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.
在 1920 年代中期的一天，一位名叫所罗门·谢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。谢列舍夫斯基的报社老板注意到他从不需要做笔记，但却能记住所有被告知的内容，并建议他去找专家检查一下记忆。

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:
卢里亚开始测试谢列谢夫斯基的记忆。他从简单的测试开始，短的单词和数字串。谢列谢夫斯基轻松地记住了这些，因此卢里亚逐渐增加了串的长度。但无论长度如何，谢列谢夫斯基都能背诵出来。卢里亚对此感到着迷，接下来的 30 年里，他继续研究谢列谢夫斯基的记忆。在总结他研究的书籍《记忆者的心智》（1968）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.
似乎 S.的记忆容量和他保留的痕迹的持久性都没有限制。实验表明，他在重现任何长度的单词序列时没有困难，即使这些单词最初是在一周、一个月、一年，甚至许多年前呈现给他的。事实上，一些旨在测试他记忆保持能力的实验是在他最初回忆这些单词的会话后十五或十六年进行的（没有提前警告他）。然而，这些实验总是成功的。

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，而拥有完美记忆的概念令人向往。与此同时，许多人对自己的记忆感到矛盾。我常常听到人们说“我的记忆不好”，有时是害羞地，有时是带着歉意，有时甚至是带着挑衅。

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*
考虑到记忆在我们思维中的核心地位，提出这个问题是很自然的计算机是否可以作为工具来帮助改善我们的记忆。这个问题被证明能够产生良好的结果。想法，追求这些想法导致了许多最重要的事情计算机历史上最早的愿景文档之一。是范尼瓦·布什 1945 年的提案** Vannevar Bush, As We May Think, The Atlantic (1945).
* 瓦内瓦·布什，我们可能的思考，《大西洋月刊》（1945 年）。 for a mechanical memory extender, the memex. Bush wrote:
为机械记忆扩展器，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.
记忆扩展器是一种设备，个人可以在其中存储所有书籍、记录和通信，并且该设备经过机械化处理，以便能够以极快和灵活的方式进行查询。它是对个人记忆的一个扩展和补充。

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*
记忆扩展器的愿景激励了许多后来的计算机先驱包括道格拉斯·恩格尔巴特关于增强的想法人类智能，泰德·尼尔森关于超文本的想法，以及，间接地，蒂姆·伯纳斯-李对万维网的构想网络** 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：道格拉斯·恩格尔巴特，增强人类智力（1962 年）；泰德·尼尔森，复杂信息处理：复杂、变化和不确定的文件结构（1965 年）；以及蒂姆·伯纳斯-李，信息管理：一项提案（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,
在他的提案中网络，伯纳斯-李描述了他雇主的需求（该粒子物理组织 CERN)开发一个集体制度记忆，

a pool of information to develop which could grow and evolve with the organization and the projects it describes.
一个信息库，可以随着组织及其所描述的项目不断发展和演变。

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.
这些只是许多尝试使用计算机增强人类记忆的一部分。从 memex 到网络，再到维基，再到org-mode，再到Project Xanadu，以及尝试制作每个人思考的每个想法的地图：记忆的增强一直是计算机领域一个极具创造性的愿景。

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，我们探讨个人记忆系统，即旨在提高单个人长期记忆的系统。在文章的第一部分，我描述了我使用这样一个系统的个人经验，名为 Anki。正如我们将看到的，Anki 可以用来记住几乎任何东西。也就是说，Anki 使记忆成为一个选择，而不是一个偶然事件，任其自然。我将讨论如何使用 Anki 理解研究论文、书籍以及其他许多内容。我还将描述 Anki 使用的许多模式和反模式。虽然 Anki 是一个极其简单的程序，但使用 Anki 可以发展出高超的技能，这种技能旨在深入理解复杂材料，而不仅仅是记忆简单的事实。

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.
文章的第二部分讨论了个人记忆系统的一般情况。许多人对记忆持有矛盾甚至贬低的态度，认为它是一种认知技能：例如，人们常常谈论“死记硬背”，仿佛它低于更高级的理解方式。我将反对这种观点，并论证记忆在解决问题和创造力中的核心作用。在这一部分中，我们还将讨论认知科学在构建个人记忆系统中的作用，以及更一般地，在构建增强人类认知的系统中的作用。在未来的文章中，《年轻女士插图启蒙书》，我将描述更多关于个人记忆系统的想法。

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.
这篇文章在风格上很不寻常。它不是一篇传统的认知科学论文，即对人类记忆及其运作方式的研究。也不是一篇计算机系统设计论文，尽管原型系统是我自己主要的兴趣所在。相反，这篇文章是对个人记忆系统如何运作的非正式的、临时观察和经验法则的提炼。我想理解这些，以便为构建我自己的系统做准备。当我收集这些观察时，似乎它们可能对其他人也有兴趣。你可以合理地将这篇文章视为一本旨在帮助发展个人记忆系统的高超技能的操作指南。但由于写这样一本指南并不是我的主要目的，它可能会显得比你想知道的要多。

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.
为了结束这段介绍，简要说明一下这篇文章不会涉及的内容。我将仅简要讨论记忆宫殿和地点法等可视化技术。文章不会描述使用药物来改善记忆，也不会讨论可能的未来脑机接口来增强记忆。这些都需要单独的讨论。但正如我们将看到的，已经有关于个人记忆系统的强大理念，这些理念完全基于信息的结构和呈现。

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*
我将从我自己的经历开始讲述个人记忆系统 Anki** 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 没有任何关联。其他类似的系统包含 Mnemosyne 和 SuperMemo。我的有限使用建议 Mnemosyne 与 Anki 非常相似。SuperMemo 运行仅在 Windows 上，我还没有机会使用它尽管我受到了一些关于……的文章的影响 SuperMemo 网站。

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.
我不会试图掩饰我对 Anki 的热情在一个体面的公正外表背后：这是一个重要的我生活的一部分。尽管如此，它有许多局限性，我会在文章中提到其中一些。. 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.
材料是的，如上所述，这非常个人化，是我自己的一个集合观察和非正式的经验法则。这些经验法则可能不适用于其他人；实际上，我可能对如何理解有误好吧，它们适用于我。这肯定不是一个适当控制的。对 Anki 使用的研究！不过，我相信这仍然是有价值的收集这样的个人经历，即使它们是轶事性的和印象派的。我不是认知科学方面的专家。记忆，如果有错误的话，我会很感激您能纠正误解。

At first glance, Anki seems nothing more than a computerized flashcard program. You enter a question:
乍一看，Anki 似乎不过是一个计算机化的抽认卡程序。你输入一个问题：

And a corresponding answer:
和相应的答案：

Later you'll be asked to review the card: that is, shown the question, and asked whether you know the answer or not.
稍后您将被要求查看卡片：也就是说，展示问题，并询问您是否知道答案。

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.
Anki 比传统的抽认卡更好的原因在于它管理复习时间表。如果你能正确回答一个问题，复习之间的时间间隔会逐渐延长。因此，复习之间的一天间隔变成两天，然后是六天，再然后是两周，依此类推。这个想法是信息在你的记忆中变得更加牢固，因此需要的复习频率减少。但是如果你错过了一个答案，时间表会重置，你需要重新建立复习之间的时间间隔。

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.
虽然计算机管理复习间隔显然是有用的，但这似乎并不是那么重要。关键在于，这实际上是一种更有效的记忆信息的方法。

How much more efficient? 更高效多少？

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.
为了回答这个问题，我们来做一些粗略的时间估算。平均来说，我大约需要 8 秒钟来复习一张卡片。假设我使用传统的抽认卡，并且每周复习一次。如果我想在接下来的 20 年里记住某件事，我需要 20 年乘以每年 52 周再乘以每张卡片 8 秒钟。这总共需要每张卡片稍超过 2 小时的复习时间。

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 不断扩展的复习间隔迅速超过一个月，然后超过一年。实际上，对于我个人的 Anki 卡片，当前的平均复习间隔是 1.2 年，并且还在增加。在下面的附录中，我估计对于一张平均卡片，我在整个 20 年内只需要 4 到 7 分钟的总复习时间。这些估计考虑了偶尔的复习失败，重置时间间隔。这比传统闪卡所需的超过 2 小时节省了超过 20 倍的时间。

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，我有两个经验法则。首先，如果记住一个事实似乎值得我在未来花 10 分钟时间，那么我就会这样做它** 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.
* 我第一次看到类似的分析是在 Gwern Branwen 对间隔重复的评论中：Gwern Branwen, Spaced-Repetition。他的数字比我的稍微乐观一些——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。Branwen 的分析又是基于以下分析：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.
第二，并取代第一条，如果一个事实看起来引人注目，那么进入 Anki 无论看起来是否值得 10 分钟的时间，都会继续进行我的未来时间或不是。例外的原因是许多我们知道的最重要的事情之一是我们不确定的事情将会很重要，但我们的直觉告诉我们这并不意味着我们应该记住所有的事情。但是值得培养对记忆内容的品味。

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，记忆不再是偶然的事件，不再依赖于运气。相反，它保证我会记住某些东西，付出最小的努力。也就是说，Anki 使记忆成为一种选择。

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.
Anki 可以用来做什么？我在生活的各个方面都使用 Anki。在职业上，我用它来学习论文和书籍；从演讲和会议中学习；帮助回忆在对话中学到的有趣事物；以及记住在日常工作中做出的关键观察。在个人生活中，我用它来记住与我的家庭和社交生活相关的各种事实；关于我的城市和旅行；以及关于我的爱好。在文章后面，我将描述一些有用的 Anki 使用模式和需要避免的反模式。

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).
我使用 Anki 在大约两年半的常规使用中创建了超过 10,000 张卡片。这包括一个 7 个月的休息期，那时我几乎没有制作新卡片。当我跟上卡片复习时，每天大约需要 15 到 20 分钟。如果时间常常超过 20 分钟，通常意味着我添加卡片的速度太快，需要放慢速度。或者，有时这也意味着我在卡片复习上落后了（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*
在实际层面上，我使用桌面版 Anki 客户端进行输入新卡片和移动客户端** 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，但移动客户端在撰写时售价为 25 美元。许多人对此感到“太贵”。就我个人而言，我发现其价值远远超过 25 美元。对我来说，移动 Anki 的价值肯定比在一家中等价位餐厅的一顿饭要高得多。 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.
为了复习。我复习我的 Anki 卡片。在去喝早晨咖啡的路上，排队等候时，在过境等情况下。只要我的心情相对放松，首先，我发现审查的体验是冥想的。如果，在另一方面，我的思维并不放松，我发现复习更多困难，Anki 可能会导致我的思维更加跳跃。

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.
我在使用 Anki 时遇到了困难。几个熟人强烈推荐它（或类似的系统），多年来我多次尝试使用它，但每次都很快放弃。回想起来，如果你想把它变成一种习惯，确实有很多障碍需要克服。

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，并将其变成习惯的是我作为玩笑而进行的一个项目。多年来，我一直对自己从未真正学会 Unix 命令行感到沮丧。我只学过最基本的命令。学习命令行对程序员来说是一种超能力，因此掌握它似乎非常有价值。因此，出于好玩，我想知道是否可以使用 Anki 来基本上完全记住一本关于 Unix 命令行的（短）书。

It was! 是的！

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 Terminal Pocket》《指南》，作者：丹尼尔·巴雷特。我并不是字面上的意思记住了整本书的所有内容** 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.
我后来做了一个实验，使用查尔斯·狄更斯的《双城记》，看看是否真的有可能记住整本书的内容。几周后，我得出结论，确实可以做到，但不值得花费时间。因此，我删除了所有的卡片。删除后发生了一件有趣的事情：书的前几句话逐渐在我的记忆中消退，现在我只剩下片段。我偶尔会想，完全记住一本好书会有什么影响；如果它极大地影响了我自己的语言和写作，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.
但我确实记住了很多内容。书中的概念知识，以及名称、语法，和书中大多数命令的选项。例外情况我无法想象使用的东西。可是我我确实记住了大多数我能想象会用到的东西。最后我覆盖了大约 60%到 70%的书籍，跳过或略过那些对我来说似乎不相关的部分。尽管如此，我的对命令行的知识大大增加。

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.
选择这个相当荒谬但极其有用的目标让我对 Anki 充满了信心。这令人兴奋，让我明显感受到 Anki 会使学习那些以前对我来说相当乏味和困难的东西变得轻而易举。这种信心反过来又使我更容易养成使用 Anki 的习惯。同时，这个项目也帮助我学习了 Anki 的界面，并让我尝试不同的提问方式。也就是说，它帮助我建立了有效使用 Anki 所需的技能。

Using Anki to thoroughly read a research paper in an unfamiliar field
使用 Anki 彻底阅读一个不熟悉领域的研究论文

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 Anki 在阅读研究论文时非常有帮助特别是在我专业领域之外的领域。作为一个例子，如何实现这一点，我将描述我在 2016 年阅读的经历纸** 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).
* 大卫·西尔弗，阿贾·黄，克里斯·J·马迪森，亚瑟·古兹等，通过深度神经网络和树搜索掌握围棋，自然（2016）。 describing AlphaGo, the computer system from Google DeepMind that beat some of the world's strongest players of the game Go.
描述 AlphaGo，这个来自计算机系统的谷歌 DeepMind 击败了一些世界上最强的选手围棋的游戏。

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** 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.

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.

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.
尽管我们在这一系列问题上进展缓慢，但在 2011 年左右，随着深度神经网络的进步，进展开始加快。例如，机器视觉系统迅速从糟糕的表现提升到在某些有限任务上与人类相当。到 AlphaGo 发布时，已经不再正确地说我们不知道如何构建计算机系统来进行直观的模式匹配。虽然我们还没有完全解决这个问题，但我们正在快速进展。AlphaGo 是这个故事的重要部分，我希望我的文章探讨构建计算机系统以捕捉人类直觉的这一概念。

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，但写这样一篇文章将会是困难。这将需要对……更深入的理解。 AlphaGo 的技术细节比典型的新闻报道更为深入文章。幸运的是，我对神经网络有相当多的了解。 – 我写了一本关于他们的书** Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press (2015).
* 迈克尔·A·尼尔森，"神经网络与深度学习"，决策出版社（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，或者关于 AlphaGo 使用的许多想法，这些想法基于一个被称为的领域作为强化学习。我需要学习这个。从头开始制作材料，并且为了写一篇好的文章，我打算需要真正理解基础技术材料。

Here's how I went about it.
我就是这样做的。

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。我很快开始阅读，几乎是在浏览。我并不是在寻找全面的理解。相反，我在做两件事。第一，我试图简单地识别论文中最重要的思想。我需要了解的关键技术的名称是什么？第二，有一种吸收的过程，寻找我可以轻松理解的基本事实，这显然会对我有益。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?”

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.

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.

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.

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.

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，我本可以选择传统的方式，而不是使用 Anki 笔记，使用类似的过程来建立对...的理解这篇论文。但使用 Anki 让我有信心我会记住很多内容。长期理解的。大约一年后 DeepMind 发布了描述后续系统的论文，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，参见：David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou 等，在没有人类知识的情况下掌握围棋，自然（2017）。关于 AlphaZero，参见：David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou 等，通过自我对弈与通用强化学习算法掌握国际象棋和将棋（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!
尽管我已经对 AlphaGo 或强化学习几乎没有考虑在这段时间里，我发现我可以阅读那些后续论文轻松地。虽然我没有试图理解那些论文。与最初的 AlphaGo 论文一样彻底，我发现我可以得到一个对这些论文有相当不错的理解，不到一个小时小时。我保留了我早期理解的很多内容！

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.
相反，如果我在最初阅读 AlphaGo 论文时使用传统的笔记方式，我的理解会更快消失，阅读后来的论文也会花费更长时间。因此，以这种方式使用 Anki 可以让你有信心在长期内保持理解。这种信心反过来又使得最初的理解过程更加愉悦，因为你相信自己在学习一些长期有用的东西，而不是一些会在一天或一周内忘记的东西。

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
好的，但这有什么用呢？…… [N]现在我拥有了所有这些力量——一个永远不会忘记、也不会让我忘记我选择的任何事情的机械傀儡——我选择记住什么呢？—— Gwern Branwen

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.
这个整个过程花了我几天的时间，分散在几周内。这是很多工作。然而，回报是我对现代深度强化学习有了相当好的基础了解。这是一个极其重要的领域，在机器人技术中非常有用，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。经过几天的工作，我从对深度强化学习一无所知，变成了对该领域一篇关键论文的持久理解，这篇论文使用了许多在整个领域中应用的技术。当然，我距离成为专家还有很长的路要走。我对 AlphaGo 的许多重要细节还没有理解，如果要在这个领域建立自己的系统，我还需要做更多的工作。但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.

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.

Study hard what interests you the most in the most undisciplined, irreverent and original manner possible. – Richard Feynman

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.

Using Anki to do shallow reads of papers

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.

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，我通常将这样的阅读作为某个项目的背景研究。我会找到一篇新的文章（或一组文章），通常花几分钟评估它。文章是否可能包含与我的项目相关的重要见解或启发——新的问题、新的想法、新的方法、新的结果？如果是这样，我就会阅读。

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，我会添加关于论文核心主张、核心问题和核心思想的 Anki 问题。从摘要、引言、结论、图表和图表说明中提取 Anki 问题特别有帮助。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变得孤立无援。后来我发现很难与这些问题产生联系。换句话说：如果一篇论文无聊到无法添加 5 个好的问题，通常最好根本不添加任何问题。

One failure mode of this process is if you Ankify*
这个过程的一种失效模式是如果你安克菲** I.e., enter into Anki. Also useful are forms such as Ankification etc.
* 即，输入到 Anki 中。像 Ankification 等这样的形式也很有用。 misleading work. Many papers contain wrong or misleading statements, and if you commit such items to memory, you're actively making yourself stupider.
误导性的工作。许多论文包含错误或误导性的信息。声明，如果你将这些内容记住，你就会主动让自己变得更愚蠢。

How to avoid Ankifying misleading work?
如何避免误导性工作的 Ankifying？

As an example, let me describe how I Ankified a paper I recently read, by the economists Benjamin Jones and Bruce Weinberg** Benjamin F. Jones and Bruce A. Weinberg, Age Dynamics in Scientific Creativity, Proceedings of the National Academy of Sciences (2011).. The paper studies the ages at which scientists make their greatest discoveries.

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.

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.

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.

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):

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.
我有一个 Anki 问题，简单地说：“想象一下 Jones 2011 年制作的物理学家在 30 岁和 40 岁时获得诺贝尔奖的概率曲线图。”答案是上面显示的图像，如果我的心理图像大致符合这些线条，我就算成功。我可以通过添加问题来加深对图表的理解，例如：“在 Jones 2011 的物理学奖获奖发现图中，40 岁时伟大成就的峰值概率是多少[即，上图中蓝线的最高点]？”（答案：大约 0.8。）实际上，人们可以轻松地为这个图表添加几十个有趣的问题。我没有这样做，因为与这些问题相关的时间投入。但我确实发现这个图表的整体形状很吸引人，知道这个图表的存在以及如果我想要更多细节时在哪里查阅也是很有用的。

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.
我在上面提到，我通常花 10 到 60 分钟来分析一篇论文，时间的长短取决于我对论文价值的判断。然而，如果我学到了很多东西，并且觉得很有趣，我会继续阅读和分析。真正好的资源值得投入时间。但大多数论文并不符合这种模式，你很快就会感到饱和。如果你觉得可以轻松找到更有价值的阅读材料，就换一下。故意练习这种切换是值得的，以避免在阅读中养成完成主义的反生产习惯。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。花太长时间阅读不重要的论文是一种失败模式。

Syntopic reading using Anki

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.
我已经谈过如何使用 Anki 对论文进行浅层阅读和更深入的阅读。还有一种方式是可以使用 Anki 不仅仅是阅读论文，而是“阅读”某个领域或子领域的整个研究文献。以下是具体方法。

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.
你可能会认为基础只是对大量论文的浅尝辄止。实际上，要真正理解一个陌生领域，你需要深入研究关键论文——比如 AlphaGo 论文。与重要论文的深度接触所获得的东西，比任何单一的事实或技术都更重要：你会对该领域中强大结果的样子有一个直观的认识。这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，以及如何将技术结合起来。你开始理解是什么让 AlphaGo 成为一个突破——以及它的局限性，以及它在某种意义上是该领域自然演变的结果。这些东西并不能通过任何单一的 Anki 问题单独捕捉到。但当你与关键论文深入接触时，所提出的问题开始集体捕捉这些内容。

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.
为了全面了解一个领域，我通常从一篇真正重要的论文开始，理想情况下是那篇让我最初对该领域产生兴趣的论文。我会仔细阅读那篇论文，按照我对 AlphaGo 的描述进行。之后，我会仔细阅读该领域的其他关键论文——理想情况下，我会阅读该领域中最好的 5 到 10 篇论文。但与此同时，我也会对数量更多的较不重要（尽管仍然不错）的论文进行较浅的阅读。在我目前的实验中，这意味着数十篇论文，尽管我预计在某些领域我最终会以这种方式阅读数百甚至数千篇论文。

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.

Over time, this is a form of what Mortimer Adler and Charles van Doren dubbed syntopic reading*
随着时间的推移，这是一种莫提默·阿德勒和查尔斯的形式 van Doren 称之为 综合阅读** 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：莫提默·J·阿德勒和查尔斯·范·多伦，《如何阅读一本书：智能阅读的经典指南》（1972）. 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.
我建立起来对整个文献的理解：已经完成了什么，尚未完成的事情。当然，这并不是字面上的。阅读整个文献。但在功能上，它是接近的。我开始识别开放问题，个人想要提出的问题像回答的，但似乎还没有被回答回答。我识别出一些看似充满潜力的技巧和观察。带有可能性，但我还不知道它的含义。还有，有时，我会识别出在我看来是整个领域的盲点我也把所有这些问题添加到 Anki 中。通过这种方式，Anki 是支持我创意研究的媒介。作为这种媒介，它有一些缺点，因为它不是旨在支持创意工作的设计 – 它是不，例如，未装备进行长时间的自由探索在一个临时空间内。但即使没有被设计成这样以这种方式，它作为创意支持是有帮助的。

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*
我一直在描述我如何使用 Anki 来学习领域对我来说大多是新的。相比之下，对于我已经了解的领域好吧，我的好奇心和我对这个领域的模型通常已经是如此强大，以至于很容易整合新事实。我仍然发现 Anki 很有用，但在新领域中最有用。伟大的英国数学家约翰·埃登索尔·利特伍德写** In “Littlewood's miscellany”, edited by Béla Bollobás (1986).
* 在贝拉·博洛巴什编辑的《利特尔伍德杂集》（1986 年）中。:

I have tried to learn mathematics outside my fields of interest; after any interval I had to begin all over again.

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*
这捕捉到了我所付出的巨大情感努力的一部分找到学习新领域所需的动力，没有足够的驱动力，在新的环境中制作大量材料是极其困难的场地棍。Anki 在很大程度上解决了这个问题。从某种意义上说，这实际上是一种情感假肢，帮助激发动力我需要达成理解。这并不能完成整个工作。如前所述，拥有其他人是非常有帮助的承诺（比如一个创意项目，或者依赖我的人）帮助创造这种动力。尽管如此，Anki 帮助我。我有信心我可以简单地决定我要阅读深入一个新领域，并保留并理解其中的许多内容我所学到的。这适用于所有概念领域。理解我尝试过的地方** 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解决问题方面的使用效果，这两个领域是我还没有尝试使用 Anki 的。.

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*
以这种方式阅读的一个令人惊讶的结果是，阅读的数量有多大越享受它就越好。我一直喜欢阅读，但在一个具有挑战性的全新领域起步有时确实很困难辛苦工作，我常常被怀疑困扰，不知道自己是否会成功真正投入到这个领域中去。那种怀疑反过来使得它变得不那么我很可能会成功。现在我有信心我可以继续前进。进入一个新领域并迅速达到良好、相对深入的水平理解，这种理解将是持久的。信心使阅读变得更加容易愉快的** Many people have written accounts of how to read using personal memory systems. My thinking was particularly stimulated by: Piotr Wozniak, Incremental Reading.
* 许多人写过关于如何使用个人记忆系统进行阅读的文章。我的思考特别受到以下人的启发：Piotr Wozniak, 增量阅读。.

More patterns of Anki use
更多的 Anki 使用模式

Having looked at the use of Anki for reading technical papers, let's return to general patterns of use*
在研究了 Anki 在阅读技术论文中的使用后，让我们回到一般模式的使用** Another useful list of patterns is: Piotr Wozniak, Effective learning: Twenty rules of formulating knowledge.
* 另一个有用的模式列表是：Piotr Wozniak, 有效学习：制定知识的二十条规则。. 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.
这一部分内容很多，经过一番整理后第一次阅读时，您可能希望浏览一下并专注于那些内容最吸引你注意的物品。

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.
尽量将 Anki 问题和答案简化为原子性：也就是说，问题和答案都只表达一个想法。举个例子，当我学习 Unix 命令行时，我输入了这个问题：“如何从 linkname 创建一个指向 filename 的软链接？”答案是：“ln -s filename linkname”。不幸的是，我经常把这个问题答错。

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”.

Breaking this question into more atomic pieces turned a question I routinely got wrong into two questions I routinely got right** 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.
* 一个更原子化的版本是将第一个问题拆分为“创建链接的 Unix 命令是什么？”和“ln 命令创建软链接的选项是什么？”实际上，我已经知道很多年了，ln 是创建链接的命令，因此这并不是必要的。. Most of all: when I wanted to create a Unix soft link in practice, I knew how to do it.

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.
我不确定是什么导致了这种效果。我怀疑这部分与专注有关。当我在综合问题上犯错时，我常常对我的错误具体在哪里感到有些模糊。这意味着我没有足够清晰地专注于错误，因此没有从失败中学到太多。当我在原子问题上失败时，我的思维确切知道该集中在哪里。

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 问题拆分得更细致可以带来显著的好处。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始问题的某个版本。我仍然想知道如何在 Unix 中创建软链接，因此在 Anki 中保留原始问题是值得的。但它变成了一个综合性的问题，成为从简单的原子事实到更复杂的思想构建的问答层次的一部分。

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，仅仅因为一个问题是原子的，并不意味着它不能涉及相当复杂的高级概念。考虑以下来自广义相对论的问题：“在罗伯逊-沃克度量中，dr² 项是什么？”答案是：dr²/(1-kr^2)。现在，除非你学习过广义相对论，否则这个问题可能看起来相当晦涩。这是一个复杂的综合性问题，假设你知道罗伯逊-沃克度量是什么，dr² 的意思是什么，k 的意思是什么，等等。但在具备这些背景知识的前提下，这实际上是一个相当原子的问答。

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.

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.
使用 Anki 最好被视为一种需要发展的高超技能： Anki 是一个极其简单的程序：它允许你输入文本或其他媒体，然后根据你的反馈在预定的时间向你展示这些媒体。尽管如此简单，它却是一个极其强大的工具。而且，像许多工具一样，使用它需要技巧。值得将 Anki 视为一种可以发展到高超水平的技能，并努力继续提升到这样的高超水平。

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 不仅仅是一个记忆简单事实的工具。它是一个理解几乎任何事物的工具。 许多人误以为 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类型。我对 AlphaGo 的问题从简单的问题开始，比如“围棋棋盘有多大？”，最后变成了关于 AlphaGo 系统设计的高层次概念性问题——涉及 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.
开发 Anki 作为一种精湛技能的一部分是培养将其用于超越基本事实的理解能力。实际上，我所做的许多观察（以及下面将要做的观察）关于如何使用 Anki，实际上是关于理解某件事的意义。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。不要放入孤立的问题。与阅读材料互动的模式。问题类型的模式（和反模式）。你想要记忆的事物的模式。Anki 技能具体体现了你对理解的理论；发展这些技能将帮助你更好地理解。说成为一个精湛的 Anki 用户就是在理解方面的精湛者，这个说法有些过于强烈。但其中确实有一些真理。

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*
使用一个大卡组：Anki 允许你组织将卡片分成牌组和子牌组。有些人用这个来创建一个复杂的组织结构。我以前是这样做的，但我逐渐** 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.)
* 这是一个渐进的过程，因为有时问题需要根据变化的上下文进行重写。例如，我的 Emacs 和 Unix 命令行卡片有非常相似的问题，类似于：“如何删除一个单词？”这些问题需要重写，例如：“在 Emacs 中，如何删除一个单词？”（顺便说一下，对于像我这样的长期 Emacs 用户，这可能听起来是个奇怪的问题。实际上，我使用 Anki 来帮助我改变在 Emacs 中删除单词的方式，这就是我在这个主题上有 Anki 问题的原因。通过这种方式，我对我的 Emacs 工作流程进行了许多改进。） 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.
合并了我的卡组和子卡组合成一大副牌。世界并不是被整齐地划分开来的。分离的组件，我认为碰撞是好的不同类型的问题。此刻，Anki 正在问我一个关于鸡肉应该煮到的温度的问题。下一个：关于 JavaScript API 的问题。这种混合是怎么回事？我真的很好吗？我不确定。我还没有找到任何。使用 JavaScript 控制烹饪的原因鸡肉。但我认为这种混合没有任何害处，并希望它具有创造性的刺激，帮助我运用我的知识不寻常的背景。

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.
避免孤立问题：假设我在网上阅读，偶然发现了一篇关于阿尔巴尼亚巨型猫鼬的梳理习惯的精彩文章，这是我之前从未意识到自己感兴趣的主题，但结果却非常吸引人。没多久，我就产生了 5 到 10 个问题。这很好，但我的经验表明，几个月后我可能会觉得这些问题相当陈旧，并且经常答错。我认为原因在于这些问题与我其他兴趣之间的联系太弱，我会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.
我称这些为孤立问题，因为它们与我记忆中的其他内容没有密切关系。在 Anki 中有一些孤立问题并不坏——很难知道哪些问题最终只会引起短暂的兴趣，哪些会发展成与我其他兴趣相关的重大兴趣。但如果你的一部分问题是孤立的，那就意味着你应该更多地集中在与主要创意项目相关的问题上，而减少对边缘材料的 Ankifying。

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.
特别值得避免孤立的孤儿问题：那些与其他内容大体上无关的单一问题。假设，例如，我正在阅读一篇关于新主题的文章，并且我学到了一个似乎特别有用的想法。我规定自己绝不只提出一个问题。相反，我尽量提出至少两个问题，最好是三个或更多。通常这足以构成一些有用知识的核心。如果是孤立的孤儿问题，我不可避免地总是答错，这样提出问题就完全是浪费。

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.
不要分享卡片：我经常被问是否愿意分享我的 Anki 卡片。我不愿意。很早我就意识到在 Anki 中放入个人信息是非常有用的。我并不是指任何非常私密的东西——我绝不会把深藏的秘密放进去。也不会放入任何需要安全保护的东西，比如密码。但我确实放入一些我不会随便提及的内容。

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.
作为一个例子，我有一份（非常短的！）表面上迷人且令人印象深刻的同事名单，我绝对不会和他们合作，因为我一直看到他们对其他人态度恶劣。将这些对待的细节进行 Ankify 是有帮助的，这样我可以清楚地记住为什么应该避免那个人。这不是应该随意传播的信息：我可能误解了对方的行为，或者误解了他们所处的环境。但对我来说，在 Anki 中拥有这些信息是有用的。

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.
构建你自己的卡组： Anki 网站有许多共享卡组，但我发现它们的用处不大。最重要的原因是制作 Anki 卡片本身就是一种理解的行为。也就是说，想出好的问题和好的答案，是深入理解新主题的一部分。使用别人的卡片意味着放弃了很多这种理解。

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.
确实，我相信构建卡片的行为实际上有助于记忆。记忆研究人员反复发现，记忆编码越详细，记忆就会越强。这里所说的详细编码，基本上是指你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，可以尝试记住一个孤立的事实，即 1962 年是第一个通信卫星 Telstar 被送入轨道的年份。但更好的记忆方式是将这个事实与其他事实联系起来。相对平淡地说，你可能会注意到 Telstar 是在第一个苏联卫星 Sputnik 发射后的 5 年内发射的。将太空用于通信并没有花费太长时间。更丰富的阐述是，我个人觉得很有趣的是，Telstar 是在引入 ASCII 的前一年被送入轨道的，ASCII 可以说是第一个现代数字文本通信标准。在我们拥有数字文本通信标准之前，人类就已经有了通信卫星！找到这种联系就是一种丰富编码的例子。

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.
构建 Anki 卡片的过程本身几乎总是一种精细编码。它迫使你思考问题的不同形式，考虑最佳答案，等等。我相信即使是最基本的卡片也是如此。如果你构建更复杂的卡片，将要记住的基本事实与其他想法（如 Telstar-ASCII 链接）联系起来，这种情况肯定会成立，逐渐建立起一个丰富相互关联的思想网络。

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*
有鉴于此，有一些有价值的甲板共享实践。例如，有医疗社区学生们发现分享和有时合作的价值建造甲板** 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).
* 请查看MedicalSchoolAnki subreddit，该论坛经常讨论最佳卡组、如何使用它们，以及不断变化的不同用途的最佳卡组列表。另请参阅论文：Michael Hart-Matyas 等，医学学生建立协作闪卡项目的十二个建议，医学教师（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.
我也发现了在包含非常基础问题的共享卡片中有价值，作为艺术卡片，它们提出的问题包括谁画了某幅作品绘画。但对于更深层次的理解，我还没有。找到了一些有效利用共享卡组的方法。

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.
培养详细编码/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是使用“相同”问题的多种变体。例如，我之前提到过我的两个问题：“Jones 2011 声称，1980-2011 年间，物理学诺贝尔奖得主获得奖项发现的平均年龄是多少？”以及：“哪篇论文声称，1980-2011 年间，物理学诺贝尔奖得主获得奖项发现的平均年龄为 48 岁？”从逻辑上讲，这两个问题显然密切相关。但就记忆的运作方式而言，它们是不同的，导致在非常不同的触发点上产生联想。

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*
记忆宫殿和类似技巧怎么样？ 有一套众所周知的记忆方法基于记忆宫殿等概念的技巧，记忆宫殿法等** An entertaining and informative overview is: Joshua Foer, “Moonwalking with Einstein” (2011).
* 一部有趣且信息丰富的概述是：乔书亚·福尔，《与爱因斯坦共舞》（2011）。. 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:
这是一种极端的详细编码形式，产生丰富的视觉效果和空间关联到您想要的材料记住。这是约书亚·福尔回忆的一段对话。记忆术专家埃德·库克描述了一种基本技巧：

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.”
埃德随后向我解释了他让名字变得难忘的程序，他在比赛中使用过这个方法来记住与九十九个不同摄影头像相关的名字和姓氏。这是一个他承诺我可以在聚会和会议上用来记住人名的技巧。“这个窍门实际上出乎意料的简单，”他说。“关键是将一个人的名字的声音与一些你能清晰想象的东西联系起来。这完全是关于在你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与这个人名字相关的视觉记忆锚定在一起。当你需要回忆这个人的名字时，你创造的形象会自然而然地浮现在你的脑海中……所以，嗯，你说你的名字是乔什·福尔，对吧？”他扬起一眉，故作戏剧性地抚摸着下巴。“好吧，我想象你在我们第一次见面时是在比赛大厅外开玩笑，而我想象自己会因此四分五裂。四/Foer，明白了吗？这个小形象对我来说比你的名字更有趣，至少应该能很好地留在脑海中。”

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，但似乎最适合用来记忆琐事——比如扑克牌的顺序、数字串等等。它们在更抽象的概念上似乎发展得不够充分，而这些抽象概念往往是深入理解的关键。从这个意义上说，它们甚至可能会分散理解的注意力。话虽如此，我可能只是需要找到更好的方法来使用这些想法，就像我需要弄清楚 Anki 一样。特别是，进一步研究一些从业者用来形成丰富联想的技巧可能是值得的。正如 Foer 所说，引用一位记忆专家的话，学习“以更容易记住的方式思考”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 的 95% 价值来自 5% 的功能：Anki 有自动生成卡片、标记卡片、插件生态系统等多种方式。实际上，我很少使用这些功能。我的卡片始终是两种类型之一：大多数是简单的问答；相当一部分是所谓的 填空题：一种填空测试。例如，我会用填空题来测试我对喜欢的名言的记忆：

“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__，那么使用它实际上会改变__的__。” __，__” (答案：新媒介，思维模式，整个文明，艾伦·凯，1989)。

Clozes can also be used to pose questions not involving quotes:
填空题也可以用来提出不涉及引用的问题：

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.
为什么不多利用 Anki 的功能呢？部分原因是我从核心功能中获得了巨大的好处。此外，学会很好地使用这小部分功能需要付出很多努力。篮球和篮筐是简单的设备，但你可以花一生的时间来学习如何很好地使用它们。同样，基本的 Anki 练习也可以得到极大的发展。因此，我专注于学习如何很好地使用这些基本功能。

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.
我认识很多人尝试使用 Anki，然后陷入学习尽可能多功能的兔子洞，以便“高效”使用它。通常，他们追求的是 1% 的改进。往往，这些人最终放弃了 Anki，认为它“太难”，这通常是“我担心自己没有完美使用它”的同义词。这真是可惜。正如之前讨论的，Anki 提供了比普通抽认卡大约 20 倍的改进。因此，他们放弃了 2000% 的改进，因为他们担心错过了最后的 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，当有人刚开始时，我建议不要使用任何高级功能，也不要安装任何插件。简而言之，不要得上程序员效率病。学习如何使用 Anki 进行基本的问答，并专注于在这个范式中探索新模式。这将比花费大量时间去摆弄功能要好得多。然后，如果你养成高质量使用 Anki 的规律习惯，你可以尝试更多高级功能。

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 存储关于朋友和家人的事实的挑战：我尝试过使用 Anki 存储（非敏感的！）关于朋友和家人的问题。这在像“[我的朋友] 是素食者吗？”这样的事情上效果很好。但我的使用在一些棘手的问题上遇到了一些困难。例如，假设我和一个新朋友谈论他们的孩子，但从未见过那些孩子。我可以输入像“[我的朋友] 的大孩子叫什么名字？”这样的问题。或者，如果我们聊过音乐，我可能会输入：“[我的朋友] 喜欢的音乐家是谁？”

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.
这种实验是出于好意。但提出这样的问题常常让我感到不舒服。这似乎太像是在假装对我的朋友感兴趣。社会上有一个相当强的规范，如果你记得朋友的音乐品味或他们孩子的名字，那是因为你对那个朋友感兴趣。使用记忆辅助工具在我看来感觉有些不真诚。

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。大多数人都告诉我同样的事情：他们很感激我一开始就费这么多心思，并觉得我担心这是否不真诚很可爱。所以也许担心是个错误。尽管如此，我仍然对此感到困扰。我已经开始使用 Anki 来记录一些不太个人化的东西，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个人信息。但目前我还是慢慢来。

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之间有很大的区别。例如，当你被 Anki 问题提示时，你可能会记住一个 Unix 命令，但这并不意味着你会在命令行的上下文中识别出使用该命令的机会，并且能够自如地输入它。而找到新颖、创造性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.
换句话说：要真正内化一个过程，仅仅复习 Anki 卡片是不够的。你需要在具体情境中执行这个过程，并且需要用它解决实际问题。

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.
说到这一点，我发现转移过程相对简单。在命令行的情况下，我使用得足够频繁，因此有很多机会真正利用我对命令行的 Ankified 知识。随着时间的推移，这种陈述性知识正在转变为我在上下文中常规使用的程序性知识。也就是说，了解转移何时有效、何时无效会更好。更好的情况是有一个能够融入我实际工作环境的记忆系统。例如，它可以在我处于实际命令行时询问我关于 Unix 命令的问题。或者它可能会在命令行时让我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.

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*
超越“名字无所谓”： 我受过理论物理学的训练。有一个著名的故事在物理学中，理查德·费曼表示，否定了……的价值知道事物的名称。作为一个孩子，费曼在外面玩耍。在一个和自以为是的小孩一起的田野里。事情是这样的，费曼的讲述** 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？一个好奇角色的进一步冒险》（1989 年）。:

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

I said, “I haven't the slightest idea what kind of a bird it is.”
我说：“我一点也不知道是什么样的。” “鸟的。”

He says, “It'a brown-throated thrush. Your father doesn't teach you anything!”
他说：“这是一只棕喉鸫。你的 “父亲什么都不教你！”

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.)

Feynman (or his father) goes on to a thoughtful discussion of real knowledge: observing behavior, understanding the reasons for it, and so on.

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.

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.

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:

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。主要原因是我喜欢回忆绘画的经历时间。另一个是给它起个名字。绘画** Actually, a better question for that is to be shown the painting and asked what its name is.
实际上，更好的问题是让人看到这幅画并问它的名字是什么。. 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.
如果我想更分析性地思考关于这个问题。绘画——比如，巧妙运用色彩渐变 – 我可以添加更详细的问题。但我很确定。快乐只是将图像的体验铭记于心。

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.
当你落后时你该怎么办？ 当你在卡片上落后时，Anki 变得具有挑战性。如果你跳过一天或两天——或者五十天——卡片就开始堆积。回来时发现你一天要复习 500 张卡片，这让人感到畏惧。更糟的是，如果你失去了使用 Anki 的习惯，你可能会落得很远。我在七个月的时间里基本停止使用 Anki，回来时发现有成千上万的积压卡片。

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，赶上并不难。我给自己设定了逐渐增加的每日卡片配额（100、150、200、250，最终达到 300），并在接下来的几周里每天完成这些配额，直到我赶上进度。

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.
虽然这并不是太困难，但有些令人沮丧和气馁。如果 Anki 有一个“赶上”功能，可以将多余的卡片分散到接下来的几周的日程中，那就更好了。但它没有。无论如何，这是一个问题，但解决起来并不太困难。

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.

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.)

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.

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.
我倾向于在阅读论文和书籍时实时进行 Ankify。对于研讨会、对话等，我更喜欢沉浸在体验中。与其拿出 Anki，我会迅速在脑海中（或纸上）记下我想要 Ankify 的内容。然后我会稍后将其输入 Anki。这需要一些自律；这也是我更喜欢设定小配额的原因之一，这样我只需稍后输入几个问题，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.
一个需要注意的地方是书籍：阅读整本书是一项很大的承诺，定期添加 Anki 问题可能会让你进展缓慢。在决定要多少内容进行 Ankify 时，值得牢记这一点。有时一本书的内容非常丰富，值得花时间添加很多问题。但不加思考地将所有内容都进行 Ankify 是一个坏习惯，我偶尔也会陷入这种情况。

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*
你所 Ankify 的并不是一个微不足道的选择：Ankify 那些事情服务于你的长期目标。在某种程度上，我们成为我们所追求的。记住，所以我们必须小心我们所做的记得** 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.”.
* 对不起，库尔特·冯内古特，他曾写道：“我们就是我们假装成的样子，因此我们必须小心我们假装成的样子。”. This is always true, but Anki makes it especially true.
这总是正确的，但 Anki 使它变得更容易。尤其如此。

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，一个有趣的模式是回到我以前的在 Anki 之前的书籍笔记，并将其转化为 Anki 格式。这通常可以是快速完成，并且让我在时间上获得更大的回报投资于现在大多被遗忘的书籍** 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.
朋友们有时抱怨许多书籍是过于冗长的论文。也许这种冗长的好处在于它强制执行类似 Anki 的间隔重复，因为读者需要几周才能读完这本书。这可能是记忆要点的一种低效方式，但总比对这本书完全没有记忆要好。.

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.
我还没有弄明白的是如何整合 Anki 用于我的创意项目的笔记记录。我无法替代笔记使用 Anki 太慢了，对于很多事情来说，对我长期记忆的利用不佳。另一方面，还有使用 Anki 处理重要事项有许多好处——流畅访问记忆是如此多创造力的基础思考。Speed of associative thought is, I believe, important in creative work. – John Littlewood 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，我发现自己本能地和无系统地做一些事情作为笔记，其他的作为 Anki 问题，以及其他一些事情作为两者。总体而言，它有效。好的，但我觉得如果我应用的话，效果会好得多更系统的思考和实验。问题的一部分我没有一个很好的笔记系统，完毕！如果我在那方面多花点时间，我怀疑整个事情会变得更好好多了。不过，它还是可以正常工作。

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:

Is computing the partition function intractable for most graphical models?
计算大多数图形模型的分区函数是否是不可处理的？

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“是的”。这没问题，至于它去。但详细阐述这些想法会有助于我理解。这个问题。我可以添加一个关于哪个图形的问题吗？模型的分区函数是可处理的吗？我可以给出一个图形模型的例子，其中的分区函数是不可处理的吗？计算分区意味着什么？函数无论如何都难以处理吗？是/否问题应该在至少，被视为问题的良好候选者重构** 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.
通过类比代码异味，我们可以谈论“问题异味”，这暗示了可能需要重构。是/否结构就是一个问题异味的例子。

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？ 对于像 Anki 这样的系统，一个常见的批评是，外部记忆设备——如 Google、维基和笔记本——实际上应该足够了。使用得当，这些系统当然是 Anki 的极好补充。但在创造性工作和解决问题时，内化理解有其特殊之处。它能够加快联想思维的速度，快速尝试多种想法的组合，并直观地识别模式，这在需要费力查找信息时是无法实现的。

Fluency matters in thinking. Alan Kay and Adele Goldberg have proposed*
流畅性在思维中很重要。艾伦·凯和阿黛尔·戈德堡有提议** Alan Kay and Adele Goldberg, Personal Dynamic Media (1977).
* 阿兰·凯和阿黛尔·戈德堡，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.
长笛的思想实验在吹气之间有“一秒钟的延迟” 注意并听到它！”当他们观察时，这就是 “荒谬”。以类似的方式，某些类型的思考在所有相关类型都存在时要容易得多理解被铭记在心。因此，Anki 是无价的。

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优秀，为什么它们没有被更广泛地使用呢？ 这个问题类似于一个老笑话，讲的是两个经济学家在散步时，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！”另一个回答：“不可能！如果真的在那里，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*
这个类比只是部分正确。实际上，Anki 看起来像是一个不断有 20 美元的钞票躺在地上。合理地问为什么它没有更广泛地使用。其中一个相关研究中被引用最多的论文文学** Frank N. Dempster, The Spacing Effect: A Case Study in the Failure to Apply the Results of Psychological Research (1988).
* 弗兰克·N·登普斯特，间隔效应：心理研究结果未能应用的案例研究 (1988)。 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.
是对……的讨论为什么这些想法在教育中没有更广泛地使用。虽然写于 1988 年，论文中的许多观察仍然有效今天是真实的。

My own personal suspicion is that there are three main factors:
我个人的怀疑是有三个主要因素：

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.
在记忆的实验研究中，人们始终低估了以类似于 Anki 的方式分散学习所带来的收益。相反，他们更喜欢临时抱佛脚，并认为这样能产生更好的结果，尽管许多研究表明并非如此。
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).
*罗伯特·A·比约克，人类训练中的记忆与元记忆考虑（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).
“可取原则” “困难”，即记忆在最大程度上在我们快要忘记的时候，如果被考验就会变得更强这表明，一个高效的记忆系统将会本质上使用起来有些困难。人类有与困难活动的复杂关系，往往不喜欢执行它们，除非有强烈的动机以防它们可能变得愉快。
Systems such as Anki are challenging to use well, and easy to use poorly.
像 Anki 这样的系统使用起来很有挑战性，容易使用不当。

It is interesting to consider developing systems which may overcome some or all of these issues.
考虑开发可能克服这些问题的系统是很有趣的。

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验看了一种特定的个人记忆系统——Anki。在第二部分较简短的内容中，我们将考虑两个关于个人记忆系统的更广泛的问题：记忆作为一种认知技能有多重要；以及认知科学在构建个人记忆系统中扮演什么角色？

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”.
长期记忆有时会被贬低。人们常常贬低“死记硬背”，尤其是在课堂上。我听过很多人说他们退掉了一些课程——有机化学很常见——因为这“只是一些事实，我想要的是更深入的理解”。

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个错误。我曾经相信关于记忆不重要的这种说法。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.
这次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.
让我从个人经历开始。

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，我常常帮助人们学习量子力学等技术性学科。随着时间的推移，你会发现人们卡住的模式。一个常见的模式是，人们认为自己卡在深奥复杂的问题上。但当你深入挖掘时，发现他们实际上在基本符号和术语上遇到了困难。当你对每第三个词或符号不清楚时，理解量子力学是很困难的！每一句话都是一种挣扎。

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.
就像他们试图用法语写一首美丽的十四行诗，但只知道 200 个法语单词。他们感到沮丧，认为问题在于找到一个好的主题、打动人心的情感和意象等的困难。但实际上，问题在于他们只有 200 个单词可以用来创作。

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.
我有些虔诚的信念是，如果人们更多地关注记住基础知识，而少担心“困难”的高级问题，他们会发现高级问题自会得到解决。

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.
但虽然我对其他人持有这种强烈的信念，我从未意识到这也适用于我。我完全不知道这对我适用的程度有多强。使用 Anki 阅读新领域的论文让我摆脱了这种错觉。我发现 Anki 让学习这些学科变得如此简单，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。如果你有像 Anki 这样的系统来克服这个障碍，那么你会发现深入新领域要容易得多。

This experience of how much easier Anki made learning a new technical field greatly increased my visceral appreciation for the importance of memory.
这段经历让我深刻体会到 Anki 在学习新技术领域时的便利性，极大增强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.
认知科学也有许多研究结果表明记忆在认知中扮演着关键角色。

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* * 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).
* 参见，例如，赫伯特·A·西蒙，一块有多大？，《科学》（1974），以及阿德里安·德·格鲁特，国际象棋中的思考与选择，阿姆斯特丹大学出版社（2008 年，1965 年再版）。. 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.

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.
西蒙估计，国际象棋大师在训练期间学习了 25,000 到 100,000 个这样的块，而学习这些块是成为一流国际象棋选手的关键因素。这些选手与初学者看待国际象棋局面的方式截然不同。

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块对发展专业知识如此有帮助？这是一个推测性的非正式模型——据我所知，它尚未得到认知科学家的验证，所以不要太认真对待。我将以数学为背景来描述它，而不是国际象棋，因为数学是我有经验与各个能力水平的人交流的领域，从初学者到杰出的专业数学家。

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.
许多人对成功数学家的印象是，他们非常聪明，智商极高，能够在脑海中处理非常复杂的想法。人们普遍认为，他们的聪明才智使他们能够应对复杂的概念。基本上，他们拥有更强大的动力引擎。

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.
确实，顶尖数学家通常非常聪明。但这里有一个不同的解释。根据西蒙的说法，许多顶尖数学家通过努力工作，内化了比普通人更多的复杂数学知识。这意味着，对我们其他人来说看起来非常复杂的数学情境，对他们来说却显得非常简单。因此，并不是说他们的思维能力更强，能够处理更多的复杂性。相反，他们的先前学习使他们具备了更好的知识块处理能力，因此大多数人认为复杂的情境，他们却认为简单，从而更容易进行推理。

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”*
现在，西蒙在他的国际象棋研究中使用的块概念玩家实际上来自乔治在 1956 年发表的一篇著名论文米勒，《神奇的七，加或减》两个* 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，《神奇的七，加或减二：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，工作记忆的容量大约是七个部分。实际上，事实证明存在变异。这个数字因人而异，并且相当可观个体工作能力之间的相关性记忆和他们的综合智力能力（智商）** 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).
* 相关性的回顾可以在 Phillip L. Ackerman, Margaret E. Beier 和 Mary O. Boyle 的工作记忆与智力：相同还是不同的构念？心理学公报 (2006) 中找到。. Typically, the better your working memory, the higher your IQ, and vice versa.
通常，越好你的工作记忆越强，你的智商就越高，反之亦然。

Exactly what Miller meant by chunks he left somewhat vague, writing:
米勒所指的“块”究竟是什么意思，他写得有些模糊：

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.
术语“位”和“块”的对比也突显了我们对什么构成信息块并不十分明确。例如，海耶斯获得的五个词的记忆跨度……同样可以称为 15 个音素的记忆跨度，因为每个词大约包含三个音素。直观上，显然受试者回忆的是五个词，而不是 15 个音素，但这种逻辑上的区别并不立即显现。我们在这里处理的是将输入组织或分组为熟悉单位或块的过程，而形成这些熟悉单位的学习投入了大量精力。

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，在米勒的描述中，块实际上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。如果这些块更复杂，那么这意味着一个棋手的工作记忆具有更高的有效容量。特别是，一个智商较低但能够调用更复杂块的人，能够对比智商较高但内化块较简单的人推理更复杂的情况。

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，在某个领域记住更多的块就像是对一个人在该领域智商的有效提升。

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*
好的，这只是一个推测性的非正式模型。无论如何无论这是否正确，确实看起来内化高层次的块是获取的关键部分专业知识。然而，这并不一定意味着使用像 Anki 这样的系统将加速获取这样的片段。这仅仅是一个关于长期记忆的论点。在某些类型的获取中发挥着关键作用专业知识。尽管如此，定期使用似乎是合理的。像 Anki 这样的系统可能会加速知识的获取专家使用的高级块** 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，了解这些片段是如何产生的将会有所帮助。对此仍然似乎理解得很不清楚。我不会感到惊讶，如果这涉及到相当多的分析和问题解决，以及长期记忆。. And that those chunks are then at the heart of effective cognition, including our ability to understand, to problem solve, and to create.
然后这些块就位于有效认知的核心，包括我们的能力去理解、解决问题和创造。

Distributed practice 分布式练习

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？在这一部分，我们简要地看一下来自认知科学的一个关键基础概念，称为 分散练习。

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.
假设你在一个聚会上被介绍给某人，他们告诉你他们的名字。如果你在认真听，而且他们的名字不是太特别，你几乎可以肯定在 20 秒后会记住他们的名字。但在一个小时后你更有可能忘记他们的名字，而在一个月后你更有可能已经忘记他们的名字。

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*
记忆会衰退。这并不是新闻！但伟大的德国心理学家赫尔曼·艾宾浩斯有个好主意，研究记忆衰退系统地和定量地** 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：实验心理学的贡献（1885 年）。艾宾浩斯结果的最近一次复制可以在以下文献中找到：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，他对速度有多快感兴趣。记忆衰退，以及导致衰退的原因。为了研究这个，艾宾浩斯记忆了一串无意义的音节像“fim”和“pes”这样的东西 – 和后来他进行了自我测试，记录了自己对这些内容的记忆保持情况不同时间间隔后的音节。

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，正确回忆一个项目的概率随着时间的推移（大致上）呈指数下降。今天，这被称为 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*
曲线的陡峭程度由什么决定，即变化的速度有多快记忆会衰退吗？实际上，衰退的程度取决于许多因素事情。例如，对于更复杂或更少的情况，它可能更陡。熟悉的概念。你可能会发现记住一个名字更容易。这听起来像你以前听过的名字：比如，理查德汉密尔顿，而不是铃木春信。所以他们会有一个较浅的曲线。同样，您可能会发现更容易记住比起语言，更倾向于视觉的东西。或者更倾向于语言而非视觉的东西。运动技能。如果你使用更复杂的记忆方式 – 记忆法，例如，或者只是注意连接一个想法与您已经知道的其他事物联系起来 – 您可能能够将曲线拉平** 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.
* 尽管这个扩展得到了广泛研究，但令人惊讶的是，关于构建详细预测模型的工作却很少。一个例外是：Burr Settles 和 Brendan Meeder, A Trainable Spaced Repetition Model for Language Learning (2016)。这篇论文建立了一个回归模型，以预测学生在在线语言学习平台 Duolingo 上的记忆衰退率。结果不仅更好地预测了衰退率，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:
假设你在一个聚会上被介绍给一个人，然后 20 分钟内没有想起他们的名字。但随后你需要把他们介绍给其他人，因此需要想起他们的名字。紧接着，你的回忆概率会再次非常高。艾宾浩斯的研究表明，重新测试后，回忆的概率会以指数方式衰减，但衰减的速度会比最初慢。事实上，随后的重新测试会进一步减缓衰减，随着记忆通过多次回忆事件的巩固，衰减曲线会逐渐平坦化。

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.
这种逐渐增加的遗忘时间是 Anki 和类似记忆系统设计的基础。这就是 Anki 逐渐延长测试之间时间间隔的原因。

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.
这些现象是一个更广泛的思想集合的一部分，科学家们对此进行了广泛研究。对于这一现象集合，有几个相关术语，但我们将使用“分散练习”这个短语，指的是在时间上分散的练习，理想情况下以最大程度促进记忆保持的方式进行。这与临时抱佛脚相对，后者通常被称为集中练习，人们试图将所有学习内容压缩到一个会话中，依赖于重复。

On the role of cognitive science in the design of systems to augment cognition
认知科学在增强认知系统设计中的作用

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*
自艾宾浩斯以来，已经有成千上万的研究不同的分布式练习变体。这些研究教会了我们很多关于长期行为的知识内存。最重要的是，它们明确表明分布式练习优于集中训练练习** 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有效性的看法。值得注意的是，他们常常认为集中练习更有效，尽管事实是分散练习的效果可靠地优于集中练习。. It's tempting to jump into that literature, and to use it as a guide to the design of memory systems*
很容易就想跳入那篇文献中，并将其作为内存设计的指南系统** 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.
* 与其进行这样的回顾，不如让我指出几篇作为有用入门的评论。本尼迪克特·凯瑞的书《我们如何学习》（2015）是一本很好的通俗介绍。关于分布式练习文献的有用评论包括：Cepeda 等，口头回忆任务中的分布式练习：一项回顾和定量综合（2006）；以及：Gwern Branwen，间隔重复。. But it's also worth thinking about the limitations of that literature as a guide to the development of systems.
但是这也值得考虑其局限性文学作为系统发展的指南。

While scientists have done a tremendous number of studies of distributed practice, many fundamental questions about distributed practice remain poorly understood.
尽管科学家们对分布式练习进行了大量研究，但关于分布式练习的许多基本问题仍然不够清楚。

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.
我们并不详细了解为什么记忆会呈指数衰减，或者何时这种模型会失效。我们没有好的模型来确定衰减的速率，以及为什么不同类型的记忆衰减速率会有所不同。我们不明白为什么在随后的回忆后衰减会花费更长时间。我们对如何最佳地扩展学习间隔的理解也很有限。

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，有许多部分理论可以回答这些和其他基本问题。但没有一个单一的、定量预测的、广泛接受的通用理论。因此，从这个意义上说，我们对分布式练习知之甚少，可能还需要几十年（如果不是更久）才能对其有一个相对全面的理解。

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，我只想提到一个有时候我们的记忆不会衰退，而是变得更加清晰随着时间的推移变得更好，即使我们没有意识到明确的行为回忆。非正式地，你可能在自己身上注意到了这一点。生活。心理学家威廉·詹姆斯开玩笑地说观察，他将其归因于一位未具名的德国作者，那** 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，正是这样的效果在实验中得到了验证 1895 年对阿克塞尔·厄恩的研究** Axel Oehrn, Experimentelle Studien zur Individualpsychologie (1895).
* 阿克塞尔·厄恩，《个体心理学的实验研究》（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.
后续实验已确认这个结果对材料类型非常敏感被记住的，准确的时间间隔，以及许多其他变量。现在，从某种意义上说，这与艾宾浩斯相矛盾。指数遗忘曲线。在实践中，效果相当不错。启发式是艾宾浩斯曲线大致成立但通常在有限的时间内会有例外，且适用于非常特定类型的材料。

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.
我提到这一点并不是为了削弱你对艾宾浩斯模型的信念，而是作为一种警示：记忆是复杂的，我们对许多大局问题的理解并不深入，在对任何特定模型过于信任之前，我们应该谨慎。

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：分布式练习和艾宾浩斯遗忘曲线背后的基本效应是真实的、显著的，并且已通过许多实验得到了证实。与 Oehrn 发现的效应相比，这些效应的重要性较低。

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.
这使我们处于一个奇特的境地：我们对记忆有足够的理解，可以得出像 Anki 这样的系统应该会有很大帮助的结论。但是，设计这样一个系统所需的许多选择必须以特设的方式做出，依赖于直觉和未经证实的假设。科学文献中的实验尚未证明这些设计选择的合理性。原因在于这些实验大多并不旨在解决这些问题。它们将专注于特定类型的信息进行记忆，或者专注于相对较短的时间段——记忆一天或一周，而不是几年。这类工作帮助我们建立更好的记忆理论，但不一定能回答设计师需要构建系统的问题。

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，系统设计师必须另寻他处，去非正式的实验和理论。例如，Anki 使用一个由 Piotr Wozniak 基于开发的间距算法个人实验** See: Piotr Wozniak, Repetition spacing algorithm used in SuperMemo 2002 through SuperMemo 2006. Anki uses algorithm SM-2.
* 见：Piotr Wozniak，在 SuperMemo 2002 到 SuperMemo 2006 中使用的重复间隔算法。Anki 使用算法 SM-2。. 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.
尽管沃兹尼亚克已发表一些论文，它们是非正式报告，不遵循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.
在某种意义上，这并不令人满意：我们对应该使用什么间隔计划并没有很好的理解。但系统必须使用某种计划，因此设计师尽力而为。这似乎比简单的方法效果要好得多，但从长远来看，基于人类记忆详细理论的方法会更好。

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，对此的一个回应是应该进行科学设计，并为所有设计选择提供良好的实验证据。我听说这被用作对 Anki 等系统设计者的批评，认为他们做了太多的临时猜测，而没有系统的科学理解作为支持。

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?
但他们应该怎么做呢？等 50 年或 100 年，直到那些答案出来？放弃设计，成为记忆科学家 30 年，以便能给出所有他们在系统设计中需要回答的问题的“科学”答案？

This isn't the way design works, nor the way it should work.

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.

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*
这样的平衡并不容易实现。人机交互（HCI）社区一直在努力实现这一目标他们构建的系统，不仅仅是为了记忆，而是为了增强人类认知总体上。但我认为这并没有奏效。好吧。在我看来，他们放弃了很多大胆的尝试。和想象力与抱负在他们的设计** 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，我知道这条评论不会让我在 HCI 社区中交到朋友。另一方面，我也认为沉默没有任何好处。当我观察社区内的重大事件，比如 CHI 会议时，绝大多数论文与早期的增强工作相比似乎显得 timid。值得注意的是，发表传统的静态论文（pdf，甚至不是互动的 JavaScript 和 HTML）在这个领域仍然是如此重要。. 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.
同时，他们也没有进行全面的认知科学研究他们没有深入理解思维。找到想象力与现实之间的正确关系设计与认知科学是工作中的一个核心问题增强，这并不是微不足道的。

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.
在类似的情况下，人们很容易想象认知科学家开始构建系统。虽然这有时可能有效，但我认为在大多数情况下不太可能产生良好的结果。构建有效的系统，甚至是原型，都是困难的。大多数认知科学家缺乏良好完成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.
这让我想到需要一个独立的人类增强领域。该领域将借鉴认知科学的成果。但它将从根本上是一门设计科学，旨在大胆、富有想象力的设计，并将系统从原型构建到大规模部署。

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，部分原因是受到Gwern Branwen、Sasha Laundy和Derek Sivers的写作启发。感谢 Andy Matuschak、Kevin Simler、Mason Hartman 和 Robert Ochshorn 与我进行的许多激动人心的关于这篇文章的讨论。我特别感谢 Andy Matuschak 与我进行的许多深思熟虑和愉快的对话，尤其是他指出 Anki 不仅仅是记忆事实的工具，而是理解的高超技能这一观点是多么不寻常。最后，感谢所有在我的 Twitter 线程上评论 Anki 的人。

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.
这是对在 20 年内学习 Anki 卡片以便记忆所需努力的粗略分析——我们可以合理地认为这是终身记忆。请注意，这个分析对所做的详细假设非常敏感，因此时间估计不应过于严肃对待。尽管如此，了解所涉及的时间是有用的。

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*
当卡片首次输入时，Anki 需要在之后进行复习只需 1 分钟，然后是 10 分钟。在那些评审之后，审查之间的间隔大幅增加，达到 1 天。之后的间隔扩展率可能会有所不同小** 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 允许您在复习卡片时指定您认为卡片是“简单”还是“困难”，除了通用的“好”（意味着您答对了）或“再来一次”（意味着您答错了）。这些额外的选项会影响间隔扩展的具体频率。实际上，我几乎总是选择“好”，或者告诉 Anki 我答错了这张卡片。, 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.

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.

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.
如果我们假设平均故障间隔时间为 4 年，那么在 20 年内意味着有 5 次故障，审查 5 次故障 * 每个周期 10 次审查 = 50 次，总共 50 * 8 秒 = 400 秒，约为 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.
如果我们假设平均故障间隔时间为 7 年，那么在 20 年内大约会发生 3 次故障，审查 3 次故障 * 每个周期 11 次审查 = 33 次，总共 33 * 8 秒 ≈ 260 秒，或大约 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，这似乎过于保守。经过一两次卡片的失败，我通常会意识到，Anki 在重置复习计划时不应该如此严厉。一个更好的复习计划将减少总学习时间，我不会感到惊讶，如果一个典型的承诺为大约 2 分钟是可能的。

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 的一个好用法是帮助学习 API。这里有一些对我有效的模式，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.
这始于我决定在一个项目中学习使用某个 API。有时，我只想使用这个 API 一点——比如，写 50 到 100 行代码，甚至只是一些 1 到 10 行的代码片段。在这种情况下，我最好是即兴发挥，从其他地方调整代码片段，并根据需要查阅文档。

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.
但是假设我知道我将在一个项目中更认真地使用 API。例如，在我的论文思想作为一种技术中，我想使用 3D 图形构建一些原型，并决定学习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.
一个诱人的失败模式是想“哦，我应该先掌握 API”，然后深入研究教程或文档。除了快速浏览一下教程或文档，这是一种错误。更好的方法是找到一小段与我项目核心功能相关的、可运行的代码。它不需要与整个项目相似，但理想情况下实现一两个相似的功能，并且代码长度在几十行或几百行之间。我让这段代码运行，然后开始进行小的调整，添加我需要的功能，去掉我不需要的部分，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
我可能倾向于让事情发生……我从赋予事物生命中获得了很多刺激……一旦它开始有了生命，它就会告诉你它是什么。 - 丹·英戈尔斯

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.
这件事的伟大之处在于，我一次只需更改 1 到 5 行代码，就能看到朝着目标迈出的有意义的进展。这令人兴奋。用机器学习的比喻来说，这就像在有意义项目的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，在这样做的过程中，我会不断查阅文档、StackOverflow 等。我还会阅读和理解我最初开始的代码片段。将所有这些内容都放入 Anki 是很诱人的，但这是一个错误：这会花费太多时间，而且你放入 Anki 的内容往往后来发现用处不大。然而，当某个概念显然是核心概念，或者我知道我会经常重用它时，值得将其添加到 Anki 中。通过这种方式，我逐渐建立起一个可以在实际项目中使用的知识库。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.
一旦我在项目上取得了真正的进展，并且对我选择的 API 充满信心，那么进行教程学习就有意义了。我通常会快速浏览几个这样的教程，并找出我认为能最快学习的那个。然后我就开始学习。在这个阶段，我会使用 Ankify，但保持相对轻松。虽然很容易想要 Ankify 所有内容，但我最终会记住很多无用的信息，耗费大量时间。最好只 Ankify 我知道会反复需要的材料。通常这意味着我能清楚地看到我现在就需要它，在项目的当前阶段。在第一次学习时，我比较保守，Ankify 的材料较少。然后，一旦我完成了一个教程，我会再回过头来，这次 Ankify 我可能稍后需要的所有内容。第二次学习通常相当快速——往往比第一次还快——但在第二次学习时，我有更多的上下文，我对 Ankify 的判断也更好。

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，在我的项目和 Anki 之间来回切换，同时通过教程和文档，以及在阅读代码时出现的材料来进行学习——包括他人的代码，甚至是我自己写的代码。我发现将我个人编写的代码的 API 转换为 Anki 非常有帮助，如果它们在未来可能会有用。仅仅因为我写过某些东西并不意味着我将来会记得它！

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.
所以：不要立刻跳入 Ankifying 的教程和文档。等一下，和你项目的认真工作一起进行。我必须承认，我建议这样做的部分原因是因为我自己也觉得这个建议很难接受。我几乎总是后悔没有遵循它。我开始一个新项目，想“哦，我需要某个 API”，然后就跳入一个教程，花费几个小时在上面。但我挣扎着，进展非常缓慢。直到我记得找一些可以作为起点的工作代码，立刻发现事情进展得好多了。然后我发誓再也不使用先看教程的方法。不幸的是，在实践中，我发现这很诱人。

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.
整体过程与通过实践学习新 API 的常见方法非常相似，您在进行项目的同时，通过重复逐渐学习 API。主要区别在于偶尔穿插使用 Anki 显著加快了您积累新知识的速度。

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.”
一个潜在的失败模式是认为“哦，我可能有一天想学习某个 API，所以我应该开始添加卡片，即使我目前没有使用该 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.
这是一种我在文章主体中描述的问题：囤积知识以备将来使用的诱惑。如果你在一个项目中认真使用 API，你会学得更快。使用 API 创造一些新东西可以帮助你识别从 API 中需要记住的重要内容。而且这也——这是推测——向你的大脑发出信号，告诉它“这真的很重要”，这对你的记忆有很大帮助。所以如果你有冲动去进行投机性的知识积累，请不要这样做。如果你发现自己开始这样做，立即停止。

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.
一个更具挑战性的部分失败模式是将其转变为孤儿 API。也就是说，我会在一个项目中使用一个新的 API，并从该 API 中提取一些材料。然后项目完成了，我没有立即有另一个项目使用相同的 API。然后我发现我的思维与这些卡片的互动不那么顺畅——心中半意识到“我为什么要学习这些无用的东西？”我发现这些卡片不再像我积极使用 API 时那样有趣。

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.
这是一个困难的情况。我遵循一个经验法则，如果我觉得不太可能再使用这个 API，我就会在卡片出现时删除它们。但如果我觉得在接下来的一年左右会使用这个 API，我就会把它们保留在牌组中。这并不是一个完美的解决方案，因为我确实会稍微与卡片断开联系。但这是我找到的最佳折衷方案。

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 彻底阅读一个不熟悉领域的研究论文

Using Anki to do shallow reads of papers

Syntopic reading using Anki

More patterns of Anki use更多的 Anki 使用模式

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？