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March 2021’s Gwern.net newsletter is now out; previous, February 2021 ( archives ).
2021 年 3 月的 Gwern.net 时事通讯现已发布；上一篇，2021 年 2 月（档案）。
This is a summary of the revision-history RSS feed, overlapping with my Changelog & /r/gwern ; brought to you by my donors on Patreon .
这是修订历史 RSS 提要的摘要，与我的变更日志 & /r/gwern 重叠；由我的 Patreon 捐助者带给您。
Gwern.net : mobile “popins” are finally enabled!
Gwern.net：移动“popins”终于启用了！
( example ); new Wikipedia popups (this 7th implementation enables recursive WP popups )
（ 例子 ）;新的维基百科弹出窗口（第 7 个实现启用了递归 WP 弹出窗口）
“Multimodal Neurons in Artificial Neural Networks” , Goh et al 2021 (dissecting CLIP concepts, discovering typographical classification ‘attacks’^1^ and a Stroop effect !
“人工神经网络中的多模态神经元”，Goh 等人 2021（剖析 CLIP 概念，发现印刷分类“攻击”^1^ 和斯特鲁普效应！
Is there anything CLIP can’t do?)
还有什么是 CLIP 做不到的吗？）
“Evolving Reinforcement Learning Algorithms” , Co-Reyes et al 2021 (evolving eg TD-learning )
“不断发展的强化学习算法”，Co-Reyes 等人 2021（不断发展，例如 TD 学习）
“Waymo Simulated Driving Behavior in Reconstructed Fatal Crashes within an Autonomous Vehicle Operating Domain” , Scanlon et al 2021 ( blog ; hard negative mining—self-driving cars, being inhuman, can learn not just from their mistakes but humans’ mistakes too)
“Waymo 在自动驾驶车辆操作域内重建致命事故中模拟驾驶行为”，Scanlon 等人 2021（博客；硬负面挖掘——自动驾驶汽车不人道，不仅可以从自己的错误中学习，还可以从人类的错误中学习）
“Debugging Reinforcement Learning Systems Without The Agonizing Pain” , Andy L.
“在没有痛苦的情况下调试强化学习系统”，Andy L.
Jones; “My Reinforcement Learning Learnings” , Clemens Winter
琼斯； “我的强化学习学习”，克莱门斯·温特
“SEER: Self-supervised Pretraining of Visual Features in the Wild” , Goyal et al 2021 ( blog ; near-SOTA by training 1b-param CNN on 1b unfiltered unlabeled Internet images—another reminder that unsupervised learning is really working!); “‘Learning From Videos’ to understand the world” (rapid FB expansion of self-supervised training to millions of photos/videos/hours-of-speech); “Contrasting Contrastive Self-Supervised Representation Learning Models” , Kotar et al 2021 (Supervised learning from ImageNet is now obsolete for transfer learning, and ImageNet just a contaminated validation set)
“SEER：野外视觉特征的自监督预训练”，Goyal 等人 2021（博客；通过在 1b 个未过滤的未标记互联网图像上训练 1b-param CNN，接近 SOTA——再次提醒我们无监督学习确实有效！）； “‘从视频中学习’了解世界”（自监督训练的FB快速扩展至数百万张照片/视频/几个小时的演讲）； “Contrasting Contrastive Self-Supervised Representation Learning Models”，Kotar et al 2021（来自 ImageNet 的监督学习对于迁移学习来说现在已经过时了，ImageNet 只是一个被污染的验证集）
“Understanding Robustness of Transformers for Image Classification” , Bhojanapalli et al 2021 ( Vision Transformers gain robustness faster than CNNs as dataset size increases)
“Understanding Robustness of Transformers for Image Classification”，Bhojanapalli et al 2021（随着数据集大小的增加，视觉 Transformers 比 CNN 更快地获得鲁棒性）
“Artificial Intelligence Index Report 2021” : technical performance and cost ( Ding questions whether this shows China catching up on AI at all, as we are incessantly told it is doing; one question to ask: ignoring fast-following, what, out of the thousands upon thousands of publications flooding out these days, are the last 3 major novel AI breakthroughs coming out of all pure-Chinese labs combined which could be plausibly equated in importance with, say, just OpenAI’s recent output of GPT-3 / DALL·E /CLIP?)
《2021 年人工智能指数报告》：技术性能和成本（丁质疑这是否表明中国正在赶上人工智能，就像我们不断被告知的那样；有一个问题要问：忽略快速跟进，在这些天涌出的成千上万份出版物中，最后 3 个来自所有纯中国实验室的重大新人工智能突破，其重要性可以合理地等同于，比如说， OpenAI最近输出了GPT-3 / DALL·E /CLIP？）
“A mathematical theory of semantic development in deep neural networks” , Saxe et al 2019 (are jumps in NN capabilities to be expected when scaling?
“深度神经网络语义发展的数学理论”，Saxe 等人 2019（在扩展时，神经网络能力的跳跃是可以预期的吗？
see also Viering & Loog 2021 ’s discussion of phase transitions & averaging of exponentials giving power-laws)
另请参阅 Viering & Loog 2021 对相变和给出幂律的指数平均的讨论）
“An early cell shape transition drives evolutionary expansion of the human forebrain” , Benito-Kwiecinski et al 2021 ( media ; a simple switch for the scaling up of the primate brain)
“早期细胞形状转变驱动人类前脑的进化扩张”，Benito-Kwiecinski 等人 2021（媒体；灵长类大脑放大的简单开关）
“Crows possess higher intelligence long thought primarily human” (the remarkable, yet not extraordinary, crow/raven brain as scaled-up bird brain )
“乌鸦拥有更高的智力，长期以来被认为主要是人类”（非凡但并不非凡的乌鸦/乌鸦大脑作为按比例放大的鸟脑）
Everything Is Heritable: “GWAS in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color” , Simcoe et al 2021
一切都是可遗传的：“近 195,000 名个体中的 GWAS 识别出 50 个先前未识别的眼睛颜色遗传位点”，Simcoe 等人 2021
“Why Do Wealthy Parents Have Wealthy Children?” , Fagereng et al 2021 (I’m always impressed just how difficult it is for rich people to pass on wealth—“shirtsleeves to shirtsleeves in 3 generations” etc)
“为什么有钱的父母会有有钱的孩子？” , Fagereng et al 2021（富人传承财富是多么困难，给我留下了深刻的印象——“三代人互不相让”等）
Evolution: “Nothing in evolution makes sense except in the light of parasites” , Hickinbotham et al 2021
进化：“除了寄生虫之外，进化中没有任何意义”，Hickinbotham 等人 2021
“Broad cross-national public support for accelerated COVID-19 vaccine trial designs” , Broockman et al 2021 (“we can’t do challenge trials with volunteers in February 2020 to save countless thousands of lives because ordinary people might think it unethical”—have you tried asking them, or was that irrelevant because it was just another noble lie?)
“对加速 COVID-19 疫苗试验设计的广泛跨国公众支持”，Broockman 等人，2021（“我们无法在 2020 年 2 月与志愿者一起进行挑战性试验来拯救无数人的生命，因为普通人可能认为这不道德”——您是否尝试过询问他们，或者这无关紧要，因为这只是另一个高尚的谎言？）
“This is the story of how I found what I believe to be scientific misconduct and what happened when I reported it” , Joe Hilgard
“这是一个关于我如何发现我认为的科学不端行为以及我举报后发生的事情的故事”，乔·希尔加德
“The Revolution in Classic Tetris: How a younger generation used the Internet to master the falling blocks” (how achieving classic Tetris maximum-scores, first done in 2010, became routine thanks to YouTube & online competition for excellence )
“经典俄罗斯方块的革命：年轻一代如何利用互联网掌握下落的方块”（由于 YouTube 和在线卓越竞赛，2010 年首次实现的经典俄罗斯方块最高分如何成为惯例）
“Magic, Explanations, and Evil: The Origins and Design of Witches and Sorcerers” , Singh 2021 (doubtless even cavemen were all “Og: sus.”)
“魔法、解释和邪恶：女巫和巫师的起源和设计”，Singh 2021（毫无疑问，即使是穴居人也是“Og：sus”。）
“Self-blinding citizen science to explore psychedelic microdosing” , Szigeti et al 2021 (related to Kaertner et al 2021 ; a self-blinding study, similar to my old self-blinding protocols, confirms that microdosing is just placebo effect, as I said in 2012 , and I’m reminded of DNB studies like Foroughi et al 2016 )
“探索迷幻微剂量的自我致盲公民科学”，Szigeti et al 2021（与 Kaertner et al 2021 相关；一项自我致盲研究，类似于我以前的自我致盲方案，证实微剂量只是安慰剂效应，正如我在 2012 年所说，我想起了 Foroughi 等人 2016 等 DNB 研究）
The 2019–2020 vaping moral panic over adulterated black-market THC products (depressing to see how irresponsibly reported & alarmist this was, and how everyone attempted to frame nicotine for it 2 .
2019-2020 年，电子烟对掺假黑市 THC 产品产生了道德恐慌（令人沮丧的是，这是多么不负责任的报道和危言耸听，以及每个人如何试图将尼古丁陷害其中 2 。
Naturally, no one involved has apologized or admitted fault—after all, their intentions were good , “won’t someone think of the children”‽ The incompetence and/or dishonesty here emphasizes how 2020–2021 was business as usual, and the only unusual part is that reality happened so fast we saw some of the unseen .)
当然，没有人道歉或认错——毕竟，他们的初衷是好的，“不会有人想到孩子们吗”‽这里的无能和/或不诚实强调了2020-2021年一切如常，唯一不寻常的部分是现实发生得太快，我们看到了一些看不见的东西。）
“Can You Ever Be Too Smart for Your Own Good?
“你会不会为了自己的利益而变得太聪明？
Comparing Linear and Nonlinear Effects of Cognitive Ability on Life Outcomes” , Brown et al 2021
比较认知能力对生活结果的线性和非线性影响”，Brown 等人 2021
“The pandemic fallacy: Inaccuracy of social scientists’ and lay judgments about COVID-19’s societal consequences in America” , Hutcherson et al 2021 (highly-inaccurate even retrospectively, typically grossly overestimating)
“大流行谬误：社会科学家对美国 COVID-19 社会后果的判断不准确”，Hutcherson 等人，2021 年（即使是回顾起来也非常不准确，通常严重高估）
“Training Working Memory for Two Years—No Evidence of Latent Transfer to Intelligence” , Watrin et al 2021 (fade-out of expectancy/placebo effects)
“训练工作记忆两年——没有证据表明潜在的智力转移”，Watrin 等人 2021（预期/安慰剂效应逐渐消失）
“Real-time dialogue between experimenters and dreamers during REM sleep” , Konkoly et al 2021
“实验者和梦想家在快速眼动睡眠期间的实时对话”，Konkoly 等人 2021
“Leroy’s elusive little people: A systematic review on lilliputian hallucinations” , Blom 2021 ( Alice in Wonderland syndrome )
“勒罗伊难以捉摸的小人物：对小人国幻觉的系统回顾”，Blom 2021（爱丽丝梦游仙境综合症）
“A Group of Orca Outcasts Is Now Dominating an Entire Sea: ‘Transient’ killer whales that feast on seals and hunt in small packs are thriving while their widely beloved ‘Resident’ siblings are dying out” (I wonder how the third orca type, ‘offshore’ , are doing?)
“一群逆戟鲸被遗弃者现在统治着整个海洋：以海豹为食并以小群捕猎的‘短暂’虎鲸正在蓬勃发展，而它们广受喜爱的‘常住’兄弟姐妹却正在灭绝”（我想知道第三种逆戟鲸，‘近海’，现在怎么样了？）
“Estimation of the total saliva volume produced per day in 5-year-old children” , Watanabe et al 1995
“5 岁儿童每天产生的唾液总量的估计”，Watanabe 等人 1995
“The Aesthetic-Usability Effect” , Moran 2017 ( “They Might Never Tell You It’s Broken” if it’s pretty enough; see also “The Third User” )
“审美可用性效应”，Moran 2017（“如果它足够漂亮，他们可能永远不会告诉你它坏了”；另请参阅“第三个用户”）
“Cameras and Lenses” , Bartosz Ciechanowski (explorable; followup to “Lights and Shadows” )
“相机和镜头”，Bartosz Ciechanowski（可探索；“光与影”的后续）
“Large Batch Simulation for Deep Reinforcement Learning” , Shacklett et al 2021 (your computer is faster than you think)
“深度强化学习的大批量模拟”，Shacklett 等人 2021（你的计算机比你想象的要快）
“Stone Walls That Stay Built: A master waller shares how to dry-lay stone walls that hold their ground for centuries” , Post 2017
“坚固的石墙：墙工大师分享如何干砌石墙，使其坚守几个世纪”，2017 年邮报
“The Use and Misuse of Income Data and Extreme Poverty in the United States” , Meyer et al 2021 (measurement error in non-registry surveys of population extremes—not quite “lizardman” but similar problem)
“美国收入数据和极端贫困的使用和滥用”，Meyer 等人 2021（极端人口非登记调查中的测量误差——不完全是“蜥蜴人”，但类似的问题）
“Is economics performative?
“经济学是表演性的吗？
Option theory and the construction of derivatives markets” , Mackenzie 2006 (the mechanics of how the Black-Scholes model changed markets: Black ran a service printing “paper” estimating optimal prices for all options which traders could consult & use with simple heuristics to try to arbitrage the market)
期权理论和衍生品市场的构建”，Mackenzie 2006（Black-Scholes 模型如何改变市场的机制：Black 运行一项打印“纸”的服务，估计所有期权的最佳价格，交易者可以通过简单的启发式咨询和使用这些价格来尝试套利市场）
“Whitewood under Siege: On the front lines of the pallet wars” (the competition between the two ecosystems of shipping pallets : ‘whitewood’ & ‘blue pallet’)
“白木围攻：托盘大战的前线”（运输托盘两个生态系统之间的竞争：“白木”和“蓝托盘”）
“Coping with mortality: responses of monkeys and great apes to collapsed, inanimate and dead conspecifics” , De Marco et al 2021
“应对死亡：猴子和类人猿对倒塌、无生命和死亡同种动物的反应”，De Marco 等人 2021
Live-action: North by Northwest ( Hitchcock 1959; for such a extremely respected movie, it felt oddly formless and like it was bouncing through genres as more of a comedic B-movie romp than a serious auteur’s effort—since James Bond started in 1953, with a TV adaptation in 1954, NbN comes off as almost a satire.
真人版：《西北偏北》（希区柯克 1959 年）；对于这样一部备受推崇的电影来说，它给人一种奇怪的无形感，更像是一部喜剧 B 级电影，而不是严肃导演的作品——自詹姆斯·邦德于 1953 年开始拍摄，到 1954 年改编成电视剧以来，《NbN》几乎是一部讽刺作品。
I mean, really, monkeying around in Presidential noses!)
我的意思是，真的，在总统的鼻子里胡闹！）
While interesting, these are ‘attacks’ only in the most generous interpretation possible (since it does know the difference ), and the fact that CLIP can read text in images to note the semantic similarity, is to considerable credit.
虽然很有趣，但这些只是在最慷慨的解释中的“攻击”（因为它确实知道其中的区别），而且 CLIP 可以读取图像中的文本以记录语义相似性，这一事实是值得赞扬的。
As the CLIP authors note , some queries benefit from ensembling, more context than a single word class name such as prefixing “A photograph of a”, and class names can be highly ambiguous: in ImageNet, the class name “crane” could refer to the bird or construction equipment; and the Oxford-IIIT Pet dataset labels one class “boxer”.
正如 CLIP 作者指出的那样，一些查询受益于集成，比单个单词类名称（例如前缀“A Photograph of a”）有更多上下文，并且类名称可能非常不明确：在 ImageNet 中，类名称“crane”可能指鸟或建筑设备； Oxford-IIIT Pet 数据集将一类标记为“拳击手”。
(CLIP is still vulnerable to regular adversarial examples , of course.)↩
（当然，CLIP 仍然容易受到常规对抗性示例的影响。）↩
It couldn’t’ve been nicotine because people had been vaping for a decade and a half without widespread near-instantaneous lung-related fatalities!
它不可能是尼古丁，因为人们吸电子烟已经有十五年了，没有发生过几乎立即发生的与肺部相关的死亡事件！
It had to be a new adulterant, and as soon as the first few black-market THC links surfaced, that meant the problem had to be THC-products-only because how would the same adulterant simultaneously get into the different supply chains?
它一定是一种新的掺假物，一旦最初的几个黑市 THC 链接浮出水面，这意味着问题一定是 THC 产品，因为相同的掺假物如何同时进入不同的供应链？
And yet, every article, health official, and activist did their paternalist best to suggest otherwise to pin the blame on regular vaping, no matter how many tests turned up clean, and it was the nicotine vaping products which got summarily banned….
然而，每一篇文章、卫生官员和活动家都尽其家长作风，建议将责任归咎于常规电子烟，无论有多少测试结果是干净的，而被立即禁止的正是尼古丁电子烟产品……。
One must assume many of those laws are still on the books, inasmuch as the shipping bans keep expanding .↩
人们必须假设其中许多法律仍然存在，因为运输禁令不断扩大。↩