineering, which is also known as “vishing,” for voice phishing. Social engineering is when a person is tricked or manipulated into providing information or performing actions that they wouldn’t normally do.

Moving Closer to Superintelligence

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 05 May 2026 10:31:00 +0000

It is difficult to keep up with AI advances and new tools. Recently, I have seen the term "superintelligence" being used and I had to look for a definition.

In AI terms, there are three kinds of intelligence. "Artificial Narrow Intelligence" is what we have now. It is "superhuman" at specific tasks like playing Go or translating languages. ChatGPT, Gemini, CoPilot and Meta AI, et al fit in there at the moment.

"Artificial General Intelligence (AGI)" is human-level across the board and can learn anything a person can learn. We’re not quite there yet as of May 2026.

"Artificial Superintelligence (ASI)" is far beyond human level. Philosopher Nick Bostrom popularized the term: and defined it as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.”

ASI is what people worry about — or get excited about — when talking about advanced AI. But AGI isn't quite the same as superintelligence. With AGI, you clone the best human brain in software, but with superintelligence that clone keeps upgrading itself until it’s as far beyond us.

Two new tools are moving closer to the next level.

Google has released TurboQuant, a new compression method that makes AI models cheaper to run and faster to respond. In Google’s reported tests, it reduced the key-value cache, the model’s short-term working memory while it responds, by at least 6x and improved performance by up to 8x on H100 chips, Nvidia’s high-end AI processors used in data centres, while keeping benchmark performance, or standard test performance, close to the original model. That is a serious technical result with a clear business consequence: one of the biggest cost pressures in modern AI may begin to ease. For the past two years, the default logic has been simple. The best AI stayed in the cloud because that is where companies could absorb the cost of running it. TurboQuant starts to weaken that logic.

Meta TRIBE v2 is a foundation AI model that acts like a “digital twin” of the human brain. In plain terms, it’s an AI trained on real brain scan data so it can predict how a person’s brain will respond to things they see, hear, or read. It takes in video, audio, and text, then maps that to about 70,000 areas of the brain to simulate neural activity. Meta itself says that you can think of it as Meta teaching an AI to “think” more as humans do, by learning directly from brain responses instead of just internet text.

Where did I get information anout Meta's products and path? From their own Muse Spark. That is Meta’s latest (well, as of today) AI assistant model.

A New AI Hub from Microsoft & The Open University

ronkowitz@gmail.com (Kenneth Ronkowitz) — Thu, 30 Apr 2026 16:20:00 +0000

This AI Hub from Microsoft & The OU is a collection allowing you to explore free, accessible courses designed to build your confidence and skills in artificial intelligence. Whether you’re just starting out or looking to deepen your knowledge, the AI Hub will support your learning journey with expert-led, trusted, easy-to-use resources created by The OU and Microsoft.

Not labeled as a MOOC (a term that seems to have fallen away in the past decade) it is a similar kind of open course, not for credit but for learning.

AI Fluency is a beginner-friendly learning path designed to build confidence and understanding in artificial intelligence. Through a series of practical sessions, learners explore AI fundamentals, generative AI, responsible AI principles, and the real-world impact of AI across work, accessibility, and society. The course also introduces Microsoft Copilot, showing how AI tools can support creativity, productivity, and everyday problem solving.
Suitable for students, professionals, and leaders alike, AI fluency helps demystify AI and equips learners with the knowledge to engage with AI technologies thoughtfully, responsibly, and effectively.

Work Smarter with AI is a 65 minute, one module, learning path to help you work better and unleash your creativity with Microsoft Copilot. In this learning path, you'll explore how to use Microsoft Copilot to help you research, find information, and generate effective content.
Prerequisites: Familiarity with Microsoft productivity applications, like Word, Excel, Outlook, and PowerPoint.

Andragogy and Microlearning

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 21 Apr 2026 09:40:00 +0000

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.

UNIVAC 1951

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 14 Apr 2026 09:45:00 +0000

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.

The AI Doc: Or How I Became an Apocaloptimist

ronkowitz@gmail.com (Kenneth Ronkowitz) — Fri, 10 Apr 2026 09:41:00 +0000

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 On Why Social Media Has Become a Freak Show

ronkowitz@gmail.com (Kenneth Ronkowitz) — Wed, 08 Apr 2026 08:35:00 +0000

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.

Twitter accounts with the most engagement so far in 2026

Your AI Is Not Free

ronkowitz@gmail.com (Kenneth Ronkowitz) — Mon, 30 Mar 2026 08:49:00 +0000

The phrase that if an app is free, you are the product means that when an app doesn’t charge you money, it usually makes money from you instead. They do that mainly by collecting your data or selling your attention to advertisers.

If that is true, then how is AI changing what that means? It is a question that deserves several posts here to really answer.

Your behavior, preferences, and time become what is being monetized. Your data becomes the product. Free apps often gather your demographics, browsing or in-app behavior, location, interests, and habits. This information is then used to target ads or sold to third parties.

The addictive nature of app design keeps you scrolling, tapping, or watching so they can show you ads. You pay with time, not dollars. “Free” is a business model, not a gift.

I will give these companies a nod that running an app costs money (servers, engineers, storage). If you are not paying, the company must earn revenue another way. Ad-free options are becoming more common as a premium. You have probably noticed that on apps and also on video streaming services. You thought that paying for Amazon Prime meant no ads on the videos. Wrong. Free is often an illusion.

In the world of AI, the difference between free and paid tiers is more than a matter of convenience. It is also about identity and privacy.

Privacy becomes the hidden cost. Data is currency. Companies track you across apps and devices, build detailed behavioral profiles, and use algorithms to influence what you see. This raises concerns about autonomy and consent.

Is there no stopping them? As long as you agree to their terms, they have a lot of power. BUT you can read those terms and privacy settings more carefully. (They rely on the fact that many users don't read the terms or adjust their settings at all.) Educate yourself and understand how digital ecosystems make money. You can choose paid or privacy-focused alternatives. And you can remove the app entirely from your life.

I see comparisons of using AI to using social media platforms. I don't think AI data is the same as social media data. Social media platforms monetize your attention. The longer you scroll, the more ads they can show. AI chatbots operate on a different axis. Your prompts aren’t just content; they’re training signals. They reveal how people think, what they struggle with, what they’re curious about, and how they phrase questions. Maybe it is anonymized (a good thing) but it is still valuable and often sensitive data.

Alarmist articles will remind you that many free AI chatbots use your prompts, your corrections, and your uploaded files. They have that photo of your family that you let them enhance. What will they do with what you give them? I can't answer that as of now, and certainly not for the future. I know that your conversation history is used to train or fine-tune future versions of the model. Hey, you are part of the product pipeline - but don't expect to be paid for your contributions.

I also concede that the business model matters and that different AI companies monetize differently. For example, Microsoft provides its own privacy commitments and policies, and those govern how your data is handled. For details, they always direct users to their Privacy Statement.

Here are 4 business models currently out there:
Ad-supported = Your attention is monetized.
Freemium = Free tier gathers usage; paid tier subsidizes development.
Enterprise licensing = Your data may be isolated; the company earns from businesses.
Open source = The model is free; the company may sell hosting or support.

So "if an app is free, you are the product" still applies, but not always in the same way. When an AI tool is free, you’re not just the product — you’re also the collaborator. You’re an unpaid teacher, tester, and a source of fuel for improvement.