Can an Algorithm Tell When Kids Are in Danger? (Published 2018) - The New York Times
Can an Algorithm Tell When Kids Are in Danger? (Published 2018) The New York Times
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Stop editing AI slop manually (free guide inside)
Universal prompt framework that works with all LLMs and writing types If you’ve ever used AI to write an email, a blog post, or a project update and felt like you spent more time editing the output than it would have taken to write it yourself, chances are your draft looks something like this: It opens with “In today’s rapidly evolving landscape.” You keep removing “delve,” “tapestry,” and “it’s worth noting.” There are enough em dashes to fill a novel. The content is accurate, but it reads like it could have been written by anyone, about anything. You publish it anyway because the deadline won’t wait. We dealt with the exact same thing for over three years at Towards AI, editing, rewriting, and occasionally questioning our life choices. Eventually, we decided to stop fixing drafts one at

Iran threatens OpenAI’s Stargate data center in Abu Dhabi
Iran's Islamic Revolutionary Guard Corps (IRGC) has published a video threatening OpenAI's planned Abu Dhabi datacenter if the US follows through on threats to attack the country's power plants, as reported earlier by Tom's Hardware. The video, which was published to an Iranian state-backed news outlet's X account on April 3rd, says the IRGC will [ ]

OpenAI calls for robot taxes, a public wealth fund, and a four-day week
Sam Altman’s 13-page policy blueprint, ‘Industrial Policy for the Intelligence Age,’ proposes auto-triggering safety nets, containment playbooks for rogue AI, and direct citizen dividends from AI-driven growth. He told Axios it is a starting point, not a prescription. OpenAI has published a 13-page policy document calling for sweeping economic reforms to prepare for what it [ ] This story continues at The Next Web
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VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors
Vision Language Models struggle with fine-grained visual perception tasks due to their language-centric training approach, performing poorly on unnamed visual entities despite having relevant information in their representations. (1 upvotes on HuggingFace)





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