Stochastic parrots, text extruders, extractive business models, and chatbots.
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"I do not have the time or emotional energy to screen out regular attacks by Large Language Models, with the knowledge that making the wrong decision costs a real human being their connection to a niche community."
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As more people look to AI to learn about the world, the people who control how it's trained and how it responds will control our prevailing narratives. That's wildly dangerous.
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The case for moving AI down the stack
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A federal court agreed that using copyrighted works to train AI is fair use - but that pirating them to do so infringes creator rights.
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Ten years ago, Google crawled two pages for every visitor it sent to a publisher. Today, Anthropic crawls 60,000.
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[Khari Johnson in The Markup] The Republican legislature is working on ensuring that AI is unencumbered by regulations or protections: "The moratorium, bundled in to a sweeping budget reconciliation bill this week, also threatens 30 bills the California Legislature is currently considering to regulate artificial intelligence, including one that
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[Mike Caulfield in The Atlantic] A smart analysis and suggestion about the current state of AI by Mike Caulfield: "I would propose a simple rule: no answers from nowhere. This rule is less convenient, and that’s the point. The chatbot should be a conduit for the information of
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[Cy Neff in The Guardian] This is straight out of Black Mirror: "Chris Pelkey was killed in a road rage shooting in Chandler, Arizona, in 2021. Three-and-a-half years later, Pelkey appeared in an Arizona court to address his killer. Sort of. [...] Pelkey’s appearance from beyond the grave was
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[Adam Wierman and Shaolei Ren in IEEE Spectrum] An interesting finding on the energy use implicit in training and offering AI services. I do think some of these principles could apply to all of cloud computing - it’s out of sight and out of mind, but certainly uses a
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[Simon Willison] Prompt injection attacks have been one of the bugbears for modern AI models: it's an unsolved problem that has meant that it can be quite dangerous to expose LLMs to direct user input, among other things. A lot of people have worked on the problem, but
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[Jeremy Keith] Jeremy Keith highlights the hammering that the public service internet is getting from LLM vendors: "When we talk about the unfair practices and harm done by training large language models, we usually talk about it in the past tense: how they were trained on other people’s
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[Allison Morrow at CNN] It's refreshing to see this kind of AI skepticism in a major outlet: "Apple, like every other big player in tech, is scrambling to find ways to inject AI into its products. Why? Well, it’s the future! What problems is it solving?