System.Xml.XmlElement
February 2021’s Gwern.net newsletter is now out; previous, January 2021 ( archives ).
2021 年 2 月的 Gwern.net 时事通讯现已发布；上一篇，2021 年 1 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 : popups: can now be moved, stickied, and full-screened (another step towards our ambition of Windows-95-in-the-browser!)
Gwern.net ：弹出窗口：现在可以移动、粘贴和全屏显示（朝着我们在浏览器中实现 Windows-95 的目标又迈出了一步！）
“Controllable Neural Text Generation” , Lilian Weng; “Recent Advances in Language Model Fine-tuning” , Sebastian Ruder (review)
“可控神经文本生成”，Lilian Weng； “语言模型微调的最新进展”，Sebastian Ruder（评论）
“Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm” , Reynolds & McDonell 2021 (original 10-shot Fr → En translation can be beaten by the better 0-shot prompt: “French: XYZ / English:…”; this is “true of most worst-performing prompts…”); “Calibrate Before Use: Improving Few-Shot Performance of Language Models” , Zhao et al 2021 (huge boost from calibrating unstable prompts; both demonstrate, as always , that “sampling can prove the presence of knowledge but not the absence.”)
“大型语言模型的提示编程：超越少样本范式”，Reynolds & McDonell 2021（原始 10 样本 Fr → En 翻译可以被更好的 0 样本提示击败：“法语：XYZ / 英语：…”；这是“最差表现的提示的真实情况…”）； “使用前校准：提高语言模型的少样本性能”，Zhao et al 2021（校准不稳定提示的巨大提升；一如既往，两者都证明“采样可以证明知识的存在，但不能证明知识的缺失。”）
“TransGAN: Two Transformers Can Make One Strong GAN” , Jiang et al 2021 (Transformer-only GAN: attention is all you need)
“TransGAN：两个 Transformer 可以组成一个强大的 GAN”，Jiang 等人 2021（仅 Transformer GAN：注意力就是你所需要的）
“PACT: Proof Artifact Co-training for Theorem Proving with Language Models” , Han et al 2021 ( GPT-f for Lean )
“PACT：使用语言模型进行定理证明的证明工件联合训练”，Han 等人 2021（精益的 GPT-f）
“Towards End-to-End In-Image Neural Machine Translation” , Mansimov et al 2020 (sure why not)
“迈向端到端图像内神经机器翻译”，Mansimov 等人 2020（当然为什么不呢）
Brains : “Artificial Neural Nets Finally Yield Clues to How Brains Learn” (short overview of biologically-plausible backprop: feedback alignment, target propagation, predictive coding, & attentional feedback; also of recent interest, VS-ML ; given their increasing success in training while respecting more biological constraints, the increasing power of backprop-trained ANNs and the neurological success of ANNs in predicting & imitating brain signals, it is increasingly clear that brains really do do backprop in some sense)
大脑：“人工神经网络最终给出了大脑如何学习的线索”（生物学上合理的反向传播的简短概述：反馈对齐、目标传播、预测编码和注意力反馈；最近也引起了人们的兴趣，VS-ML；考虑到它们在训练中越来越成功，同时尊重更多的生物约束，反向传播训练的人工神经网络的能力不断增强，以及人工神经网络在预测和模仿大脑信号方面的神经学成功，越来越清楚的是，大脑确实确实做到了这一点某种意义上的反向传播）
“NSD: A massive 7-tesla fMRI dataset to bridge cognitive and computational neuroscience” , Jean et al 2021 (“…The availability of NSD thus opens the door to using brain activity to directly guide the optimization of deep neural networks.”)
“NSD：连接认知和计算神经科学的大型 7 特斯拉 fMRI 数据集”，Jean 等人，2021（“……NSD 的可用性因此为利用大脑活动直接指导深度神经网络的优化打开了大门。”）
“Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity” , Le et al 2021 (reconstructing Dr. Who )
“Brain2Pix：根据大脑活动进行全卷积自然视频重建”，Le et al 2021（重建 Dr. Who）
“High-performance brain-to-text communication via imagined handwriting” , Willett et al 2020
“通过想象的手写进行高性能的大脑到文本的通信”，Willett 等人 2020
“Brain-computer interface for generating personally attractive images” , Spape et al 2021 (many ways to improve this…)
“用于生成具有个人吸引力的图像的脑机接口”，Spape 等人 2021（有很多方法可以改进这一点......）
“Scaling Laws for Transfer” , Hernandez et al 2021 (“We find that pre-training effectively multiplies the fine-tuning dataset size”; a shot across the bow of anyone floating on a proprietary-dataset moat: large models can drop data requirements by orders of magnitude overnight, even surpassing you)
“Scaling Laws for Transfer”，Hernandez 等人，2021 年（“我们发现预训练有效地使微调数据集大小成倍增加”；对任何漂浮在专有数据集护城河上的人来说，这是一击：大型模型可以在一夜之间将数据需求降低几个数量级，甚至超过你）
“ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision” , Jia et al 2021 (see also CC-12M ; CLIP -like w/EfficientNet trained on 1.8 billion images on a TPUv3-1024— DM argues that fancier cross-modal Transformers are better, nevertheless, ‘TPUs go brrr’ .
“ALIGN：通过噪声文本监督扩展视觉和视觉语言表示学习”，Jia 等人 2021（另请参阅 CC-12M；类似 CLIP，使用 EfficientNet 在 TPUv3-1024 上对 18 亿张图像进行训练 - DM 认为更高级的跨模态 Transformer 更好，尽管如此，“TPU 会前进”。
Given DALL·E, CLIP, ALIGN, VDVAE , CW-VAE , AIPO et al, are GANs already dead, and just don’t realize it yet?
鉴于 DALL·E、CLIP、ALIGN、VDVAE、CW-VAE、AIPO 等，GAN 是否已经死了，只是还没有意识到？
Or at least soon to be relegated to only DRL-like uses as a final finetuning phase to sharpen up a self-supervised model?); “WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training” , Huo et al 2021
或者至少很快就会被降级为仅类似 DRL 的用途，作为最终的微调阶段以增强自监督模型？）； “文澜：通过大规模多模态预训练桥接视觉和语言”，Huo 等人 2021
“DALL·E: Zero-Shot Text-to-Image Generation” , Ramesh et al 2021 ( original blog ); “M6: A Chinese Multimodal Pretrainer” , Lin et al 2021 (Chinese DALL·E: 1.9TB images/0.29TB text for 10b-parameter dense/100b-parameter MoE Transformer; shockingly fast Chinese replication of DALL·E/CLIP)
“DALL·E：零镜头文本到图像生成”，Ramesh 等人 2021（原创博客）； “M6：中文多模态预训练器”，Lin 等人 2021（中文 DALL·E：10b 参数密集/100b 参数 MoE Transformer 的 1.9TB 图像/0.29TB 文本；DALL·E/CLIP 的中文复制速度惊人）
“Explaining Neural Scaling Laws” , Bahri et al 2021/ “Learning Curve Theory” , Hutter 2021 ( Rohin Shah commentary ; more on the manifold hypothesis)
“解释神经尺度法则”，Bahri 等人 2021/“学习曲线理论”，Hutter 2021（Rohin Shah 评论；更多关于流形假设）
Everything Is Heritable: “Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals” , Kemper et al 2021
一切都是可遗传的：“860 亿对个体的整个相关性范围内的表型协方差”，Kemper 等人，2021
“Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical and behavioural sciences” , Malanchini et al 2021 (not the focus, but the IQ PGS is a slight improvement over Allegrini et al 2018 due to less phenotype measurement error?)
“Pathfinder：一种将一般认知能力整合到生物、医学和行为科学中的游戏化测量”，Malanchini et al 2021（不是重点，但由于表型测量误差较小，IQ PGS 比 Allegrini et al 2018 略有改善？）
“Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants” , Saarentaus et al 2021
“与大多数罕见和高风险拷贝数变异相比，多基因负担对健康、认知和社会经济结果具有更广泛的影响”，Saarentaus et al 2021
Recent Evolution: “Million-Year-Old DNA Rewrites the Mammoth Family Tree: Genomic data—the oldest ever recovered from a fossil—reveals the origin and evolution of the Columbian mammoth”
最近的进化：“百万年前的 DNA 改写了猛犸象的家谱：基因组数据——从化石中发现的最古老的数据——揭示了哥伦比亚猛犸象的起源和进化”
“Kin selection explains the evolution of cooperation in the gut microbiota” , Simonet & McNally 2021
“亲缘选择解释了肠道微生物群合作的进化”，Simonet 和 McNally 2021
“Lessons from Gerolamo Cardano’s The Book of My Life ” (progress studies; see also Newton’s anthropic argument , Bakewell & inventing progress , The Autobiography of Benvenuto Cellini )
“杰罗拉莫·卡尔达诺的《我的生命之书》的教训”（进步研究；另见牛顿的人择论证、贝克韦尔与发明进步、本韦努托·切利尼自传）
“How Many Microcovids Would You Spend on a Burrito?” (on the microCOVID Project Calculator )
“你会花多少个 Microcovid 来买一个墨西哥卷饼？” （在 microCOVID 项目计算器上）
“On the enfeeblement of mathematical skills by ‘Modern Mathematics’ and by similar soft intellectual trash in schools and universities” , Hammersley 1968 ( Knuth highlights as also amusing: “A Note on Piffles” , Smith 1967; “A rebuke of A.
“关于‘现代数学’以及学校和大学中类似的软知识垃圾对数学技能的削弱”，Hammersley 1968（Knuth 强调也很有趣：“关于 Piffles 的注释”，Smith 1967；“对 A.
B.
B.
Smith’s paper, ‘A Note on Piffles’” , Farlow 1980)
史密斯的论文，‘关于 Piffles 的注释’”，Farlow 1980）
“Artifact and Recording Concepts in EEG” , Tatum et al 2011 (on the EEG signals of Jell-O , or, the importance of negative controls )
“EEG 中的工件和记录概念”，Tatum 等人 2011（关于 Jell-O 的 EEG 信号，或者阴性对照的重要性）
“The Logic of Fashion Cycles” , Acerbi et al 2012; “Fashion and art cycles are driven by counter-dominance signals of elite competition: quantitative evidence from music styles” , Klimek et al 2019; “The hipster effect: When anti-conformists all look the same” , Touboul 2019; “Right Is The New Left” , Scott Alexander (see also Han et al 2010 , Downs 1972 / Gupta & Jenkins-Smith 2015 , Lorenz-Spreen et al 2019 / Candia et al 2019 , Loury 1994 )
“时尚周期的逻辑”，Acerbi 等人，2012 年； “时尚和艺术周期是由精英竞争的反主导信号驱动的：来自音乐风格的定量证据”，Klimek et al 2019； “时髦效应：当反墨守成规的人看起来都一样时”，Touboul 2019； “右派就是新左派”，斯科特·亚历山大（另见 Han et al 2010，Downs 1972 / Gupta & Jenkins-Smith 2015，Lorenz-Spreen et al 2019 / Candia et al 2019，Loury 1994）
“What can we learn from the lunar pandemic that never was?” (NASA’s lunar quarantine was a sham intended to mollify the public as they covered up repeated major failures & lab leaks both before & after—had there been any dangerous lunar organisms, they would have escaped easily)
“我们可以从从未发生过的月球大流行中学到什么？” （美国宇航局的月球隔离是一场骗局，旨在安抚公众，因为他们掩盖了前后重复的重大失败和实验室泄漏——如果月球上有任何危险的生物，它们很容易逃脱）
MrBeast (the new aristocracy of prestige ?
MrBeast（声望新贵族？
Borrowed plumage, perhaps, but effective…)
也许是借来的羽毛，但很有效……）
Semaglutide : “Once-Weekly Semaglutide in Adults with Overweight or Obesity” , Wilding et al 2021; “Effect of Subcutaneous Semaglutide vs Placebo as an Adjunct to Intensive Behavioral Therapy on Body Weight in Adults With Overweight or Obesity: The STEP 3 Randomized Clinical Trial” , Wadden et al 2021
索马鲁肽：“超重或肥胖成人每周一次索马鲁肽”，Wilding 等人，2021 年； “皮下注射索马鲁肽与安慰剂作为强化行为疗法的辅助疗法对超重或肥胖成人体重的影响：STEP 3 随机临床试验”，Wadden 等人，2021 年
A longer-acting version of the insulin/appetite peptide liraglutide , semaglutide greatly reduces weight, fat, blood sugar, cholesterol etc, with an upcoming oral version ; background: Kushner et al 2020 , Aroda et al 2019 , Nauck & Meier 2019 , O’Neil et al 2018 , Blundell et al 2017 , Nauck et al 2016 , Lau et al 2015 .
司马鲁肽是胰岛素/食欲肽利拉鲁肽的长效版本，可大大降低体重、脂肪、血糖、胆固醇等，即将推出口服版本；背景：Kushner et al 2020，Aroda et al 2019，Nauck & Meier 2019，O’Neil et al 2018，Blundell et al 2017，Nauck et al 2016，Lau et al 2015。
“Lessons from the host defences of bats, a unique viral reservoir” , Irving et al 2021 ( bat-borne viruses ; previously, Trevor Klee )
“蝙蝠宿主防御的教训，独特的病毒库”，Irving 等人 2021（蝙蝠传播的病毒；以前，Trevor Klee）
“Beneficial & Detrimental Effects of Reactive Oxygen Species on Lifespan: A Comprehensive Review of Comparative & Experimental Studies” , Shields et al 2021 (antioxidants still aren’t the fountain of youth, and may be harmful; animal studies still frequently inconsistent)
“活性氧对寿命的有益和有害影响：比较和实验研究的综合回顾”，Shields 等人 2021（抗氧化剂仍然不是青春之泉，而且可能有害；动物研究仍然经常不一致）
“Positive expectations predict improved mental-health outcomes linked to psychedelic microdosing” , Kaertner et al 2021 (placebo)
“积极的期望预示着与迷幻微剂量相关的心理健康结果的改善”，Kaertner et al 2021（安慰剂）
“Sleep & Sex: What Can Go Wrong?
“睡眠与性：会出什么问题？
A Review of the Literature on Sleep Related Disorders and Abnormal Sexual Behaviors & Experiences” , Schenck et al 2007
睡眠相关障碍和异常性行为与经历的文献综述”，Schenck 等人 2007 年
Wringing gauge blocks (“With their precisely-flat metal faces, gauge blocks can be stuck together non-magnetically via a process calling ‘wringing’, requiring substantial effort to separate.
拧紧量块（“量块具有精确平坦的金属面，可以通过称为‘拧紧’的过程以非磁性方式粘在一起，需要付出很大的努力才能分离。
Scientists are still uncertain exactly how wringing works.”)
科学家们仍然不确定绞拧到底是如何工作的。”）
“Why did renewables become so cheap so fast?
“为什么可再生能源这么快就变得如此便宜？
And what can we do to use this global opportunity for green growth?” , Max Roser (specifically, why such an extreme experience curve ?)
我们可以做些什么来利用这个全球绿色增长的机会？” ，Max Roser（具体来说，为什么会有如此极端的经验曲线？）
“IQ, trading behavior, and performance” , Grinblatt et al 2012; “Genetic Endowments and Wealth Inequality” , Barth et al 2020 (why, despite notorious setbacks, did Isaac Newton & LTCM’s founders die wealthy?
“智商、交易行为和绩效”，Grinblatt 等人，2012 年； “遗传禀赋与财富不平等”，Barth 等人 2020（为什么艾萨克·牛顿和长期资本管理公司的创始人尽管经历了臭名昭著的挫折，但死时却很富有？
Why, in general, are more intelligent people so much better investors?
一般来说，为什么更聪明的人是更好的投资者？
‘The indifference of the indicator’: it’s not one thing, it’s everything—more intelligent people have lower discount rates, save more for longer & are less risk-averse, more accurately predict future growth or inflation, are more likely to participate in +EV opportunities like the stock market, to use low-fee rather than high-fee (and thus, underperforming) mutual funds, succumb less to biases like herding as they trade better & at better times, trade less, and harvest losses more efficiently when trading poorly.)
“指标的冷漠”：这不是一回事，而是一切——更聪明的人有较低的贴现率，储蓄时间更长，风险规避程度较低，更准确地预测未来的增长或通胀，更有可能参与股票市场等+EV机会，使用低费用而不是高费用（因此表现不佳）共同基金，更少屈服于羊群效应等偏见，因为他们在更好的时间进行更好的交易，更少的交易，更有效地收获损失当交易不佳时。）
Are ethics experts more ethical ?
道德专家是否更有道德？
“The Behavior of Ethicists” , Schwitzgebel & Rust 2016 (most recently: “The moral behavior of ethics professors: A replication-extension in German-speaking countries” , Schönegger et al 2019; given moral licensing & activism, perhaps we should be surprised we don’t hear about more ethicists doing things like posting enemy lists or trying to dox reviewers.
“伦理学家的行为”，Schwitzgebel & Rust 2016（最近：“伦理学教授的道德行为：德语国家的复制延伸”，Schönegger 等人 2019；鉴于道德许可和行动主义，也许我们应该感到惊讶的是，我们没有听到更多的伦理学家做诸如发布敌人名单或试图人肉评论员之类的事情。
“Woe to you Pharisees!”)
“你们法利赛人有祸了！”）
“Meta-analysis on belief in free will manipulations” , Genschow et al 2021 (another noble lie turns out to be ignoble)
“对自由意志操纵信念的元分析”，Genschow et al 2021（另一个崇高的谎言被证明是不光彩的）