Augmenting Long-term Memory

Nielsen, Michael

One day in the mid-1920s, a Moscow newspaper reporter named Solomon Shereshevsky entered the laboratory of the psychologist Alexander Luria. Shereshevsky's boss at the newspaper had noticed that Shereshevsky never needed to take any notes, but somehow still remembered all he was told, and had suggested he get his memory checked by an expert.
20 世纪 20 年代中期的一天，一位名叫所罗门-舍列舍夫斯基的莫斯科报社记者走进了心理学家亚历山大-卢里亚的实验室。谢列舍夫斯基在报社的上司注意到，谢列舍夫斯基从来不需要做任何笔记，但不知为什么，他仍然记得所有告诉他的事情，于是建议他找专家检查一下自己的记忆力。

Luria began testing Shereshevsky's memory. He began with simple tests, short strings of words and of numbers. Shereshevsky remembered these with ease, and so Luria gradually increased the length of the strings. But no matter how long they got, Shereshevsky could recite them back. Fascinated, Luria went on to study Shereshevsky's memory for the next 30 years. In a book summing up his research ("The Mind of a Mnemonist", 1968), Luria reported that:
卢里亚开始测试舍雷舍夫斯基的记忆力。他从简单的测试开始，测试单词和数字的短串。谢列舍夫斯基很容易就记住了所以卢里亚逐渐增加了单词串的长度但无论字符串有多长，谢列舍夫斯基都能背诵出来。卢里亚被深深吸引住了，在接下来的 30 年里，他一直在研究舍列舍夫斯基的记忆力。在一本总结其研究的书（《记忆大师的头脑》，1968 年）中，卢里亚报告说：

[I]t appeared that there was no limit either to the capacity of S.'s memory or to the durability of the traces he retained. Experiments indicated that he had no difficulty reproducing any lengthy series of words whatever, even though these had originally been presented to him a week, a month, a year, or even many years earlier. In fact, some of these experiments designed to test his retention were performed (without his being given any warning) fifteen or sixteen years after the session in which he had originally recalled the words. Yet invariably they were successful.
[S.的记忆能力和他所保留痕迹的持久性似乎都没有极限。实验表明，他在重现任何一长串单词时都毫无困难，即使这些单词最初是在一周前、一个月前、一年前甚至多年前出现在他面前的。事实上，其中一些旨在测试他记忆力的实验是在他最初回忆单词的那节课后十五六年才进行的（没有给他任何警告）。然而，这些实验无一例外地取得了成功。

Such stories are fascinating. Memory is fundamental to our thinking, and the notion of having a perfect memory is seductive. At the same time, many people feel ambivalent about their own memory. I've often heard people say “I don't have a very good memory”, sometimes sheepishly, sometimes apologetically, sometimes even defiantly.
这样的故事令人着迷。记忆是我们思维的基础，拥有完美记忆的概念很有诱惑力。与此同时，许多人对自己的记忆力感到矛盾。我经常听到有人说 "我的记忆力不太好"，有时是羞怯地，有时是抱歉地，有时甚至是蔑视地。

Given how central memory is to our thinking, it's natural to ask whether computers can be used as tools to help improve our memory. This question turns out to be highly generative of good ideas, and pursuing it has led to many of the most important vision documents in the history of computing. One early example was Vannevar Bush's 1945 proposal*
鉴于记忆对我们的思维如此重要，我们自然会问，计算机是否可以作为帮助我们改善记忆的工具。事实证明，这个问题极易产生好的想法，而对这个问题的追问也促成了计算史上许多最重要的愿景文件。一个早期的例子就是范内瓦-布什在 1945 年提出的建议*。* Vannevar Bush, As We May Think, The Atlantic (1945).
* Vannevar Bush，As We May Think，The Atlantic（1945 年）。 for a mechanical memory extender, the memex. Bush wrote:
机械式内存扩展器 memex。布什写道

A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.
备忘录是个人存储其所有账簿、记录和通信的一种装置，它是机械化的，因此可以以超乎寻常的速度和灵活性进行查阅。它是个人记忆的放大和贴心补充。

The memex vision inspired many later computer pioneers, including Douglas Engelbart's ideas about the augmentation of human intelligence, Ted Nelson's ideas about hypertext, and, indirectly, Tim Berners-Lee's conception of the world wide web*
memex的愿景启发了许多后来的计算机先驱，包括道格拉斯-恩格尔巴特（Douglas Engelbart）关于增强人类智能的想法、特德-尼尔森（Ted Nelson）关于超文本的想法，以及间接启发了蒂姆-伯纳斯-李（Tim Berners-Lee）关于万维网的构想*。* See, for example: Douglas Engelbart, Augmenting Human Intellect (1962); Ted Nelson, Complex information processing: a file structure for the complex, the changing and the indeterminate (1965); and Tim Berners-Lee, Information Management: a Proposal (1989).
* 例如，见Douglas Engelbart，《增强人类智力》（1962 年）；Ted Nelson，《复杂信息处理：复杂、多变和不确定的文件结构》（1965 年）；以及 Tim Berners-Lee，《信息管理：一项建议》（1989 年）。. In his proposal for the web, Berners-Lee describes the need for his employer (the particle physics organization CERN) to develop a collective institutional memory,
.伯纳斯-李在他的网络提案中描述了他的雇主（粒子物理学组织欧洲核子研究中心）发展集体机构记忆的必要性、

a pool of information to develop which could grow and evolve with the organization and the projects it describes.
建立一个信息库，该信息库可以随着组织及其描述的项目的发展而发展。

These are just a few of the many attempts to use computers to augment human memory. From the memex to the web to wikis to org-mode to Project Xanadu to attempts to make a map of every thought a person thinks: the augmentation of memory has been an extremely generative vision for computing.
这些只是利用计算机增强人类记忆的众多尝试中的一小部分。从memex到网络，从维基到org-mode，从 "仙境计划 "到试图绘制一个人每一次思考的地图：记忆增强一直是计算机领域极富创造力的愿景。

In this essay we investigate personal memory systems, that is, systems designed to improve the long-term memory of a single person. In the first part of the essay I describe my personal experience using such a system, named Anki. As we'll see, Anki can be used to remember almost anything. That is, Anki makes memory a choice, rather than a haphazard event, to be left to chance. I'll discuss how to use Anki to understand research papers, books, and much else. And I'll describe numerous patterns and anti-patterns for Anki use. While Anki is an extremely simple program, it's possible to develop virtuoso skill using Anki, a skill aimed at understanding complex material in depth, not just memorizing simple facts.
在这篇文章中，我们将研究个人记忆系统，即旨在改善单个人长期记忆的系统。在文章的第一部分，我将介绍我使用这种名为 Anki 的系统的个人经验。我们将看到，Anki 几乎可以用来记忆任何东西。也就是说，Anki 使记忆成为一种选择，而不是任凭机会的偶然事件。我将讨论如何利用 Anki 理解研究论文、书籍和其他许多东西。此外，我还将介绍许多使用 Anki 的模式和反模式。虽然 Anki 是一个极其简单的程序，但使用 Anki 可以培养出精湛的技能，这种技能旨在深入理解复杂的材料，而不仅仅是记住简单的事实。

The second part of the essay discusses personal memory systems in general. Many people treat memory ambivalently or even disparagingly as a cognitive skill: for instance, people often talk of “rote memory” as though it's inferior to more advanced kinds of understanding. I'll argue against this point of view, and make a case that memory is central to problem solving and creativity. Also in this second part, we'll discuss the role of cognitive science in building personal memory systems and, more generally, in building systems to augment human cognition. In a future essay, Toward a Young Lady's Illustrated Primer, I will describe more ideas for personal memory systems.
文章的第二部分从总体上讨论了个人记忆系统。许多人对记忆这种认知技能的态度是矛盾的，甚至是轻蔑的：例如，人们经常谈论 "死记硬背"，好像它不如更高级的理解能力。我将反驳这种观点，并说明记忆是解决问题和创造力的核心。在第二部分中，我们还将讨论认知科学在构建个人记忆系统中的作用，以及在构建增强人类认知能力的系统中的作用。在今后的一篇文章《走向年轻女士的图解入门》中，我将介绍有关个人记忆系统的更多想法。

The essay is unusual in style. It's not a conventional cognitive science paper, i.e., a study of human memory and how it works. Nor is it a computer systems design paper, though prototyping systems is my own main interest. Rather, the essay is a distillation of informal, ad hoc observations and rules of thumb about how personal memory systems work. I wanted to understand those as preparation for building systems of my own. As I collected these observations it seemed they may be of interest to others. You can reasonably think of the essay as a how-to guide aimed at helping develop virtuoso skills with personal memory systems. But since writing such a guide wasn't my primary purpose, it may come across as a more-than-you-ever-wanted-to-know guide.
这篇文章的风格与众不同。它不是一篇传统的认知科学论文，即关于人类记忆及其工作原理的研究。它也不是一篇计算机系统设计论文，尽管系统原型设计是我的主要兴趣所在。相反，这篇文章是对个人记忆系统如何工作的非正式、临时观察和经验法则的提炼。我想了解这些，为建立自己的系统做准备。当我收集这些观察结果时，我觉得它们可能会引起其他人的兴趣。你有理由把这篇文章看作是一本如何使用个人记忆系统的指南，其目的是帮助开发精湛的个人记忆系统技能。但由于撰写这样的指南并不是我的主要目的，因此它可能会被认为是一个你永远都不想知道的指南。

To conclude this introduction, a few words on what the essay won't cover. I will only briefly discuss visualization techniques such as memory palaces and the method of loci. And the essay won't describe the use of pharmaceuticals to improve memory, nor possible future brain-computer interfaces to augment memory. Those all need a separate treatment. But, as we shall see, there are already powerful ideas about personal memory systems based solely on the structuring and presentation of information.
最后，我想谈谈这篇文章不会涉及的内容。我只会简要讨论记忆宫殿和定位法等可视化技术。文章也不会描述使用药物来改善记忆，以及未来可能的脑机接口来增强记忆。这些都需要单独讨论。但是，正如我们将要看到的，已经有一些关于个人记忆系统的强大想法，它们完全基于信息的结构化和呈现。

Part I: How to remember almost anything: the Anki system
第一部分：如何记住几乎任何东西：Anki 系统

I'll begin with an account of my own experience with the personal memory system Anki*
首先，我要介绍一下我自己使用个人记忆系统 Anki* 的经验* I've no affiliation at all with Anki. Other similar systems include Mnemosyne and SuperMemo. My limited use suggests Mnemosyne is very similar to Anki. SuperMemo runs only on Windows, and I haven't had an opportunity to use it, though I have been influenced by essays on the SuperMemo website.
* 我与 Anki 没有任何关系。其他类似的系统包括 Mnemosyne 和 SuperMemo。根据我有限的使用经验，Mnemosyne 与 Anki 非常相似。SuperMemo 只能在 Windows 上运行，我还没有机会使用它，不过我受到了 SuperMemo 网站上文章的影响。

I won't try to hide my enthusiasm for Anki behind a respectable facade of impartiality: it's a significant part of my life. Still, it has many limitations, and I'll mention some of them through the essay.
我不会试图用公正的外表来掩饰我对 Anki 的热情：它是我生活的重要组成部分。不过，它也有很多局限性，我将在文中提到其中一些。 . The material is, as mentioned above, quite personal, a collection of my own observations and informal rules of thumb. Those rules of thumb may not apply to others; indeed, I may be mistaken about how well they apply to me. It's certainly not a properly controlled study of Anki usage! Still, I believe there is value in collecting such personal experiences, even if they are anecdotal and impressionistic. I am not an expert on the cognitive science of memory, and I'd appreciate corrections to any errors or misconceptions.
.如上所述，这些材料是我的个人观点和非正式经验法则的结晶。这些经验法则可能并不适用于其他人；事实上，我可能搞错了它们对我的适用程度。这当然不是对 Anki 使用情况的适当控制研究！尽管如此，我相信收集这些个人经验还是有价值的，即使它们只是传闻和印象。我不是记忆认知科学方面的专家，如果有任何错误或误解，我希望得到指正。

At first glance, Anki seems nothing more than a computerized flashcard program. You enter a question:
乍一看，Anki 似乎只是一个电脑化的闪存卡程序。你输入一个问题：

And a corresponding answer:
以及相应的答案：

Later you'll be asked to review the card: that is, shown the question, and asked whether you know the answer or not.
稍后，你会被要求复习这张卡：即向你展示问题，并询问你是否知道答案。

What makes Anki better than conventional flashcards is that it manages the review schedule. If you can answer a question correctly, the time interval between reviews gradually expands. So a one-day gap between reviews becomes two days, then six days, then a fortnight, and so on. The idea is that the information is becoming more firmly embedded in your memory, and so requires less frequent review. But if you ever miss an answer, the schedule resets, and you again have to build up the time interval between reviews.
与传统的卡片相比，Anki 的优势在于它能管理复习时间表。如果你能正确回答一个问题，复习的时间间隔就会逐渐延长。因此，一天的复习间隔会变成两天，然后是六天，再然后是两周，依此类推。这样做的目的是让信息在你的记忆中更加牢固，从而减少复习的频率。但是，如果你错过了一个答案，时间表就会重置，你就必须再次增加复习的时间间隔。

While it's obviously useful that the computer manages the interval between reviews, it perhaps doesn't seem like that big a deal. The punchline is that this turns out to be a far more efficient way to remember information.
计算机管理复习的间隔时间显然很有用，但这似乎并不是什么大问题。问题的关键在于，事实证明这是一种更有效的信息记忆方式。

How much more efficient?
效率提高了多少？

To answer that question, let's do some rough time estimates. On average, it takes me about 8 seconds to review a card. Suppose I was using conventional flashcards, and reviewing them (say) once a week. If I wanted to remember something for the next 20 years, I'd need 20 years times 52 weeks per year times 8 seconds per card. That works out to a total review time of just over 2 hours for each card.
要回答这个问题，我们先来粗略估算一下时间。我复习一张卡片平均需要 8 秒钟。假设我使用传统的闪存卡，每周复习一次。如果我想在接下来的 20 年里记住一些东西，那么我需要 20 年乘以每年 52 周，再乘以每张卡片 8 秒钟。这样算下来，每张卡片的总复习时间略高于 2 小时。

By contrast, Anki's ever-expanding review intervals quickly rise past a month and then out past a year. Indeed, for my personal set of Anki cards the average interval between reviews is currently 1.2 years, and rising. In an appendix below I estimate that for an average card, I'll only need 4 to 7 minutes of total review time over the entire 20 years. Those estimates allow for occasional failed reviews, resetting the time interval. That's a factor of more than 20 in savings over the more than 2 hours required with conventional flashcards.
相比之下，Anki 不断扩大的审查间隔很快就会超过一个月，然后超过一年。事实上，就我个人的 Anki 卡集而言，目前的平均复习间隔是 1.2 年，而且还在不断延长。在下面的附录中，我估计平均每张卡片在整个 20 年中只需要 4 到 7 分钟的复习时间。这些估算考虑到了偶尔的审查失败，重新设定了时间间隔。这比传统认字卡所需的 2 个多小时节省了 20 多倍。

I therefore have two rules of thumb. First, if memorizing a fact seems worth 10 minutes of my time in the future, then I do it*
因此，我有两条经验法则。第一，如果记住一个事实在将来看来值得我花 10 分钟的时间，那么我就去做*；第二，如果记住一个事实在将来看来值得我花 10 分钟的时间，那么我就去做*。* I first saw an analysis along these lines in Gwern Branwen's review of spaced repetition: Gwern Branwen, Spaced-Repetition. His numbers are slightly more optimistic than mine – he arrives at a 5-minute rule of thumb, rather than 10 minutes – but broadly consistent. Branwen's analysis is based, in turn, on an analysis in: Piotr Wozniak, Theoretical aspects of spaced repetition in learning.
* 我第一次看到这样的分析，是在格韦恩-布兰文（Gwern Branwen）对间隔重复的评论中：Gwern Branwen, Spaced-Repetition.他的数据比我的略微乐观一些--他得出的经验法则是 5 分钟，而不是 10 分钟--但大体上是一致的。布兰文的分析反过来又是基于 Piotr Wozniak 的分析：Piotr Wozniak, Theoretical aspects of spaced repetition in learning.. Second, and superseding the first, if a fact seems striking then into Anki it goes, regardless of whether it seems worth 10 minutes of my future time or not. The reason for the exception is that many of the most important things we know are things we're not sure are going to be important, but which our intuitions tell us matter. This doesn't mean we should memorize everything. But it's worth cultivating taste in what to memorize.
.其次，比第一条更重要的是，如果一个事实看起来很醒目，那么不管它是否值得我花 10 分钟的时间，我都会把它放到 Anki 中去。之所以有这种例外情况，是因为我们知道的许多最重要的事情都是我们不确定是否重要，但直觉告诉我们很重要的事情。这并不意味着我们应该记住所有的东西。但值得培养的是对记忆内容的品味。

The single biggest change that Anki brings about is that it means memory is no longer a haphazard event, to be left to chance. Rather, it guarantees I will remember something, with minimal effort. That is, Anki makes memory a choice.
Anki 带来的最大变化是，它意味着记忆不再是一件杂乱无章、听天由命的事情。相反，它能保证我以最小的努力记住一些东西。也就是说，Anki 让记忆成为一种选择。

What can Anki be used for? I use Anki in all parts of my life. Professionally, I use it to learn from papers and books; to learn from talks and conferences; to help recall interesting things learned in conversation; and to remember key observations made while doing my everyday work. Personally, I use it to remember all kinds of facts relevant to my family and social life; about my city and travel; and about my hobbies. Later in the essay I describe some useful patterns of Anki use, and anti-patterns to avoid.
安基可以用来做什么？我在生活的各个方面都使用安奇。在工作中，我用它来学习论文和书籍；从演讲和会议中学习；帮助回忆谈话中的趣事；以及记住日常工作中的重要观察结果。就个人而言，我用它来记忆与我的家庭和社会生活有关的各种事实；与我的城市和旅行有关的事实；以及与我的爱好有关的事实。在本文后面，我将介绍一些有用的 Anki 使用模式，以及需要避免的反模式。

I've used Anki to create a little over 10,000 cards over about 2 and a half years of regular use. That includes a 7-month break when I made very few new cards. When I'm keeping up with my card review, it takes about 15 to 20 minutes per day. If it routinely rises to much more than 20 minutes it usually means I'm adding cards too rapidly, and need to slow down. Alternately, it sometimes means I'm behind on my card review (which I'll discuss later).
在大约两年半的时间里，我用 Anki 制作了 10,000 多张卡片。其中有 7 个月的间歇期，我很少制作新卡片。当我坚持复习卡片时，每天大约需要 15 到 20 分钟。如果经常超过 20 分钟，通常意味着我添加卡片的速度太快，需要放慢速度。另外，有时这也意味着我的贺卡审核进度落后了（我稍后会讨论这个问题）。

At a practical level, I use the desktop Anki client for entering new cards, and the mobile client*
在实际操作中，我使用桌面版 Anki 客户端输入新卡，而使用手机客户端*输入新卡。* The desktop client is free, but the mobile client is, at the time of writing, 25 dollars. Many people balk at that as “too expensive”. Personally, I've found the value is several orders of magnitude beyond 25 dollars. Mobile Anki is certainly far more valuable to me than a single meal in a moderately priced restaurant.
* 桌面客户端是免费的，但在撰写本报告时，手机客户端的价格为 25 美元。很多人对此不屑一顾，认为 "太贵了"。就我个人而言，我发现它的价值远远超过 25 美元。对我来说，手机 Anki 的价值远远超过在价格适中的餐馆吃一顿饭。 for reviewing. I review my Anki cards while walking to get my morning coffee, while waiting in line, on transit, and so on. Provided my mind is reasonably relaxed to begin with, I find the review experience meditative. If, on the other hand, my mind is not relaxed, I find review more difficult, and Anki can cause my mind to jump around more.
用于复习。我一边走着去买早咖啡，一边排队等候，一边在公交车上等着，一边复习我的 Anki 卡。只要我的精神一开始就相当放松，我就会觉得复习是一种冥想体验。相反，如果我的头脑不放松，我就会觉得复习比较困难，而且 Anki 会让我的头脑更加跳跃。

I had trouble getting started with Anki. Several acquaintances highly recommended it (or similar systems), and over the years I made multiple attempts to use it, each time quickly giving up. In retrospect, there are substantial barriers to get over if you want to make it a habit.
我在开始使用 Anki 时遇到了困难。几个熟人极力推荐它（或类似系统），多年来我多次尝试使用，每次都很快放弃。现在回想起来，如果想把它变成一种习惯，需要克服很多障碍。

What made Anki finally “take” for me, turning it into a habit, was a project I took on as a joke. I'd been frustrated for years at never really learning the Unix command line. I'd only ever learned the most basic commands. Learning the command line is a superpower for people who program, so it seemed highly desirable to know well. So, for fun, I wondered if it might be possible to use Anki to essentially completely memorize a (short) book about the Unix command line.
让我最终 "接受 "Anki，并把它变成一种习惯的，是我作为一个玩笑接手的一个项目。多年来，我一直为没有真正学会 Unix 命令行而苦恼。我只学过最基本的命令。对于编程人员来说，学习命令行是一种超能力，因此，掌握命令行似乎是非常有必要的。因此，为了好玩，我想知道是否有可能用 Anki 来完全记住一本关于 Unix 命令行的（短）书。

It was! 就是这样！

I chose O'Reilly Media's “Macintosh Terminal Pocket Guide”, by Daniel Barrett. I don't mean I literally memorized the entire text of the book*
我选择了 O'Reilly Media 的《Macintosh 终端袖珍指南》，作者是 Daniel Barrett。我并不是说我真的背下了这本书的全部内容*。* I later did an experiment with Charles Dickens' “A Tale of Two Cities”, seeing if it might actually be possible to memorize the entire text. After a few weeks I concluded that it would be possible, but would not be worth the time. So I deleted all the cards. An interesting thing has occurred post-deletion: the first few sentences of the book have gradually decayed in my memory, and I now have no more than fragments. I occasionally wonder what the impact would be of memorizing a good book in its entirety; I wouldn't be surprised if it greatly influenced my own language and writing.
* 后来，我用狄更斯的《双城记》做了一个实验，看看是否真的有可能背诵全文。几周后，我得出结论：有可能，但不值得花时间。于是，我删除了所有的卡片。删除后发生了一件有趣的事：这本书的前几句话在我的记忆中逐渐衰减，现在只剩下一些片段。我偶尔会想，把一本好书完整地背下来会有什么影响；如果它对我自己的语言和写作有很大影响，我也不会感到惊讶。. But I did memorize much of the conceptual knowledge in the book, as well as the names, syntax, and options for most of the commands in the book. The exceptions were things I had no frame of reference to imagine using. But I did memorize most things I could imagine using. In the end I covered perhaps 60 to 70 percent of the book, skipping or skimming pieces that didn't seem relevant to me. Still, my knowledge of the command line increased enormously.
.但我确实记住了书中的很多概念性知识，以及书中大部分命令的名称、语法和选项。例外的情况是，我没有任何参照系，无法想象如何使用这些命令。但我还是记住了大部分我可以想象到的用法。最后，我大概掌握了全书 60% 到 70% 的内容，跳过或略过了与我无关的部分。不过，我对命令行的了解还是大大增加了。

Choosing this rather ludicrous, albeit extremely useful, goal gave me a great deal of confidence in Anki. It was exciting, making it obvious that Anki would make it easy to learn things that would formerly have been quite tedious and difficult for me to learn. This confidence, in turn, made it much easier to build an Anki habit. At the same time, the project also helped me learn the Anki interface, and got me to experiment with different ways of posing questions. That is, it helped me build the skills necessary to use Anki well.
选择这个尽管非常有用但却相当可笑的目标让我对 Anki 充满信心。它让我兴奋不已，让我清楚地认识到 Anki 可以让我轻松地学习以前学起来相当乏味和困难的东西。这种信心反过来又使我更容易养成 Anki 的习惯。同时，这个项目还帮助我学习了 Anki 界面，并让我尝试了不同的提问方式。也就是说，它帮助我掌握了使用好 Anki 的必要技能。

Using Anki to thoroughly read a research paper in an unfamiliar field
使用 Anki 透彻阅读陌生领域的研究论文

I find Anki a great help when reading research papers, particularly in fields outside my expertise. As an example of how this can work, I'll describe my experience reading a 2016 paper*
我发现在阅读研究论文时，Anki 可以提供很大的帮助，尤其是在我专业领域之外的领域。下面我就以 2016 年的一篇论文*为例，介绍我的阅读经验* David Silver, Aja Huang, Chris J. Maddison, Arthur Guez et al, Mastering the game of Go with deep neural networks and tree search, Nature (2016).
* David Silver、Aja Huang、Chris J. Maddison、Arthur Guez 等人，《用深度神经网络和树搜索掌握围棋》，《自然》（2016 年）。 describing AlphaGo, the computer system from Google DeepMind that beat some of the world's strongest players of the game Go.
描述了谷歌 DeepMind 的计算机系统 AlphaGo，它击败了世界上最强的围棋棋手。

After the match where AlphaGo beat Lee Sedol, one of the strongest human Go players in history, I suggested to Quanta Magazine that I write an article about the system*
在 AlphaGo 击败史上最强人类围棋手之一李世石的比赛之后，我向《广达》杂志建议撰写一篇关于该系统的文章*。* Michael Nielsen, Is AlphaGo Really Such a Big Deal?, Quanta (2016).. AlphaGo was a hot media topic at the time, and the most common angle in stories was human interest, viewing AlphaGo as part of a long-standing human-versus-machine narrative, with a few technical details filled in, mostly as color.
.当时，AlphaGo 是媒体的热门话题，报道中最常见的角度是人文关怀，把 AlphaGo 看作是长期存在的人机对战叙事的一部分，并填充了一些技术细节，主要是作为色彩。

I wanted to take a different angle. Through the 1990s and first decade of the 2000s, I believed human-or-better general artificial intelligence was far, far away. The reason was that over that time researchers made only slow progress building systems to do intuitive pattern matching, of the kind that underlies human sight and hearing, as well as in playing games such as Go. Despite enormous effort by AI researchers, many pattern-matching feats which humans find effortless remained impossible for machines.
我想换一个角度。在 20 世纪 90 年代和 21 世纪头十年，我一直认为人类或更好的通用人工智能离我们还很遥远。原因是在那段时间里，研究人员在构建直观模式匹配系统方面进展缓慢，而这种模式匹配正是人类视觉和听觉以及下围棋等游戏的基础。尽管人工智能研究人员付出了巨大努力，但许多人类认为毫不费力的模式匹配对机器来说仍然是不可能的。

While we made only very slow progress on this set of problems for a long time, around 2011 progress began to speed up, driven by advances in deep neural networks. For instance, machine vision systems rapidly went from being terrible to being comparable to human beings for certain limited tasks. By the time AlphaGo was released, it was no longer correct to say we had no idea how to build computer systems to do intuitive pattern matching. While we hadn't yet nailed the problem, we were making rapid progress. AlphaGo was a big part of that story, and I wanted my article to explore this notion of building computer systems to capture human intuition.
虽然在很长一段时间里，我们在这一系列问题上的进展非常缓慢，但在深度神经网络进步的推动下，进展在 2011 年左右开始加速。例如，在某些有限的任务中，机器视觉系统迅速从糟糕透顶变得与人类不相上下。到 AlphaGo 发布时，说我们不知道如何构建计算机系统来进行直观模式匹配已不再正确。虽然我们还没有解决这个问题，但我们正在取得快速进展。AlphaGo 是这一故事的重要组成部分，我希望我的文章能探讨构建计算机系统以捕捉人类直觉这一概念。

While I was excited, writing such an article was going to be difficult. It was going to require a deeper understanding of the technical details of AlphaGo than a typical journalistic article. Fortunately, I knew a fair amount about neural networks – I'd written a book about them*
虽然我很兴奋，但写这样一篇文章会很困难。与一般的新闻报道相比，它需要对 AlphaGo 的技术细节有更深入的了解。幸运的是，我对神经网络有相当的了解--我写过一本关于神经网络的书*。* Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press (2015).
* Michael A. Nielsen，"Neural Networks and Deep Learning"，Determination Press (2015)。. But I knew nothing about the game of Go, or about many of the ideas used by AlphaGo, based on a field known as reinforcement learning. I was going to need to learn this material from scratch, and to write a good article I was going to need to really understand the underlying technical material.
.但我对围棋一无所知，对基于强化学习领域的 AlphaGo 所使用的许多想法也一无所知。我需要从头开始学习这方面的知识，要写出一篇好文章，我需要真正了解底层的技术材料。

Here's how I went about it.
我是这样做的

I began with the AlphaGo paper itself. I began reading it quickly, almost skimming. I wasn't looking for a comprehensive understanding. Rather, I was doing two things. One, I was trying to simply identify the most important ideas in the paper. What were the names of the key techniques I'd need to learn about? Second, there was a kind of hoovering process, looking for basic facts that I could understand easily, and that would obviously benefit me. Things like basic terminology, the rules of Go, and so on.
我从 AlphaGo 论文本身开始。我开始快速阅读，几乎是略读。我并不是在寻求全面的理解。相反，我在做两件事。第一，我只是想找出论文中最重要的观点。我需要学习的关键技术名称是什么？其次，我还在进行一种 "地毯式搜索"，寻找那些我很容易理解的基本事实，这些事实显然会对我有帮助。比如基本术语、围棋规则等等。

Here's a few examples of the kind of question I entered into Anki at this stage: “What's the size of a Go board?”; “Who plays first in Go?”; “How many human game positions did AlphaGo learn from?”; “Where did AlphaGo get its training data?”; “What were the names of the two main types of neural network AlphaGo used?”
以下是我在这一阶段向 Anki 输入问题的几个例子："围棋棋盘的大小是多少？"；"围棋中谁先下棋？"；"AlphaGo 从多少人类棋局中学习？"；"AlphaGo 从哪里获得训练数据？"；"AlphaGo 使用的两种主要神经网络的名称是什么？"

As you can see, these are all elementary questions. They're the kind of thing that are very easily picked up during an initial pass over the paper, with occasional digressions to search Google and Wikipedia, and so on. Furthermore, while these facts were easy to pick up in isolation, they also seemed likely to be useful in building a deeper understanding of other material in the paper.
如你所见，这些都是基本问题。这些问题很容易在初读论文时找到，偶尔也会跑题去搜索谷歌和维基百科等。此外，虽然这些事实很容易被孤立地理解，但它们似乎也有助于加深对论文中其他材料的理解。

I made several rapid passes over the paper in this way, each time getting deeper and deeper. At this stage I wasn't trying to obtain anything like a complete understanding of AlphaGo. Rather, I was trying to build up my background understanding. At all times, if something wasn't easy to understand, I didn't worry about it, I just keep going. But as I made repeat passes, the range of things that were easy to understand grew and grew. I found myself adding questions about the types of features used as inputs to AlphaGo's neural networks, basic facts about the structure of the networks, and so on.
我用这种方法快速浏览了这篇论文好几次，每次都越看越深。在这个阶段，我并没有试图完全理解 AlphaGo。相反，我试图建立起自己的背景知识。在任何时候，如果有什么不容易理解的地方，我都不会担心，而是继续前进。但随着我不断重复，容易理解的东西越来越多。我发现自己又增加了一些问题，涉及 AlphaGo 神经网络输入的特征类型、网络结构的基本事实等等。

After five or six such passes over the paper, I went back and attempted a thorough read. This time the purpose was to understand AlphaGo in detail. By now I understood much of the background context, and it was relatively easy to do a thorough read, certainly far easier than coming into the paper cold. Don't get me wrong: it was still challenging. But it was far easier than it would have been otherwise.
在对论文进行了五六次这样的通读之后，我又回去尝试进行了一次彻底的阅读。这次的目的是详细了解 AlphaGo。现在，我已经了解了很多背景知识，彻底读完论文相对容易多了，当然也比冷冰冰地看论文要容易得多。别误会我的意思：这仍然很有挑战性。但是，这比我想象中要容易得多。

After doing one thorough pass over the AlphaGo paper, I made a second thorough pass, in a similar vein. Yet more fell into place. By this time, I understood the AlphaGo system reasonably well. Many of the questions I was putting into Anki were high level, sometimes on the verge of original research directions. I certainly understood AlphaGo well enough that I was confident I could write the sections of my article dealing with it. (In practice, my article ranged over several systems, not just AlphaGo, and I had to learn about those as well, using a similar process, though I didn't go as deep.) I continued to add questions as I wrote my article, ending up adding several hundred questions in total. But by this point the hardest work had been done.
在对 AlphaGo 的论文进行了一次全面检查之后，我又以类似的方式进行了第二次全面检查。然而，更多的问题迎刃而解。此时，我已经相当了解 AlphaGo 系统了。我在 Anki 中输入的许多问题都是高水平的，有时甚至濒临原创研究方向。当然，我对 AlphaGo 的理解已经足够深入，以至于我有信心写出文章中涉及它的部分。(实际上，我的文章涉及多个系统，而不仅仅是 AlphaGo，我也必须用类似的方法了解这些系统，尽管我没有那么深入）。我在写文章的过程中不断添加问题，最后总共添加了几百个问题。但此时最艰巨的工作已经完成。

Of course, instead of using Anki I could have taken conventional notes, using a similar process to build up an understanding of the paper. But using Anki gave me confidence I would retain much of the understanding over the long term. A year or so later DeepMind released papers describing followup systems, known as AlphaGo Zero and AlphaZero*
当然，如果不使用 Anki，我也可以做传统的笔记，用类似的方法建立对论文的理解。但使用 Anki 让我有信心能够长期保持对文章的理解。一年左右后，DeepMind 发布了描述后续系统的论文，这些系统被称为 AlphaGo Zero 和 AlphaZero*。* For AlphaGo Zero, see: David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou et al, Mastering the game of Go without human knowledge, Nature (2017). For AlphaZero, see: David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou et al, Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017).
* 关于 AlphaGo Zero，请参阅：David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou et al, Mastering the game of Go without human knowledge, Nature (2017)。关于 AlphaZero，见David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou et al, Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017).. Despite the fact that I'd thought little about AlphaGo or reinforcement learning in the intervening time, I found I could read those followup papers with ease. While I didn't attempt to understand those papers as thoroughly as the initial AlphaGo paper, I found I could get a pretty good understanding of the papers in less than an hour. I'd retained much of my earlier understanding!
.尽管在这段时间里，我对 AlphaGo 或强化学习的思考很少，但我发现我可以轻松地阅读这些后续论文。虽然我没有尝试像理解最初的 AlphaGo 论文那样透彻地理解这些论文，但我发现我可以在不到一个小时的时间里很好地理解这些论文。我保留了很多之前的理解！

By contrast, had I used conventional note-taking in my original reading of the AlphaGo paper, my understanding would have more rapidly evaporated, and it would have taken longer to read the later papers. And so using Anki in this way gives confidence you will retain understanding over the long term. This confidence, in turn, makes the initial act of understanding more pleasurable, since you believe you're learning something for the long haul, not something you'll forget in a day or a week.
相比之下，如果我在最初阅读 AlphaGo 论文时使用传统的记笔记方法，我的理解会更快地消失，阅读后面的论文也需要更长的时间。因此，以这种方式使用 Anki 会让你有信心长期保持理解。这种信心反过来又会让最初的理解行为变得更加愉悦，因为你相信你是在长期学习，而不是在一天或一周内就会忘记的东西。

OK, but what does one do with it? … [N]ow that I have all this power – a mechanical golem that will never forget and never let me forget whatever I chose to – what do I choose to remember? – Gwern Branwen
好吧，但该如何使用它呢？......现在我拥有了这一切的力量--一个永远不会忘记、也不会让我忘记任何我选择忘记的东西的机械高仑--我选择记住什么呢？- 格温-布兰文

This entire process took a few days of my time, spread over a few weeks. That's a lot of work. However, the payoff was that I got a pretty good basic grounding in modern deep reinforcement learning. This is an immensely important field, of great use in robotics, and many researchers believe it will play an important role in achieving general artificial intelligence. With a few days work I'd gone from knowing nothing about deep reinforcement learning to a durable understanding of a key paper in the field, a paper that made use of many techniques that were used across the entire field. Of course, I was still a long way from being an expert. There were many important details about AlphaGo I hadn't understood, and I would have had to do far more work to build my own system in the area. But this foundational kind of understanding is a good basis on which to build deeper expertise.
整个过程花费了我几天的时间，分散在几个星期里。这是一个很大的工作量。不过，我得到的回报是，我在现代深度强化学习方面打下了相当好的基础。这是一个非常重要的领域，在机器人学中大有用武之地，许多研究人员认为它将在实现通用人工智能方面发挥重要作用。通过几天的努力，我从对深度强化学习一无所知到对该领域的一篇重要论文有了持久的了解，这篇论文使用了整个领域的许多技术。当然，我离成为专家还有很长的路要走。关于 AlphaGo，我还有很多重要的细节没有理解，要想在这一领域建立自己的系统，我还需要做更多的工作。但是，这种基础性的理解是建立更深层次专业知识的良好基础。

It's notable that I was reading the AlphaGo paper in support of a creative project of my own, namely, writing an article for Quanta Magazine. This is important: I find Anki works much better when used in service to some personal creative project.
值得注意的是，我阅读 AlphaGo 论文是为了支持我自己的一个创意项目，即为《Quanta》杂志撰写一篇文章。这一点很重要：我发现 Anki 在为个人创作项目服务时效果更好。

It's tempting instead to use Anki to stockpile knowledge against some future day, to think “Oh, I should learn about the geography of Africa, or learn about World War II, or […]”. These are goals which, for me, are intellectually appealing, but which I'm not emotionally invested in. I've tried this a bunch of times. It tends to generate cold and lifeless Anki questions, questions which I find hard to connect to upon later review, and where it's difficult to really, deeply internalize the answers. The problem is somehow in that initial idea I “should” learn about these things: intellectually, it seems like a good idea, but I've little emotional commitment.
我很想用 Anki 来储备知识，以备将来的某一天，我想 "哦，我应该学习非洲地理，或者学习第二次世界大战，或者[......]"。对我来说，这些目标在知识上很有吸引力，但在感情上我并不投入。我曾多次尝试这样做。这样做往往会产生冷冰冰、毫无生气的 Anki 问题，我在日后复习时很难与这些问题联系起来，也很难真正、深刻地将答案内化。问题就出在我 "应该 "学习这些东西的最初想法上：从理智上讲，这似乎是个好主意，但我却没有什么情感投入。

Study hard what interests you the most in the most undisciplined, irreverent and original manner possible. – Richard Feynman
以最不严谨、最不羁、最新颖的方式，努力学习你最感兴趣的东西。- 理查德-费曼

By contrast, when I'm reading in support of some creative project, I ask much better Anki questions. I find it easier to connect to the questions and answers emotionally. I simply care more about them, and that makes a difference. So while it's tempting to use Anki cards to study in preparation for some (possibly hypothetical) future use, it's better to find a way to use Anki as part of some creative project.
相比之下，当我为支持某个创意项目而阅读时，我提出的 Anki 问题要好得多。我发现更容易与问题和答案建立情感联系。我只是更关心它们，这就产生了不同。因此，虽然使用 Anki 卡为将来的某些用途（可能是假设的）做准备很诱人，但最好还是想办法把 Anki 用作某些创造性项目的一部分。

Using Anki to do shallow reads of papers
使用 Anki 进行论文浅读

Most of my Anki-based reading is much shallower than my read of the AlphaGo paper. Rather than spending days on a paper, I'll typically spend 10 to 60 minutes, sometimes longer for very good papers. Here's a few notes on some patterns I've found useful in shallow reading.
我大多数基于 Anki 的阅读都比我对 AlphaGo 论文的阅读浅得多。我通常不会在一篇论文上花上好几天的时间，而是花上 10 到 60 分钟，有时对于非常好的论文，我会花更长的时间。下面是我在浅阅读中发现的一些有用的模式。

As mentioned above, I'm usually doing such reading as part of the background research for some project. I will find a new article (or set of articles), and typically spend a few minutes assessing it. Does the article seem likely to contain substantial insight or provocation relevant to my project – new questions, new ideas, new methods, new results? If so, I'll have a read.
如上所述，我通常是作为某个项目背景研究的一部分来进行此类阅读的。我会找到一篇新文章（或一组文章），通常会花几分钟对其进行评估。这篇文章是否可能包含与我的项目相关的重要见解或启发--新问题、新观点、新方法、新成果？如果是，我会读一读。

This doesn't mean reading every word in the paper. Rather, I'll add to Anki questions about the core claims, core questions, and core ideas of the paper. It's particularly helpful to extract Anki questions from the abstract, introduction, conclusion, figures, and figure captions. Typically I will extract anywhere from 5 to 20 Anki questions from the paper. It's usually a bad idea to extract fewer than 5 questions – doing so tends to leave the paper as a kind of isolated orphan in my memory. Later I find it difficult to feel much connection to those questions. Put another way: if a paper is so uninteresting that it's not possible to add 5 good questions about it, it's usually better to add no questions at all.
这并不意味着要阅读论文中的每一个字。相反，我会在 Anki 中添加有关论文核心主张、核心问题和核心观点的问题。从摘要、引言、结论、图表和图表标题中提取 Anki 问题尤其有帮助。通常，我会从论文中提取 5 到 20 个 Anki 问题。提取少于 5 个问题通常是个坏主意--这样做往往会使论文在我的记忆中成为一种孤立的孤儿。后来我发现很难再与这些问题产生联系。换一种说法：如果一篇论文非常无趣，以至于无法添加 5 个有关它的好问题，那么通常最好不添加任何问题。

One failure mode of this process is if you Ankify*
该过程的一种失败模式是，如果您安克里** I.e., enter into Anki. Also useful are forms such as Ankification etc.
* 即输入 Anki。Ankification 等形式也很有用。 misleading work. Many papers contain wrong or misleading statements, and if you commit such items to memory, you're actively making yourself stupider.
误导性工作。许多论文都包含错误或误导性的陈述，如果你把这些内容记在脑子里，就会主动把自己变得更愚蠢。

How to avoid Ankifying misleading work?
如何避免 Ankifying 误导工作？

As an example, let me describe how I Ankified a paper I recently read, by the economists Benjamin Jones and Bruce Weinberg*
举个例子，让我描述一下最近读到的经济学家本杰明-琼斯和布鲁斯-温伯格*的一篇论文，我是如何对其进行分析的。* Benjamin F. Jones and Bruce A. Weinberg, Age Dynamics in Scientific Creativity, Proceedings of the National Academy of Sciences (2011).
* 本杰明-F-琼斯和布鲁斯-A-温伯格，《科学创造力的年龄动态》，《美国国家科学院院刊》（2011 年）。. The paper studies the ages at which scientists make their greatest discoveries.
.这篇论文研究了科学家做出最伟大发现的年龄段。

I should say at the outset: I have no reason to think this paper is misleading! But it's also worth being cautious. As an example of that caution, one of the questions I added to Anki was: “What does Jones 2011 claim is the average age at which physics Nobelists made their prizewinning discovery, over 1980-2011?” (Answer: 48). Another variant question was: “Which paper claimed that physics Nobelists made their prizewinning discovery at average age 48, over the period 1980-2011?” (Answer: Jones 2011). And so on.
首先我要说的是：我没有理由认为这篇论文有误导性！但是，谨慎行事也是值得的。作为谨慎的一个例子，我在 Anki 中添加的一个问题是"琼斯（Jones 2011）声称，在 1980-2011 年间，诺贝尔物理学奖获得者的平均获奖年龄是多少？另一个变式问题是"哪篇论文声称，在 1980-2011 年期间，诺贝尔物理学奖获得者的平均获奖年龄是 48 岁？"（答案：琼斯 2011）。以此类推。

Such questions qualify the underlying claim: we now know it was a claim made in Jones 2011, and that we're relying on the quality of Jones and Weinberg's data analysis. In fact, I haven't examined that analysis carefully enough to regard it as a fact that the average age of those Nobelists is 48. But it is certainly a fact that their paper claimed it was 48. Those are different things, and the latter is better to Ankify.
这些问题限定了基本说法：我们现在知道这是琼斯 2011 年的说法，我们依赖的是琼斯和温伯格的数据分析质量。事实上，我还没有仔细研究过那份分析报告，因此无法将这些诺贝尔奖获得者的平均年龄为 48 岁视为一个事实。但他们的论文声称平均年龄是 48 岁，这肯定是事实。这两件事是不同的，后者更好理解。

If I'm particularly concerned about the quality of the analysis, I may add one or more questions about what makes such work difficult, e.g.: “What's one challenge in determining the age of Nobel winners at the time of their discovery, as discussed in Jones 2011?” Good answers include: the difficulty of figuring out which paper contained the Nobel-winning work; the fact that publication of papers is sometimes delayed by years; that sometimes work is spread over multiple papers; and so on. Thinking about such challenges reminds me that if Jones and Weinberg were sloppy, or simply made an understandable mistake, their numbers might be off. Now, it so happens that for this particular paper, I'm not too worried about such issues. And so I didn't Ankify any such question. But it's worth being careful in framing questions so you're not misleading yourself.
如果我特别关注分析的质量，我可能会增加一个或多个问题，比如："如 Jones 2011 所述，确定诺贝尔奖获得者发现时的年龄有什么困难？"如《琼斯 2011》一书所讨论的，在确定诺贝尔奖获得者发现时的年龄方面有什么困难？好的答案包括：难以确定哪篇论文包含诺贝尔奖得主的研究成果；论文的发表有时会延迟数年；有时研究成果分散在多篇论文中；等等。想到这些难题，我不禁想到，如果琼斯和温伯格马虎了，或者只是犯了一个可以理解的错误，他们的数字可能就会有偏差。现在，就这篇论文而言，我并不太担心这些问题。因此，我没有提出任何此类问题。不过，在提出问题时还是要小心谨慎，以免误导自己。

Another useful pattern while reading papers is Ankifying figures. For instance, here's a graph from Jones 2011 showing the probability a physicist made their prizewinning discovery by age 40 (blue line) and by age 30 (black line):
阅读论文时另一个有用的模式是Ankifying数字。例如，这里有一张琼斯 2011 年的图表，显示了物理学家在 40 岁（蓝线）和 30 岁（黑线）之前做出获奖发现的概率：

I have an Anki question which simply says: “Visualize the graph Jones 2011 made of the probability curves for physicists making their prizewinning discoveries by age 30 and 40”. The answer is the image shown above, and I count myself as successful if my mental image is roughly along those lines. I could deepen my engagement with the graph by adding questions such as: “In Jones 2011's graph of physics prizewinning discoveries, what is the peak probability of great achievement by age 40 [i.e., the highest point in the blue line in the graph above]?” (Answer: about 0.8.) Indeed, one could easily add dozens of interesting questions about this graph. I haven't done that, because of the time commitment associated to such questions. But I do find the broad shape of the graph fascinating, and it's also useful to know the graph exists, and where to consult it if I want more details.
我有一道 Anki 题，题目很简单："请直观显示琼斯 2011 年绘制的物理学家在 30 岁和 40 岁之前做出获奖发现的概率曲线图"。答案就是上图所示的图形，如果我的思维图像大致如此，我就算是成功了。我可以通过增加问题来加深对图表的理解，比如"在琼斯 2011 年的物理学获奖发现图中，40 岁之前（即上图中蓝线的最高点）取得巨大成就的峰值概率是多少？"（答案：约 0.8）事实上，人们可以很容易地就这张图提出几十个有趣的问题。我没有这样做，因为这类问题需要花费大量时间。不过，我确实发现这个图表的大致形状很吸引人，而且知道这个图表的存在，以及如果我想了解更多细节，在哪里可以查阅它，也是很有用的。

I said above that I typically spend 10 to 60 minutes Ankifying a paper, with the duration depending on my judgment of the value I'm getting from the paper. However, if I'm learning a great deal, and finding it interesting, I keep reading and Ankifying. Really good resources are worth investing time in. But most papers don't fit this pattern, and you quickly saturate. If you feel you could easily find something more rewarding to read, switch over. It's worth deliberately practicing such switches, to avoid building a counter-productive habit of completionism in your reading. It's nearly always possible to read deeper into a paper, but that doesn't mean you can't easily be getting more value elsewhere. It's a failure mode to spend too long reading unimportant papers.
我在上文说过，我通常会花 10 到 60 分钟安克一篇论文，时间长短取决于我对论文价值的判断。不过，如果我学到了很多东西，而且觉得很有意思，我就会继续阅读和安克。真正好的资源值得投入时间。但大多数论文不符合这种模式，你很快就会饱和。如果你觉得很容易就能找到更有价值的内容，那就换一换。值得刻意练习这种切换，以避免在阅读中养成完成主义的反作用习惯。深入阅读一篇论文几乎总是可能的，但这并不意味着你不能轻松地在其他地方获得更多价值。花太多时间阅读不重要的论文是一种失败模式。

Syntopic reading using Anki
使用 Anki 进行句法阅读

I've talked about how to use Anki to do shallow reads of papers, and rather deeper reads of papers. There's also a sense in which it's possible to use Anki not just to read papers, but to “read” the entire research literature of some field or subfield. Here's how to do it.
我已经谈到过如何使用 Anki 进行论文的浅阅读和深阅读。从某种意义上说，使用 Anki 不仅可以阅读论文，还可以 "阅读 "某个领域或子领域的全部研究文献。下面是如何做到这一点。

You might suppose the foundation would be a shallow read of a large number of papers. In fact, to really grok an unfamiliar field, you need to engage deeply with key papers – papers like the AlphaGo paper. What you get from deep engagement with important papers is more significant than any single fact or technique: you get a sense for what a powerful result in the field looks like. It helps you imbibe the healthiest norms and standards of the field. It helps you internalize how to ask good questions in the field, and how to put techniques together. You begin to understand what made something like AlphaGo a breakthrough – and also its limitations, and the sense in which it was really a natural evolution of the field. Such things aren't captured individually by any single Anki question. But they begin to be captured collectively by the questions one asks when engaged deeply enough with key papers.
你可能会认为，浅阅读大量论文是基础。事实上，要真正了解一个陌生的领域，你需要深入阅读关键论文，比如 AlphaGo 的论文。深入阅读重要文献所获得的东西，比任何单一的事实或技术都更有意义：你可以了解到该领域的强大成果是什么样的。它有助于你吸收该领域最健康的规范和标准。它帮助你内化如何在该领域提出好问题，以及如何将技术整合在一起。你会开始理解是什么让 AlphaGo 这样的技术取得了突破--同时也理解了它的局限性，以及它在某种意义上是该领域的自然演进。任何一道 Anki 题目都无法单独捕捉到这些东西。但是，当我们深入研究关键论文时，我们提出的问题就能共同捕捉到这些信息。

So, to get a picture of an entire field, I usually begin with a truly important paper, ideally a paper establishing a result that got me interested in the field in the first place. I do a thorough read of that paper, along the lines of what I described for AlphaGo. Later, I do thorough reads of other key papers in the field – ideally, I read the best 5-10 papers in the field. But, interspersed, I also do shallower reads of a much larger number of less important (though still good) papers. In my experimentation so far that means tens of papers, though I expect in some fields I will eventually read hundreds or even thousands of papers in this way.
因此，为了了解整个领域的情况，我通常会从一篇真正重要的论文开始，理想的情况是，这篇论文建立了一个成果，让我一开始就对这个领域产生了兴趣。我会按照我为 AlphaGo 所描述的方法，对这篇论文进行深入阅读。之后，我会深入阅读该领域的其他重要论文--理想情况下，我会阅读该领域最好的 5-10 篇论文。但同时，我也会穿插阅读更多不太重要（但仍然不错）的论文。在我目前的实验中，这意味着要阅读几十篇论文，不过我预计在某些领域，我最终会以这种方式阅读几百篇甚至几千篇论文。

You may wonder why I don't just focus on only the most important papers. Part of the reason is mundane: it can be hard to tell what the most important papers are. Shallow reads of many papers can help you figure out what the key papers are, without spending too much time doing deeper reads of papers that turn out not to be so important. But there's also a culture that one imbibes reading the bread-and-butter papers of a field: a sense for what routine progress looks like, for the praxis of the field. That's valuable too, especially for building up an overall picture of where the field is at, and to stimulate questions on my own part. Indeed, while I don't recommend spending a large fraction of your time reading bad papers, it's certainly possible to have a good conversation with a bad paper. Stimulus is found in unexpected places.
你可能会问，为什么我不只关注最重要的论文呢？部分原因很平凡：最重要的论文是什么可能很难说清楚。对许多论文进行浅层阅读可以帮助你找出关键论文，而不用花太多时间去深层阅读那些原来并不那么重要的论文。但是，阅读一个领域的基础性论文也能熏陶出一种文化：一种对常规进展和该领域实践的感觉。这也是很有价值的，尤其是可以让我对该领域的发展有一个整体的了解，并激发我自己的问题。事实上，虽然我不建议把大部分时间花在阅读糟糕的论文上，但与一篇糟糕的论文进行良好的对话肯定是有可能的。刺激就在意想不到的地方。

Over time, this is a form of what Mortimer Adler and Charles van Doren dubbed syntopic reading*
随着时间的推移，这种形式被莫蒂默-阿德勒和查尔斯-范多伦称为 "句法阅读 "*。* In their marvelous “How to Read a Book”: Mortimer J. Adler and Charles van Doren, “How to Read a Book: The Classic Guide to Intelligent Reading” (1972)
* 在他们精彩的 "如何阅读一本书 "中：莫蒂默-J.-阿德勒和查尔斯-范多伦，《如何阅读一本书》：智慧阅读经典指南》（1972 年）. I build up an understanding of an entire literature: what's been done, what's not yet been done. Of course, it's not literally reading an entire literature. But functionally it's close. I start to identify open problems, questions that I'd personally like answered, but which don't yet seem to have been answered. I identify tricks, observations that seem pregnant with possibility, but whose import I don't yet know. And, sometimes, I identify what seem to me to be field-wide blind spots. I add questions about all these to Anki as well. In this way, Anki is a medium supporting my creative research. It has some shortcomings as such a medium, since it's not designed with supporting creative work in mind – it's not, for instance, equipped for lengthy, free-form exploration inside a scratch space. But even without being designed in such a way, it's helpful as a creative support.
.我对整部文学作品有了一定的了解：哪些已经完成，哪些尚未完成。当然，这并不是真正意义上的阅读整部文学作品。但从功能上来说，已经很接近了。我开始发现一些悬而未决的问题，一些我个人希望得到解答，但似乎尚未得到解答的问题。我还会发现一些窍门，一些看起来很有可能，但我还不知道其意义的观察结果。有时，我还会发现一些在我看来是整个领域的盲点。我也会把这些问题添加到 Anki 中。这样，Anki 就成了支持我进行创造性研究的媒介。作为这样一种媒介，它也有一些不足之处，因为它在设计时并没有考虑到支持创造性工作--例如，它并不适合在划痕空间内进行长时间的自由探索。不过，即使没有这样的设计，它作为一种创作辅助工具还是很有帮助的。

I've been describing how I use Anki to learn fields which are largely new to me. By contrast, with a field I already know well, my curiosity and my model of the field are often already so strong that it's easy to integrate new facts. I still find Anki useful, but it's definitely most useful in new areas. The great English mathematician John Edensor Littlewood wrote*
我一直在描述我是如何使用 Anki 来学习对我来说很陌生的领域的。相比之下，对于我已经非常熟悉的领域，我的好奇心和我对该领域的模型往往已经非常强大，因此很容易整合新的事实。我仍然觉得 Anki 很有用，但它在新领域无疑是最有用的。伟大的英国数学家约翰-爱登瑟-利特尔伍德写道* 。* In “Littlewood's miscellany”, edited by Béla Bollobás (1986).
* 《利特尔伍德杂录》，贝拉-波尔洛巴什编（1986 年）。:

I have tried to learn mathematics outside my fields of interest; after any interval I had to begin all over again.
我曾尝试学习自己感兴趣领域之外的数学，但每隔一段时间，我又得重新开始。

This captures something of the immense emotional effort I used to find required to learn a new field. Without a lot of drive, it was extremely difficult to make a lot of material in a new field stick. Anki does much to solve that problem. In a sense, it's an emotional prosthetic, actually helping create the drive I need to achieve understanding. It doesn't do the entire job – as mentioned earlier, it's very helpful to have other commitments (like a creative project, or people depending on me) to help create that drive. Nonetheless, Anki helps give me confidence that I can simply decide I'm going to read deeply into a new field, and retain and make sense of much of what I learn. This has worked for all areas of conceptual understanding where I've tried it*
这反映了我在学习一个新领域时所付出的巨大情感努力。如果没有强大的动力，就很难将新领域的大量材料融会贯通。Anki 在很大程度上解决了这个问题。从某种意义上说，它是一种情感假体，实际上帮助我产生了理解所需的动力。但它并不能完成全部工作--如前所述，有其他承诺（如创作项目，或依赖于我的人等）来帮助创造这种动力是非常有帮助的。尽管如此，Anki 还是给了我信心，让我相信我只要决定深入阅读一个新领域，就能保留并理解所学的大部分内容。这在我尝试过的所有概念理解领域都很有效*。* I'm curious how well it could be used for motor skills and problem solving, two areas where I haven't tried using Anki.
* 我很好奇它在运动技能和问题解决方面的应用效果如何，我还没有尝试过在这两个领域使用 Anki。.

One surprising consequence of reading in this way is how much more enjoyable it becomes. I've always enjoyed reading, but starting out in a challenging new field was sometimes a real slog, and I was often bedeviled by doubts that I would ever really get into the field. That doubt, in turn, made it less likely that I would succeed. Now I have confidence that I can go into a new field and quickly attain a good, relatively deep understanding, an understanding that will be durable. That confidence makes reading even more pleasurable*
以这种方式进行阅读的一个令人惊讶的结果是，阅读变得更加令人愉快。我一直都很喜欢阅读，但刚开始进入一个具有挑战性的新领域时，有时真的很艰难，我常常怀疑自己是否能真正进入这个领域。这种怀疑反过来又降低了我成功的可能性。现在，我有信心进入一个新领域，并迅速获得良好的、相对深刻的理解，这种理解将是持久的。这种自信让阅读变得更加愉悦*。* Many people have written accounts of how to read using personal memory systems. My thinking was particularly stimulated by: Piotr Wozniak, Incremental Reading.
* 许多人都写过关于如何利用个人记忆系统进行阅读的文章。我的思考尤其受到以下书籍的启发Piotr Wozniak，《增量阅读》。.

More patterns of Anki use
使用 Anki 的更多模式

Having looked at the use of Anki for reading technical papers, let's return to general patterns of use*
在了解了 Anki 在阅读技术论文方面的应用之后，我们再来看看一般的使用模式*。* Another useful list of patterns is: Piotr Wozniak, Effective learning: Twenty rules of formulating knowledge.
* 另一份有用的模式清单是Piotr Wozniak, Effective learning：有效学习：形成知识的二十条规则》。. There's a lot in this section, and upon a first read you may wish to skim through and concentrate on those items which most catch your eye.
.这部分内容很多，初读时不妨略读，集中精力阅读那些最吸引你眼球的项目。

Make most Anki questions and answers as atomic as possible: That is, both the question and answer express just one idea. As an example, when I was learning the Unix command line, I entered the question: “How to create a soft link from linkname to filename?” The answer was: “ln -s filename linkname”. Unfortunately, I routinely got this question wrong.
让大多数 Anki 问题和答案尽可能原子化：也就是说，问题和答案都只表达一个意思。例如，当我学习 Unix 命令行时，我输入的问题是"如何创建从 linkname 到 filename 的软链接？"答案是" ln -s filename linkname "。不幸的是，我经常出错。

The solution was to refactor the question by breaking it into two pieces. One piece was: “What's the basic command and option to create a Unix soft link?” Answer: “ln -s …”. And the second piece was: “When creating a Unix soft link, in what order do linkname and filename go?” Answer: “filename linkname”.
解决办法是对问题进行重构，将其分成两部分。一块是"创建 Unix 软链接的基本命令和选项是什么？"答案是" ln -s … "。第二部分是"创建 Unix 软链接时， linkname 和 filename 的顺序是什么？答案：""：" filename linkname "。

Breaking this question into more atomic pieces turned a question I routinely got wrong into two questions I routinely got right*
将这道题分解成更多的原子块，把我经常出错的一道题变成了我经常答对的两道题** An even more atomic version would be to break the first question into “What's the Unix command to create a link?” and “What's the option to the ln command to create a soft link?” In practice, I've known for years that ln is the command to create a link, and so this wasn't necessary.
* 一个更加原子化的版本是把第一个问题分成 "创建链接的 Unix 命令是什么？"和 "创建软链接的 ln 命令的选项是什么？"。实际上，多年来我一直知道 ln 是创建链接的命令，因此没有必要这样做。. Most of all: when I wanted to create a Unix soft link in practice, I knew how to do it.
.最重要的是：当我想在实践中创建 Unix 软链接时，我知道如何去做。

I'm not sure what's responsible for this effect. I suspect it's partly about focus. When I made mistakes with the combined question, I was often a little fuzzy about where exactly my mistake was. That meant I didn't focus sharply enough on the mistake, and so didn't learn as much from my failure. When I fail with the atomic questions my mind knows exactly where to focus.
我不知道是什么造成了这种效果。我怀疑部分原因是注意力不集中。当我在综合题上出错时，我常常对自己到底错在哪里有些模糊。这意味着我没有足够专注于错误，因此没有从失败中学到很多东西。而当我做原子题失败时，我的大脑会清楚地知道应该把注意力集中在哪里。

In general, I find that you often get substantial benefit from breaking Anki questions down to be more atomic. It's a powerful pattern for question refactoring.
一般来说，我发现将 Anki 问题分解成更原子化的问题往往能带来很大的好处。这是一种强大的问题重构模式。

Note that this doesn't mean you shouldn't also retain some version of the original question. I still want to know how to create a soft link in Unix, and so it's worth keeping the original question in Anki. But it becomes an integrative question, part of a hierarchy of questions building up from simple atomic facts to more complex ideas.
请注意，这并不意味着你不应该保留原问题的某个版本。我仍然想知道如何在 Unix 中创建软链接，因此值得在 Anki 中保留原始问题。但是，这将成为一个综合问题，成为从简单的原子事实到更复杂的想法的层级问题的一部分。

Incidentally, just because a question is atomic doesn't mean it can't involve quite complex, high-level concepts. Consider the following question, from the field of general relativity: “What is the dr² term in the Robertson-Walker metric?” Answer: dr²/(1-kr^2). Now, unless you've studied general relativity that question probably seems quite opaque. It's a sophisticated, integrative question, assuming you know what the Robertson-Walker metric is, what dr² means, what k means, and so on. But conditional on that background knowledge, it's quite an atomic question and answer.
顺便提一下，一个问题是原子问题并不意味着它不涉及相当复杂的高层次概念。请看下面这个来自广义相对论领域的问题："罗伯逊-沃克公设中的 dr ² 项是什么？答案：dr ² /(1-kr^2)。现在，除非你研究过广义相对论，否则这个问题可能看起来很不透彻。这是一个复杂的综合性问题，假设你知道什么是罗伯逊-沃克度量，dr ² 意味着什么，k 意味着什么，等等。但是，如果以这些背景知识为条件，这个问题的问答就相当原子化了。

One benefit of using Anki in this way is that you begin to habitually break things down into atomic questions. This sharply crystallizes the distinct things you've learned. Personally, I find that crystallization satisfying, for reasons I (ironically) find difficult to articulate. But one real benefit is that later I often find those atomic ideas can be put together in ways I didn't initially anticipate. And that's well worth the trouble.
这样使用 Anki 的一个好处是，你开始习惯性地将事物分解成原子问题。这使你所学到的独特知识更加具体化。就我个人而言，我觉得这种结晶很令人满意，原因是我（讽刺的是）觉得很难说清楚。但一个真正的好处是，我后来经常发现，这些原子式的想法可以用我最初没有预料到的方式组合起来。而这是非常值得的。

Anki use is best thought of as a virtuoso skill, to be developed: Anki is an extremely simple program: it lets you enter text or other media, and then shows you that media on a schedule determined by your responses. Despite that simplicity, it's an incredibly powerful tool. And, like many tools, it requires skill to use well. It's worth thinking of Anki as a skill that can be developed to virtuoso levels, and attempting to continue to level up toward such virtuosity.
Anki 的使用最好被视为一种有待开发的高超技能：Anki 是一个极其简单的程序：它可以让你输入文字或其他媒体，然后根据你的反应来确定播放时间。尽管简单，但它却是一个非常强大的工具。而且，和许多工具一样，它也需要技巧才能用好。我们应该把 Anki 看作是一种可以发展到精湛水平的技能，并尝试不断提高这种精湛水平。

Anki isn't just a tool for memorizing simple facts. It's a tool for understanding almost anything. It's a common misconception that Anki is just for memorizing simple raw facts, things like vocabulary items and basic definitions. But as we've seen, it's possible to use Anki for much more advanced types of understanding. My questions about AlphaGo began with simple questions such as “How large is a Go board?”, and ended with high-level conceptual questions about the design of the AlphaGo systems – on subjects such as how AlphaGo avoided over-generalizing from training data, the limitations of convolutional neural networks, and so on.
Anki 不仅仅是记忆简单事实的工具。它几乎是理解任何东西的工具。一个常见的误解是，Anki 只是用来记忆简单的原始事实，比如词汇和基本定义。但正如我们所看到的，Anki 可以用于更高级的理解。我对 AlphaGo 的提问从 "围棋棋盘有多大？"这样的简单问题开始，到有关 AlphaGo 系统设计的高层次概念性问题--例如 AlphaGo 如何避免从训练数据中过度概括、卷积神经网络的局限性等等。

Part of developing Anki as a virtuoso skill is cultivating the ability to use it for types of understanding beyond basic facts. Indeed, many of the observations I've made (and will make, below) about how to use Anki are really about what it means to understand something. Break things up into atomic facts. Build rich hierarchies of interconnections and integrative questions. Don't put in orphan questions. Patterns for how to engage with reading material. Patterns (and anti-patterns) for question types. Patterns for the kinds of things you'd like to memorize. Anki skills concretely instantiate your theory of how you understand; developing those skills will help you understand better. It's too strong to say that to be a virtuoso Anki user is to be a virtuoso in understanding. But there's some truth to it.
将安基作为一项高超技能来培养的一部分，就是要培养使用它来理解基本事实之外的类型的能力。事实上，我对如何使用 Anki 所做的许多观察（以及在下文中将做的观察）实际上都是关于理解事物的意义。将事物分解为原子事实。建立丰富的相互联系和综合问题的层次结构。不要提出无意义的问题。阅读材料的模式。问题类型的模式（和反模式）。你想要记忆的内容的模式。Anki 技能具体体现了你的理解理论；发展这些技能将帮助你更好地理解。说精通 Anki 的用户就是精通理解，未免言过其实。但这也有一定道理。

Use one big deck: Anki allows you to organize cards into decks and subdecks. Some people use this to create a complicated organizational structure. I used to do this, but I've gradually*
使用一个大牌组Anki 允许你将纸牌组织成牌组和子牌组。有些人会用它来创建复杂的组织结构。我以前也这样做，但现在我已经逐渐**了。* It's gradual because questions sometimes need to be rewritten due to the changed context. For instance, both my Emacs and Unix command line decks had very similar questions, along the lines of: “How to delete a word?” Those questions need to be rewritten, e.g. as: “In Emacs, how to delete a word?” (This, by the way, may seem a strange question for a long-time Emacs user such as myself. In fact, I've used Anki to help me change the way I delete words in Emacs, which is why I have an Anki question on the subject. I have made many improvements to my Emacs workflow this way.)
* 这是循序渐进的，因为有时问题需要根据语境的变化进行改写。例如，我在 Emacs 和 Unix 命令行中遇到的问题非常相似，大致如下："如何删除一个单词？"这些问题需要改写，例如："在 Emacs 中，如何删除一个单词？"在 Emacs 中，如何删除一个单词？"（顺便说一句，对于像我这样的 Emacs 老用户来说，这可能是个奇怪的问题。事实上，我已经用 Anki 帮助我改变了在 Emacs 中删除单词的方式，这就是为什么我有一个关于这个问题的 Anki 问题。通过这种方法，我的 Emacs 工作流程有了很大的改进。） merged my decks and subdecks into one big deck. The world isn't divided up into neatly separated components, and I believe it's good to collide very different types of questions. One moment Anki is asking me a question about the temperature chicken should be cooked to. The next: a question about the JavaScript API. Is this mixing doing me any real good? I'm not sure. I have not, as yet, found any reason to use JavaScript to control the cooking of a chicken. But I don't think this mixing does any harm, and hope it is creatively stimulating, and helps me apply my knowledge in unusual contexts.
将我的卡组和子卡组合并成一个大卡组。世界并不是被分割成整整齐齐的几个部分，我认为不同类型的问题碰撞在一起是件好事。前一秒，Anki 还在问我鸡肉应该煮到什么温度。下一秒，Anki 又问我关于 JavaScript API 的问题。这种混合对我有什么好处吗？我不确定。到目前为止，我还没有找到任何理由使用 JavaScript 来控制鸡肉的烹饪。但我不认为这种混合有什么坏处，我希望它能激发我的创造力，帮助我在不同寻常的环境中应用我的知识。

Avoid orphan questions: Suppose I'm reading online and stumble across a great article about the grooming habits of the Albanian giant mongoose, a subject I never previously knew I was interested in, but which turns out to be fascinating. Pretty soon I've Ankified 5 to 10 questions. That's great, but my experience suggests that in a few months I'll likely find those questions rather stale, and frequently get them wrong. I believe the reason is that those questions are too disconnected from my other interests, and I will have lost the context that made me interested.
避免孤儿问题：假设我在网上阅读时，偶然发现了一篇关于阿尔巴尼亚巨獴梳理毛发习惯的好文章。很快，我就回答了 5 到 10 个问题。这很好，但我的经验表明，几个月后，我可能会发现这些问题相当陈旧，而且经常出错。我认为原因是这些问题与我的其他兴趣太脱节，我将失去让我感兴趣的背景。

I call these orphan questions, because they're not closely related to anything else in my memory. It's not bad to have a few orphan questions in Anki – it can be difficult to know what will turn out to be of only passing interest, and what will grow into a substantial interest, connected to my other interests. But if a substantial minority of your questions are orphans, that's a sign you should concentrate more on Ankifying questions related to your main creative projects, and cut down on Ankifying tangential material.
我称这些问题为 "孤儿问题"，因为它们与我记忆中的其他问题没有密切联系。在安基中有一些孤儿问题并不是坏事--很难知道哪些问题只会引起我一时的兴趣，哪些问题会发展成为与我其他兴趣相关的实质性兴趣。但是，如果你的大部分问题都是 "孤儿"，这就表明你应该把更多的精力放在与你的主要创作项目有关的安基问题上，而减少安基切题材料。

It's particularly worth avoiding lonely orphans: single questions that are largely disconnected from everything else. Suppose, for instance, I'm reading an article on a new subject, and I learn an idea that seems particularly useful. I make it a rule to never put in one question. Rather, I try to put at least two questions in, preferably three or more. That's usually enough that it's at least the nucleus of a bit of useful knowledge. If it's a lonely orphan, inevitably I get the question wrong all the time, and it's a waste to have entered it at all.
尤其值得避免的是 "孤独的孤儿"：与其他事物基本脱节的单个问题。例如，假设我正在阅读一篇关于新主题的文章，我学到了一个似乎特别有用的想法。我的原则是绝不只提一个问题。相反，我尽量至少提出两个问题，最好是三个或更多。这通常就足够了，它至少是一点有用知识的核心。如果只是孤零零的一个问题，我就难免会经常出错，这样就白白浪费了这个问题。

Don't share decks: I'm often asked whether I'd be willing to share my Anki decks. I'm not. Very early on I realized it would be very useful to put personal information in Anki. I don't mean anything terribly personal – I'd never put deep, dark secrets in there. Nor do I put anything requiring security, like passwords. But I do put some things I wouldn't sling about casually.
不要共享字组经常有人问我是否愿意分享我的 Anki 卡组。我不愿意。很早以前，我就意识到把个人信息放在 Anki 中会非常有用。我指的不是什么非常私人的东西--我绝不会把深藏不露的秘密放进去。我也不放任何需要安全的东西，比如密码。但我确实会放一些我不会随便乱放的东西。

As an example, I've a (very short!) list of superficially charming and impressive colleagues who I would never work with, because I've consistently seen them treat other people badly. It's helpful to Ankify some details of that treatment, so I can clearly remember why that person should be avoided. This isn't the kind of information that is right to spread casually: I may have misinterpreted the other person's actions, or have misunderstood the context they were operating in. But it's personally useful for me to have in Anki.
举个例子，我有一份（非常短的！）名单，上面列出了一些表面上很有魅力、让人印象深刻的同事，但我绝不会与他们共事，因为我总是看到他们对别人不好。把他们对待别人的一些细节整理出来会很有帮助，这样我就能清楚地记住为什么要避开这个人。这种信息不能随便传播：我可能曲解了对方的行为，或者误解了他们当时的工作环境。但对我个人来说，在 Anki 中保存这些信息还是很有用的。

Construct your own decks: The Anki site has many shared decks, but I've found only a little use for them. The most important reason is that making Anki cards is an act of understanding in itself. That is, figuring out good questions to ask, and good answers, is part of what it means to understand a new subject well. To use someone else's cards is to forgo much of that understanding.

Indeed, I believe the act of constructing the cards actually helps with memory. Memory researchers have repeatedly found that the more elaborately you encode a memory, the stronger the memory will be. By elaborative encoding, they mean essentially the richness of the associations you form.
事实上，我认为制作卡片的行为实际上有助于记忆。记忆研究人员多次发现，对记忆进行的编码越精细，记忆就越牢固。他们所说的精心编码，主要指的是你所形成的联想的丰富程度。

For instance, it's possible to try to remember as an isolated fact that 1962 was the year the first telecommunications satellite, Telstar, was put into orbit. But a better way of remembering it is to relate that fact to others. Relatively prosaically, you might observe that Telstar was launched just 5 years after the first Soviet satellite, Sputnik. It didn't take long to put space to use for telecommunications. Less prosaically – a richer elaboration – I personally find it fascinating that Telstar was put into orbit the year before the introduction of ASCII, arguably the first modern digital standard for communicating text. Humanity had a telecommunications satellite before we had a digital standard for communicating text! Finding that kind of connection is an example of an elaborative encoding.
例如，1962 年是第一颗电信卫星 Telstar 进入轨道的年份，我们可以尝试将这一事实作为一个孤立的事实来记忆。但更好的记忆方法是将这一事实与其他事实联系起来。相对来说，你可以观察到，在苏联第一颗人造卫星 "人造地球卫星 "发射 5 年后，"泰尔斯塔 "号才发射升空。将太空用于电信领域并没有花费太长的时间。我个人认为，泰事达卫星被送入轨道的时间比 ASCII（可以说是第一个现代文本通信数字标准）问世的时间早一年，这一点非常吸引人。人类在拥有文字通信的数字标准之前就拥有了通信卫星！找到这种联系就是精心编码的一个例子。

The act of constructing an Anki card is itself nearly always a form of elaborative encoding. It forces you to think through alternate forms of the question, to consider the best possible answers, and so on. I believe this is true for even the most elementary cards. And it certainly becomes true if you construct more complex cards, cards relating the basic fact to be remembered to other ideas (like the Telstar-ASCII link), gradually building up a web of richly interrelated ideas.
制作 Anki 卡的行为本身几乎总是一种精心编码的形式。它迫使你思考问题的其他形式，考虑最佳答案等等。我相信即使是最基本的卡片也是如此。如果你制作更复杂的卡片，把要记住的基本事实与其他想法联系起来（如 Telstar-ASCII 链接），逐渐建立起一个相互关联的丰富想法网络，那么这当然也是正确的。

With that said, there are some valuable deck-sharing practices. For instance, there are communities of medical students who find value in sharing and sometimes collaboratively constructing decks*
尽管如此，还是有一些有价值的甲板共享做法。例如，有一些医学生社区发现了共享甲板的价值，有时甚至是合作构建甲板*。* See the MedicalSchoolAnki subreddit, which contains frequent discussion of the best decks, how to use them, as well as an ever-changing canon of best decks to use for different purposes. See also the paper: Michael Hart-Matyas et al, Twelve tips for medical students to establish a collaborative flashcard project, Medical Teacher (2018).
* 请参阅 MedicalSchoolAnki 子版块，该版块经常讨论最佳卡组、如何使用这些卡组，以及针对不同用途不断变化的最佳卡组。另请参阅论文：Michael Hart-Matyas et al, Twelve tips for medical students to establish a collaborative flashcard project, Medical Teacher (2018).. I've also found value in shared decks containing very elementary questions, such as art decks which ask questions such as who painted a particular painting. But for deeper kinds of understanding, I've not yet found good ways of using shared decks.
.我也发现了包含非常基本的问题的共享牌组的价值，比如问谁画了某幅画等问题的艺术牌组。但对于更深层次的理解，我还没有找到使用共享牌组的好方法。

Cultivate strategies for elaborative encoding / forming rich associations: This is really a meta-strategy, i.e., a strategy for forming strategies. One simple example strategy is to use multiple variants of the “same” question. For instance, I mentioned earlier my two questions: “What does Jones 2011 claim is the average age at which physics Nobelists made their prizewinning discovery, over 1980-2011?” And: “Which paper claimed that physics Nobelists made their prizewinning discovery at average age 48, over the period 1980-2011?” Logically, these two questions are obviously closely related. But in terms of how memory works, they are different, causing associations on very different triggers.
培养精心编码/形成丰富联想的策略：这实际上是一种元策略，即形成策略的策略。一个简单的示例策略就是使用 "同一 "问题的多个变体。例如，我之前提到过我的两个问题："琼斯 2011》称，1980-2011 年间，诺贝尔物理学奖获得者的平均获奖年龄是多少？还有"哪篇论文声称，1980-2011年间，物理学诺贝尔奖获得者平均年龄为48岁？从逻辑上讲，这两个问题显然密切相关。但就记忆的工作原理而言，它们是不同的，引起联想的触发点也截然不同。

What about memory palaces and similar techniques? There is a well-known set of memory techniques based around ideas such as memory palaces, the method of loci, and others*
那么记忆宫殿和类似的技术呢？围绕记忆宫殿、定位法等思想，有一套著名的记忆技术*。* An entertaining and informative overview is: Joshua Foer, “Moonwalking with Einstein” (2011).
* 乔舒亚-福尔（Joshua Foer），《与爱因斯坦漫步月球》（2011 年）：Joshua Foer，《与爱因斯坦漫步月球》（2011 年）。. This is an extreme form of elaborative encoding, making rich visual and spatial associations to the material you want to remember. Here's Joshua Foer recounting a conversation where mnemonist Ed Cooke describes one basic technique:
.这是精心编码的一种极端形式，即对想要记住的材料进行丰富的视觉和空间联想。约书亚-福尔（Joshua Foer）在这里讲述了记忆大师埃德-库克（Ed Cooke）在一次谈话中描述的一种基本技巧：

Ed then explained to me his procedure for making a name memorable, which he had used in the competition to memorize the first and last names associated with ninety-nine different photographic head shots in the names-and-faces event. It was a technique he promised I could use to remember people's names at parties and meetings. “The trick is actually deceptively simple,” he said. “It is always to associate the sound of a person's name with something you can clearly imagine. It's all about creating a vivid image in your mind that anchors your visual memory of the person's face to a visual memory connected to the person's name. When you need to reach back and remember the person's name at some later date, the image you created will simply pop back into your mind… So, hmm, you said your name was Josh Foer, eh?” He raised an eyebrow and gave his chin a melodramatic stroke. “Well, I'd imagine you joshing me where we first met, outside the competition hall, and I'd imagine myself breaking into four pieces in response. Four/Foer, get it? That little image is more entertaining—to me, at least—than your mere name, and should stick nicely in the mind.”
随后，艾德向我解释了他让人记住名字的方法，他曾在 "姓名与面孔 "比赛中用这一方法记住了与 99 张不同头像照片相关的名字和姓氏。他向我保证，我可以用这种方法在聚会和会议上记住别人的名字。"他说："这个技巧其实很简单。"总是把一个人的名字与你能清楚想象到的事物联系起来。这就是要在你的脑海中创造一个生动的形象，将你对这个人面孔的视觉记忆与这个人名字的视觉记忆联系起来。当你以后需要回想这个人的名字时，你所创造的形象就会简单地回到你的脑海中......那么，嗯，你说你叫乔希-福尔，是吗？"他挑了挑眉毛，夸张地摸了摸下巴。"好吧，我会想象你在我们初次见面的地方，在比赛大厅外面，跟我开玩笑，而我会想象自己被你打成四瓣。Four/Foer，明白吗？至少对我来说，这个小形象比你的名字更有趣，也更容易让人记住"。

I've experimented with these techniques, and while they're fun, they seem most useful for memorizing trivia – sequences of playing cards, strings of digits, and so on. They seem less well developed for more abstract concepts, and such abstractions are often where the deepest understanding lies. In that sense, they may even distract from understanding. That said, it's possible I simply need to figure out better ways of using these ideas, much as I needed to figure out Anki. In particular, it may be worth further investigating some of the techniques used by practitioners to form rich associations. As Foer says, quoting a memory expert, there is great value in learning to “think in more memorable ways”.
我尝试过这些技巧，虽然它们很有趣，但它们似乎最适用于记忆琐事--扑克牌顺序、数字串等。对于更抽象的概念，它们似乎没有那么好用，而这些抽象概念往往是最深刻的理解所在。从这个意义上说，它们甚至会分散理解力。也就是说，我可能只是需要找出更好的方法来使用这些想法，就像我需要找出 Anki 一样。特别是，也许值得进一步研究实践者用来形成丰富联想的一些技巧。正如福尔引用一位记忆专家的话所说，学习 "以更难忘的方式思考 "是非常有价值的。

95% of Anki's value comes from 5% of the features: Anki has ways of auto-generating cards, of tagging cards, a plugin ecosystem, and much else. In practice, I rarely use any of these features. My cards are always one of two types: the majority are simple question and answer; a substantial minority are what's called a cloze: a kind of fill-in-the-blanks test. For instance, I'll use clozes to test myself on favorite quotes:
Anki 95% 的价值来自于 5% 的功能：Anki 有自动生成卡片的方法、标记卡片的方法、插件生态系统以及其他很多方法。实际上，我很少使用这些功能。我的卡片总是两种类型中的一种：大多数是简单的问答题；相当一部分是所谓的 "掐头去尾"：一种填空测试。例如，我会用 "掐头去尾 "来测试自己最喜欢的名言：

“if the personal computer is truly a __ then the use of it would actually change the __ of an __", __, __” (Answer: new medium, thought patterns, entire civilization, Alan Kay, 1989).
"如果个人电脑真的是一种__，那么它的使用实际上将改变一个__的__"、__、__"（答案：新媒介、思维模式、整个文明，艾伦-凯，1989 年）。

Clozes can also be used to pose questions not involving quotes:
Clozes 也可用于提出不涉及引语的问题：

The Adelson illusion is also known as the ___ illusion. (Answer: checker-shadow)
阿德尔森错觉也被称为____错觉。(答案：棋盘阴影）

Why not use more of Anki's features? Part of the reason is that I get an enormous benefit from just the core features. Furthermore, learning to use this tiny set of features well has required a lot of work. A basketball and hoop are simple pieces of equipment, but you can spend a lifetime learning to use them well. Similarly, basic Anki practice can be developed enormously. And so I've concentrated on learning to use those basic features well.
为什么不使用 Anki 的更多功能呢？部分原因是我只从核心功能中获得了巨大的收益。此外，要想很好地使用这一小部分功能，需要付出很多努力。篮球和篮球架都是简单的设备，但你可以用一生的时间来学习如何使用它们。同样，Anki 的基本练习也可以得到极大的发展。因此，我集中精力学习如何使用好这些基本功能。

I know many people who try Anki out, and then go down a rabbit hole learning as many features as possible so they can use it “efficiently”. Usually, they're chasing 1% improvements. Often, those people ultimately give up Anki as “too difficult”, which is often a synonym for “I got nervous I wasn't using it perfectly”. This is a pity. As discussed earlier, Anki offers something like a 20-fold improvement over (say) ordinary flashcards. And so they're giving up a 2,000% improvement because they were worried they were missing a few final 5%, 1% and (in many cases) 0.1% improvements. This kind of rabbit hole seems to be especially attractive to programmers.
我认识很多人，他们试用了 Anki 之后，就钻进了一个兔子洞，尽可能多地学习各种功能，以便 "高效 "地使用它。通常，他们追求的是 1%的进步。通常，这些人最终会以 "太难了 "为由放弃 Anki，而 "太难了 "往往就是 "我没有完美地使用它而感到紧张 "的同义词。这太可惜了。正如前面所讨论的，Anki 比（比如说）普通的闪存卡提高了大约 20 倍。因此，他们放弃了 2000% 的改进，因为他们担心会错过最后 5%、1% 和（在许多情况下）0.1% 的改进。这种兔子洞似乎对程序员特别有吸引力。

For this reason, when someone is getting started I advise not using any advanced features, and not installing any plugins. Don't, in short, come down with a bad case of programmer's efficiency disease. Learn how to use Anki for basic question and answer, and concentrate on exploring new patterns within that paradigm. That'll serve you far better than any number of hours spent fiddling around with the features. Then, if you build a regular habit of high-quality Anki use, you can experiment with more advanced features.
因此，在新手入门时，我建议不要使用任何高级功能，也不要安装任何插件。总之，不要患上程序员效率病。学习如何使用 Anki 进行基本的问答，并集中精力在这一范例中探索新模式。这远比你花大量时间在功能上瞎折腾要好得多。然后，如果你养成了定期使用高质量 Anki 的习惯，你就可以尝试使用更高级的功能了。

The challenges of using Anki to store facts about friends and family: I've experimented with using Anki to store (non-sensitive!) questions about friends and family. It works well for things like “Is [my friend] a vegan?” But my use has run somewhat aground on thornier questions. For instance, suppose I talk with a new friend about their kids, but have never met those kids. I could put in questions like “What is the name of [my friend's] eldest child?” Or, if we'd chatted about music, I might put in: “What is a musician [my friend] likes?”
使用 Anki 存储有关朋友和家人的事实所面临的挑战：我曾经尝试过用 Anki 来存储有关朋友和家人的问题（非敏感问题！）。它在诸如"（我的朋友）是素食主义者吗？但我对更复杂的问题的使用就有点搁浅了。例如，假设我和一位新朋友谈论他们的孩子，但我从未见过他们的孩子。我可以问这样的问题："（我朋友的）大孩子叫什么名字？或者，如果我们聊到音乐，我可以问："[我朋友]喜欢哪位音乐家？"

This kind of experiment is well intentioned. But posing such questions often leaves me feeling uncomfortable. It seems too much like faking interest in my friends. There's a pretty strong social norm that if you remember your friends' taste in music or their kids' names, it's because you're interested in that friend. Using a memory aid feels somehow ungenuine, at least to me.
这种实验的初衷是好的。但提出这样的问题常常让我感到不自在。这似乎太像假装对我的朋友感兴趣了。如果你记得朋友的音乐品味或他们孩子的名字，那是因为你对这个朋友感兴趣。至少在我看来，使用记忆辅助工具会让人觉得不真实。

I've talked with several friends about this. Most have told me the same thing: they appreciate me going to so much trouble in the first place, and find it charming that I'd worry so much about whether it was ungenuine. So perhaps it's a mistake to worry. Nonetheless, I still have trouble with it. I have adopted Anki for less personal stuff – things like people's food preferences. And maybe over time I'll use it for storing more personal facts. But for now I'm taking it slow.
我和几个朋友谈过这个问题。大多数人都对我说了同样的话：他们很感激我当初费了这么大的周折，而且觉得我这么担心是否是假的很有魅力。所以，也许担心是个错误。尽管如此，我还是有这方面的困扰。我采用 Anki 来处理一些不那么私人的东西--比如人们的饮食偏好。也许随着时间的推移，我会用它来存储更多的个人事实。但现在我还在慢慢来。

Procedural versus declarative memory: There's a big difference between remembering a fact and mastering a process. For instance, while you might remember a Unix command when cued by an Anki question, that doesn't mean you'll recognize an opportunity to use the command in the context of the command line, and be comfortable typing it out. And it's still another thing to find novel, creative ways of combining the commands you know, in order to solve challenging problems.
程序性记忆与陈述性记忆：记住一个事实和掌握一个过程有很大区别。例如，虽然你可能会在 Anki 问题的提示下记住一个 Unix 命令，但这并不意味着你会意识到在命令行上下文中使用该命令的机会，并能自如地敲出它。而找到新颖、有创意的方法来组合你所知道的命令，以解决具有挑战性的问题，则是另一回事。

Put another way: to really internalize a process, it's not enough just to review Anki cards. You need to carry out the process, in context. And you need to solve real problems with it.
换一种说法：要真正内化一个过程，光复习 Anki 卡是不够的。你需要在语境中执行该过程。你需要用它来解决实际问题。

With that said, I've found the transfer process relatively easy. In the case of the command line, I use it often enough that I have plenty of opportunities to make real use of my Ankified knowledge of the command line. Over time, that declarative knowledge is becoming procedural knowledge I routinely use in context. That said, it'd be good to better understand when the transfer works and when it doesn't. Even better would be a memory system that integrates into my actual working environment. For instance, it could query me on Unix commands, while placing me at an actual command line. Or perhaps it would ask me to solve higher-level problems, while at the command line.
尽管如此，我发现转移过程相对简单。就命令行而言，我经常使用它，因此我有很多机会真正利用我的命令行知识。随着时间的推移，这些陈述性知识逐渐变成了我经常在上下文中使用的程序性知识。尽管如此，如果能更好地了解传输何时有效、何时无效就更好了。如果记忆系统能融入我的实际工作环境，那就更好了。例如，它可以让我置身于实际的命令行中，询问我有关 Unix 命令的信息。或者，它可以让我在命令行中解决更高层次的问题。

I've tried one experiment in this vein: miming the action of typing commands while I review my Anki cards. But my subjective impression was that it doesn't work so well, and it was also quite annoying to do. So I stopped.
在这方面，我曾尝试过一个实验：在复习 Anki 卡时模仿输入命令的动作。但我的主观印象是，效果并不好，而且做起来也很烦人。所以我就停止了。

Getting past “names don't matter”: I'm a theoretical physicist by training. There is a famous story in physics, told by Richard Feynman, dismissing the value of knowing the names of things. As a child, Feynman was out playing in a field with a know-it-all kid. Here's what happened, in Feynman's telling*
摆脱 "名字并不重要 "的束缚：我是一名理论物理学家。理查德-费曼（Richard Feynman）讲过一个物理学界的著名故事，其中否定了知道事物名称的价值。小时候，费曼和一个无所不知的孩子在田野里玩耍。以下是费曼讲述的事情经过** Richard P. Feynman, “What Do You Care What Other People Think? Further Adventures of a Curious Character” (1989).
* 理查德-P-费曼，《你在乎别人怎么想吗？一个好奇人物的进一步历险》（1989 年）。:

One kid says to me, “See that bird? What kind of bird is that?”
一个孩子对我说："看到那只鸟了吗？那是什么鸟？"

I said, “I haven't the slightest idea what kind of a bird it is.”
我说："我根本不知道这是什么鸟。"

He says, “It'a brown-throated thrush. Your father doesn't teach you anything!”
他说："这是褐喉鸫。你爸爸什么都没教你！"

But it was the opposite. He [Feynman's father] had already taught me: “See that bird?” he says. “It's a Spencer's warbler.” (I knew he didn't know the real name.) “Well, in Italian, it's a Chutto Lapittida. In Portuguese, it's a Bom da Peida… You can know the name of that bird in all the languages of the world, but when you're finished, you'll know absolutely nothing whatever about the bird! You'll only know about humans in different places, and what they call the bird. So let's look at the bird and see what it's doing — that's what counts.” (I learned very early the difference between knowing the name of something and knowing something.)
但事实恰恰相反。他（费曼的父亲）已经教过我了："看到那只鸟了吗？"他说"那是斯宾塞莺" （我知道他不知道真名）(我知道他不知道真名）"嗯，意大利语叫 Chutto Lapittida。葡萄牙语叫 Bom da Peida......你可以用世界上所有的语言知道这种鸟的名字，但是当你学完之后，你就会对这种鸟一无所知！你只知道不同地方的人类，以及他们是如何称呼这只鸟的。所以，让我们看看这只鸟，看看它在做什么--这才是最重要的。(我很早就知道了知道某样东西的名字和知道某样东西之间的区别）。

Feynman (or his father) goes on to a thoughtful discussion of real knowledge: observing behavior, understanding the reasons for it, and so on.
费曼（或他的父亲）接着对真正的知识进行了深思熟虑的讨论：观察行为、理解行为的原因等等。

It's a good story. But it goes too far: names do matter. Maybe not as much as the know-it-all kid thought, and they're not usually a deep kind of knowledge. But they're the foundation that allows you to build up a network of knowledge.
这是个好故事。但它太过分了：名字确实很重要。也许没有那个万事通孩子想的那么重要，而且名字通常也不是什么深奥的知识。但它们是让你建立知识网络的基础。

This trope that names don't matter was repeatedly drilled into me during my scientific training. When I began using Anki, at first I felt somewhat silly putting questions about names for things into the system. But now I do it enthusiastically, knowing that it's an early step along the way to understanding.
在我接受科学训练期间，这种 "名称并不重要 "的说法被反复灌输给我。当我开始使用 Anki 时，起初我觉得把有关事物名称的问题输入系统有点傻。但现在我热衷于这样做，因为我知道这是通往理解的第一步。

Anki is useful for names of all kinds of things, but I find it particularly helpful for non-verbal things. For instance, I put in questions about artworks, like: “What does the artist Emily Hare's painting Howl look like?” Answer:
Anki 对各种事物的名称都很有用，但我发现它对非语言事物特别有帮助。比如，我输入关于艺术作品的问题，比如："艺术家艾米莉-哈雷的画作《嚎叫》是什么样的？"答案是

I put that question in for two reasons. The main reason is that I like to remember the experience of the painting from time to time. And the other is to put a name to the painting*
我提出这个问题有两个原因。最主要的原因是，我喜欢时不时回忆一下绘画的经历。另一个原因是为画作命名*。* Actually, a better question for that is to be shown the painting and asked what its name is.
* 实际上，更好的问题是让你看这幅画，然后问它叫什么名字。. If I wanted to think more analytically about the painting – say, about the clever use of color gradients – I could add more detailed questions. But I'm pretty happy just committing the experience of the image to memory.
.如果我想对这幅画进行更多的分析思考--比如，关于色彩渐变的巧妙运用--我还可以提出更详细的问题。不过，我很乐意把画面的体验记在脑子里。

What do you do when you get behind? Anki becomes challenging when you get behind with cards. If you skip a day or two – or fifty – the cards begin to back up. It's intimidating to come back to find you have 500 cards to review in a day. Even worse, if you fall out of the Anki habit, you can get a very long way behind. I largely stopped using Anki for a 7-month period, and came back to thousands of backlogged cards.
落后时该怎么办？当你落后的时候，Anki 就变得很有挑战性了。如果跳过一两天或五十天，卡片就会开始倒退。当你回来的时候发现一天要复习 500 张卡片，这是很吓人的。更糟糕的是，如果你放弃了 Anki 的习惯，你就会落后很长时间。我曾在 7 个月的时间里停止使用 Anki，结果回来时发现积压了数千张卡片。

Fortunately, it wasn't that hard to catch up. I set myself gradually increasing quotas (100, 150, 200, 250, and eventually 300) of cards per day, and worked through those quotas each day for several weeks until I'd caught up.
幸运的是，赶上进度并不难。我给自己设定了每天逐渐增加的卡片配额（100 张、150 张、200 张、250 张，最后是 300 张），连续几周每天都完成这些配额，直到我赶上为止。

While this wasn't too difficult, it was somewhat demoralizing and discouraging. It'd be better if Anki had a “catch up” feature that would spread the excess cards over the next few weeks in your schedule. But it doesn't. In any case, this is a gotcha, but it's not too difficult to address.
虽然这并不难，但却有些打击士气和泄气。如果 Anki 有 "追赶 "功能，把多余的卡片分散到接下来几周的计划中，那就更好了。但它没有。无论如何，这是一个问题，但并不难解决。

Using Anki for APIs, books, videos, seminars, conversations, the web, events, and places: Nearly everything I said earlier about Ankifying papers applies also to other resources. Here's a few tips. I've separated out the discussion for APIs into an appendix, which you can read below, if interested.
将 Anki 用于 API、书籍、视频、研讨会、对话、网络、活动和地点：我前面说的关于安基论文的几乎所有内容也适用于其他资源。这里有一些提示。我把有关 API 的讨论单独列了一个附录，感兴趣的朋友可以在下面阅读。

For seminars and conversations with colleagues I find it surprisingly helpful to set Anki quotas. For instance, for seminars I try to find at least three high-quality questions to Ankify. For extended conversations, at least one high-quality question to Ankify. I've found that setting quotas helps me pay more attention, especially during seminars. (I find it much easier a priori to pay attention in one-on-one conversation.)
对于研讨会和与同事的谈话，我发现设定 Anki 配额会有意想不到的帮助。例如，在研讨会上，我尽量找至少三个高质量的问题放到 Ankify 中。对于长时间的对话，至少要找一个高质量的问题来安岐。我发现设置配额能让我更加专注，尤其是在研讨会上。(我发现在一对一的谈话中，先验地集中注意力要容易得多）。

I'm more haphazard about videos, events, and places. It'd be good to, say, systematically Ankify 3-5 questions after going on an outing or to a new restaurant, to help me remember the experience. I do this sometimes. But I haven't been that systematic.
我对视频、活动和地点的记忆比较随意。比方说，在外出旅行或去一家新餐厅用餐后，我可以系统地回答 3-5 个问题，帮助自己记住这段经历。我有时会这么做。但我还没有那么系统。

I tend to Ankify in real time as I read papers and books. For seminars, conversations, and so on I prefer to immerse myself in the experience. Instead of getting out Anki, I will quickly make a mental (or paper) note of what I want to Ankify. I then enter it into Anki later. This requires some discipline; it's one reason I prefer to set a small quota, so that I merely have to enter a few questions later, rather than dozens.
在阅读论文和书籍时，我倾向于实时 "注释"。对于研讨会、对话等，我更喜欢让自己沉浸在体验中。我不会拿出 Anki，而是迅速在脑中（或纸上）记下我想 Ankify 的内容。然后再输入到 Anki 中。这需要一些纪律；这也是我喜欢设定一个小配额的原因之一，这样我以后只需要输入几个问题，而不是几十个。

One caution is with books: reading an entire book is a big commitment, and adding Anki questions regularly can slow you down a lot. It's worth keeping this in mind when deciding how much to Ankify. Sometimes a book is so dense with great material that it's worth taking the time to add lots of questions. But unmindfully Ankifying everything in sight is a bad habit, one I've occasionally fallen into.
在阅读书籍时要注意：阅读一整本书是一项很大的投入，经常添加 Anki 问题会让你的阅读速度大大减慢。在决定安岐化的程度时，值得牢记这一点。有时，一本书的内容非常丰富，值得花时间添加大量问题。但是，不加思索地安利眼前的一切是个坏习惯，我偶尔也会陷入其中。

What you Ankify is not a trivial choice: Ankify things that serve your long-term goals. In some measure we become what we remember, so we must be careful what we remember*
Ankify 并不是一个微不足道的选择：安化的东西要符合你的长期目标。在某种程度上，我们记住了什么，就会成为什么，所以我们必须小心记住什么*。* With apologies to Kurt Vonnegut, who wrote: “We are what we pretend to be, so we must be careful about what we pretend to be.”.
* 向库尔特-冯内古特致歉，他写道："我们是我们所假装的，所以我们必须小心我们所假装的"。. This is always true, but Anki makes it especially true.
.这始终是正确的，但 Anki 让它变得尤其正确。

With all that said, one fun pattern is to go back to my old, pre-Anki notes on books, and to Ankify them. This can often be done quickly, and gives me a greater return on the time I've invested in now mostly-forgotten books*
说了这么多，有一个有趣的模式，就是回到我以前的、安基之前的读书笔记，对它们进行安基化。这样做通常很快就能完成，而且能让我在已经基本被遗忘的书籍上投入的时间得到更大的回报*。* Friends sometimes complain that many books are over-padded essays. Perhaps a benefit of such padding is that it enforces an Anki-like spaced repetition, since readers take weeks to read the book. This may be an inefficient way to memorize the main points, but is better than having no memory of the book at all.
* 朋友们有时会抱怨很多书都是填充过多的散文。也许这种填充的好处是，由于读者需要花费数周的时间来阅读这本书，因此它可以强制执行类似 Anki 的间隔重复。这可能是一种低效的记忆要点的方法，但总比完全不记忆这本书要好。.

Something I haven't yet figured out is how to integrate Anki with note taking for my creative projects. I can't replace note taking with Anki – it's too slow, and for many things a poor use of my long-term memory. On the other hand, there are many benefits to using Anki for important items – fluid access to memory is at the foundation of so much creative thought.
我还没有想好的是如何将 Anki 与我的创作项目笔记结合起来。我不能用 Anki 来代替记笔记--它太慢了，而且对很多事情来说，我的长期记忆力都用不上。另一方面，用 Anki 来做重要的事情也有很多好处--流畅的记忆是很多创造性思维的基础。Speed of associative thought is, I believe, important in creative work. – John Littlewood
我认为，联想速度对创造性工作非常重要。- 约翰-利特尔伍德 In practice, I find myself instinctively and unsystematically doing some things as notes, others as Anki questions, and still other things as both. Overall, it works okay, but my sense is that it could be a lot better if I applied more systematic thought and experimentation. Part of the problem is that I don't have a very good system for note taking, period! If I worked more on that, I suspect the whole thing would get a lot better. Still, it works okay.
在实践中，我发现自己本能地、不系统地把一些事情当作笔记来做，把另一些事情当作 Anki 问题来做，还有一些事情则两者兼而有之。总的来说，效果还可以，但我觉得，如果我能进行更系统的思考和尝试，效果会更好。部分问题在于我没有一个很好的笔记系统！如果我在这方面多下功夫，我想整个事情会好很多。不过，效果还是可以的。

Avoid the yes/no pattern: One bad habit I sometimes slide into is having lots of Anki questions with yes/no answers. For instance, here's a not-very-good question I added when learning about graphical models in machine learning:
避免 "是/否 "模式：我有时会养成一个坏习惯，那就是在 Anki 问题中加入很多 "是/否 "答案。例如，这是我在学习机器学习中的图形模型时添加的一个不太好的问题：

Is computing the partition function intractable for most graphical models?
对于大多数图形模型而言，计算分割函数是否难以实现？

The answer is “yes”. That's fine, as far as it goes. But it'd help my understanding to elaborate the ideas in the question. Can I add a question about for which graphical models the partition function is tractable? Can I give an example of a graphical model for which the partition function is intractable? What does it mean for computing the partition function to be intractable anyway? Yes/no questions should, at the least, be considered as good candidates for question refactoring*
答案是 "是"。就其本身而言，这很好。但如果能详细说明问题中的观点，会有助于我的理解。我能否补充一个问题：对于哪些图形模型，分割函数是可控的？我能举例说明哪些图形模型的分割函数是难以计算的吗？计算分割函数是棘手的到底意味着什么？是/否问题至少应被视为问题重构的良好候选*。* By analogy with code smells, we can speak of “question smells”, as suggesting a possible need for refactoring. A yes/no construction is an example of a question smell.
* 通过与代码气味的类比，我们可以把 "问题气味 "称为可能需要重构的气味。是/否结构就是问题气味的一个例子。

Aren't external memory aids enough? One common criticism of systems such as Anki is that external memory devices – systems such as Google, wikis, and notebooks – really ought to be enough. Used well, such systems are, of course, extremely useful as a complement to Anki. But for creative work and for problem-solving there is something special about having an internalized understanding. It enables speed in associative thought, an ability to rapidly try out many combinations of ideas, and to intuit patterns, in ways not possible if you need to keep laboriously looking up information.
外部记忆辅助工具还不够吗？对Anki等系统的一个常见批评是，外部记忆设备--谷歌、维基和笔记本等系统--真的应该足够了。当然，如果使用得当，这些系统作为 Anki 的补充是非常有用的。但是，对于创造性工作和解决问题来说，内化的理解有其特殊之处。它可以加快联想速度，快速尝试多种想法的组合，以及直觉模式，而如果你需要不断费力地查找信息，就不可能做到这一点。

Fluency matters in thinking. Alan Kay and Adele Goldberg have proposed*
思维的流畅性很重要。艾伦-凯和阿黛尔-戈德堡提出** Alan Kay and Adele Goldberg, Personal Dynamic Media (1977).
* Alan Kay 和 Adele Goldberg，《个人动态媒体》（1977 年）。 the thought experiment of a flute in which there is “a one-second delay between blowing a note and hearing it!” As they observe, this is “absurd”. In a similar way, certain types of thoughts are much easier to have when all the relevant kinds of understanding are held in mind. And for that, Anki is invaluable.
的思想实验中，"吹出一个音符与听到它之间有一秒钟的延迟！"。正如他们所观察到的，这是 "荒谬的"。同样，如果对所有相关的理解都牢记于心，某些类型的思考就会容易得多。为此，Anki 的作用不可估量。

If personal memory systems are so great, why aren't they more widely used? This question is analogous to the old joke about two economists who are walking along when one of them spots a $20 bill. They say: “Look! There's $20 on the ground!” The other replies: “Impossible! If it were really there, someone would have picked it up already.”
如果个人记忆系统如此出色，为什么没有得到更广泛的应用？这个问题类似于一个老笑话：两个经济学家走在路上，其中一个发现了一张 20 美元的钞票。他们说"看，地上有 20 美元！地上有 20 美元！"另一个回答说另一个回答说："不可能！如果真有的话，早就有人捡起来了。"

The analogy is only partial. In fact, Anki seems like a continual supply of $20 bills lying on the ground. And it's reasonable to ask why it's not more widely used. One of the most cited papers in the relevant research literature*
这个比喻只是局部的。事实上，Anki 就像是躺在地上的 20 美元钞票。我们有理由问，为什么它没有得到更广泛的应用。相关研究文献中被引用最多的论文之一*是* Frank N. Dempster, The Spacing Effect: A Case Study in the Failure to Apply the Results of Psychological Research (1988).
* Frank N. Dempster, The Spacing Effect：心理学研究成果应用失败的案例研究》（1988 年）。 is a discussion of why these ideas aren't more widely used in education. Although written in 1988, many of the observations in the paper remain true today.
是关于为什么这些理念没有在教育中得到更广泛应用的讨论。虽然这篇文章写于 1988 年，但其中的许多观点至今仍然适用。

My own personal suspicion is that there are three main factors:
我个人认为主要有三个因素：

In experimental research on memory, people consistently underestimate the gains that come from distributing their study in a manner similar to Anki. Instead, they prefer last-minute cramming, and believe it produces better results, though many studies show it does not.
在有关记忆的实验研究中，人们总是低估以类似 Anki 的方式分散学习所带来的收益。相反，他们更喜欢在最后一刻恶补，并认为这样做能取得更好的效果，尽管许多研究表明这样做效果并不好。
The psychologist Robert Bjork has suggested*
心理学家罗伯特-比约克建议**Robert A. Bjork, Memory and Metamemory Considerations in the Training of Human Beings (1994). the “principle of desirable difficulty”, the idea that memories are maximally strengthened if tested when we're on the verge of forgetting them. This suggests that an efficient memory system will intrinsically be somewhat difficult to use. Human beings have a complex relationship to difficult activities, and often dislike performing them, unless strongly motivated (in which case they may become pleasurable).
"理想难度原则 "认为，如果在我们濒临遗忘时对记忆进行测试，就能最大程度地强化记忆。这表明，高效的记忆系统在本质上会有一定的使用难度。人类与困难的活动之间有着复杂的关系，通常不喜欢进行这些活动，除非有强烈的动机（在这种情况下，这些活动可能会变得令人愉悦）。
Systems such as Anki are challenging to use well, and easy to use poorly.
Anki 等系统用得好有难度，用不好也很容易。

It is interesting to consider developing systems which may overcome some or all of these issues.
考虑开发可以克服部分或所有这些问题的系统是很有意义的。

Part II: Personal Memory Systems More Broadly
第二部分：更广泛的个人记忆系统

In the first part of this essay we looked at a particular personal memory system, Anki, through the lens of my personal experience. In the second, briefer, part of this essay we'll consider two broader questions about personal memory systems: how important is memory as a cognitive skill; and what is the role of cognitive science in building personal memory systems?
在这篇文章的第一部分，我们通过我的个人经历透视了一个特殊的个人记忆系统--Anki。在本文的第二部分，我们将考虑有关个人记忆系统的两个更广泛的问题：记忆作为一种认知技能有多重要；认知科学在建立个人记忆系统中的作用是什么？

How important is long-term memory, anyway?
长期记忆到底有多重要？

Long-term memory is sometimes disparaged. It's common for people to denigrate “rote memory”, especially in the classroom. I've heard from many people that they dropped some class – organic chemistry is common – because it was “just a bunch of facts, and I wanted something involving more understanding”.
长期记忆有时会受到贬低。人们经常诋毁 "死记硬背"，尤其是在课堂上。我听很多人说过，他们放弃了一些课程--有机化学就是常见的一种--因为它 "只是一堆事实，而我想要的是涉及更多理解的东西"。

I won't defend bad classroom teaching, or the way organic chemistry is often taught. But it's a mistake to underestimate the importance of memory. I used to believe such tropes about the low importance of memory. But I now believe memory is at the foundation of our cognition.
我不会为糟糕的课堂教学或有机化学的教学方式辩护。但低估记忆的重要性是错误的。我曾经相信这种低估记忆重要性的说法。但我现在相信，记忆是我们认知的基础。

There are two main reasons for this change, one a personal experience, the other based on evidence from cognitive science.
这种变化有两个主要原因，一个是个人经历，另一个是基于认知科学的证据。

Let me begin with the personal experience.
让我从个人经历说起。

Over the years, I've often helped people learn technical subjects such as quantum mechanics. Over time you come to see patterns in how people get stuck. One common pattern is that people think they're getting stuck on esoteric, complex issues. But when you dig down it turns out they're having a hard time with basic notation and terminology. It's difficult to understand quantum mechanics when you're unclear about every third word or piece of notation! Every sentence is a struggle.
多年来，我经常帮助人们学习量子力学等技术科目。随着时间的推移，你会发现人们被卡住的模式。一种常见的模式是，人们认为自己被深奥、复杂的问题卡住了。但当你深入研究后发现，他们在基本符号和术语方面遇到了困难。当你不清楚每三个单词或每一个符号时，就很难理解量子力学！每一句话都很费劲。

It's like they're trying to compose a beautiful sonnet in French, but only know 200 words of French. They're frustrated, and think the trouble is the difficulty of finding a good theme, striking sentiments and images, and so on. But really the issue is that they have only 200 words with which to compose.
这就好比他们想用法语创作一首优美的十四行诗，却只知道 200 个法语单词。他们很沮丧，认为问题出在难以找到好的主题、鲜明的情感和形象等等。但实际上，问题在于他们只有 200 个单词可以创作。

My somewhat pious belief was that if people focused more on remembering the basics, and worried less about the “difficult” high-level issues, they'd find the high-level issues took care of themselves.
我有点虔诚地认为，如果人们更专注于记住基础知识，少担心 "困难的 "高层次问题，他们就会发现高层次问题会自己解决。

But while I held this as a strong conviction about other people, I never realized it also applied to me. And I had no idea at all how strongly it applied to me. Using Anki to read papers in new fields disabused me of this illusion. I found it almost unsettling how much easier Anki made learning such subjects. I now believe memory of the basics is often the single largest barrier to understanding. If you have a system such as Anki for overcoming that barrier, then you will find it much, much easier to read into new fields.
但是，当我把这作为对其他人的坚定信念时，我从未意识到它也适用于我自己。而且，我根本不知道它对我的影响有多大。用 Anki 来阅读新领域的论文，让我消除了这种错觉。我发现 Anki 让学习这些科目变得如此容易，这几乎让我感到不安。我现在相信，基础知识的记忆往往是理解的最大障碍。如果你有一个像 Anki 这样的系统来克服这一障碍，那么你就会发现阅读新领域的论文要容易得多。

This experience of how much easier Anki made learning a new technical field greatly increased my visceral appreciation for the importance of memory.
Anki 让我学习一个新的技术领域变得容易多了，这种经历大大提高了我对记忆重要性的直观认识。

There are also many results from cognitive science on the key role memory plays in cognition.
认知科学中也有许多关于记忆在认知中的关键作用的研究成果。

One striking line of work was done (separately) by the researchers Adriaan de Groot and Herbert Simon, studying how people acquire expertise, focusing particularly on chess*
研究人员阿德里安-德-格鲁特和赫伯特-西蒙（分别）完成了一项引人注目的工作，他们研究了人们如何获得专业知识，尤其侧重于国际象棋*。* See, for instance, Herbert A. Simon, How Big is a Chunk?, Science (1974), and Adriaan de Groot, Thought and Choice in Chess, Amsterdam University Press (2008, reprinted from 1965).
* 例如，见赫伯特-A-西蒙（Herbert A. Simon），《大块有多大》（How Big is a Chunk?），《科学》（1974 年），以及阿德里安-德-格鲁特（Adriaan de Groot），《国际象棋中的思想与选择》（Thought and Choice in Chess），阿姆斯特丹大学出版社（2008 年，重印自 1965 年）。. They found that world-class chess experts saw the board differently to beginners. A beginner would see “a pawn here, a rook there”, and so on, a series of individual pieces. Masters, by contrast, saw much more elaborate “chunks”: combinations of pieces that they recognized as a unit, and were able to reason about at a higher level of abstraction than the individual pieces.
.他们发现，世界级的国际象棋专家和初学者看到的棋盘是不一样的。初学者看到的是 "这里一个兵，那里一个车 "等一系列单个棋子。相比之下，大师看到的 "大块 "要复杂得多：他们将棋子组合视为一个整体，并能以比单个棋子更高的抽象水平进行推理。

Simon estimated chess masters learn between 25,000 and 100,000 of these chunks during their training, and that learning the chunks was a key element in becoming a first-rate chess player. Such players really see chess positions very differently from beginners.
据西蒙估计，国际象棋大师在训练过程中学会了 25000 到 100000 个这样的棋块，而学习这些棋块是成为一流棋手的关键因素。这些棋手对棋势的看法确实与初学者截然不同。

Why does learning to recognize and reason about such chunks help so much in developing expertise? Here's a speculative, informal model – as far as I know, it hasn't been validated by cognitive scientists, so don't take it too seriously. I'll describe it in the context of mathematics, instead of chess, since mathematics is an area where I have experience talking with people at all ranges of ability, from beginners to accomplished professional mathematicians.
为什么学习识别和推理这些大块内容对发展专业知识有如此大的帮助呢？这里有一个推测性的、非正式的模型--据我所知，它还没有经过认知科学家的验证，所以不要太当真。我将以数学而非国际象棋为背景来描述它，因为在数学领域，我有与各种能力的人交流的经验，从初学者到有成就的专业数学家。

Many people's model of accomplished mathematicians is that they are astoundingly bright, with very high IQs, and the ability to deal with very complex ideas in their mind. A common perception is that their smartness gives them the ability to deal with very complex ideas. Basically, they have a higher horsepower engine.
在许多人的印象中，有成就的数学家都是聪明绝顶、智商极高、能够在头脑中处理非常复杂的想法。一个普遍的看法是，他们的聪明使他们有能力处理非常复杂的想法。从根本上说，他们的发动机马力更大。

It's true that top mathematicians are usually very bright. But here's a different explanation of what's going on. It's that, per Simon, many top mathematicians have, through hard work, internalized many more complex mathematical chunks than ordinary humans. And what this means is that mathematical situations which seem very complex to the rest of us seem very simple to them. So it's not that they have a higher horsepower mind, in the sense of being able to deal with more complexity. Rather, their prior learning has given them better chunking abilities, and so situations most people would see as complex they see as simple, and they find it much easier to reason about.
顶级数学家通常都非常聪明，这是事实。但这里有一个不同的解释。按照西蒙的说法，许多顶尖数学家通过艰苦的努力，比普通人内化了许多更复杂的数学块。这意味着，在我们看来非常复杂的数学情境，在他们看来却非常简单。因此，这并不是说他们的思维能力更强，能够处理更复杂的问题。相反，他们之前的学习给了他们更好的分块能力，所以在大多数人看来很复杂的情况，在他们看来却很简单，他们发现这更容易推理。

Now, the concept of chunks used by Simon in his study of chess players actually came from a famous 1956 paper by George Miller, “The Magical Number Seven, Plus or Minus Two”*
西蒙在研究棋手时使用的 "大块 "概念实际上来自乔治-米勒 1956 年发表的一篇著名论文《神奇的数字七，正负二》*。* George A. Miller, The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information (1956).
* 乔治-A-米勒，《神奇的数字七，加减二：我们处理信息能力的一些限制》（1956 年）。. Miller argued that the capacity of working memory is roughly seven chunks. In fact, it turns out that there is variation in that number from person to person, and a substantial correlation between the capacity of an individual's working memory and their general intellectual ability (IQ)*
.米勒认为，工作记忆的容量大致为七块。事实上，这个数字因人而异，而且个人的工作记忆能力与其一般智力（IQ）之间存在着很大的相关性*。* A review of the correlation may be found in Phillip L. Ackerman, Margaret E. Beier, and Mary O. Boyle, Working Memory and Intelligence: The Same or Different Constructs? Psychological Bulletin (2006).
* 有关相关性的综述见 Phillip L. Ackerman, Margaret E. Beier, and Mary O. Boyle, Working Memory and Intelligence：相同还是不同的结构？心理学通报》（2006 年）。. Typically, the better your working memory, the higher your IQ, and vice versa.
.通常情况下，工作记忆越好，智商越高，反之亦然。

Exactly what Miller meant by chunks he left somewhat vague, writing:
至于米勒所说的 "大块 "究竟是什么意思，他在写作时说得有些含糊不清：

The contrast of the terms bit and chunk also serves to highlight the fact that we are not very definite about what constitutes a chunk of information. For example, the memory span of five words that Hayes obtained… might just as appropriately have been called a memory span of 15 phonemes, since each word had about three phonemes in it. Intuitively, it is clear that the subjects were recalling five words, not 15 phonemes, but the logical distinction is not immediately apparent. We are dealing here with a process of organizing or grouping the input into familiar units or chunks, and a great deal of learning has gone into the formation of these familiar units.
位（bit）和块（chunk）这两个词的对比也突出了这样一个事实，即我们对什么是信息块并不十分明确。例如，海耶斯获得的五个单词的记忆跨度......也可以恰当地称为 15 个音素的记忆跨度，因为每个单词都有大约三个音素。从直观上看，受试者回忆的显然是五个单词，而不是 15 个音素，但逻辑上的区别并不是一目了然的。我们在这里处理的是将输入内容组织或归类为熟悉的单元或语块的过程，而这些熟悉单元的形成需要大量的学习。

Put another way, in Miller's account the chunk was effectively the basic unit of working memory. And so Simon and his collaborators were studying the basic units used in the working memory of chess players. If those chunks were more complex, then that meant a player's working memory had a higher effective capacity. In particular, someone with a lower IQ but able to call on more complex chunks would be able to reason about more complex situations than someone with a higher IQ but less complex internalized chunks.
换句话说，在米勒看来，"块 "实际上就是工作记忆的基本单位。因此，西蒙和他的合作者正在研究棋手工作记忆中使用的基本单位。如果这些大块更为复杂，那么就意味着棋手的工作记忆具有更高的有效容量。特别是，与智商较高但内化块复杂度较低的人相比，智商较低但能够调用更复杂块的人能够推理出更复杂的情况。

In other words, having more chunks memorized in some domain is somewhat like an effective boost to a person's IQ in that domain.
换句话说，在某个领域记忆更多的内容，就像是有效地提高了一个人在该领域的智商。

Okay, that's a speculative informal model. Regardless of whether it's correct, it does seem that internalizing high-level chunks is a crucial part of acquiring expertise. However, that doesn't then necessarily imply that the use of systems such as Anki will speed up acquisition of such chunks. It's merely an argument that long-term memory plays a crucial role in the acquisition of some types of expertise. Still, it seems plausible that regular use of systems such as Anki may speed up the acquisition of the high-level chunks used by experts*
好吧，这是一个推测性的非正式模型。不管它是否正确，内化高水平的语块似乎确实是获取专业知识的关键部分。然而，这并不意味着使用 Anki 等系统就能加快掌握这些语块。这只是一种论证，即长期记忆在掌握某些类型的专业知识中起着至关重要的作用。不过，经常使用 Anki 等系统可能会加快掌握专家使用的高级语块*，这似乎还是有道理的。* To determine this it would help to understand exactly how these chunks arise. That still seems to be poorly understood. I wouldn't be surprised if it involved considerable analysis and problem-solving, in addition to long-term memory.
* 要确定这一点，就必须了解这些块体是如何产生的。人们对这一点似乎仍然知之甚少。如果除了长期记忆外，还涉及大量的分析和解决问题，我也不会感到惊讶。. And that those chunks are then at the heart of effective cognition, including our ability to understand, to problem solve, and to create.
.而这些片段正是有效认知的核心，包括我们理解、解决问题和创造的能力。

Distributed practice 分布式实践

Why does Anki work? In this section we briefly look at one of the key underlying ideas from cognitive science, known as distributed practice.
Anki 为什么有效？在本节中，我们将简要介绍认知科学的一个重要基本思想，即分布式练习。

Suppose you're introduced to someone at a party, and they tell you their name. If you're paying attention, and their name isn't too unusual, you'll almost certainly remember their name 20 seconds later. But you're more likely to have forgotten their name in an hour, and more likely still to have forgotten their name in a month.
假设你在聚会上认识了一个人，他告诉了你他的名字。如果你留心听，而且他们的名字不太特别，你几乎肯定会在 20 秒钟后记住他们的名字。但你更有可能在一小时后忘记他们的名字，更有可能在一个月后忘记他们的名字。

That is, memories decay. This isn't news! But the great German psychologist Hermann Ebbinghaus had the good idea of studying memory decay systematically and quantitatively*
也就是说，记忆会衰退。这并不是什么新闻！但伟大的德国心理学家赫尔曼-艾宾浩斯（Hermann Ebbinghaus）却想到了一个好主意，那就是系统地、定量地研究记忆衰减*。* Hermann Ebbinghaus, Memory: A Contribution to Experimental Psychology (1885). A recent replication of Ebbinghaus's results may be found in: Jaap M. J. Murre and Joeri Dros, Replication and Analysis of Ebbinghaus' Forgetting Curve (2015).
* 赫尔曼-艾宾浩斯，《记忆》：对实验心理学的贡献》（1885 年）。对艾宾浩斯研究成果的最新复制可参见Jaap M. J. Murre 和 Joeri Dros，《艾宾浩斯遗忘曲线的复制与分析》（2015 年）。. In particular, he was interested in how quickly memories decay, and what causes the decay. To study this, Ebbinghaus memorized strings of nonsense syllables – things like “fim“ and “pes” – and later tested himself, recording how well he retained those syllables after different time intervals.
.特别是，他对记忆衰减的速度以及导致记忆衰减的原因很感兴趣。为了研究这个问题，艾宾浩斯记住了一串无意义的音节--比如 "fim "和 "pes"--然后对自己进行测试，记录下自己在不同时间间隔后对这些音节的保留程度。

Ebbinghaus found that the probability of correctly recalling an item declined (roughly) exponentially with time. Today, this is called the Ebbinghaus forgetting curve:
艾宾浩斯发现，随着时间的推移，正确回忆一个项目的概率呈指数下降（大致如此）。如今，这条曲线被称为艾宾浩斯遗忘曲线：

What determines the steepness of the curve, i.e., how quickly memories decay? In fact, the steepness depends on many things. For instance, it may be steeper for more complex or less familiar concepts. You may find it easier to remember a name that sounds similar to names you've heard before: say, Richard Hamilton, rather than Suzuki Harunobu. So they'd have a shallower curve. Similarly, you may find it easier to remember something visual than verbal. Or something verbal rather than a motor skill. And if you use more elaborate ways of remembering – mnemonics, for instance, or just taking care to connect an idea to other things you already know – you may be able to flatten the curve out*
是什么决定了曲线的陡度，即记忆衰减的速度？事实上，陡度取决于很多因素。例如，对于更复杂或不那么熟悉的概念，它可能更陡峭。你可能会发现，记住一个听起来与你以前听过的名字相似的名字更容易：比如，理查德-汉密尔顿（Richard Hamilton），而不是铃木春信（Suzuki Harunobu）。因此，他们的记忆曲线会更浅一些。同样，你可能会觉得视觉上的东西比语言上的东西更容易记住。或者是语言而不是运动技能。如果你使用更精细的记忆方法--比如记忆法，或者只是注意将某个概念与你已经知道的其他事物联系起来--你可能就能将曲线拉平*。* Although this expansion is much studied, there is surprisingly little work building detailed predictive models of the expansion. An exception is: Burr Settles and Brendan Meeder, A Trainable Spaced Repetition Model for Language Learning (2016). This paper builds a regression model to predict the decay rate of student memory on Duolingo, the online language learning platform. The result was not only better prediction of decay rates, but also improved Duolingo student engagement.
* 尽管对这种扩展的研究很多，但建立详细的扩展预测模型的工作却少得令人吃惊。Burr Settles 和 Brendan Meeder，《语言学习的可训练间隔重复模型》（2016 年）是一个例外。这篇论文建立了一个回归模型，用于预测在线语言学习平台 Duolingo 上学生记忆的衰减率。结果不仅更好地预测了衰减率，还提高了Duolingo学生的参与度。.

Suppose you're introduced to a person at a party, and then don't think about their name for 20 minutes. But then you need to introduce them to someone else, and so need to bring it to mind. Immediately after that, your probability of recall will again be very high. Ebbinghaus's research suggested that the probability will decay exponentially after the re-test, but the rate of decay will be slower than it was initially. In fact, subsequent re-tests will slow the decay still more, a gradually flattening out of the decay curve as the memory is consolidated through multiple recall events:
假设你在一次聚会上被介绍给一个人，然后在 20 分钟内都没有想起他的名字。但随后你需要把他介绍给其他人，因此需要把他的名字想起来。紧接着，你的回忆概率又会非常高。艾宾浩斯的研究表明，在重新测试后，概率会以指数形式衰减，但衰减速度会比最初慢一些。事实上，随后的再测试会进一步减缓衰减速度，衰减曲线会逐渐趋于平缓，因为记忆会通过多次回忆事件得到巩固：

This gradual increase in decay time underlies the design of Anki and similar memory systems. It's why Anki gradually expands the time periods between testing.
衰减时间的逐渐延长是 Anki 和类似记忆系统设计的基础。这也是 Anki 逐渐延长测试间隔时间的原因。

These phenomena are part of a broader set of ideas which have been extensively studied by scientists. There are several related terms used for this set of phenomena, but we'll use the phrase “distributed practice”, meaning practice which is distributed in time, ideally in a way designed to maximally promote retention. This is in contrast to cramming, often known as massed practice, where people try to fit all their study into just one session, relying on repetition.
这些现象是科学家们广泛研究的一系列观点的一部分。对于这一系列现象，有几个相关的术语，但我们将使用 "分布式练习 "这一短语，意思是在时间上分布的练习，理想的方式是最大限度地促进记忆。这与填鸭式练习形成了鲜明对比，填鸭式练习通常被称为大量练习，人们试图将所有的学习都集中在一次练习中，依赖于重复练习。

On the role of cognitive science in the design of systems to augment cognition
论认知科学在设计增强认知系统中的作用

Since Ebbinghaus, there's been thousands of studies of different variations of distributed practice. These studies have taught us a great deal about the behavior of long-term memory. Most of all, they show emphatically that distributed practice outperforms massed practice*
从艾宾浩斯开始，我们已经对分布式练习的不同变化进行了数千次研究。这些研究为我们提供了大量有关长期记忆行为的信息。最重要的是，它们有力地证明了分散练习优于集中练习*。* Many experiments also try to assess participants' perception of the effectiveness of massed practice versus distributed practice. Remarkably, they often believe that massed practice is more effective, despite the fact that it is reliably outperformed by distributed practice.
* 许多实验还试图评估参与者对集中练习与分散练习的效果的看法。值得注意的是，尽管大量练习的效果明显优于分散练习，但他们往往认为大量练习更有效。. It's tempting to jump into that literature, and to use it as a guide to the design of memory systems*
.我们很容易跳入这些文献，并将其作为设计内存系统的指南*。* Rather than do such a review, let me point to several reviews which serve as useful entry points. Benedict Carey's book “How We Learn” (2015) is a good introduction at a popular level. Useful reviews of the distributed practice literature include: Cepeda et al, Distributed Practice in Verbal Recall Tasks: A Review and Quantitative Synthesis (2006); and: Gwern Branwen, Spaced-Repetition.
* 与其做这样的评论，不如让我指出几篇评论，作为有用的切入点。本尼迪克特-凯里（Benedict Carey）的著作《我们如何学习》（How We Learn）（2015年）是一本很好的大众入门读物。关于分布式实践文献的有用评论包括Cepeda et al, Distributed Practice in Verbal Recall Tasks：Cepeda et al, Distributed Practice in Verbal Recall Tasks: A Review and Quantitative Synthesis (2006); and：Gwern Branwen, Spaced-Repetition.. But it's also worth thinking about the limitations of that literature as a guide to the development of systems.
.但同样值得思考的是，这些文献作为系统开发指南的局限性。

While scientists have done a tremendous number of studies of distributed practice, many fundamental questions about distributed practice remain poorly understood.
虽然科学家们对分布式实践进行了大量研究，但对分布式实践的许多基本问题仍然知之甚少。

We don't understand in detail why exponential decay of memory occurs, or when that model breaks down. We don't have good models of what determines the rate of decay, and why it varies for different types of memories. We don't understand why the decay takes longer after subsequent recalls. And we have little understanding of the best way of expanding the inter-study intervals.
我们并不清楚为什么会出现记忆的指数衰减，也不知道这种模式何时会崩溃。我们没有很好的模型来说明是什么决定了记忆的衰减速度，以及为什么不同类型的记忆衰减速度不同。我们不明白为什么在随后的回忆中衰减的时间会更长。我们对扩大研究间隔的最佳方法也知之甚少。

Of course, there are many partial theories to answer these and other fundamental questions. But there's no single, quantitatively predictive, broadly accepted general theory. And so in that sense, we know little about distributed practice, and are probably decades (if not more) away from a reasonably full understanding.
当然，有许多局部理论可以回答这些问题和其他基本问题。但还没有一个单一的、可量化预测的、广为接受的通用理论。因此，从这个意义上说，我们对分布式实践知之甚少，而且可能还需要几十年（如果不是更长的话）才能有一个合理全面的认识。

To illustrate this point concretely, let me mention just one example: there are times when our memories don't decay, but get better over time, even when we're not aware of explicit acts of recall. Informally, you may have noticed this in your own life. The psychologist William James made the tongue-in-cheek observation, which he attributed to an unnamed German author, that*
为了具体说明这一点，我只想举一个例子：有时候，我们的记忆不会衰退，反而会随着时间的推移而变得更好，即使我们没有意识到明确的回忆行为。非正式地说，你可能已经在自己的生活中注意到了这一点。心理学家威廉-詹姆士（William James）曾不无调侃地指出，他认为这是一位不知名的德国作家的观点*。* William James, “The Principles of Psychology” (1890).
* 威廉-詹姆斯：《心理学原理》（1890 年）。

we learn to swim during the winter and to skate during the summer.
我们在冬天学习游泳，在夏天学习滑冰。

In fact, exactly such an effect was experimentally verified in an 1895 study of Axel Oehrn*
事实上，阿克塞尔-奥尔恩* 1895 年的一项研究正是通过实验验证了这种效果* Axel Oehrn, Experimentelle Studien zur Individualpsychologie (1895).. While subsequent experiments have confirmed this result, it depends sensitively on the type of material being memorized, on the exact time intervals, and many other variables. Now, in some sense this contradicts the Ebbinghaus exponential forgetting curve. In practice, a pretty good heuristic is that the Ebbinghaus curve holds approximately, but there are exceptions, usually over limited times, and for very specific types of materials.
.虽然后来的实验证实了这一结果，但它的敏感性取决于记忆材料的类型、确切的时间间隔以及许多其他变量。现在，从某种意义上说，这与艾宾浩斯指数遗忘曲线相矛盾。在实践中，艾宾浩斯曲线近似成立，但也有例外，通常是在有限的时间内，针对非常特殊的材料类型。

I don't mention this to undermine your belief in the Ebbinghaus model. But rather as a caution: memory is complicated, we don't understand many of the big picture questions well, and we should be careful before we put too much faith in any given model.
我说这些并不是要削弱你对艾宾浩斯模型的信心。我只是想提醒你：记忆是复杂的，我们对许多大问题还不太了解，在过于相信任何一个模型之前，我们都应该小心谨慎。

With all that said: the basic effects underlying distributed practice and the Ebbinghaus forgetting curve are real, large, and have been confirmed by many experiments. Effects like that discovered by Oehrn are less important by comparison.
综上所述：分布式实践和艾宾浩斯遗忘曲线的基本效应是真实的、巨大的，并已被许多实验所证实。相比之下，奥恩发现的效应就不那么重要了。

This places us in a curious situation: we have enough understanding of memory to conclude that a system like Anki should help a lot. But many of the choices needed in the design of such a system must be made in an ad hoc way, guided by intuition and unconfirmed hypotheses. The experiments in the scientific literature do not yet justify those design choices. The reason is that those experiments are mostly not intended to address those questions. They'll focus on specific types of information to memorize. Or they'll focus on relatively short periods of time – memorization over a day or a week, not for years. Such work helps us build a better theory of memory, but it's not necessarily answering the questions designers need to build systems.
这让我们陷入了一个奇怪的境地：我们对记忆有了足够的了解，可以断定像 Anki 这样的系统应该有很大的帮助。但是，在设计这样一个系统时，许多必要的选择必须在直觉和未经证实的假设的指导下临时做出。科学文献中的实验还不能证明这些设计选择是正确的。原因在于，这些实验大多不是为了解决这些问题。它们将重点放在需要记忆的特定类型的信息上。或者它们关注的是相对较短的时间--一天或一周的记忆，而不是几年的记忆。这些工作有助于我们建立更好的记忆理论，但并不一定能回答设计者建立系统所需的问题。

As a consequence, system designers must look elsewhere, to informal experiments and theories. Anki, for example, uses a spacing algorithm developed by Piotr Wozniak on the basis of personal experimentation*
因此，系统设计者必须将目光转向别处，转向非正式的实验和理论。例如，Anki 使用了 Piotr Wozniak 在个人实验基础上开发的间距算法*。* See: Piotr Wozniak, Repetition spacing algorithm used in SuperMemo 2002 through SuperMemo 2006. Anki uses algorithm SM-2.
* 参见：Piotr Wozniak，《超级记忆法 2002》至《超级记忆法 2006》中使用的重复间距算法。Anki 使用 SM-2 算法。. Although Wozniak has published a number of papers, they are informal reports, and don't abide by the norms of the conventional cognitive science literature.
.尽管沃兹尼亚克发表了许多论文，但它们都是非正式报告，并不遵守传统认知科学文献的规范。

In some sense, this is not satisfactory: we don't have a very good understanding of what spacing schedule to use. But a system has to use some schedule, and so designers do the best they can. This seems likely to work much better than naive approaches, but over the long run it'd be good to have an approach based on a detailed theory of human memory.
从某种意义上说，这并不令人满意：我们并不十分清楚应该使用什么样的间距表。但系统必须使用某种时间表，因此设计者只能尽力而为。这似乎比天真无邪的方法要有效得多，但从长远来看，最好还是能有一种基于人类记忆的详细理论的方法。

Now, one response to this is to say that you should design scientifically, and have good experimental evidence for all design choices. I've heard this used as a criticism of the designers of systems such as Anki, that they make too many ad hoc guesses, not backed by a systematic scientific understanding.
现在，对此的一种回应是，你应该科学地进行设计，并为所有的设计选择提供良好的实验证据。我听过有人用这一点来批评 Anki 等系统的设计者，说他们做了太多的临时猜测，而没有系统的科学认识作为支撑。

But what are they supposed to do? Wait 50 or 100 years, until those answers are in? Give up design, and become memory scientists for the next 30 years, so they can give properly “scientific” answers to all the questions they need answered in the design of their systems?
但他们该怎么办呢？等待 50 年或 100 年，直到得到答案？放弃设计，在接下来的 30 年里成为记忆科学家，这样他们就能对系统设计中需要回答的所有问题给出正确的 "科学 "答案？

This isn't the way design works, nor the way it should work.
这不是设计的工作方式，也不应该是设计的工作方式。

If designers waited until all the evidence was in, no-one would ever design anything. In practice, what you want is bold, imaginative design, exploring many ideas, but inspired and informed (and not too constrained) by what is known scientifically. Ideally, alongside this there would be a much slower feedback loop, whereby design choices would suggest questions about memory, which would lead to new scientific experiments, and thence to an improved understanding of memory, which would in turn suggest new avenues for design.
如果设计者等到所有的证据都出来了，就不会有人设计任何东西了。在实践中，我们需要的是大胆、富有想象力的设计，探索多种想法，但同时也要受到科学知识的启发和影响（不要过于受限）。理想的情况是，与此同时会有一个更缓慢的反馈回路，即设计选择会提出有关记忆的问题，从而导致新的科学实验，进而提高对记忆的理解，这反过来又会为设计提供新的途径。

Such a balance is not easy to achieve. The human-computer interaction (HCI) community has tried to achieve it in the systems they build, not just for memory, but for augmenting human cognition in general. But I don't think it's worked so well. It seems to me that they've given up a lot of boldness and imagination and aspiration in their design*
实现这种平衡并非易事。人机交互（HCI）领域一直试图在他们构建的系统中实现这种平衡，不仅是记忆系统，还包括增强人类认知能力的系统。但我认为效果并不理想。在我看来，他们在设计中放弃了很多大胆的想象力和愿望*。* As an outsider, I'm aware this comment won't make me any friends within the HCI community. On the other hand, I don't think it does any good to be silent, either. When I look at major events within the community, such as the CHI conference, the overwhelming majority of papers seem timid when compared to early work on augmentation. It's telling that publishing conventional static papers (pdf, not even interactive JavaScript and HTML) is still so central to the field.
* 作为一个局外人，我知道这番话不会让我在人机交互社区交到任何朋友。另一方面，我也不认为保持沉默有什么好处。当我看到人机交互社区内的重大事件时，比如 CHI 会议，与早期的增强工作相比，绝大多数论文都显得畏首畏尾。发表传统的静态论文（pdf，甚至不是交互式 JavaScript 和 HTML）仍然是该领域的核心，这很能说明问题。. At the same time, they're not doing full-fledged cognitive science either – they're not developing a detailed understanding of the mind. Finding the right relationship between imaginative design and cognitive science is a core problem for work on augmentation, and it's not trivial.
.与此同时，他们也没有进行全面的认知科学研究--他们并没有深入了解人的思想。在想象力设计和认知科学之间找到正确的关系，是增强功能工作的核心问题，而且并非易事。

In a similar vein, it's tempting to imagine cognitive scientists starting to build systems. While this may sometimes work, I think it's unlikely to yield good results in most cases. Building effective systems, even prototypes, is difficult. Cognitive scientists for the most part lack the skills and the design imagination to do it well.
同样，认知科学家开始构建系统的想法也很诱人。虽然这有时会奏效，但我认为在大多数情况下，这不太可能产生好的结果。建立有效的系统，甚至是原型，都是很困难的。认知科学家大多缺乏做好这项工作的技能和设计想象力。

This suggests to me the need for a separate field of human augmentation. That field will take input from cognitive science. But it will fundamentally be a design science, oriented toward bold, imaginative design, and building systems from prototype to large-scale deployment.
这向我表明，人类增强技术需要一个独立的领域。该领域将从认知科学中汲取营养。但从根本上说，它将是一门设计科学，面向大胆、富有想象力的设计，以及从原型到大规模部署的系统构建。

Acknowledgments 致谢

I initially became intrigued by Anki in part due to the writing of Gwern Branwen, Sasha Laundy, and Derek Sivers. Thanks to Andy Matuschak, Kevin Simler, Mason Hartman, and Robert Ochshorn for many stimulating conversations about this essay. I'm particularly grateful to Andy Matuschak for many thoughtful and enjoyable conversations, and especially for pointing out how unusual is the view that Anki can be a virtuoso skill for understanding, not just a means of remembering facts. Finally, thanks to everyone who commented on my Twitter thread about Anki.
我最初对 Anki 产生兴趣，部分是因为格温-布兰文、萨沙-劳迪和德里克-西弗斯的文章。感谢安迪-马图沙克（Andy Matuschak）、凯文-西姆勒（Kevin Simler）、梅森-哈特曼（Mason Hartman）和罗伯特-奥克肖恩（Robert Ochshorn）就这篇文章进行的许多激励性对话。我尤其要感谢安迪-马图夏克（Andy Matuschak），他与我进行了多次深思熟虑而又愉快的对话，尤其是他指出了安基可以成为一种理解的高超技能而不仅仅是一种记忆事实的手段这一观点是多么不同寻常。最后，感谢在我的Twitter上发表关于Anki的评论的所有人。

Appendix 1: analysis of Anki study time
附录 1：Anki 学习时间分析

Here's a ballpark analysis of the effort required to study an Anki card for recall over 20 years – what we might reasonably consider lifetime recall. Note that the analysis is sensitive to the detailed assumptions made, so the time estimates shouldn't be taken too seriously. Nonetheless, it's useful to get a sense of the times involved.
下面是对研究一张 Anki 记忆卡 20 年（我们可以合理地认为是终生记忆）所需时间的大致分析。请注意，该分析对所做的详细假设很敏感，因此对时间的估计不应过于认真。不过，了解相关时间还是很有用的。

When a card is initially entered, Anki requires reviews after just 1 minute and then 10 minutes. After those reviews the interval between reviews rises substantially, to 1 day. The interval expansion rate after that may vary a little*
最初输入卡片时，Anki 要求在 1 分钟和 10 分钟后进行复查。在这些复查之后，复查间隔会大幅增加，达到 1 天。之后的间隔扩展率可能会略有不同*。* The reason is that Anki allows you to specify that you found a card “easy” or “hard” when you review it, in addition to the generic “good” (meaning you got it right) or “again” (meaning you got it wrong). Those additional options vary the exact rate of interval expansion. In practice, I nearly always choose “good”, or tell Anki that I got the card wrong.
* 原因是 Anki 允许你在复习时，除了一般的 "好"（表示你做对了）或 "又"（表示你做错了）之外，还可以指定你觉得某张牌 "容易 "或 "难"。这些附加选项会改变间隔扩展的确切速度。在实践中，我几乎总是选择 "好"，或者告诉 Anki 我做错了。, but for my cards the typical expansion rate is by a factor of about 2.4 for each successful review. That means that successful reviews will raise the interval to 2.4 days, then to 2.4 * 2.4 = 6.76 days, and so on. On average, I get about 1 in 12 cards wrong, so by the 12th card we're up to about 2.4⁹ = 2,642 days between reviews. Note that we raise to the 9^th power rather than the 12^th power, because it's not until the third repetition of a card that the interval reaches 1 day.
但对于我的卡片来说，每成功审核一次，典型的扩展率大约是 2.4 倍。这意味着成功的审核会将间隔时间延长至 2.4 天，然后延长至 2.4 * 2.4 = 6.76 天，以此类推。平均来说，我每 12 张牌中就有 1 张出错，所以到第 12 张牌时，我们的审核间隔就达到了 2.4 ⁹ = 2,642 天。请注意，我们提高到的是 9 ^th 的幂，而不是 12 ^th 的幂，因为直到第三次重复卡片时，间隔才会达到 1 天。

If you sum those intervals all up, it suggests the typical time between failed reviews is about 12 years. Note, however, that I haven't been using Anki for nearly that long, and this estimate may be over-optimistic. We can get a lower bound on the time between failures by observing that my mean interval between card reviews is already 1.2 years. To achieve an interval of 1.2 years requires about 0.9 years of successful prior reviews, so on average my cards involve at least 2.1 years between failures. However, the real number may be much higher, since there's no reason to assume my next review on most of those cards is going to fail. So let's say that a conservative estimate is a mean time between failures of between 4 and 7 years.
如果将这些时间间隔相加，则表明两次失败的复习之间的典型间隔时间约为 12 年。不过要注意的是，我使用 Anki 的时间还没有这么长，这个估计可能过于乐观了。我们可以通过观察我的卡片审查平均间隔时间已经是 1.2 年来获得失败间隔时间的下限。要达到 1.2 年的间隔时间，需要约 0.9 年的成功审核时间，因此我的卡片平均至少需要 2.1 年的失败间隔时间。不过，实际数字可能要高得多，因为我们没有理由假设我下一次对这些显卡的评测会失败。因此，保守估计平均故障间隔时间在 4 到 7 年之间。

If we assume the mean time between failures is 4 years, then over 20 years that means 5 failures, and reviewing 5 failures * 10 reviews per period = 50 times, for a total of 50 * 8 seconds = 400 seconds, or about 7 minutes.
如果我们假设两次故障之间的平均间隔时间为 4 年，那么 20 年内就意味着发生 5 次故障，审查 5 次故障 * 每期审查 10 次 = 50 次，总计 50 * 8 秒 = 400 秒，即大约 7 分钟。

If instead we assume the mean time between failures is 7 years, then over 20 years that means roughly 3 failures, and reviewing 3 failures * 11 reviews per period = 33 times, for a total of 33 * 8 seconds ≈ 260 seconds, or about 4 minutes.
如果我们假设两次故障之间的平均间隔时间为 7 年，那么 20 年内大约会发生 3 次故障，每期审查 3 次故障 * 11 次审查 = 33 次，总计 33 * 8 秒 ≈ 260 秒，即大约 4 分钟。

Note that in Anki's model a failure resets the review interval back to 10 minutes, then to 1 day, 2.4 days, and so on. In practice, that seems much too conservative. After one or two failures with a card I usually catch on, and it would be better if Anki wasn't so draconian in resetting the review schedule. A better review schedule would reduce the total study time, and I wouldn't be surprised if a typical commitment of ˜2 minutes was possible.
请注意，在 Anki 的模型中，失败会将复习间隔重置为 10 分钟，然后是 1 天、2.4 天，依此类推。实际上，这似乎太保守了。经过一两次失败后，我通常就能掌握了，如果 Anki 在重置复习时间表时不那么苛刻就更好了。一个更好的复习计划可以减少总的学习时间，如果一般只需要 2 分钟，我也不会感到惊讶。

Appendix 2: Using Anki to learn APIs
附录 2：使用 Anki 学习 API

A good use for Anki is to assist in learning APIs. Here's some patterns which work for me, and a few warnings about anti-patterns.
Anki 的一个很好的用途就是帮助学习 API。这里有一些适合我的模式，以及一些关于反模式的警告。

It begins with me deciding there's some API I'd like to learn to use in a project. Some of the time, I just want to use the API a little – say, for 50-100 lines of code, or even just some 1-10 line code snippets. In that case I'm best off winging it, adapting snippets from elsewhere, and consulting the docs as needed.
一开始，我决定在某个项目中学习使用某个 API。有些时候，我只是想使用一下 API，比如 50-100 行代码，甚至只是一些 1-10 行的代码片段。在这种情况下，我最好是随机应变，改编其他地方的代码片段，并在需要时查阅文档。

But suppose I know I will use the API more seriously in a project. For instance, for my essay Thought as a Technology I wanted to build some prototypes using 3d graphics, and decided to learn the basics of the three.js Javascript library.
但是，如果我知道我会在某个项目中更认真地使用应用程序接口。例如，在我的论文 "思想作为一种技术 "中，我想用 3D 图形制作一些原型，于是决定学习 three.js Javascript 库的基础知识。

One tempting failure mode is to think “Oh, I should master the API first”, and then to dive into tutorials or the documentation. Apart from a quick skim of a tutorial or the documentation, that's a mistake. A better approach is to find a small, functioning piece of code that does something related to the core functionality of my project. It doesn't need to be similar to the whole project, but ideally implements one or two similar features, and is a few tens or hundreds of lines of code long. I get that code running, then start making small tweaks, adding bits of functionality I need, taking out bits that I don't, and trying to understand and improve the code.
一种诱人的失败模式是认为 "哦，我应该先掌握应用程序接口"，然后一头扎进教程或文档中。除了快速浏览教程或文档外，这是一个错误。更好的方法是找到一小段与我的项目核心功能相关的功能代码。它不需要与整个项目类似，但最好能实现一两个类似的功能，并且只有几十行或几百行代码。我先让代码运行起来，然后开始做一些小的调整，添加一些我需要的功能，删除一些我不需要的功能，并尝试理解和改进代码。

I probably err on the side of just making things happen… I get so much of a thrill bringing things to life… as soon as it comes to life it starts telling you what it is. - Dan Ingalls
我可能会偏向于把事情做成......我很喜欢把事情做成现实......一旦它变成现实，它就会告诉你它是什么。- 丹-英格尔斯

The great thing about this is that I need only change 1 to 5 lines of code at a time, and I see meaningful progress toward my goals. That's exciting. To use a metaphor from machine learning, it's like doing gradient descent in the space of meaningful projects.
这样做的好处是，我每次只需修改 1 到 5 行代码，就能看到实现目标的显著进展。这太令人兴奋了。用机器学习中的一个比喻来说，这就像是在有意义的项目空间中进行梯度下降。

Of course, while doing this, I'll constantly be looking up things in the docs, on StackOverflow, and so on. I'll also be reading and understanding pieces of the code I started from. It's tempting to Ankify all this, but it's a mistake: it takes too much time, and you Ankify too much that later turns out to be little use. However, when something is clearly a central concept, or I know I'll reuse it often, it's worth adding to Anki. In this way, I gradually build up a knowledge base of things I can use in real, live projects. And, slowly, I get better and better.
当然，在这样做的同时，我会不断在文档、StackOverflow 等网站上查找资料。我还会阅读和理解我最初的代码片段。Ankify 所有这些都很诱人，但这是个错误：它会花费太多时间，而且你 Ankify 的太多东西后来会发现用处不大。不过，如果某些内容显然是一个中心概念，或者我知道我会经常使用它，那就值得把它添加到安基中。通过这种方式，我逐渐建立了一个知识库，可以在实际项目中使用。慢慢地，我变得越来越好。

Once I'm making real progress on my project, and confident I've made a good choice of API, then it makes sense to work through a tutorial. I usually dip quickly into several such tutorials, and identify the one I believe I can learn most quickly from. And then I work through it. I do Ankify at this stage, but keep it relatively light. It's tempting to Ankify everything, but I end up memorizing lots of useless information, at great time cost. It's much better to only Ankify material I know I'll need repeatedly. Usually that means I can already see I need it right now, at the current stage of my project. On the first pass, I'm conservative, Ankifying less material. Then, once I've gone through a tutorial once, I go back over it, this time Ankifying everything I'm likely to need later. This second pass is usually quite rapid – often faster than the first pass – but on the second pass I have more context, and my judgment about what to Ankify is better.
一旦我的项目取得了实际进展，并且确信自己已经选择了一个好的应用程序接口，那么通过教程来学习就很有意义了。我通常会快速浏览几本这样的教程，然后找出我认为能最快速学到知识的那一本。然后我就开始学习。在这个阶段，我也会进行 Ankify，但会保持相对轻松。我很想把所有的东西都 Ankify，但最终会记住很多无用的信息，耗费大量时间。最好只对我知道会反复用到的材料进行 Ankify。通常情况下，这意味着我已经知道在项目的当前阶段我需要它。在第一遍时，我会采取保守的做法，减少材料的安可化。然后，当我看完一遍教程后，我会再看一遍，这次我会把以后可能会用到的所有内容都标注出来。第二遍通常会很快，往往比第一遍更快，但在第二遍时，我有更多的背景资料，对哪些内容需要安卡也有更好的判断。

I continue doing this, bouncing back and forth between working on my project and working on Anki as I make my way through tutorials and documentation, as well as material that comes up while reading code – code from others, and even code I've written myself. I find it surprisingly helpful to Ankify the APIs for code I've personally written, if they're likely to be useful in the future. Just because I wrote something doesn't mean I'll remember it in future!
我继续这样做，一边做我的项目，一边做 Anki，一边阅读教程和文档，以及阅读代码时出现的资料--别人的代码，甚至是我自己写的代码。我发现，如果我亲自编写的代码的应用程序接口在将来可能有用，那么对这些应用程序接口进行 Ankify 会有意想不到的帮助。我写的东西并不意味着我将来会记住它！

So: don't jump into Ankifying tutorials and documentation straight away. Wait, and do it in tandem with serious work on your project. I must admit, part of the reason I advise this is because I find the advice hard to take myself. I nearly always regret not following it. I start a new project, think “Oh, I need such-and-such an API”, and then dive into a tutorial, spending hours on it. But I struggle and struggle and make very slow progress. Until I remember to find some working code to start from, and immediately find things are going much better. I then swear to never use the tutorial-first approach again. Unfortunately, in practice, I find it seductive.
因此：不要马上就去学习 Ankifying 教程和文档。等待，在认真完成项目的同时进行。我必须承认，我之所以提出这样的建议，部分原因是我发现自己很难接受这样的建议。我几乎总是后悔没有遵循这个建议。我开始了一个新项目，心想 "哦，我需要这样那样的 API"，然后一头扎进教程中，花上几个小时。但我挣扎了又挣扎，进展非常缓慢。直到我想起找一些工作代码作为起点，立刻发现事情变得好多了。于是我发誓再也不采用教程先行的方法了。不幸的是，在实践中，我发现这种方法很有诱惑力。

The overall process is much like the common learning-by-doing approach to a new API, where you gradually learn the API through repetition, while working on a project. The main difference is that the occasional interspersed use of Anki considerably speeds up the rate at which you agglomerate new knowledge.
整个过程很像学习新 API 的常见 "边做边学 "方法，即在完成一个项目的同时，通过反复练习逐步学习 API。主要区别在于，偶尔穿插使用 Anki 可以大大加快你积累新知识的速度。

A potential failure mode is to think “Oh, I might want to learn such-and-such an API one day, so I should start adding cards, even though I don't currently have a project where I'm using the API.”
一种潜在的失败模式是认为 "哦，有一天我可能会想学习这样那样的 API，所以我应该开始添加卡片，尽管我目前还没有使用 API 的项目"。

I've tried this a couple of times, and my advice is: don't do it.
我试过几次，我的建议是：不要这样做。

It's a form of a problem I described in the main body of the essay: the temptation to stockpile knowledge against some day when you'll use it. You will learn far more quickly if you're simultaneously using the API seriously in a project. Using the API to create something new helps you identify what is important to remember from the API. And it also – this is speculation – sends a signal to your brain saying “this really matters”, and that helps your memory quite a bit. So if you're tempted to do speculative Ankification, please don't. And if you find yourself starting, stop.
这是我在文章主体中描述的问题的一种形式：囤积知识，以防有一天用得上。如果同时在一个项目中认真使用 API，学习速度会快很多。使用应用程序接口来创建新的东西，可以帮助你从应用程序接口中找出需要记住的重要内容。同时，这也是一种推测，它会向你的大脑发出 "这真的很重要 "的信号，这对你的记忆有很大帮助。因此，如果你想进行投机性的 Ankification，请不要这样做。如果你发现自己开始了，请停止。

A more challenging partial failure mode is Ankifying what turn into orphan APIs. That is, I'll use a new API for a project, and Ankify some material from the API. Then the project finishes, and I don't immediately have another project using the same API. I then find my mind won't engage so well with the cards – there's a half-conscious thought of “why am I learning this useless stuff?” I just no longer find the cards as interesting as when I was actively using the API.
更具挑战性的部分失效模式是将变成孤儿 API 的内容进行 Ankifying。也就是说，我会在一个项目中使用一个新的 API，并对 API 中的一些材料进行 Ankify。然后项目结束了，而我并没有立即有另一个项目使用相同的 API。这时，我发现自己的大脑不再那么喜欢这些卡片了--会半梦半醒地想："我为什么要学这些没用的东西？我觉得这些卡片不再像我积极使用 API 时那么有趣了。

This is a difficult situation. I use the rule of thumb that if it seems likely I'm not going to use the API again, I delete the cards when they come up. But if it seems likely I'll use the API in the next year or so, I keep them in the deck. It's not a perfect solution, since I really do slightly disconnect from the cards. But it's the best compromise I've found.
这种情况很棘手。我使用的经验法则是，如果我很可能不会再使用 API，我就会在卡片出现时删除它们。但如果我有可能在未来一年左右使用 API，我就会把它们保留在卡组中。这并不是一个完美的解决方案，因为我确实与卡牌略有脱节。但这是我找到的最好的折中方案。

Augmenting Long-term Memory增强长期记忆

Part I: How to remember almost anything: the Anki system第一部分：如何记住几乎任何东西：Anki 系统

Using Anki to thoroughly read a research paper in an unfamiliar field使用 Anki 透彻阅读陌生领域的研究论文

Using Anki to do shallow reads of papers使用 Anki 进行论文浅读

Syntopic reading using Anki使用 Anki 进行句法阅读

More patterns of Anki use使用 Anki 的更多模式

Part II: Personal Memory Systems More Broadly第二部分：更广泛的个人记忆系统

How important is long-term memory, anyway?长期记忆到底有多重要？