历史与人物twobithistory.org编辑精选
阿达·洛芙莱斯的程序究竟做了什么？
What Did Ada Lovelace’s Program Actually Do?
2018 — 2024，它一次次回到首页
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导读
这篇文章深入解读了 Ada Lovelace 在 1843 年为巴贝奇分析机所写的“Note G”程序，逐条分析它实际计算的内容（伯努利数），并附上作者将其翻译成的 C 代码。文章还讨论了 Lovelace 是否配得上“第一位程序员”的称号，以及她对计算机构想超越巴贝奇的远见。
为什么经久不衰：它用现代编程语言把史上第一个程序讲透，还发现并修复了其中的一个 bug，让 180 年前的计算史变得可验证、可运行。
HN 评论精选
文章作者在程序中发现了 1843 年的一个真实 bug，令人震撼
太震撼了。当我意识到 Sinclair 竟然发现并修复了 1843 年写下的第一个程序中的 bug 时，我起了一身鸡皮疙瘩——在 Lovelace 的“开发图”中，第四个操作写成了 v5 / v4，但正确的顺序应该是 v4 / v5。评论者还提到一本含此算法的皮面初版 Lovelace 著作近期以约 12 万美元成交，并认为这个 bug 更可能出在转录而非原稿。
zaroth查看英文原文
Holy shit. I got shivers the moment I realized Sinclair actually finds and fixes a bug from the very first program written in 1843. In her “diagram of development,” Lovelace gives the fourth operation as v5 / v4. But the correct ordering here is v4 / v5. 1843! I was recently at the Musee des arts et metiers in Paris, and they have several original Pascalines on display. So many marvels packed into a very tight space, it is one of my favorite in Paris and I would highly recommend it. I'm not sure what they have on Lovelace, I'll admit I was pretty overwhelmed with the collection. Their computer collection alone is a museums worth, and they cover much, much more than just computer history (a lot of mechanical engineering for starters). If they don't have this algorithm on display, they absolutely should. A leather-bound first edition Lovelace including this algorithm recently sold for about $120,000. That actually seems like a bargain when you think about the level of the accomplishment. [1] I agree with OP that most likely the bug is in the transcription and not present in the original manuscript. [1] - https://www.theguardian.com/books/2018/jul/24/ada-lovelace-first-edition-pioneering-algorithm-program
有人正在尝试按巴贝奇的设计真正造出分析机（Plan 28 项目）
给好奇的人：John Graham-Cumming 发起的 Plan 28 项目正尝试真正建造巴贝奇的分析机，目前需要从巴贝奇浩繁的手稿中整理大量零散的知识。项目博客提到，仅一个带 1000 个寄存器的存储器就需要约 180 万个齿轮齿。
imhoguy查看英文原文
Just for anyone curious there is attempt to build Babbage's Analytical Engine started by John Graham-Cumming's Plan 28 project[0]. Currently there is a lot of fragmented knowledge to be collected from the vast Babbage's writings. To understand the size of the project here is the excerpt from their blog[1]: >in the context of manufacturing methods Babbage calculates that the total number of teeth to be formed for a store with 1,000 registers would be 1,800,000. [0] https://plan28.org/ [1] http://blog.plan28.org/2018/05/spring-2018-report-to-computer.html
C 翻译版不简单；分析机的十进制定点设计与指令集值得玩味
文章作者把这个程序翻译成的 C 版本绝不简单。值得注意的是 C 版用了浮点数，而很多简单 CPU 没有浮点；分析机用 40 位（后来 50 位）十进制定点数表示，而且它的指令集直接带除法——许多简单 CPU 反而没有。既然已有模拟器，也许有人可以把 Lovelace 的程序做成打孔卡真正跑起来。
acqq查看英文原文
The "C translation" of the program by the article author: https://gist.github.com/sinclairtarget/ad18ac65d277e453da5f479d6ccfc20e It's definitely far from being trivial! Also note the use of floats in the C version. A lot of simpler CPUs didn't and still don't have floats. Fascinatingly, the Analytical Engine's number representation was 40 digits (and later: 50)(!) base 10 (but not "floating" but "fixed" point): https://www.fourmilab.ch/babbage/authentic.html Moreover, a lot of simpler CPUs also don't have the "division" in the "instruction set", the Engine has it! Given the emulator already existing, maybe somebody will now create the cards from Lovelace's program, and run it as intended? https://www.fourmilab.ch/babbage/emulator.html
Lovelace 的伟大在于她对机器潜力的远见，而不只是写了第一个程序
好文章。作者认为 Ada 配得上“第一位程序员”的称号，既因为她发表了一个精巧的算法，也因为她比巴贝奇和 Menabrea 更深刻地理解了分析机的潜力——计算机（或足够复杂的动态系统）能“回馈给你未曾输入的东西”。这一思想如今主要通过深度学习的进展被大众重新发现。
kristoff_it查看英文原文
Great article. In some ways it reminds me of Jonathan Blow's presentation titled "Truth in Game Design" [1] The author is saying that Ada deserves the title of first programmer both because she did publish an elaborate algorithm and because she understood the potential of the Analytical Engine better than Babbage and Menabrea. Jonathan Blow's presentation is more about that second point and how computers (or rather, sufficiently complex dynamical systems) can "give you something back that you didn't put in". System theory is nothing new so it might not be exact equivalent of thinking about engines that can compose music in 1842, but it seems to me that it is not a widespread and well-understood concept as it should be, and that the public opinion is re-discovering it mainly through Deep Learning advancements. [1] https://www.youtube.com/watch?v=C5FUtrmO7gI
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