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Algorithm SM-2 was first used in learning on Dec 13, 1987, and with minor tweaks survived to this day in a number of applications. SuperMemo abandoned the algorithm in 1989, however, Algorithm SM-2 keeps popping up in new applications with a frequency that must be approaching a few new developments each month. I lost count long ago. Some of the mutations contradict the principles of SuperMemo, and still take on its label. Most often, the violations include intervals measured in minutes, or halving intervals at fail grade (Leitner style). These mutations also lead to some fake news about SuperMemo. Note that fake news was one of the greatest incentives for writing this article.
Algorithm SM-2最早是在1987年12月13日被用于学习的，并且在一些应用程序中保留了一些细微的调整。SuperMemo在[1989](https://supermemo.guru/wiki/First_adaptable_spaced_repetition_algorithm: _algorithm_mc -4)放弃了该算法，然而，算法sm2不断出现在新的应用程序中，其频率必须每月接近一些新的开发。我很久以前数不清了。有些突变与SuperMemo的原则相抵触，但仍保留其标签。大多数情况下，违规包括以分钟为单位的间隔，或者在不及格 时将间隔减半(Leitner。这些突变也导致了一些关于SuperMemo的假新闻。请注意，假新闻是写这篇文章的最大动机之一。
When Duolingo speaks in their paper of hand-picked parameters in reference to SuperMemo, it must be a result of relying on some older texts, perhaps second-hand texts, perhaps texts written in reference to Algorithm SM-2. After all, SuperMemo was pretty adaptable as of 1989 and Algorithm SM-17 is the most adaptable specimen in existence.
Some of the blame for misinformation is mine as I stopped caring about peer review, and let the information wild on the web with insufficient mythbusting effort.
当 Duolingo说在他们的论文中精心挑选参数的引用SuperMemo,它必须依靠一些老的文献的结果,也许二手短信,也许文字写在参考算法SM-2。毕竟，SuperMemo自1989年起就具有很强的适应性，算法手册-17是现存的适应性最强的样本。
错误信息的部分责任在我，因为我不再关心同行评审，让信息在网络上肆意传播，却没有付出足够的努力来戳破谣言。
The first applications to use Algorithm SM-2 were non-commercial offshoots of SuperMemo for Atari in the 1980s. Later, minor clones of SuperMemo (e.g. for handheld computers) opted for variants of Algorithm SM-2 with various own innovations, of which many provided painful lessons on the impact of disrespect of memory in the name of cramming.
By 2001, SuperMemo World moved ahead by five major generations of the algorithm. All major software lines, incl. on-line SuperMemo and SuperMemo for Windows adopted the data driven variants of the algorithm. supermemo.net became one of the pioneering e-learning platforms, and has now evolved into supermemo.com. SuperMemo for Windows pioneered self-learning solutions such as incremental reading, sleep-and-learning optimization, or neural creativity. In the meantime, Algorithm SM-2 became an easy first-choice option for other developers.
最早使用Algorithm SM-2的应用程序是20世纪80年代雅达利公司SuperMemo的非商业分支。后来，SuperMemo的小型克隆(如掌上电脑)选择了SM-2算法的变体，并进行了各种各样的创新，其中许多以cramming的名义提供了关于不尊重内存的影响的痛苦教训。
到2001年，SuperMemo World的算法向前发展了5代。所有主要的软件,包括。在线SuperMemo和SuperMemo Windows采用[数据驱动的变体](https://supermemo.guru/wiki/First_data-driven_spaced_repetition_algorithm: _Algorithm_SM-8)算法。supermemo.net成为最早的电子学习平台之一，现已发展为supermemo.com。SuperMemo Windows首创自学解决方案,比如增量阅读, sleep-and-learning优化,或神经创造力。同时，Algorithm SM-2成为其他开发人员的首选。
On May 10, 1998, Algorithm SM-2 was opened to the public and published on the web here.
Mnemosyne was first to pick the tool as the offshoot of neural network MemAid created in 2003. As of 2006, Mnemosyne keeps collecting repetition history data running a mutation of Algorithm SM-2. As a free multi-platform application, Mnemosyne quickly reached a large base of users, e.g. on Linux, or those users who have Latex requirements.
Anki was born on Oct 6, 2006. It was based on Algorithm SM-2 and for nearly a decade provided the widest reach for the algorithm. It is still going strong. Anki introduced a great deal of innovations into their algorithm but refused to advance beyond its basic principles (see: criticism of SM3+).
1998年5月10日，Algorithm SM-2向公众开放，并在网上发布here。
Mnemosyne是第一个选择该工具作为神经网络的分支MemAid创建于2003年。从2006年开始，Mnemosyne一直在收集重复历史的数据，这些数据运行的是一个经过修改的算法sm2。作为一个免费的多平台应用程序，Mnemosyne很快就吸引了大量的用户，例如Linux上的用户，或者有Latex需求的用户。
Anki出生于2006年10月6日。它基于Algorithm SM-2，并在近10年的时间里为该算法提供了最广泛的应用。它仍在强劲增长。Anki在他们的算法中引入了大量的创新，但拒绝超越其基本原则(参见:批评SM3+)。
In 2007, when we met Gary Wolf, SuperMemo looked like a sad deserted island that begged a question: if it is so good, why others don't try to copy the algorithm. Anki and Mnemosyne were little known at that time. Wolf's article in Wired in 2008 caused a nice rush for education software developers to implement a form of spaced repetition. Algorithm SM-2 seems like a low fruit to pick and its expansion accelerated. Many users of SuperMemo claim they would never find the program without Wolf's article in Wired. Krzysztof Biedalak likes to joke though that Wolf's article was indeed a breakthrough. However, it was not a break for SuperMemo. It simply opened the floodgates for the competition to rush in into the field of spaced repetition.
2007年，当我们遇到加里•沃尔夫Gary Wolf时，(SuperMemo)看起来就像一个令人悲伤的荒岛，它提出了一个问题:如果它是如此之好，为什么其他人不尝试复制这个算法?那时，Anki和Mnemosyne鲜为人知。沃尔夫2008年在《连线》(Wired)杂志上发表的一篇文章引发了教育软件开发人员实施一种“间隔重复”形式的热潮。算法SM-2似乎是一个低果子采摘和它的扩展加速。SuperMemo的许多用户声称，如果没有沃尔夫发表在《连线》杂志上的文章，他们永远也找不到这个程序。Krzysztof Biedalak喜欢开玩笑，虽然沃尔夫的文章确实是一个突破。然而，这并不是SuperMemo的突破。它只是为竞争进入间隔重复领域打开了闸门。
Quizlet was written in 2005 and released in 2007. It was initially a typical cramming tool, however, by 2015, backed by venture capital, Quizlet announced a higher emphasis on long-term retention, which resulted in adopting a variant of Algorithm SM-2. By 2017, they decided to use machine learning to deploy a new algorithm that would capitalize on billions of repetition records collected. The short stint for SuperMemo at Quizlet must have given a mutation of Algorithm SM-2 an exposure to the largest user base ever. At the time, Quizlet reported reaching every second high school student in the US.
The new approach taken by Quizlet is based on a strong foundation, and can lead to a very strong tool, however, this is very disappointing to hear the motivation behind the move towards better algorithms: "Cramming is a reality for many students, and we want to help them make the best of their study time however they spend it". Algorithm SM-17 provides for more freedom to students: (1) to advance learning when in need, or (2) to delay low priority material. However, we always discourage cramming as a bad practice. It is schools that need to adapt to human brain, not the other way around. This stubborn stance on learning efficiency hurts SuperMemo, but it will never change.
That move away from a simple review schedule by Quizlet in 2017 is probably the move past the peak of popularity for the old venerable algorithm. New competitors will need to go for intelligent tools, or perhaps for licensing Algorithm SM-17. The news is good.
1855/5000
Quizlet写于2005年，2007年发布。它最初是一种典型的填词工具，然而，到了2015年，在风险资本的支持下，Quizlet宣布更加重视长期用户留存，因此采用了Algorithm SM-2的变体。到2017年，他们决定使用机器学习来部署一种新的算法，该算法将利用收集到的数十亿次重复记录。在Quizlet上短暂的SuperMemo一定让Algorithm SM-2的一个突变暴露给了有史以来最大的用户群。当时，Quizlet报告说，在美国，每两个高中生都能收到Quizlet的邮件。
Quizlet采取的新方法是基于一个坚实的基础,并能导致一个非常强大的工具,然而,这是非常令人失望,听到背后的动机走向更好的算法:“死记硬背是许多学生的现实,我们要帮助他们充分利用学习时间但是他们花”。算法SM-17为学生提供了更多的自由:(1)在需要的时候推进学习，或(2)延迟低优先级的材料。然而，我们总是不鼓励填鸭式教学，因为这是一种不好的做法。是学校需要适应人类的大脑，而不是其他方式。这种顽固的学习效率立场伤害了SuperMemo，但它永远不会改变。
在2017年，Quizlet改变了简单的复习时间表，这可能是旧算法最受欢迎的时候了。新的竞争者将需要使用智能工具，或者可能需要获得许可Algorithm SM-17。这是个好消息。
In mid-1991, one of my classmates tried to cheer me up. He predicted we will be successful and we will manage to sell 10-20 copies of SuperMemo. I was more optimistic. In 1993, I predicted 1 million users by 1996. In 1994, Enter, Poland, mentioned similar optimism of Marczello Georgiew:
1991年年中，我的一个同学试图让我振作起来。他预测我们会成功，我们将设法销售10-20份SuperMemo。我比较乐观。1993年，我预测到1996年将有100万用户。1994年，波兰的Enter提到了类似的乐观主义Marczello Georgiew:
In questionnaires received at SuperMemo World, when asked what they like most in the program, users of SuperMemo overwhelmingly indicate its effectiveness. The software may be OK, but what really counts is results in learning. How about dislikes? Users are not pleased with this or that, most often with the fact that, even in Poland, SuperMemo is always released first in English. But there is no particular turn-off that takes precedence. Definitely, nobody questions the fact that with SuperMemo, one can learn faster and never worry about forgetting. Taking this rosy picture into heart, one might wonder why has SuperMemo not yet sold in millions of copies worldwide. Marczello Georgiew, Marketing Director at SuperMemo World proposed to recall the problems Graham Bell experienced when trying to introduce his funny machine for talking over a wire, or how pessimistic the predictions of industry futurologists were about the expansion of the air-polluting mechanical horse. Then he adds confidently: It took Wozniak 10 years to turn necessity into invention, give us half this time, and we will turn his invention into a global necessity.
在SuperMemo World收到的问卷中，当被问及他们最喜欢的是什么程序时，SuperMemo的用户绝大多数都表示了它的有效性。软件可能还行，但真正重要的是学习的结果。不喜欢呢?用户对这个或那个并不满意，最常见的情况是，即使在波兰，SuperMemo也总是先用英文发布。但并没有什么特别的障碍是优先考虑的。毫无疑问，没有人会质疑这样一个事实:使用SuperMemo，一个人可以更快地学习，而且永远不用担心遗忘。考虑到这幅美好的画面，人们可能会想，为什么SuperMemo还没有在全球售出数百万份。Marczello Georgiew,营销总监SuperMemo世界提出召回问题·格雷汉姆·贝尔经历当试图引入他的有趣的机器说通过电线,或悲观的预测行业未来学家是如何污染空气的膨胀机械马。然后他自信地补充道:“***沃兹尼亚克花了10年时间才把必需品变成了发明，这次给我们一半的时间，我们将把他的发明变成全球必需品。
In my 1 million users prediction, I was off by 3 years, and had to make a distinction between short-timers and active users. The proportion of active users of spaced repetition kept dropping with wider adoption. In 2007, we estimated the reach of SuperMemo to be 5 million, of which most were freeware and partwork users. Of those 5 million, only 0.4-4.0% were active users. This might have been as few as 20,000 students.
In 2009, Gwern Branwen estimated the population of active users to be around 100,000, which seems to agree with my numbers. This does not sound too optimistic for two decades of hard work at SuperMemo World.
在我对100万用户的预测中，我差了3年，必须区分短期用户和活跃用户。间隔重复的活跃用户比例随着更广泛的采用不断下降。2007年，我们估计SuperMemo的用户达到500万，其中大部分是freeware和partwork用户。在这500万用户中，只有0.4-4.0%是活跃用户。这可能只有2万名学生。
2009年，Gwern Branwen估计活跃用户大约有10万，这似乎和我的数字一致。对于SuperMemo World 20年来的艰苦努力，这听起来并不太乐观。
Let's then have a closer look at the reach of spaced repetition today. My estimates below met with a great deal of skepticism. I agree that they are based on a great deal of guesswork. However, once you are on an exponential curve of growth, even large estimate errors make little difference. You can overestimate by 200% and still catch up quickly in no time.
This is why I do not hesitate to say that the exponential growth in the adoption of spaced repetition streaks towards the big B: one billion users. Amazon's Kindle has added spaced repetition to its Flashcard option in Vocabulary Builder. Even users of SuperMemo who use Kindle may know nothing of the fact. Flashcards with books is the general idea that was to bring SuperMemo to NASDAQ, back in 1996, if we only succeeded in persuading venture capital that the idea made sense.
However, to hit a billion users we need another breakthrough. The first obvious candidate that comes to mind is Facebook, which might wire spaced repetition into the cacophony of social interaction, and make free learning transparent, i.e. where users learn without ever showing intent.
让我们今天来仔细看看间隔重复的影响范围。我下面的估计遭到了许多人的怀疑。我同意他们是基于大量的猜测。然而，一旦你处在指数增长曲线上，即使是很大的估计误差也不会有什么不同。你可以高估200%，但仍然可以很快赶上。
这就是为什么我毫不犹豫地说，采用间隔重复的用户呈指数级增长，接近10亿用户。亚马逊的Kindle在词汇构建器的闪存卡选项中增加了间隔重复。即使是使用Kindle的SuperMemo用户也可能对此一无所知。带书的抽认卡是将SuperMemo带回到1996年纳斯达克的主要想法，如果我们成功地说服了风险投资，这个想法是有意义的。
然而，要达到10亿用户，我们需要另一个突破。首先想到的显然是Facebook，它可能会将间隔重复与不和谐的社交互动连接起来，并让免费学习变得透明，也就是说，用户在那里学习，却从未表现出任何意图。
If you think Facebook and spaced repetition are incompatible worlds, consider the world of advertising. These days we all hate advertising. No matter how well it is targeted. However, the pestering party can maximize the memory effect and minimize the annoyance (i.e. retrievability) by employing spaced repetition. Even the most captivating TV advert will get on your nerves by the third exposure. Spaced review could ensure that retrievability is low and retention high.
Last but not least, spaced review may be taken on by the bad guys: the makers of fake news and worse. A publicity charlatan might pull strings behind the back of a world leader. He may shake the world in spaces. This may expose the whole world to spaced repetition to be sure we all remember.
The top of the pyramid is so bad that I will not even list it. I don't want to give bad guys any ideas.
My estimates below include a couple of points that are pretty certain. The first user in 1985, second in 1987, one million by 2000, and my laborious estimate of 5 million in 2007. Today, Duolingo claims 200,000 users. Quizlet claims even more. The growth is still showing few sign of saturation.
如果你认为Facebook和“间隔重复”(https://supermemo.guru/wiki/Spaced_repetition)是两个不兼容的世界，那么想想广告的世界吧。现在我们都讨厌广告。不管目标有多明确。然而，通过使用[间隔重复](https://supermemo.guru/wiki/Spaced_repetition)，纠缠一方可以最大化[记忆效果](https://supermemo.guru/wiki/Retrievability) (https://supermemo.guru/wiki/Spaced_repetition)。即使是最吸引人的电视广告，在第三次曝光时也会让你紧张。间隔评审可以确保[可检索性](https://supermemo.guru/wiki/Retrievability)较低，而[保留率](https://supermemo.guru/wiki/Retention)较高。
最后但并非最不重要的是，隔行审查可能会被坏人利用:假新闻的制造者和更糟的人。一个在公众面前装腔作势的人可能会在一个世界领导人背后拉皮条。他可以在空间里撼动世界。这可能会使整个世界暴露于间隔重复，以确保我们都能记住。
金字塔的顶端是如此糟糕，我甚至不会列出它。我不想给坏人任何主意。
我下面的估计包括一些非常确定的观点。1985年的第一个用户， 1987年的第二个用户，到2000年是100万，2007年我艰难的估计是500万。如今，Duolingo号称拥有20万用户。Quizlet声称甚至更多。增长仍未显示出饱和的迹象。
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Figure: We expected spaced repetition to show signs of saturation long ago. However, through transmutation, it will inevitably hit a billion users at some point. Once it becomes integrated with human digital life, it will affect nearly everyone. If my estimate is right, the speed of adoption, aided by the web, is still ahead of the telephone, car, and the radio. We never thought it was possible to compete with Pokemons or Angry Birds though. The exponential regression formula in the graph is: Reach=exp((year-1984)*0.63). The red line determined by that formula crosses 1 billion just about now
***图:*我们期望间隔重复很久以前就显示出饱和的迹象。然而，通过嬗变，它将不可避免地在某一时刻触及十亿用户。一旦它与人类数字生活融为一体，它将影响到几乎每一个人。如果我的估计是正确的，在web的帮助下，采用的速度仍然领先于电话、汽车和收音机。我们从来没有想过可以和口袋妖怪或愤怒的小鸟竞争。图中指数回归公式为:Reach=exp((year-1984)*0.63)。由该公式确定的红线现在刚好越过10亿
Today, with almost no barrier of entry, there are many students who try and drop out after weeks or even days of use. The proportion of active users may be very low. A billion users with negligible learning is still little learning. The next step in the job is to produce a cultural paradigm shift that will add value to efficient long-term learning. We need to begin with a change to the system of schooling and to adopt the principles of free learning.
Once spaced repetition hits a billion users, cultural paradigm shift will be necessary to convert usership to actual benefits in long-term quality learning
The road ahead is still very long.
如今，在几乎没有入学门槛的情况下，许多学生在使用了几周甚至几天后就试图辍学。活跃用户的比例可能非常低。10亿用户的微不足道的学习仍然是很少的学习。这项工作的下一步是产生一种文化范式的转变，这种转变将为有效的长期学习增加价值。我们需要从改变学校教育系统开始，并采用免费学习的原则。
一旦间隔重复触及10亿用户，文化范式的转变将是必要的，以转化为长期的实际利益优质学习
前面的路还很长。