【Nature 578, 502-504 (2020)】为什么脸并不总是表达真实的感情？

Clipped from: https://www.nature.com/articles/d41586-020-00507-5?utm_source=weibo&utm_medium=social&utm_term=d41586-020-00507-5

Credit: Adapted from GL Archive/Alamy

Human faces pop up on a screen, hundreds of them, one after another. Some have their eyes stretched wide, others show lips clenched. Some have eyes squeezed shut, cheeks lifted and mouths agape. For each one, you must answer this simple question: is this the face of someone having an orgasm or experiencing sudden pain?

屏幕上出现了人脸，成百上千张，一个接一个。有的睁大眼睛，有的紧闭双唇。有些人眼睛紧闭，脸颊抬高，嘴巴张开。对于每一个人，你必须回答这个简单的问题:这是一个人的高潮或经历突然疼痛的脸吗?

Psychologist Rachael Jack and her colleagues recruited 80 people to take this test as part of a study1 in 2018. The team, at the University of Glasgow, UK, enlisted participants from Western and East Asian cultures to explore a long-standing and highly charged question: do facial expressions reliably communicate emotions?

心理学家雷切尔·杰克和她的同事在2018年招募了80人参加这项测试，作为一项研究的一部分。英国格拉斯哥大学的研究小组邀请了来自西方和东亚文化的参与者来探讨一个长期存在且高度敏感的问题:面部表情能可靠地传达情感吗?

Researchers have been asking people what emotions they perceive in faces for decades. They have questioned adults and children in different countries and Indigenous populations in remote parts of the world. Influential observations in the 1960s and 1970s by US psychologist Paul Ekman suggested that, around the world, humans could reliably infer emotional states from expressions on faces — implying that emotional expressions are universal2,3.

【存在】几十年来，研究人员一直在问人们他们在脸上看到了什么情绪。他们询问了不同国家的成年人和儿童，以及世界偏远地区的土著居民。20世纪60年代和70年代，美国心理学家保罗·埃克曼(Paul Ekman)的一项颇具影响力的观察表明，在世界各地，人们可以可靠地从面部表情推断出情绪状态，这意味着情绪表情是普遍存在的

These ideas stood largely unchallenged for a generation. But a new cohort of psychologists and cognitive scientists has been revisiting those data and questioning the conclusions. Many researchers now think that the picture is a lot more complicated, and that facial expressions vary widely between contexts and cultures. Jack’s study, for instance, found that although Westerners and East Asians had similar concepts of how faces display pain, they had different ideas about expressions of pleasure.

【但存在争议】这些思想在一代人的时间里基本上没有受到挑战。但一群新的心理学家和认知科学家一直在重新审视这些数据，并对结论提出质疑。许多研究人员现在认为，这幅图要复杂得多，而且面部表情在不同的语境和文化之间存在很大差异。例如，杰克的研究发现，尽管西方人和东亚人对脸部如何表现痛苦有相似的概念，但他们对快乐的表达有不同的看法。【很期待科技公司开始关注文化产业啊，知识的共融总是让人很兴奋】

【companies and governments】Researchers are increasingly split over the validity of Ekman’s conclusions. But the debate hasn’t stopped companies and governments accepting his assertion that the face is an emotion oracle — and using it in ways that are affecting people’s lives. In many legal systems in the West, for example, reading the emotions of a defendant forms part of a fair trial. As US Supreme Court judge Anthony Kennedy wrote in 1992, doing so is necessary to “know the heart and mind of the offender”.

研究人员对埃克曼的结论的有效性分歧越来越大。但这场辩论并没有阻止公司和政府接受他的说法，即脸是一种情感的神谕——并以影响人们生活的方式使用它。例如，在西方的许多法律体系中，解读被告的情绪是公平审判的一部分。正如美国最高法院法官安东尼·肯尼迪在1992年所写的那样，这样做对于“了解罪犯的内心和思想”是必要的。

Decoding emotions is also at the core of a controversial training programme designed by Ekman for the US Transportation Security Administration (TSA) and introduced in 2007. The programme, called SPOT (Screening Passengers by Observation Techniques), was created to teach TSA personnel how to monitor passengers for dozens of potentially suspicious signs that can indicate stress, deception or fear. But it has been widely criticized by scientists, members of the US Congress and organizations such as the American Civil Liberties Union for being inaccurate and racially biased.

解码情绪也是埃克曼为美国运输安全管理局(TSA)设计的备受争议的培训项目的核心，该项目于2007年推出。该项目名为SPOT(通过观察技术对乘客进行筛查)，目的是教会运输安全管理局的工作人员如何对乘客进行监控，以发现几十种潜在的可疑迹象，这些迹象可能暗示着压力、欺骗或恐惧。但它受到了科学家、美国国会议员和美国公民自由联盟(American Civil Liberties Union)等组织的广泛批评，称它不准确，存在种族偏见。【你的朋友，美国的政治正确争议上线】

Such concerns haven’t stopped leading tech companies running with the idea that emotions can be detected readily, and some firms have created software to do just that. The systems are being trialled or marketed for assessing the suitability of job candidates, detecting lies, making adverts more alluring and diagnosing disorders from dementia to depression. Estimates place the industry’s value at tens of billions of dollars. Tech giants including Microsoft, IBM and Amazon, as well as more specialist companies such as Affectiva in Boston, Massachusetts, and NeuroData Lab in Miami, Florida, all offer algorithms designed to detect a person’s emotions from their face.

这样的担忧并没有阻止领先的科技公司，他们认为情绪很容易被察觉，一些公司已经开发了软件来做到这一点。这些系统正在试用或推广，用于评估求职者的适合程度、检测谎言、让广告更具吸引力，以及诊断从痴呆症到抑郁症的各种疾病。【这部分是我想尝试的】据估计，该行业的价值高达数百亿美元。包括微软(Microsoft)、IBM和亚马逊(Amazon)在内的科技巨头，以及更多的专业公司，如马萨诸塞州波士顿的艾里蒂瓦(affect tiva)和佛罗里达州迈阿密的神经数据实验室(NeuroData Lab)，都提供了旨在从面部识别一个人的情绪的算法

With researchers still wrangling over whether people can produce or perceive emotional expressions with fidelity, many in the field think efforts to get computers to do it automatically are premature — especially when the technology could have damaging repercussions. The AI Now Institute, a research centre at New York University, has even called for a ban on uses of emotion-recognition technology in sensitive situations, such as recruitment or law enforcement4.

由于研究人员仍在争论人们是否能以逼真的方式表达或感知情感，许多业内人士认为，让电脑自动表达情感的努力还为时过早——尤其是在这项技术可能产生破坏性影响的情况下。纽约大学的研究中心Al Now研究所甚至呼吁禁止在招聘或执法等敏感场合使用情绪识别技术。

Facial expressions are extremely difficult to interpret, even for people, says Aleix Martinez, who researches the topic at the Ohio State University in Columbus. With that in mind, he says, and given the trend towards automation, “we should be very concerned”.

俄亥俄州立大学哥伦布分校研究面部表情的阿历克斯·马丁内斯(Aleix Martinez)说，即使是对人来说，面部表情也极难解读。考虑到这一点，他说，考虑到自动化的趋势，“我们应该非常谨慎。关注"。

Skin deep 【表层深挖？这个咋翻译啊】

The human face has 43 muscles, which can stretch, lift and contort it into dozens of expressions. Despite this vast range of movement, scientists have long held that certain expressions convey specific emotions.

人类的脸有43块肌肉，可以拉伸、举起和扭曲成几十种表情。尽管人类活动范围很广，但科学家们长期以来一直认为，某些特定的表情传达了特定的情感。

One person who pushed this view was Charles Darwin. His 1859 book On the Origin of Species, the result of painstaking fieldwork, was a masterclass in observation. His second most influential work, The Expression of the Emotions in Man and Animals (1872), was more dogmatic.

查尔斯·达尔文是推动这一观点的人之一。他1859年出版的《物种起源》一书，是艰苦野外工作的成果，是观察方面的大师之作。他的第二部最有影响力的作品《人与动物的情感表达》(1872年)则更加教条。【原来达尔文写过这个书，这个题材被论证的时间比我想的早好久啊】

Darwin noted that primates make facial movements that look like human expressions of emotion, such as disgust or fear, and argued that the expressions must have some adaptive function. For example, curling the lip, wrinkling the nose and narrowing the eyes — an expression linked to disgust — might have originated to protect the individual against noxious pathogens. Only as social behaviours started to develop, did these facial expressions take on a more communicative role.

达尔文注意到，灵长类动物的面部表情看起来像人类的情绪表达，比如厌恶或恐惧，并认为这些表情一定具有某种适应功能。例如，噘起嘴唇、皱起鼻子和眯起眼睛——一种与厌恶有关的表情——可能是为了保护个人免受有害病原体的侵害。只有当社交行为开始发展时，这些面部表情才会有更多的交流作用

Darwin’s treatise on emotions featured plenty of posed expressions, such as these subjects doing their best to imitate grief.Credit: Alamy

达尔文的《情感论》中有大量的摆姿势的表达，比如这些被试者尽力模仿悲伤。

The first cross-cultural field studies, carried out by Ekman in the 1960s, backed up this hypothesis. He tested the expression and perception of six key emotions — happiness, sadness, anger, fear, surprise and disgust — around the world, including in a remote population in New Guinea2,3.

埃克曼在20世纪60年代进行的第一次跨文化实地研究支持了这一假设。他在全世界范围内测试了六种主要情绪——快乐、悲伤、愤怒、恐惧、惊讶和厌恶——的表达和感知，包括在新几内亚的一个偏远地区。【好喜欢这种跨文化的氛围】

Ekman chose these six expressions for practical reasons, he told Nature. Some emotions, such as shame or guilt, do not have obvious readouts, he says. “The six emotions that I focused on do have expressions, which meant that they were amenable to study.”

埃克曼告诉《自然》杂志，他选择这六种表达方式是出于实际原因。他说，有些情绪，如羞愧或内疚，没有明显的读数。“我关注的六种情绪确实有表达方式，这意味着它们是可以研究的。”

Those early studies, Ekman says, showed evidence of the universality that Darwin’s evolution theory expected. And later work supported the claim that some facial expressions might confer an adaptive advantage5.

埃克曼说，这些早期的研究证明了达尔文进化论所期望的普遍性。后来的研究也支持了这一观点，即某些面部表情可能具有适应优势。

“The assumption for a long time was that facial expressions were obligatory movements,” says Lisa Feldman Barrett, a psychologist at Northeastern University in Boston who studies emotion. In other words, our faces are powerless to hide our emotions. The obvious problem with that assumption is that people can fake emotions, and can experience feelings without moving their faces. Researchers in the Ekman camp acknowledge that there can be considerable variation in the ‘gold standard’ expressions expected for each emotion.

波士顿东北大学(Northeastern University)研究情绪的心理学家丽莎•费尔德曼•巴雷特(Lisa Feldman Barrett)表示:“长期以来，人们一直认为面部表情是一种必须的动作。”换句话说，我们的脸无法隐藏我们的情绪。这种假设的一个明显的问题是，人们可以伪造情感，可以在不移动面部的情况下体验情感。埃克曼阵营的研究人员承认，人们对每种情绪的“黄金标准表达”可能存在相当大的差异。

But a growing crowd of researchers argues that the variation is so extensive that it stretches the gold-standard idea to the breaking point. Their views are backed up by a vast literature review6. A few years ago, the editors of the journal Psychological Science in the Public Interest put together a panel of authors who disagreed with one another and asked them to review the literature.

但越来越多的研究人员认为，这种差异是如此之大，以至于将黄金标准的观点延伸到了临界点。他们的观点得到了大量文学评论的支持。【哪类文章可以称作文学评论呢？】几年前，《公共利益心理科学》(Psychological Science in the Public Interest)杂志的编辑们召集了一组意见相左的作者，请他们回顾相关文献。

“We did our best to set aside our priors,” says Barrett, who led the team. Instead of starting with a hypothesis, they waded into the data. “When there was a disagreement, we just broadened our search for evidence.” They ended up reading around 1,000 papers. After two and a half years, the team reached a stark conclusion: there was little to no evidence that people can reliably infer someone else’s emotional state from a set of facial movements.

“我们尽了最大的努力把我们的前科放在一边，”领导这个团队的巴雷特说。他们没有从一个假设开始，而是深入研究了数据。“当出现分歧时，我们只是扩大了寻找证据的范围。”他们最终阅读了大约1000篇论文。两年半之后，研究小组得出了一个明显的结论:几乎没有证据表明，人们可以从一组面部动作可靠地推断出他人的情绪状态。【狠话来了】

Faces alone only reveal so much about mood. Scroll down for the full picture.

Credits: Lance King/Hector Vivas/Ronaldo Schemidt/Kevin Winter/Getty

单是面部表情就能透露很多情绪。向下滚动查看全图。

工作人员:兰斯·金/赫克托·维瓦斯/罗纳尔多·斯莫伊特/凯文·温特/盖蒂

At one extreme, the group cited studies that found no clear link between the movements of a face and an internal emotional state. Psychologist Carlos Crivelli at De Montfort University in Leicester, UK, has worked with residents of the Trobriand islands in Papua New Guinea and found no evidence for Ekman’s conclusions in his studies. Trying to assess internal mental states from external markers is like trying to measure mass in metres, Crivelli concludes.

在一个极端情况下，该研究小组引用的研究结果表明，面部的动作和内心的情绪状态之间没有明显的联系。英国莱斯特德蒙福特大学的心理学家卡洛斯·克里维利曾与巴布亚新几内亚特罗布里南群岛的居民合作过，但在他的研究中没有发现支持埃克曼结论的证据。克里维利总结道，试图通过外部标记来评估内部精神状态，就像试图以米为单位来测量质量一样。

Another reason for the lack of evidence for universal expressions is that the face is not the whole picture. Other things, including body movement, personality, tone of voice and changes in skin tone have important roles in how we perceive and display emotion. For example, changes in emotional state can affect blood flow, and this in turn can alter the appearance of the skin. Martinez and his colleagues have shown that people are able to connect changes in skin tone to emotions7. The visual context, such as the background scene, can also provide clues to someone’s emotional state8.

缺乏普遍表情证据的另一个原因是，面部表情并不是全部。其他因素，包括身体运动、性格、声调和肤色的变化，在我们如何感知和表达情感方面也起着重要作用。例如，情绪状态的变化会影响血液流动，而这反过来又会改变皮肤的外观。马丁内斯和他的同事们已经证明，人们能够把肤色的变化与情绪联系起来。视觉环境，比如背景场景，也可以为一个人的情绪状态提供线索。【神奇，显著度超过百分之五了嘛】

Clockwise, from top left: basketball player Zion Williamson celebrates a dunk; Mexico fans celebrate a win in a World Cup group match; singer Adele wins Album of the Year at the Grammys in 2012; Justin Bieber fans cry at a concert in Mexico City.Credits: Lance King/Hector Vivas/Ronaldo Schemidt/Kevin Winter/Getty

顺时针方向，从左上角开始:篮球运动员锡安·威廉姆森庆祝扣篮成功:墨西哥球迷庆祝世界杯小组赛胜利;歌手阿黛尔获得2012年格莱美年度最佳专辑奖;贾斯汀·比伯的粉丝们在墨西哥城的一场音乐会上哭泣。工作人员:兰斯·金/赫克托·维瓦斯/罗纳尔多·斯米德特·凯文·温特/盖蒂

Mixed emotions

Other researchers think the push-back on Ekman’s results is a little overzealous — not least Ekman himself. In 2014, responding to a critique from Barrett, he pointed to a body of work that he says supports his previous conclusions, including studies on facial expressions that people make spontaneously, and research on the link between expressions and underlying brain and bodily state. This work, he wrote, suggests that facial expressions are informative not only about individuals’ feelings, but also about patterns of neurophysiological activation (see go.nature.com/2pmrjkh). His views have not changed, he says.

其他研究人员认为，对埃克曼研究结果的反驳有点过分——尤其是埃克曼本人。2014年，在回应巴雷特(Barrett)的一篇评论时，他提到了大量的工作，他说这些工作支持了他之前的结论，包括对人们自发做出的面部表情的研究，以及对表情与潜在的大脑和身体状态之间联系的研究。他写道，这项研究表明，面部表情不仅能传达个人感受，还能反映神经生理活动的模式(见go.nature.com/2pmrjkh)。他说，他的观点没有改变

According to Jessica Tracy, a psychologist at the University of British Columbia in Vancouver, Canada, researchers who conclude that Ekman’s theory of universality is wrong on the basis of a handful of counterexamples are overstating their case. One population or culture with a slightly different idea of what makes an angry face doesn’t demolish the whole theory, she says. Most people recognize an angry face when they see it, she adds, citing an analysis of nearly 100 studies9. “Tons of other evidence suggests that most people in most cultures all over the world do see this expression is universal.”

加拿大温哥华英属哥伦比亚大学(University of British Columbia)的心理学家杰西卡·特蕾西(Jessica Tracy)表示，一些研究人员基于少数反例得出结论，认为埃克曼的普遍性理论是错误的，他们夸大了自己的观点。她说，一个民族或文化对什么是生气的表情有稍微不同的理解，并不会推翻整个理论。她引用了一项对近100项研究的分析，补充说，大多数人看到一张生气的脸时都能认出来。“大量的其他证据表明，在世界各地的大多数文化中，大多数人都认为这个表达是通用的。”

Tracy and three other psychologists argue10 that Barrett’s literature review caricatures their position as a rigid one-to-one mapping between six emotions and their facial movements. “I don’t know any researcher in the field of emotion science who thinks this is the case,” says Disa Sauter at the University of Amsterdam, a co-author of the reply.

特蕾西和其他三位心理学家认为，巴雷特的文献综述把他们的处境描绘成六种情绪和他们的面部动作之间严格的一一对应关系。“我不知道任何情绪科学领域的研究人员认为这是事实，”阿姆斯特丹大学的迪萨·索特说，他是这篇论文的合著者。

Sauter and Tracy think that what is needed to make sense of facial expressions is a much richer taxonomy of emotions. Rather than considering happiness as a single emotion, researchers should separate emotional categories into their components; the happiness umbrella covers joy, pleasure, compassion, pride and so on. Expressions for each might differ or overlap.

索特和特蕾西认为，要理解面部表情所需要的是一种更为丰富的情绪分类。研究人员不应将幸福视为一种单一的情感，而应将情感类别划分为不同的组成部分;幸福的伞涵盖了快乐、愉悦、同情、骄傲等等。每个表达式可能不同或重叠。

Some studies use computers to randomly generate faces. In Rachael Jack’s 2018 study, participants were asked how strongly each face matched their idea of an expression of pain or orgasm.Credit: C. Chen et al./PNAS (CC by 4.0）【gif】

一些研究使用电脑随机生成人脸。在瑞秋·杰克(Rachael Jack) 2018年的研究中，参与者被问及每张脸与他们对疼痛或高潮表情的匹配程度。来源:C. Chen等/PNAS (CC by 4.0)

At the heart of the debate is what counts as significant. In a study in which participants choose one of six emotion labels for each face they see, some researchers might consider that an option that is picked more than 20% of the time shows significant commonality. Others might think 20% falls far short. Jack argues that Ekman’s threshold was much too low. She read his early papers as a PhD student. “I kept going to my supervisor and showing him these charts from the 1960s and 1970s and every single one of them shows massive differences in cultural recognition,” she says. “There’s still no data to show that emotions are universally recognized.”

争论的核心是什么才是有意义的。在一项研究中，参与者为他们看到的每一张脸从六种情绪标签中选择一种，一些研究人员可能会认为，一个被选中超过20%的选项显示出明显的共性。其他人可能认为20%远远不够。杰克认为埃克曼的门槛太低了。她在读博士时读过他早期的论文。她说:“我不断去找我的导师，给他看这些60年代和70年代的图表，每一张都显示出文化认知上的巨大差异。”“目前还没有数据显示人们的情绪会受到影响普遍认可。

Significance aside, researchers also have to battle with subjectivity: many studies rely on the experimenter having labelled an emotion at the start of the test, so that the end results can be compared. So Barrett, Jack and others are trying to find more neutral ways to study emotions. Barrett is looking at physiological measures, hoping to provide a proxy for anger, fear or joy. Instead of using posed photographs, Jack uses a computer to randomly generate facial expressions, to avoid fixating on the common six. Others are asking participants to group faces into as many categories as they think are needed to capture the emotions, or getting participants from different cultures to label pictures in their own language.

撇开重要性不谈，研究人员还必须与主观性作斗争:许多研究依赖于实验者在测试开始时给一种情绪贴上标签，以便比较最终结果。因此，巴雷特、杰克和其他人正试图找到更中立的方法来研究情绪。巴雷特正在研究生理测量，希望能提供愤怒、恐惧或快乐的替代指标。杰克没有使用摆姿势的照片，而是用电脑随机生成面部表情，以避免专注于常见的六种表情。其他人则要求参与者根据他们认为捕捉情绪所需要的不同类别来分组，或者让来自不同文化的参与者用他们自己的语言来标记图片。

In silico sentiment

 

Software firms tend not to allow their algorithms such scope for free association. A typical artificial intelligence (AI) program for emotion detection is fed millions of images of faces and hundreds of hours of video footage in which each emotion has been labelled, and from which it can discern patterns. Affectiva says it has trained its software on more than 7 million faces from 87 countries, and that this gives it an accuracy in the 90th percentile. The company declined to comment on the science underlying its algorithm. Neurodata Lab acknowledges that there is variation in how faces express emotion, but says that “when a person is having an emotional episode, some facial configurations occur more often than a chance would allow”, and that its algorithms take this commonality into account. Researchers on both sides of the debate are sceptical of this kind of software, however, citing concerns over the data used to train algorithms and the fact that the science is still debated.

软件公司往往不允许他们的算法有这样的自由联想空间。一个典型的用于情绪检测的人工智能(Al)程序被输入数百万张人脸图像和数百小时的视频片段，其中每种情绪都被贴上了标签，并从中识别模式。affect tiva表示，他们已经对来自87个国家的700多万张面孔进行了软件培训，这使它的准确率达到了90%。该公司拒绝就其算法背后的科学原理置评。神经数据实验室承认，面部表情表达情感的方式存在差异，但该实验室表示，“当一个人情绪波动时，某些面部表情的出现频率超过了偶然性所允许的频率”，其算法也考虑了这种共性。然而，争论双方的研究人员都对这类软件持怀疑态度，理由包括对用于训练算法的数据的担忧，以及该科学仍然存在争议。

Ekman says he has challenged the firms’ claims directly. He has written to several companies — he won’t reveal which, only that “they are among the biggest software companies in the world” — asking to see evidence that their automated techniques work. He has not heard back. “As far as I know, they’re making claims for things that there is no evidence for,” he says.

埃克曼说，他已经直接质疑了这些公司的说法。他已经写信给几家公司——他不愿透露是哪家公司，只是说“它们是世界上最大的软件公司之一”——要求看看它们的自动化技术是否奏效。他没有得到回音。他说:“据我所知，他们对一些没有证据的事情提出了要求。”

Martinez concedes that automated emotion detection might be able to say something about the average emotional response of a group. Affectiva, for example, sells software to marketing agencies and brands to help predict how a customer base might react to a product or marketing campaign.

马丁内斯承认，自动化的情绪检测可能能够反映一个群体的平均情绪反应。例如，affect tiva向营销机构和品牌销售软件，以帮助预测客户基础对产品或营销活动的反应。

If this software makes a mistake, the stakes are low — an advert might be slightly less effective than hoped. But some algorithms are being used in processes that could have a big impact on people’s lives, such as in job interviews and at borders. Last year, Hungary, Latvia and Greece piloted a system for prescreening travellers that aims to detect deception by analysing microexpressions in the face.

如果这个软件出了错，风险很低——广告的效果可能会比预期的略差。但一些算法正被用于可能对人们生活产生重大影响的过程中，比如在工作面试和边境。去年，匈牙利、拉脱维亚和希腊试行了一套旅客预筛选系统，旨在通过分析面部微表情来发现欺骗。

Settling the emotional-expressions debate will require different kinds of investigation. Barrett — who is often asked to present her research to technology companies, and who visited Microsoft this month — thinks that researchers need to do what Darwin did for On the Origin of Species: “Observe, observe, observe.” Watch what people actually do with their faces and their bodies in real life — not just in the lab. Then use machines to record and analyse real-world footage.

解决情感表达的争论需要不同种类的调查。巴雷特经常被要求向科技公司介绍她的研究成果，这个月她还访问了微软公司。巴雷特认为，研究人员需要像达尔文在《物种起源》一书中所做的那样:“观察，观察，再观察。”观察人们在现实生活中，而不仅仅是在实验室里，对自己的脸和身体做些什么。然后用机器来记录和分析真实世界的镜头

Barrett thinks that more data and analytical techniques could help researchers to learn something new, instead of revisiting tired data sets and experiments. She throws down a challenge to the tech companies eager to exploit what she and many others increasingly see as shaky science. “We’re really at this precipice,” she says. “Are AI companies going to continue to use flawed assumptions or are they going do what needs to be done?”

巴雷特认为，更多的数据和分析技术可以帮助研究人员学习新的东西，而不是重新访问疲惫的数据集和实验。她向那些渴望利用她和其他许多人日益认为不可靠的科学的科技公司发起了挑战。“我们真的在悬崖边上，”她说。“Al公司是会继续使用有缺陷的假设，还是会做需要做的事情?”

 

Nature 578, 502-504 (2020)

doi: 10.1038/d41586-020-00507-5

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