The rise and fall of robots.txt
David Pierce,
The Verge,
2024/02/16
This article is getting a lot of circulation around social media. It describes robots.txt, a text file that sits in the home directory of every web site (including mine) and instructs crawlers about what they can index and what they should avoid. As David Pierce reports, following robots.txt is not a legal requirement, but it's something considerate web crawlers and search engine indexers did. But no more, as AI engines are insatiable in their quest for data. They run right through robots.txt as though it didn't exist. And that's bad for the long-term future of the web. On the other hand, replacing robots.txt with something more enforceable is also bad for the long-term future of the web.
Web: [Direct Link] [This Post]
OpenAI Launches AI Text-to-Video Generator Sora
Sergio De Simone,
InfoQ,
2024/02/16
As I type Sora is broken but as Donald Clark reports "Sora, from OpenAI, may go down in the history of movies or moving pictures, as a pivot point. It is significant as that filmed train thundering into La Ciotat, scaring the theatre audience or The Jazz Singer, the first feature-length talking film." For the record, Sora is supposed to create (short) videos from text prompts. But more, "We're teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction."
Web: [Direct Link] [This Post]
Learning to Surf: Supporting a Campus’s AI Needs
Leslie Bruce, PhD,
Faculty Focus | Higher Ed Teaching & Learning,
2024/02/16
"On most campuses, faculty fall into three camps: those who want to 'lock and block' AI (just stay out of the water!), those who encourage students to use AI freely in their writing processes (open-ocean swimmers), and, finally, those who want to embrace AI with guardrails (learning to surf)." Not surprisingly, this article endorses the third approach, and recommends some strategy.
Web: [Direct Link] [This Post]
Large Language Models: A Survey
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao,
arXiv,
2024/02/16
This is an outstanding paper. It delivers exactly what the title promises, an up-to-date survey of large language models. The illustrations are lavish, expertly executed, detailed and informative. The text is clear and well-presented. Overall what we have is not just a list of LLM engines but detailed accounts of how they work, what they can do, what makes them different from each other, how they're developed, generations of different types of engines, and more. If you can, take a day, work through this paper slowlt, and create something out of it (a PowerPoint presentation, say) and then share what you've done. After this you will be more knowledgable than pretty much every edtech pundit in the field.
Web: [Direct Link] [This Post]
(Almost) Every infrastructure decision I endorse or regret after 4 years running infrastructure at a startup
Jack Lindamood,
Jack's home on the web,
2024/02/16
Want to be daunted? Read through this list of infrastructure decisions made at a large-scale startup. What most of these services do is autimate the management of a larrge cloud application infrastructure supporting a range of applications and platforms (which aren't even mentioned, they're so far down the stack). I recognized maybe half the tech being used in this, and it gives me a sense of how much learning would be required to master the entire stack. If you're lookming at a major MOOC or online learning application, this is what you're looking at.
Web: [Direct Link] [This Post]
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