New prototype to share! My main purpose in building it is to inspire you to think more about how open content and open generative AI can be combined to create openly licensed, freely available, interactive learning resources that run privately and locally. This proof-of-concept combines (1) an OpenStax textbook, (2) a knowledge-graph representation of the book’s contents, and (3) an open weights LLM that runs locally inside your web browser. The locally-running LLM means you can chat forever without having to pay per-token costs, and the knowledge graph means you can browse a visual map of the book that is interlinked with the content itself. ...

I’m a huge proponent of OER. In fact, I’m the original OER advocate - since 1998, when I created the first open license for educational materials and began working to persuade people to openly license them. Creating and sharing OER is a powerful lever for increasing educational opportunity. (Generative AI is now an even more powerful way of accomplishing this goal, but that’s a subject for another post.) Because I’m an OER advocate, I’m frustrated by the unfounded claims I hear people make about OER, especially the claim that ‘adopting OER improves student outcomes!’ That’s simply not true. Adopting OER does NOT reliably improve student success. In some studies adopting OER is associated with better outcomes, in some studies there’s no change at all, and it some studies adopting OER is actually associated with decreases in student success. This variability in study results is partly due to problems with the ways studies are designed. ...

Generative AI tools have become superhuman in their capacity to discover vulnerabilities in computer code. Describing the Claude Mythos Preview, Anthropic recently wrote: During our testing, we found that Mythos Preview is capable of identifying and then exploiting zero-day vulnerabilities in every major operating system and every major web browser when directed by a user to do so. The vulnerabilities it finds are often subtle or difficult to detect. Many of them are ten or twenty years old, with the oldest we have found so far being a now-patched 27-year-old bug in OpenBSD—an operating system known primarily for its security. ...

When I started blogging in the late 1990s, I wrote essays, marked them up with HTML, and linked them from my personal website. I started using blogging software in the early 2000s. First was Movable Type. Then, in 2004 when Movable Type changed licenses from open source to proprietary, I made the jump to some new hosting software called Wordpress (along with a lot of other people). From then until now - for the last 22 years - my blog has been powered by Wordpress. ...

Rather than imagining “prompt engineering” as a new form of writing that appeared *ex nihilo* three years ago, I find it helpful to think about the ways this new kind of writing remixes existing forms of writing. For example, the primary goal of prompt engineering is getting a model to behave in a specific way. We do that by providing it with very clear, unambiguous instructions. There’s a clear connection to technical writing here. Some prompt engineering frameworks claim that adding phrases like “my job depends on it!” to a prompt can improve the quality of responses, so there’s likely an opportunity to draw in aspects of persuasive writing as well. &c. And of course there are the interesting differences between prompt writing and technical or persuasive writing, such as the difference in audience (when you write a prompt, your audience is an LLM). But it’s still the case that knowing something about your audience and how they think (in this case, knowing something about how LLMs work under the hood) can make you a more effective writer. ...

tl;dr - Go play around with generativetextbooks.org and let me know what you think. Earlier this year I began prototyping an open source tool for learning with AI in order to explore ways generative AI and OER could intersect. I’m specifically interested in trying to combine the technical power of generative AI with the participatory power of OER, in order to both increase access to educational opportunity and improve outcomes for those students who access it. I did some preliminary writing on this topic back in July of 2023, calling the artifacts that result from combining generative AI and OER “generative textbooks” and have continued to ruminate on the topic. ...

“Generative AI models don’t understand, they just predict the next token.” You’ve probably heard a dozen variations of this theme. I certainly have. But I recently heard a talk by Shuchao Bi that changed the way I think about the relationship between prediction and understanding. The entire talk is terrific, but the section that inspired this post is between 19:10 and 21:50. Saying a model can “just do prediction,” as if there were no relationship between understanding and prediction, is painting a woefully incomplete picture. Ask yourself: why do we expend all the time, effort, and resources we do on science? What is the primary benefit of, for example, understanding the relationship between force, mass, and acceleration? The primary benefit of understanding this relationship is being able to make accurate predictions about a huge range of events, from billiard balls colliding to planets crashing into each other. In fact, the relationship between understanding and prediction is so strong that the primary way we test people’s understanding of the relationship between force, mass, and acceleration is by asking them to make predictions. “A 100kg box is pushed to the right with a force of 500 N. What is its acceleration?” A student who understands the relationships will be able to predict the acceleration accurately; one who doesn’t, won’t. ...

Last week I had the amazing opportunity to speak at the 3rd Annual AI Summit at UNC Charlotte. The entire event was wonderful and the organizing team were terrific. My keynote wasn’t recorded, so I thought I would serialize it across a series of blog posts. This post is the first in that series, and this section of the talk was titled Writing Is Thinking. David McCullough said, “Writing is thinking. To write well is to think clearly. That’s why it’s so hard… We all know the old expression, ‘I’ll work my thoughts out on paper.’ There’s something about the pen that focuses the brain in a way that nothing else does.” ...

Back in the mid-1990s I read an absolutely amazing article that had a lasting impact on my thinking. Despite looking for it several times over the years, I’ve never been able to find it again. This was the era of 14.4k, 28.8k, and 56k modems, when we used our home landlines to dial up and connect to the internet. The article’s main argument was that, just as architects have to understand and account for gravity in their designs of bridges and buildings, web architects have to understand and account for bandwidth in their website designs. Back in the day, including too many large images on a webpage could “weigh it down” to the point of “collapse.” Your 28.8k connection provided so little bandwidth to your home that you simply couldn’t download that much data in a reasonable amount of time, so after waiting a minute for the page to load you just gave up and went somewhere else. ...

Human Teachers and AI Teachers Would you be surprised if you pulled a random person off the street, shoved them into a classroom full of students, and then found that they weren’t a particularly effective teacher? Of course not. And why wouldn’t that be surprising? Because effective teaching requires a great deal of knowledge and skill, and the person you pulled off the street most likely had no relevant training. ...