也是一只自由泳的小鸟
Where humans are ambiguous, often contradictory and hard to understand, science is trustworthy and clear. It doesn’t lie to you, mask its meaning or talk behind your back.
It was something I knew instinctively then, but would only come to understand properly years later: the way we think as humans is not so different from how a computer program operates. Every one of you reading this is currently processing thoughts. Just like a computer algorithm, we ingest and respond to data – instructions, information and external stimuli. We sort that data, using it to make conscious and unconscious decisions. And we categorize it for later use, like directories within a computer, stored in order of priority. The human mind is an extraordinary processing machine, one whose awesome power is the distinguishing feature of our species.
完全同意！我早就觉得人跟电脑没什么区别！有外行人问我机器学习到底怎么回事，不懂怎么运行的，我说就跟你不懂人脑怎么运行的一回事，一大堆神经细胞 0 啊 1 啊的莫名其妙就算出了一堆东西
我就想当一枚电脑程序
As a young child I even insisted on sleeping in a cardboard box, day and night, enjoying the feeling of being cocooned in its safe enclosure (with my mum passing biscuits to me through a ‘cat flap’ cut in the side).
我小时候喜欢在衣柜里蹲着！
It’s why statistics uses standard error as a basic principle, building in an assumption that there will always be things that don’t accord with expectations and predictions. [...]
People, on the other hand, can be less sanguine when things don’t go according to plan. You won’t find many commuters cheerfully quoting standard error when their train gets delayed or cancelled.
这和我前两天读到的 #TheDemonHauntedWorld 是一样的思路呀！https://rhabarberbarbara.bar/@unagi/107544607279401820
但我认为没有人 cheerfully quoting standard error when their train gets delayed or cancelled 是因为 no one likes seeing their train gets delayed or cancelled, but I strongly suggest that 德铁在列车时刻表的晚点时间旁边加上置信区间 to get people mentally prepared, otherwise 我打算自己统计一下我上班途中的晚点规律。
不过我自己统计有一个很大的问题就是我只能统计晚点，如果车子出发太早而我还没赶到车站那就统计不进去。。我又不想提前那么久等车
第一章读完了，感觉很怪，我的设想是它会对比 machine learning 和 human learning 的相似性，类似于“智慧背囊”那种通过生活中一个可以做类比的事件来讲道理，只不过类比的对象是科学研究的内容。但看起来 machine learning 只是一个发散点而已，作者最后讲到的内容其实关联性并没有那么大，而且结构松散，一个话题没讲清楚就又跳到别的内容去了，过一会儿又跳回来。。莫非这就是 ADHD 写作法？？
不过如果自己主动 interpret 一下就还是有所启发的，比如我的理解就是“要有计划地行事，但同时也要把错误和可能出的状况计划进去”。
这不是程序员笑话吗
我现在相信笑话来源于生活了