To be fair, I wasn't calling you a hypocrite. I was implying that the "journalists" who shovel shit, whose prospects of being replaced by the predictor is almos

To be fair, I wasn't calling you a hypocrite. I was implying that the "journalists" who shovel shit, whose prospects of being replaced by the predictor is almost certain at this point, are hypocrites. I do enjoy a little schadenfreude every now and then. And certainly, other people, including two-faced elected politicians. I apologize if you felt it was directed towards you. >The idea that mid and late career tech, creative, medical, etc. professional will be able to easily "reskill," or that there will be good jobs for them if they manage to, is magical thinking The jobs aren't going anywhere. Some will be made redundant, a lot will be augmented, and if we're so lucky, there will be more content shoveled out by the text predictor than ever before (garbage.) There is no medical, tech, or "truly" creative professional that is going to be replaced wholly by an LLM or diffusion model, not anytime soon. I'm assuming you're referencing the "study" done on reddit with /r/AskDoctors, and it's a laughable study. Certainly, docs can augment themselves, and they should, but not wholly rely on the model. Even 70B+ LLMs do not produce consistent facts, they produce plausibility, despite the facts most definitely being in their corpus. They predict. If you read Microsoft/OpenAI's paper around a month ago, it appears that RLHF actually reduces the quality in many aspects, despite it increasing the quality of output for whatever they deem quality elsewhere, so it's a double edged source instead of "add more tokens" + "add more layers." But more importantly, new jobs will come, more than before. >without being curious about what the actual human beings involved I'm back in the rust belt with family after moving around the US for software jobs in the last five years, I see it first hand. LLMs are like Google on steroids for a (compressed) corpus (the internet.) But you have to know that it's simply predicting. It's plausibilities. Once you know their limitations in that respect, they are useful.