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When Research Starts Accelerating Itself, the Real Danger Is “Everything Looks Fine” When feedback loops shorten, the quality of oversight matters as much as speed. A model clears a training log, catches a few bugs in d...
当研发开始自己加速，真正危险的是“看起来一切正常” 反馈回路变短之后，监督的质量和速度一样重要。 一套模型帮人排完训练日志，改掉几个分布式训练的 bug，又把下一轮实验的参数摸得更准。新模型因此更强，也更会做同样的事。下一次迭代里，试验更多、反馈更快、人工参与更少。真正让人不安的，不是机器忽然拥有了一个无所不能的大脑，而是这条链路里每一步看上去都很合理。 人工智能研发正在被拆成越来越多可重复、可评分、可并行的工作：训练小模型，调整优化...
When AI Starts Doing the Work: The Real Divide Is Not Better Answers, but Reliable Delivery Once models enter workflows, “good answers” matter less than dependable delivery. At two in the morning, the most expensive thi...
当 AI 开始做事：真正的分水岭不是更会回答，而是更会交付 当模型开始进入流程，能力的意义会从“答得好”转向“交付得稳”。 凌晨两点，材料实验室里最贵的东西往往不是超算，而是卡在交接处的那一小时：有人把晶体结构导进模拟器，有人去找势函数，有人盯着队列，有人再把结果翻成下一轮实验能用的判断。单个步骤并不神秘，真正耗掉研究者时间的，是步骤之间彼此不认识。 AI 正在撞上这堵墙。过去，人们围着模型的回答质量打分：会不会写、会不会算、会不会解...
Why Meta Sent Contractors to Test Rival Chatbots as Teens Illustration prepared for this article. No company logos or real people are depicted. The easy version of this story is too easy. Some headlines have turned WIRE...