Recently I had the opportunity to attend the inaugural Emotion AI Conference, organized by Seth Grimes, a leading analyst and business consultant in the areas of natural language processing (NLP), text analytics, sentiment analysis and their business applications. (Seth also organized the first Text Analytics Summit 15 years ago, which I also had the privilege to attend, and his next conference, CX Emotion, takes place July 22nd online.) The conference was attended by about 70 people (including presenters and panelists) from industry and academia in the US, Canada, and Europe.

最近，我有机会参加了由自然语言处理(NLP)，文本分析，情感分析及其业务应用领域的领先分析师和业务顾问Seth Grimes组织的首届Emotion AI Conference 。 (Seth还于15年前组织了第一次Text Analytics峰会，我也有幸参加了该峰会，他的下一次会议CX Emotion将于7月22日在线举行。)该会议有大约70人参加(包括演讲者和小组成员) )来自美国，加拿大和欧洲的工业界和学术界。

Given the conference topic, what is emotion AI, why is it relevant, and what do you need to know about it? Read on to find out, but first, some background.

给定会议主题，什么是情感AI，它为何相关，您需要了解什么？ 请继续阅读以了解相关知识，但首先要了解一些背景知识。

情绪是人类智慧和决策制定的基石 (Emotions are a cornerstone of human intelligence and decision making)

We humans are highly emotional beings, and emotions impact everything we do, even if we are not, for the most part, aware of it. They guide our attention, impact what and how we learn and remember, how we perceive ourselves and others, and ultimately how we grow as individuals and who we become. As Yann LeCun, one of the godfathers of AI and of deep learning, said: “It is impossible to have intelligence without emotions” (Quoted in Daniel McDuff presentation).

我们人类是高度情绪化的人，即使大多数时候我们没有意识到，情绪也会影响我们所做的一切。 它们引导我们的注意力，影响我们学习和记忆的方式和方式，以及我们如何看待自己和他人，最终影响我们如何成长为个人以及成为什么样的人。 正如AI和深度学习的教父之一 Yann LeCun所说：“没有情感就不可能拥有智慧”(引自Daniel McDuff的演讲)。

Emotions are highly personal, yet also social. Emotional responses in others in general and in reaction to our own actions are some of the first things we learn as infants. They are also the reason why we love storytelling. And why stories are highly effective for learning, for influencing and inspiring others, and for instigating action and change. In a well-constructed story, the plot (its narrative arc) is closely intertwined with the characters’ emotional evolution (the emotional arc), forming the double helix of narrative + emotion. Also, what is history, politics, and news if not a collection of stories — true or otherwise — and the emotions therein?

情绪是高度个人化的，但也是社交的。 总体上，他人的情绪React以及对自身行为的React是我们婴儿期学习的第一件事。 这也是我们喜欢讲故事的原因。 以及为什么故事对于学习，影响和启发他人以及鼓励行动和变革非常有效。 在一个结构良好的故事中，情节(其叙事弧)与角色的情感演变(情感弧)紧密交织在一起，形成了叙事+情感的双螺旋。 此外，历史，政治和新闻(如果不是故事的集合)(无论是真实的还是其他的)及其情感是什么？

We continue to be governed by emotions in subtle and not-so-subtle ways throughout our lives. As one of the conference presenters, Diana Lucaci of True Impact said, “People say what they think and act on how they feel.”

在我们的一生中，我们继续以微妙而不是微妙的方式受到情感的支配。 作为会议演讲者之一， True Impact的戴安娜·卢卡奇(Diana Lucaci)说：“人们说出自己的想法并根据自己的感受采取行动。”

It is not surprising then, that neuroscience research and emotional design have for years been staples in marketing and advertising and in product, service, and website design. Emotions nudge you, the user, to click on a link and buy a product or follow the emotional breadcrumbs through a website, just like its designer intended.

不足为奇的是，神经科学研究和情感设计多年来一直是营销和广告以及产品，服务和网站设计的主要内容。 就像用户的设计者所希望的那样，情绪会推动用户(用户)单击链接并购买产品或通过网站关注情绪面包屑。

情感人工智能旨在理解，复制和模拟人类情感 (Emotion artificial intelligence seeks to understand, replicate, and simulate human emotions)

In the field of artificial intelligence (AI), researchers and practitioners have likewise for years looked into ways to mimic and explore human emotions. The field took off in 1995 when MIT Media lab professor Rosalind Picard published an article entitled “Affective Computing.” It gave rise to a new discipline by the same name, from which emotional artificial intelligence spun off. The goal of emotion AI? To understand, replicate, and simulate human emotions in and by machines.

在人工智能(AI)领域，研究人员和从业人员多年来一直在研究模仿和探索人类情感的方法。 1995年 ，麻省理工学院媒体实验室教授罗莎琳德·皮卡德(Rosalind Picard)发表了一篇题为“ 情感计算 ”的文章，这一领域开始 兴起 。 它催生了同名的新学科，从中产生了情感人工智能。 情感AI的目标是什么？ 理解，复制和模拟机器中和机器上的人类情感。

Affective computing and emotion AI incorporate many technologies and application areas. The Affective Computing group at MIT, for example, “aims to bridge the gap between human emotions and computational technology.” Its projects range from “finding new ways to forecast and prevent depression before there are any clear outward signs of it; to inventing ways to help people with special needs who face communication, motivation, and emotion regulation challenges; to enabling robots and computers to receive natural emotional feedback and improve human experiences.” And this is just scratching the surface; there are many more projects, applications, and use cases on the lab’s website.

情感计算和情感AI融合了许多技术和应用领域。 例如，麻省理工学院的情感计算小组 “旨在弥合人类情感与计算技术之间的鸿沟”。 它的项目范围从“在没有明显外部迹象之前找到新的预测和预防抑郁的方法； 发明一些方法来帮助有特殊需要的人，他们面临沟通，动机和情绪调节方面的挑战； 使机器人和计算机能够接收自然的情感反馈并改善人类体验。” 这只是表面上的划痕； 实验室网站上还有更多项目，应用程序和用例。

情绪分析进入多模式 (Sentiment analysis goes multi-modal)

One of the areas of emotion AI is sentiment analysis, a field that has existed since at least the early 2000s. Sentiment analysis is usually conducted on textual data, be it emails, chats, social media posts, or survey responses. It uses NLP, computational linguistics, and text analytics to infer positive or negative attitude (aka “orientation”) of the text writer: Do they say good or bad things about your brand and your products or services?

情感AI的领域之一是情感分析，至少从2000年代初开始就存在这一领域。 情绪分析通常是针对文本数据进行的，例如电子邮件，聊天，社交媒体帖子或调查回复。 它使用NLP，计算语言学和文本分析来推断文本作者的积极或消极态度(又称“方向”)：他们对您的品牌和产品或服务说得好还是不好？

The obvious applications of sentiment analysis have been brand/reputation management (especially on social media), recommender systems, content-based filtering, semantic search, and understating user/consumer opinions and needs to inform product design, triaging customer complaints, etc.

情感分析的明显应用包括品牌/声誉管理(尤其是在社交媒体上)，推荐系统，基于内容的过滤，语义搜索以及低估了用户/消费者的意见和需要告知产品设计，对客户投诉进行分类等。

Several of the conference presentations were devoted to this topic, which, despite all the recent progress in NLP and related fields, is still hard. Not least because there is little agreement among researchers on even what constitutes basic human emotions and how many of them are there, said Bing Liu, Professor of Computer Science at the University of Illinois at Chicago.

会议的几场演讲都针对该主题，尽管NLP和相关领域最近取得了所有进展，但仍然很难。 伊利诺伊大学芝加哥分校的计算机科学教授刘兵说，这不仅是因为研究人员之间甚至就基本的人类情感以及其中存在多少情感都没有达成共识。

Emotions are also notoriously hard to identify and code (label), since they are ambiguous, shifting, overlapping, and adjacent. For example, one can feel anger, sadness, and disgust at the same time. Moreover, emotions are not always easy to pin down. And clear, unambiguous labels are important: AI — or at least the 70% of it that is known as supervised learning — depends on data that has been tagged (“annotated” or “labeled’) by humans. That’s how machines learn. (Hence “supervised”.)

众所周知，情感也难以识别和编码(标签)，因为它们是模棱两可的，转移的，重叠的和相邻的。 例如，一个人可以同时感到愤怒，悲伤和厌恶。 而且，情绪并不总是容易被抑制的。 清晰，明确的标签很重要：人工智能-或至少70％的被称为监督学习-依赖于人类已经标记(“注释”或“标记”)的数据。 机器就是这样学习的。 (因此为“监督”。)

Then there is complexity behind how emotions are conveyed, explained Professor Liu. When speaking, emotions are communicated through a broad range of linguistic and paralinguistic cues, such as intonation, facial expressions, body movements, gestures and posture, and bio-physical signals (sweating, skin flushing, etc.). In writing, they are signaled by punctuation, capitalization, emoticons, and other creative expressions, for example, word lengthening (e.g. “soooo slow” or “so sloooow”). And that is in addition to word choices and grammar!

刘教授解释说，然后是如何表达情感的背后是复杂的。 说话时，情绪通过广泛的语言和副语言提示进行交流，例如语调，面部表情，身体动作，手势和姿势以及生物物理信号(出汗，皮肤潮红等)。 在书面形式中，标点符号，大写字母，表情符号和其他创造性表达(例如，单词加长(例如“ soooo slow”或“ so sloooow”))以信号形式发出信号。 这是单词选择和语法的补充！

There are also cultural differences in how people convey emotions. To complicate things further, there is a phenomenon known as the “cognitive gap.” What people say and how they truly feel do not always match, for multiple reasons: they are trying to be polite or avoid hurting other people’s feelings. Or maybe they are trying to keep their emotions to themselves.

人们传达情感的方式也存在文化差异。 更复杂的是，有一种现象称为“认知鸿沟”。 人们说的话和他们的真实感受并不总是相符的，原因有很多：他们试图礼貌或避免伤害他人的感受。 也许他们正在努力保持自己的情绪。

Professor Liu said that context and multi-modal data may be helpful to resolve many such ambiguities. And in fact, with the advancement of biometrics and wearables, the field has expanded to analyzing emotions from sensor data, which includes heart rate, temperature, brain waves, blood flow, and muscle bio-signals, as well as voice, facial expressions, images, and video.

刘教授说，情境和多模式数据可能有助于解决许多此类歧义。 实际上，随着生物识别技术和可穿戴设备的发展，该领域已扩展为从传感器数据分析情绪，这些数据包括心率，温度，脑电波，血流和肌肉生物信号以及语音，面部表情，图片和视频。

This trend of leveraging sensors will continue, predicted strategist and “tech emotionographer” Pamela Pavliscak (who also teaches at Pratt Institute), with the exception of, perhaps, facial recognition technologies (FRTs) and haptic/touch data. FRTs recently came under fire because of privacy concerns. (Some of my previous notes on this topic can be found here.) And touch data is a “no-go” for obvious reasons: the COVID-19 pandemic.

预测策略家和“技术情感学家” Pamela Pavliscak (也曾在Pratt Institute任教)的人预计，利用传感器的趋势将继续下去，除了面部识别技术(FRT)和触觉/触摸数据外。 由于隐私问题，FRT最近遭到抨击。 (有关此主题的我以前的一些笔记可以在这里找到。)出于明显的原因，触摸数据是“不可行的”：COVID-19大流行。

可穿戴设备可以用来识别情绪吗？ (Can wearables be used to identify emotions?)

A meta-analysis by Professor Przemysław Kazienko of Wroclaw University of Science and Technology focused on wearables and tried to answer the following question: “Can they be used to identify emotions in everyday life?” If we could do that, we could, for example, improve health and well-being and clinical outcomes of patients suffering from diseases that have mood altering effects. (The example he used is kidney dysfunction.)

弗罗茨瓦夫科技大学的PrzemysławKazienko教授进行的荟萃分析着重于可穿戴设备，并试图回答以下问题：“它们可用于识别日常生活中的情绪吗？” 如果我们能够做到这一点，例如，我们可以改善患有情绪改变效应的疾病的患者的健康和福祉以及临床结果。 (他使用的例子是肾脏功能障碍。)

We could also use wearables for stress control, mental health, and autism. One such app developed at the MIT Media Lab monitors a person’s heartbeat to detect whether they are experiencing negative emotions such as stress, pain, or frustration and it releases a scent to help the wearer to cope.

我们还可以将可穿戴设备用于压力控制，心理健康和自闭症。 麻省理工学院媒体实验室开发的一款这样的应用程序可以监控人的心跳，以检测他们是否正遭受诸如压力，疼痛或沮丧之类的负面情绪，它会释放出一种气味，帮助穿戴者应对。

And of course, we could use emotions detected from wearables for “good old” personalization and product/service improvement: from online content and product recommendations, to virtual assistants and gaming experience. (Although I am concerned about privacy implications for several of the use cases Professor Kazienko mentioned.)

当然，我们可以将从可穿戴设备中检测到的情绪用于“过时的”个性化和产品/服务的改进：从在线内容和产品推荐，到虚拟助手和游戏体验。 (尽管我担心Kazienko教授提到的几个用例对隐私的影响。)

Emotion data from wearable devices can also be used to prevent car accidents (when the driver gets drowsy, for example), to track students’ attention and hence academic success, and to improve social interactions, among several other things, he said.

他说，来自可穿戴设备的情绪数据还可以用于预防交通事故(例如，驾驶员困倦时)，跟踪学生的注意力并因此获得学术上的成功，以及改善社交互动等。

Ms. Pavliscak illustrated the latter with US+ project by Lauren Lee McCarthy and Kyle McDonald, which is “a Google Hangout video chat app that uses audio, facial expression, and linguistic analysis to optimize conversations.” The app analyzes what users say and whether they use common vocabulary and sentence structures, which we humans tend to do as a conversation unfolds. (This is known as “linguistic style matching.”) For each of the chat participants the app displays a quick visualization and pop-up notifications, for example: “Stop talking about yourself so much” or “What are you hiding? Clare is speaking much more honestly.” It can even automute a participant “when the conversation gets out of balance.”

Pavliscak女士在Lauren Lee McCarthy和Kyle McDonald的US +项目中举例说明了后者，该项目是“一种Google Hangout视频聊天应用程序，它使用音频，面部表情和语言分析来优化对话。” 该应用程序分析用户所说的内容以及他们是否使用常见的词汇和句子结构，随着对话的进行，我们人类倾向于这样做。 (这被称为“语言风格匹配”。)对于每个聊天参与者，该应用都会显示快速的可视化和弹出通知，例如：“停止谈论太多自己”或“您隐藏了什么？ 克莱尔讲的话要诚实得多。” 当对话失去平衡时，它甚至可以使参与者自动化。

Image: US+ project, accessed May 12, 2020

图片： US +项目 ，2020年5月12日访问

There were many more fascinating applications of emotional data and emotion analysis in Ms. Pavliscak’s keynote, “Design for an Emotionally Intelligent Future.” The video recordings of all conference sessions are available on the conference site for $200, with a discounted price of $100 for additional viewers from your organization, and I highly recommend that you watch them.

帕夫利斯卡克(Pavliscak)女士在主题演讲“为情感智能的未来而设计”中还有更多有趣的情感数据和情感分析应用程序。 会议期间的所有会议录像都可以在会议站点上以200美元的价格购买，对于组织中其他观众来说则是100美元的折扣价，我强烈建议您观看。

Going back to the question, “Can wearables be used to identify emotions in everyday life?”, Professor Kazienko’s conclusion is: “Emotion recognition with wearables is the future of personalized affective computing.” However, he continued, we need more field studies, more and better data, active collaboration between researchers, a common model on how to classify emotions, data and code sharing, and several other things to improve recognition quality and reproducibility.

回到问题“可穿戴设备可用于识别日常生活中的情感吗？”，卡齐坚科教授的结论是：“可穿戴设备的情感识别是个性化情感计算的未来。” 但是，他继续说，我们需要更多的领域研究，更多更好的数据，研究人员之间的积极合作，关于如何对情感进行分类的通用模型，数据和代码共享以及其他几项事情，以提高识别质量和可重复性。

利用情感使AI听起来像人一样 (Using emotions to make AI sound (and act like a) human)

As more of our experiences and daily lives are mediated by artificial intelligence, we increasingly want AI to look, “feel,” sound, and (re)act like a human. Perhaps, because humans are in general less frightening than machines. (Or are they?) Or, maybe because our imagination is constrained by our own physiological characteristics — similar to the Sapir-Whorf hypothesis which postulates that the structure of a language affects how its speakers perceive the world. (Add emotions to the mix, and it becomes quite complex very quickly.)

随着越来越多的经验和日常生活受到人工智能的影响，我们越来越希望AI看起来，“感觉”到声音并(像人类一样)做出React。 也许是因为人类通常没有机器那么可怕。 (或者是？)或者，也许是因为我们的想象力受到了我们自身生理特征的限制-类似于Sapir-Whorf假说 ，该假说假设一种语言的结构会影响其说话者对世界的感知。 (将情绪添加到混合中，它很快就会变得非常复杂。)

Or maybe because we humans like to remodel the world to our image and liking. Have you seen Atlas the robot by Boston Robotics performing gymnastics? In all his mechanical glory, he is sort-of alright, no? But the company’s headless mechanical dogs on the other hand are rather unsettling…

也许是因为我们人类喜欢按照自己的形象改造世界并喜欢我们。 您看到波士顿机器人公司(Boston Robotics) 进行体操 的机器人阿特拉斯(Atlas)吗？ 在他所有的机械荣耀中，他还算不错，不是吗？ 但是另一方面，该公司的无头机械狗却令人不安。

Now think about how you would like Atlas to sound if he could talk. Obviously, he can’t (yet), but we increasingly interact with chatbots and virtual assistants. And if we could imbue machines with empathy, they could provide better consumer and brand experience, and by doing this build familiarity, stronger bonds, and deeper trust, said Greg Hedges, Chief Experience Officer at Rain, a consultancy. His firm creates “emotionally-intelligent voice experiences” for brands such as Nike, Starbucks,Tiffany, Tide, and Sesame Street.

现在考虑一下，如果Atlas可以讲话，您希望他如何听起来。 显然，他还不能(但是)，但是我们越来越多地与聊天机器人和虚拟助手互动。 咨询公司Rain的首席经验官Greg Hedges表示，如果我们可以使机器充满同情心，它们可以提供更好的消费者和品牌体验，并以此来建立熟悉度，更牢固的联系和更深的信任。 他的公司为耐克，星巴克，蒂芙尼，泰德和芝麻街等品牌创造了“情感智能的语音体验”。

从聊天机器人到“情感聊天机器”？ (From chatbots to “emotional chatting machines”?)

Since potentially significant business benefits can be gained from emotionally aware AI it is perhaps not surprising that “emotionally intelligent” chatbots are a hot research area. Professor Liu mentioned above is working “to create chatbots that can perceive and express emotions and learn continually during conversations.” He calls them “Emotional Chatting Machines.”

由于可以从具有情感意识的AI中获得潜在的重大业务收益，因此“具有情感智能”的聊天机器人成为热门研究领域也就不足为奇了。 上面提到的刘教授正在努力“创建可以感知和表达情感并在对话中不断学习的聊天机器人。” 他称它们为“情感聊天机器”。

“Emotional intelligence is a vital aspect of human intelligence,” he said, and moreover, research shows that emotion in dialogue systems can enhance user satisfaction. Chatbots that express empathy decrease user frustration and stress. They lead to fewer breakdowns in dialogues. And they also inspire people to cooperate rather than rage about “stupid machines,” he continued.

他说：“情绪智力是人类智力的重要方面。”此外，研究表明，对话系统中的情绪可以提高用户满意度。 表达同情心的聊天机器人可以减少用户的沮丧感和压力。 它们导致对话的故障更少。 他继续说，他们还激励人们合作而不是为“愚蠢的机器”而愤怒。

“Systems that mimic human style are more natural to interact with,” said Daniel McDuff, Principal Researcher at Microsoft. His team is building embodied agents, among other things. (See also the last section for details on his other work.) We humans constantly adapt to each other and this adaptation creates social cohesion. When people interact with a virtual reality agent over time, they similarly try to adapt to its style, he said. And so virtual agents and chatbots will need to adapt to humans.

微软首席研究员丹尼尔·麦克杜夫(Daniel McDuff)说：“模仿人类风格的系统更自然地可以与之交互。” 他的团队正在建立具体的代理商。 (有关他的其他作品的详细信息，另请参阅最后一部分。)我们人类不断相互适应，这种适应创造了社会凝聚力。 他说，当人们随着时间与虚拟现实代理互动时，他们同样会尝试适应其风格。 因此，虚拟代理和聊天机器人将需要适应人类。

Emotional chatbots in business could mean more satisfied customers and citizens and perhaps lower costs, as chatbots will be able to take care of most inquiries and free human operators for less common or more complex interactions. Especially in these times of crisis. More than two months after the WHO declared the novel coronavirus disease a global pandemic, telephone lines at banks, insurance companies, major retailers, and government offices remain jammed. IBM, who offered its Watson Assistant to help deploy chatbots to governments saw a 40% increase in traffic to its chatbot platform. Google promptly launched its own Rapid Response Virtual Agent to “[q]uickly build and implement a customized Contact Center AI virtual agent to respond to questions your customers have due to COVID-19 over chat, voice, and social channels.”

商业上的情感聊天机器人可能意味着更满意的客户和市民，并且也许会降低成本，因为聊天机器人将能够处理大多数查询，并让人工操作者可以进行较不常见或较复杂的交互。 特别是在危机时期。 在世界卫生组织宣布新的冠状病毒病在全球大流行之后两个多月，银行，保险公司，主要零售商和政府办公室的电话线仍然拥堵。 IBM提供了其Watson Assistant来帮助向政府部署聊天机器人，其聊天机器人平台的流量增加了40％ 。 GoogleSwift启动了自己的Rapid Response虚拟代理，以“ 快速构建并实施定制的Contact Center AI虚拟代理，以通过聊天，语音和社交渠道来响应客户因COVID-19而引起的问题。”

还没… (Not yet…)

But beware vendor promises. Chatbots in general require extensive training and/or scripting, and so the costs may not be significantly lower — depending on organization size and number, type and complexity of use cases, and other parameters.

但是要当心供应商的承诺。 聊天机器人通常需要大量的培训和/或脚本编写，因此成本可能不会大大降低-取决于组织的规模和数量，用例的类型和复杂性以及其他参数。

“Emotionally aware” chatbots add another layer of complexity and challenges to chatbot technologies. Here are a few mentioned by Professor Liu:

“具有情感意识”的聊天机器人增加了聊天机器人技术的复杂性和挑战性。 以下是刘教授提到的一些内容：

• Emotion-labeled data that such emotionally aware chatbots and VAs rely on is hard to obtain at the scale needed to train the machines.
  这样的具有情感意识的聊天机器人和VA所依赖的带有情感标签的数据很难以训练机器所需的规模获得。
• Annotation (i.e. labels), where available, are subjective and classification may be inaccurate. And with AI, just like with other data-dependent applications, “garbage in, garbage out.”
  注释(即标签)(如果可用)是主观的，分类可能不准确。 与AI一样，就像其他依赖数据的应用程序一样，“垃圾填入，垃圾填入”。
• An emotionally enabled bot must also be able to balance understanding the emotions of the speaker with whom it is conversing with generating its own emotional and linguistics responses in return. This is really hard to do because there is a clear dependency between the two processes. And because they need to happen almost simultaneously.
  具有情感功能的机器人还必须能够在理解与之交谈的说话者的情感与产生自己的情感和语言学响应之间取得平衡。 这确实很难做到，因为这两个过程之间存在明显的依赖性。 并且因为它们需要几乎同时发生。
• All this becomes even harder in what is know as “open domain,” unconstrained by bot use cases, application areas, industry, etc.
  在所谓的“开放域”中，这一切都变得更加困难，不受机器人用例，应用领域，行业等的限制。

Professor Liu’s takeaway on the state of emotionally aware chatbots: “Chatting with emotions is vital for dialogue systems,” but there are massive quality issues, and because of that, the technology is not ready for prime time.

刘教授对具有情感意识的聊天机器人的状态进行了总结：“与情感聊天对于对话系统至关重要。”但是仍然存在大量的质量问题，因此，该技术还没有准备就绪。

He said, there are examples of deployed chatbots with emotions, but such bots are mainly based on rules — they are scripted, so there is no intelligence there. These bots are also domain-constrained: they are typically used in narrow cases such as customer service or as emotional companions. To create more human-like “emotional chatting machines,” he said, we will need to get better at multi-modal emotion detection and generation (using sensor data mentioned earlier).

他说，有部署带有情感的聊天机器人的示例，但是这类机器人主要基于规则-它们是脚本编写的，因此那里没有情报。 这些漫游器也受域限制：它们通常在狭窄的情况下使用，例如客户服务或情感伴侣。 他说，要创建更多类似人类的“情感聊天机器”，我们将需要更好地进行多模式情感检测和生成(使用前面提到的传感器数据)。

情绪启发式机器学习 (Emotion-inspired machine learning)

The most intriguing session at the conference was the keynote by Daniel McDuff, Principal Researcher at Microsoft, who asked the question “How can machine learning leverage emotions to learn and explore?” He spoke about “visceral machines” — the idea that machine learning/AI systems should have some emotional mechanisms similar to those of humans. Or, at least that they should be able to model emotions for the reasons mentioned above: we want technology to interact with us humans as if it were human.

大会上最有趣的会议是微软首席研究员丹尼尔·麦克杜夫(Daniel McDuff)的主题演讲，他提出了一个问题：“机器学习如何利用情绪来学习和探索？” 他谈到“内脏机器”，即机器学习/人工智能系统应该具有与人类相似的情感机制。 或者，至少由于上述原因，他们应该能够为情绪建模：我们希望技术能够像人类一样与人类互动。

But there is another reason. Emotions, said Mr. McDuff, help us to understand and explore the world. They are fundamental to understanding what it means to take risks, to achieve positive outcomes, and so on. As we interact with the world, he continued, we receive either positive or negative response that guides our further actions. What if we could incorporate emotions into machine learning systems to help inform them? (And to improve their performance.)

但是还有另一个原因。 麦克杜夫先生说，情感可以帮助我们理解和探索世界。 它们对于了解冒险，取得积极成果等的意义至关重要。 他继续说，当我们与世界互动时，我们会收到正面或负面的回应，这将指导我们采取进一步的行动。 如果我们可以将情绪整合到机器学习系统中以帮助告知他们该怎么办？ (并提高其性能。)

One of the examples he showed involved using drivers’ heartbeats to guide machine learning. (Heart rate is one of the ways we express and experience emotions. Others include at the biological level: changes in body temperature; pupil and blood vessel dilation/constriction and changes in blood flow, breathing, and brain waves; increased/ decreased production of saliva, hormones and digestive enzymes, and so on.) Mr. McDuff’s team used drivers’ emotional responses as expressed through their heartbeat in training a neural network model to drive a car. (In addition to other data, I presume.) The result? The model was able to drive a vehicle longer than the state-of-the-art model without emotions.

他展示的示例之一涉及使用驾驶员的心跳来指导机器学习。 (心率是我们表达和体验情感的方法之一。其他生物学方面的方法包括：体温变化；瞳Kong和血管的扩张/收缩以及血流，呼吸和脑电波的变化；产生/减少情绪的产生。麦克杜夫的团队利用驾驶员心跳所表达的情绪React来训练神经网络模型来驾驶汽车。 (除了其他数据，我想。)结果是？ 该模型能够比没有噪音的最新模型驾驶车辆更长的时间。

Image: Daniel McDuff, “Emotion Inspired (Machine) Leaning”, keynote at The Emotion AI Conference, May 5, 2020.

图片：Daniel McDuff，“情感启发(机器)学习”，在2020年5月5日举行的Emotion AI Conference上的主题演讲。

His team also looked at applying emotions (or rather human physiological response data) to teach machines to avoid car crashes, in this particular case, using human drivers’ facial expressions. It seems that it worked too. Their next project is looking to “combine various emotional response signals, risk-aversity and curiosity to nudge machines to explore more and do so in a safe way.”

他的团队还研究了如何运用情绪(或更确切地说是人类的生理React数据)来教机器，以防止在这种情况下使用人类驾驶员的面部表情来防止车祸。 似乎它也起作用。 他们的下一个项目旨在“结合各种情绪React信号，风险平均水平和好奇心，以推动机器进行更多探索并以一种安全的方式进行。”

我们的看法 (Our take)

In Russian there is a proverb which, loosely translated, says that when we meet a person, we judge them by their dress, and when they leave, we judge them by their intelligence. (It is roughly equivalent in English to “You only get one chance to make a first impression” and “You can’t judge a book by its cover.”) Interestingly enough — and this is perhaps one of the rare occasions when ancient folk wisdom does not ring true — people do not usually remember what you wore or said or did, but they do remember how you made them feel — in other words, the emotions you stirred within them.

俄语中有一句谚语，松散地翻译成一句话：当我们遇到一个人时，我们根据他们的着装来判断他们，而当他们离开时，我们根据他们的才智来判断他们。 (在英语中，这相当于“您只有一次机会给人留下第一印象”和“您不能凭其封面来判断一本书。”)有趣的是，这也许是古代民间罕见的场合之一智慧不能说真的-人们通常不会记住您的穿衣，说话或做过的事情，但是他们确实会记住您的感受 -换句话说，就是您在其中产生的情感。

Emotions impact all aspects of our intelligence and behavior, at the individual and group level. In aggregate, they determine the behavior of the markets, social cohesion, the health of local and global economies, and the progress of nations. The Nordics, for example, are some of the happiest nations in the world, and they are among the richest, too.

情绪会影响我们个人和团体层面的智力和行为的各个方面。 总体而言，它们决定市场的行为，社会凝聚力，本地和全球经济的健康状况以及国家的进步 。 例如，北欧人是世界上最幸福的国家之一 ，它们也是最富有的国家之一 。

So, can we take emotions — this fundamental quality that makes us human — decode it, and re-code it into machines? Well, look at language and speech technologies or computer vision. Significant successes have been achieved in the past decade as a result of big data, cheap compute power, powerful hardware, rapidly improving algorithms, and collaboration/open-source software.

那么，我们能否接受情感-这种使我们成为人类的基本素质-将其解码并重新编码为机器？ 好吧，看看语言和语音技术或计算机视觉。 大数据，廉价的计算能力，强大的硬件，快速改进的算法以及协作/开源软件在过去十年中取得了巨大的成功。

However, if the field of AI is any indication, we may never be able to create truly emotionally aware machines. We just do not understand emotions — and intelligence — well enough. What started 60 years ago with an ambitious goal to simulate human intelligence and where the field’s creators thought “a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer,” evolved into something more narrow and brittle: applied AI (or narrow AI), as the field is now known.

但是，如果AI领域有任何迹象，我们可能永远无法创建真正具有情感意识的机器。 我们只是对情感和智力不够了解。 演变始于60年前，其宏伟目标是模拟人类智能，并且该领域的创造者认为： “如果精心挑选的一组科学家一起研究一个夏天，就可以在一个或多个问题上取得重大进步”。变得更加狭窄和脆弱：应用AI(或狭窄AI)，这一领域现已广为人知。

Does this mean that emotion AI is too hard, and that we should just give up? Despite more limited scope, many narrow AI applications generate significant business benefits and human value, and emotion AI has the potential do that, too, as The Emotion AI conference has illustrated. As long as we are aware of the challenges the field is facing and have rational expectations. But that would be so unlike us, humans — to be rational — that is.

这是否意味着情感AI太难了，我们应该放弃？ 尽管范围有限，但许多狭窄的AI应用程序仍可产生显着的业务收益和人文价值，情感AI也有潜力做到这一点，正如Emotion AI会议所说明的那样。 只要我们意识到该领域面临的挑战并抱有合理的期望。 但这将与我们如此不同，人类是理性的。

下一步 (Next steps)

To learn more about emotion AI, check out the conference website, watch the recordings, attend the July CX Emotion conference, or give us a call to learn more about this field and how to get started with AI and machine learning in the context of your organization.

要了解有关情感AI的更多信息，请访问会议网站 ， 观看录音 ，参加7月CX Emotion会议或致电我们以了解更多有关该领域的知识以及如何在您的背景下开始使用AI和机器学习组织。