| Correct. Entirely. And I'm yuge on LLMs. It is very much one of those things that makes me feel old and/or scared, because I don't believe this would have been swallowed as easily, say, 10 years ago. As neutrally as possible, I think everyone can agree: - There was a good but very long overview of LLMs from an ex-OpenAI employee. Good stuff, really well-written, - Rapidly it concludes by hastily drawing a graph of "relative education level of AI" versus "year", draw a line from high school 2023 => college grad 2024 => phd 2025 => post-phd 2026 => agi 2027. - Later, this gets published by same OpenAI guy, then the SlateStarCodex guy, and some other guy. - You could describe it as taking the original, cut out all the boring leadup, jumped right to "AGI 2027", then wrote out a too-cute-by-half, way too long, geopolitics ramble about China vs. US. It's mildly funny to me, in that yesteryear's contrarians are today's MSM, and yet, they face ~0 concerted criticism. In the last comment thread on this article, someone jumped in to discuss the importance of more "experts in the field" contributing, meaning, psychiatrist Scott Siskind. The idea is writing about something makes you an expert, which leads us to tedious self-fellating like Scott's recent article letting us know LLMs don't have to have an assistant character, and how he predicted this years ago It's not so funny, in that the next time a science research article is posted here, as is tradition, 30% will be claiming science writers never understand anything and can't write etc. etc. |