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It didn’t take long for Tom Fishburne’s cartoon to come true: Generative AI is increasingly treated as a magic trick. Most tricks aren’t really useful or truly “real,” yet they remain entertaining until we figure out, often the hard way, how the illusion is performed.

My conversations show that some executives are cautiously optimistic, integrating only what is truly useful into their business processes and project workstreams, while others are applauding the tricks and wanting more. The former group is already AI-literate, using machine learning and algorithms to augment and automate processes in the broader, more accurate definition of AI. The latter, more easily impressed group, seems to lack this foundation.

We can expect that more of the applauding executives will eventually join the cautiously optimistic ones once the magic show ends and we move past the peak of the hype cycle.

This is an (almost) complete picture of LLMs, highlighting the underlying math for probability calculations. It’s a great companion for the 64-picture visual summary I curated for a talk earlier this year.

Good reminder that “Life’s most important questions are, for the most part, nothing but probability problems.” Pierre-Simon Laplace in Théorie Analytique Des Probabilités

I once quoted Abraham Maslow in my note “Using predictive modeling as a hammer when the nail needs more thinking“, a problem that has since eased as focus shifts to causal modeling and optimization.

Now, if the problem is extracting handwritten digits from a PDF or an image, what’s the solution? I guess good old reliable OCR? There is nothing generative about extraction, so using an LLM may be moot, if not actively detrimental (due to hallucinations). OCR is widely available through cloud like AWS Textract and Google Cloud Vision, some of which also offer human validation.

Instead, the Reuters team intriguingly chose the shiniest hammer, an LLM, to extract data from handwritten prison temperature logs. They uploaded 20,326 pages of logs to Gemini 2.5 Pro, followed by manual cleaning and merging. One big problem with this approach is the inevitable made-up text (hallucinations), which required the team to hand-code 384 logs.

So why not OCR but an LLM? All I can think of is, LLMs may be useful in extraction when the text is highly unstructured and contextual understanding is needed, neither of which seems to apply here. Surprisingly, the methodological note doesn’t even mention OCR as a solution.

Putting the tool choice aside, though, Reuters asks an important question here: “How hot does it get inside prisons?” and the answer is “Very.” I applaud the effort and data-centric journalism, and I recommend reading the story.

Credit for the image goes to Adolfo Arranz.

Source – Project GitHub

In January, I wrote a short note based on one of my talks: “How to use LLMs for learning in 2025.” In that note, I differentiated between using LLMs (1) to learn and (2) to do. With the new semester now underway, I’ve checked some usage numbers and read the Ammari et al. (2025) paper on how students use ChatGPT. I was particularly interested in the second RQ: “Which usage patterns correlate with continued or increased reliance on ChatGPT over time?”

An over-reliance on any tool, regardless of what it is, is a potential red flag for persistent learning, especially when the goal is comprehension. For example, understanding derivatives and calculating them using a computer are two distinct learning objectives. If the reliance on a tool substitutes for understanding, long-term implications may not be a net positive.

The article does not really answer the reliance part of the question. It does, however, report some interesting correlations between LLM behavior and student engagement. Notably, when ChatGPT asks for clarifications, provides unintended or inconsistent answers, or communicates its limitations, students are less likely to continue using it.

Plausible, but what these correlations mean for learning and comprehension is unclear. What is the next step after disengagement? Do they switch to another LLM to get a direct answer without having to answer follow-up questions, or do they go back to figuring it out on their own?

Class of 2029, I guess the answer lies with you. Welcome!

Source – Paper