Stephen Downes

Good article describing how neuroscience is looking to artificial intelligence to solve some longstanding issues in the field. They're doing it because they can: "They trained a battery of different language models on those same sentences, and created a mapping model between human and machine neural activity. And they discovered that the networks not only produced humanlike text, but did so in a broadly humanlike way." This, in turn, tells us some things about artificial intelligence. "When they hallucinate information, it is not their failing, but ours: We are forcing them to answer a query that is outside their narrow competence... One of the things we've really learned from the last 20 years of cognitive neuroscience is that language and thought are separate in the brain."

This article (15 page PDF) is an introduction to open educational resources, covering "the OER movement, its milestones, and its integration into educational practice" as well as arguments for OER, criticisms, and an examination of "current OER implementation in higher education and its promise of innovation." Via Alan Levine.

I really do love 'do it yourself' posts and have an entire YouTube playlist devoted to my adventures with them. This set of instructions, however, begins with the requirement for "one cluster that had 4,088 H100 GPUs spread across 511 computers, with eight GPUs to a computer." Um, that's a bit much for my home office, let alone my budget. Then you have to make sure every computer works (they don't always), set up the software, train a single node, "burn InfiniBand burn," make sure again that the machines are working, and more. I like the 'reflections and learnings' at the end and look forward to the day when home hobbiests can set up their own 70B models in their living rooms.

This is a really good article discussing where Neal Stephenson's concept of The Young Lady's Illustrated Primer works and, more importantly, where it fails. "It works its magic through manipulation," writes Andy Matuschuk, "through isolation from people or projects with real meaning, and through a misguided reliance on aesthetic pleasures. I see no way to incrementally rescue its structure from these flaws." To a large degree, I agree with his argument. A personal learning environment should be engaged with reality, should offer control to the learner, and should serve as a multi-purpose thinking and learning tool, "an ever-present conduit, providing the support, structure, and representations we need to do things we care about."

"The answer to the problem or challenge of generative AI in education is generative teaching," writes Steve Hargadon. What does that mean? "How can we help students understand and use these amazing new tools in a way that lights the fires of their intellectual curiosity and growth, rather than just filling the pails through of traditional instruction and assessment?" I think this is the right approach - it is often better to run toward a challenge than to retreat from it. But as Hargadon notes, "t we have to become capable enough ourselves with an understanding AI in order to manage the process." This is a longer post that frames both the question and answer; worth a read.

Though this is not immediately useful it's the sort of approach necessary to accomplish a genuinely decentralized internet. "The web democratized the exchange of information, but it's missing a key layer: identity. We struggle to secure personal data with hundreds of accounts and passwords we can't remember. On the web today, identity and personal data have become the property of third parties. Web5 brings decentralized identity and data storage to your applications. It lets devs focus on creating delightful user experiences, while returning ownership of data and identity to individuals." It's based on Node, which I think is far too fragile an environment for critical web applications. But it's a step in the right direction. Here's the blog, which I'll be watching.