推荐一篇文章：how to be good at researchhttps://r.slax.com/b/9f317e59-61bb-441b-8be8-9a097f4ec53f大家都有 AI 工具，我就不狗尾续貂地翻译了，这篇文章读下来，没有复杂的理论术语，挺对我胃口。没看过的，建议直接去看看，比看我的公众号有营养多了。he'd ask whoever sat near him what the important problems in their field were, then ask why they weren't working on them. 从问题入手，问问自己，你所在的行业，有什么最重要的，未被解决的问题？静下来想这件事，还挺奇妙的。如果想不出来，你一直在干嘛？如果解决不了，你一直在干嘛？如果没问题？你一直在干嘛？old material is criminally underpriced. this field reruns its own past on a delay: mixture of experts dates to 1991, lstms to 1997, backprop went mainstream in 1986. rich sutton needed about a thousand words in 2019 to write the bitter lesson, and it predicts the shape of the field better than surveys ten times its length. claude shannon gave a talk on creative thinking in 1952 where his opening move was to shrink a problem until it's nearly trivial, crack the small version, then reintroduce the difficulty one piece at a time. that single trick will carry you through more walls than any modern productivity advice.这段话里提到了香农和萨顿，他们是谁？这段话的背后有什么故事或者文章？有人停下来了解了吗？a descending loss curve is not analysis, it's reassurance. your experiments throw off far more information than you consume: transcripts, failure cases, the strange tail of the distribution. most of it dies unread in a logs folder.我自己也有一个大问题：沉迷于创造，希望灵光一闪能带来数据上的突破。但是缺少对输出的仔细观察、调优、改进。那些脏活累活简单的活里，可能潜藏着价值。这可能也是我们说的“闭环”。顺便说一句，我是用 Slax Reader 分享的这篇文章，文章里做了些评论——你如果有不同观点，可以登录了，一起讨论。
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