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"Staying on the subject of "we live in a dystopian digital hellscape": a Gizmodo investigation identified 32 data brokers selling access to the unique mobile IDs of people pegged as "actively pregnant" or "shopping for maternity products". At least one company was also offering access to a catalogue of people using the same sorts of emergency contraceptives that some Republican's want to outlaw or restrict."

"Ultimately, he suggests, each user would have their own "large language model" (LLM), the underlying technology of services such as ChatGPT, which would be trained on their own speech and behaviour patterns, to let them generate extremely personalised responses to queries and requests. Such systems could be a natural progression from AI tools that already exist today. Services such as Gmail can summarise and suggest replies to emails based on previous messages, while Microsoft Teams will transcribe and summarise video conferences, automatically generating a to-do list from the contents."

"A core selling point of machine learning is discovery without understanding, which is why errors are particularly common in machine-learning-based science. Three years ago, we compiled evidence revealing that an error called leakage - the machine learning version of teaching to the test - was pervasive, affecting hundreds of papers from 17 disciplines. Since then, we have been trying to understand the problem better and devise solutions.  This post presents an update. In short, we think things will get worse before they get better, although there are glimmers of hope on the horizon."

Are Social Media Platforms the Next Dying Malls? It was cool to hang out at the mall-until it wasn't

"This is a broader pattern we're seeing across other fields where LLMs are being deployed. Whether it's coding, writing, design, law, or medicine, the most effective AI users are people who already have deep domain expertise. Expertise isn't obsolete; it's more important than ever-because the value isn't just in producing outputs quickly. It's in being able to vet, steer, and improve those outputs. The future of computer science education isn't about teaching less. It's about teaching differently. We still need students who can understand how software works at a fundamental level. But we also need to train them to collaborate with AI-to become fluent in prompting, reviewing, debugging, and refining AI-generated outputs. Mastering this hybrid skillset will be critical not just for engineers, but for anyone hoping to thrive in a world where knowledge work is increasingly AI-augmented. Practically speaking, AI could dramatically lower the barrier to entry for students. When I was in high school, it would take months (if not years) of training in CS before you could create a game or app that was genuinely cool to people that aren't inherently curious and nerdy."

"This cycle is particularly painful for non-engineers because they lack the mental models to understand what's actually going wrong. When an experienced developer encounters a bug, they can reason about potential causes and solutions based on years of pattern recognition. Without this background, you're essentially playing whack-a-mole with code you don't fully understand."

"And so I think this is a crisis. I think the biggest part of the crisis is for teachers. A lot of people suggest teachers go back to Blue Book exams, right? At least then we can make sure that the student is really doing their work. But that's just a huge burden on teachers now to completely reorganize the entire way that they administer education. I couldn't agree more. Kevin Roose of the New York Times recently said, "if students can cheat with ChatGPT then you need to rethink your teaching." Well, I've been working in education for almost two decades, that's exactly what people said when smartphones came around. And now, 10 years later, we've come around to just banning them in school, or trying to anyway. And I wonder if we're going to need to wait a decade before reaching that conclusion in education."