OLDaily by Stephen Downes

I think this is a useful exercise that should be watched by educators. "We wanted to come up with a method to encourage people watching videos to do what's called 'lateral reading,' which is that you go look at other places on the web to establish whether something is credible or true, as opposed to diving deep into the thing itself," says Amy X. Zhang, one of the authors of a paper (20 page PDF) describing the project. Creating the mechanism is only the first step, though, as the authors need to consider things like bad actors and circular citation networks.

"When will the first American college or university charge $100,000 or more to attend?" asks Bryan Alexander. "What might that mean for higher education?" There's a number of them in the $US 90K range already, so it's probably not long now. The survey paints a picture of an education system that has gone very wrong, and is designed to preserve privilege rather than advance the interests of society.

The gist of the story is this: "Broadcasters have been focused on fitting the streaming TV business into the traditional TV mold. Meanwhile, YouTube has shattered the mold with its democratic approach to TV and is eating the broadcaster's lunch!" Almost all the video I watch online (and I watch a lot) does not fit into the traditional TV mode. "YouTube has figured out how to harness the creativity of anyone with talent and get them onto the TV screens of just about everyone... Disney spends $30 billion a year on content destined for television. YouTube doesn't pay anything for its content. Instead, it relies on many creators to keep viewers coming back." At some point, we'll see educational media follow the same path (things like TeachersPayTeachers were trailblazing in this way).

This post offers an overview of GPT-4o, the new model released by chatGPT. "GPT-4o appears to be a step up over GPT-4 and is the smartest model I have used. However, it does not represent a major leap over the previous version of GPT-4, the way that GPT-4 was a 10x improvement over the free GPT-3.5." That accords with my own experience. Also, "soon everyone, whether they are paying or not, will get access to GPT-4o." I've been paying for GPT-4, and will probably keep paying, but as Ethan Mollick says, "that $20 a month barrier kept many people from understanding how impressive AI can be, and for gaining any benefit from AI. That is no longer true."

"I was at an event recently and a speaker was late so was asked to do a 20 minute slot, unscripted, no props/slides etc.," writes Andrew Jacobs. "If it happened to you, what could you do a tight 20 on?" My answer - and it should be your answer too - would be "anything". That's the different between knowledge as 'remembering' and knowledge as 'learning'. Anything I've ever heard about, I could talk about for twenty minutes simply by organizing and presenting my thoughts on the topic. Do a 'quick three' - past, present or future; my view, your view, synthesis; etc. For each six minute segment, build the case - something concrete, something general, a conclusion to be drawn (or: an odd phenomenon, a general principle, an explanation to be given). Each of these is only two minutes, and you'll be pressed for time to make the point, but try: a couple points of reference or evidence, and the point is made. Anyone can do this about anything - if they've learned how. And if they've learned how, they know how to learn about any new subject they encounter.

According to this article, " A 2024 Gallup poll found that nearly 25% of workers worry that their jobs can become obsolete because of AI, up from 15% in 2021." This would not be an issue except for the fact that we currently structure society such that, if you do not have a job, you are relegated to a life of pain and poverty. We should rethink this. Certainly, as the IBM blog suggests, lets think about reskilling. But articles like this that focus only on reskilling serve as a distraction from the larger issues. But let's also reimagine the social and corporate compact to ensure everybody earns a share of the increased productivity AI will generate. Because the changing workplace isn't the employee's problem. It's everybody's problem.

I find it interesting to juxtapose two documents, a statement from the Stanford Social Innovation Review on what it means to govern for all, and this document from the Wikimedia Movement declaring "the values, rights, relationships, and mutual responsibilities of all participants in the shared mission of this movement." There are numerous points of contrast, starting from Wikimedia's embracing of "a factual, verifiable, open, and inclusive approach to knowledge," and SSIR's failure to address the topic at all. There's the difference between 'inclusion' (in Wikimedia) and 'equity' (in SSIR). At the same time, Wikimedia embraces a 'shared vision', while SSIR limits this to fairness and consistency under the law. And, taking a few steps back, the SSIR document seems to be about the relation between us (the governors) and them (the people) while Wikimedia "entrusts decisions to the most immediate or the lowest possible level of participation". I don't think you get democracy without education, or education without democracy, but what either of those two things amount to is still very much open to debate.

This is a headline I would have considered current in around 2003 or so, but hey, educational podcasts were a good idea then and they're a good idea now. As true today as it was twenty years ago, "educational podcasts come in diverse forms, including podcasts produced by teaching practitioners for learners, by learners for teachers (for instance as summative assessment) by students for each other (for instance as peer-supportive resources) and by teachers for other teachers." What the article does point to is a resurgence of interest in podcasts (not coincidentally, I think, with the collapse of cable media, the fragmentation of streaming video services, and the toxification of social media). What it doesn't talk about is the use of an open access format (RSS) to distribute and collect free media. Image: EduTech Wiki.

Martin Weller reflects on "a way to think about post-Twitter life," bringing us the personal engagement environment, a nod back to the personal learning environment (PLE), where "you may have a main platform (eg blog), a work focused one eg LinkedIn, a personal one eg Instagram, a general one eg Threads, a course focused one eg podcasts, etc. For any one engagement activity you may post to all, one or some of these." Not quite though - a PLE was one place we could use to reach out to these other services, not merely a collection of platforms (the collection of platforms was the 'personal learning network', which is what people used in lieu of an actual functioning PLE (which never materialized).

The main takeaway from this oddly-written story is that "More media and tech companies are launching educational courses as they venture deeper into video." AI-written? You can read more about the specific Bloomberg-Emeritus project in the press release. Of course, online courses are more than just nice video presentations; you need to do something to justify the $2500 price tag. So they'll get a Bloomberg subscription too.

This article "follows Gert Biesta's recent call to query the 'common sense' understanding of educational research" as it relates to social media, reflecting on the "subtle demand that the contemporary academic present to the world as a coherent, consistent, and orderly self." Twitter, we are told, "discourages a fluid and complex self. It prioritizes stability over self-creation." But as Richard Rorty would say, we need people to see us as complex and sometimes changing individuals. From here the article diverges a bit, on the one hand considering the new toxic Twitter (you can leave only iof you have a well-established identity) and on the other considering what's lost by staying on Twitter (intimacy, vulnerability and acknowledgement). Either way, networking on social media involves presenting oneself as an abstraction lacking the nuance found in a physical space "which draws people together, which fosters dwelling, and which invites care and connectedness." Image: LSE Blog, Time to Rethink Academic Twitter.

If you're curious to know what the hypercard experience was like for Mac users back in the day, you might want to try Decker, "a multimedia platform for creating and sharing interactive documents, with sound, images, hypertext, and scripted behavior." I'm not a fan of the small sized and black-and-white aesthetic, but that's purely a matter of taste. Via Alan Levine. More fun tools from Internet Janitor.

My first question on reading the title was whether this is the same concept as I have been talking about over the years? It seems to be. "To both companies (OpenAI and Google), personal AI is a personalized service—from them. It's not something you own and control. It's not about individual empowerment and agency," writes Doc Searls. "For individual empowerment and scale to happen, we need to be self-sovereign and independent. Personal AI can give that to us... by working as agents that represent us as human beings—rather than mere users." Let's call it, um, a Personal Living Environment. A PLE - for the AI age. P.S. if you read to the bottom you find that Searls's post is mostly an advertisement for Kwaai, which kind of makes me sign with disappointment.

I like the idea of a 'nutrition label' for publications, as we see described here about the Public Knowledge Project (PKP) example. However, I think there is room for debate about what is relevant, and what is not, in such a label. There's information about the reviewers, editorial and review process, and funding. But are the articles cited? Are they relevant, influential, or factual? What's the percentage of articles that had to be retracted? Does the journal publish replications of previous studies? Does it allow discussion? Are the papers used in courses? We need to start seeing these resources from a public, academic and scientific perspective, and not merely from a publication perspective.

"AI is going to be an enterprise transformation technology, just like digital," writes Josh Bersin. In this post he described a company called ServiceNow that pulls the pieces together to address a single problem: "Every role I have ever experienced in business, and I've been around for a long time, has to do with somebody trying to do aspiring work and finding themselves stymied because they can't find the tools or the information they need," he says. However, the transition is challenged by the cost of such systems and the complexity of implementing them. "You're now buying another complex enterprise platform on top of your existing complex enterprise platforms."

You've probably already read AI-generated articles without knowing it. This article reports on an investigation into AdVon Commerce, the AI contractor at the heart of scandals at USA Today and Sports Illustrated. Author Maggie Harrison Dupré outlines the company's strategy of using AI to author review articles to be published on high profile websites. The idea is that the articles contain affiliate links that generate revenue if a reader clicks through and buys something. The articles are sometimes attributed to AI-generated authors whos profiles are reused over and over. This is a lengthy and detailed article that presents a lot of evidence along with the back-and-forth between the author and AdVon as the company denies many of the allegations. For my own part, I see no problem with AI-generated content if it's accurate, well-written, and not intended to deceive. But as the publishing industry spirals downward we see more and more the influence of bad actors filling the web with dishonest pollution. Via TWIG.

What I like about this course is that I was learning things only two clicks in (specifically, I was manipulating 3D models of proteins). There's no login (unless I want to mark segments as complete) and the course is fully open access. AlphaFold, by the way, is a tool "for advanced modelling, such as predicting protein-protein interactions, modelling large protein complexes and alternative structural states." The course is offered by EMBL-EBI Training on the ELIXIR Training Platform.(see more). There's a lot of stuff here; it can all be overwhelming, but this course will guide you through the resources they offer.

This is a tool that allows you to search for relevant topics from among the 1763 papers presented at the ACM CHI Conference on Human Factors in Computing Systems (CHI 2024) conference. It returns a similarity graph plotting the most relevant matches to your search. You can filter for specific paper tracks and, interact with the scatterplot to explore the different papers. "The system works by computing sentence embeddings on the papers text, then applying dimensionality reduction on the embeddings to compute the scatterplot. You can even change the dimensionality reduction and embeddings algorithm and parameters." You may also enjoy the way the source code for this is presented. It calculates "CHI 2024 papers similarity using sentence embeddings via HuggingFace Transformer.js and dimensionality reduction methods using Druid.js."

The 51th edition contains selected professional development opportunities that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. 252 page MS-Word Document.

I thought this would be a paean to the joys of cognitive science in education, but surprisingly this short article (based on a longer research paper) not only describes some of the principles they authors applied but also the difficulties they faced while applying them. For example, they discuss the concern that the 'coherence principle', which "recommends avoiding redundant visuals or information", stands in the way of clarity, "where improving clarity meant making explanations longer, presenting ideas in more than one way."