在过去的十年中，计算机愿景，旨在了解视觉世界的人工智能分支，从简单地识别图像中的物体来描述图片，回答有关图像的问题，以及围绕物理空间的机器人操纵甚至产生新的视觉内容。随着这些任务和应用程序的现代化，因此依赖更多数据，用于模型培训或评估。在本章中，我们展示了新颖的互动策略可以为计算机愿景提供新的数据收集和评估。首先，我们提出了一种众群界面，以通过数量级加速付费数据收集，喂养现代视觉模型的数据饥饿性质。其次，我们探索使用自动社交干预措施增加志愿者贡献的方法。第三，我们开发一个系统，以确保人类对生成视觉模型的评估是可靠的，实惠和接地在心理物理学理论中。我们结束了人机互动的未来机会，以帮助计算机愿景。

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

情绪分析中最突出的任务是为文本分配情绪，并了解情绪如何在语言中表现出来。自然语言处理的一个重要观察结果是，即使没有明确提及情感名称，也可以通过单独参考事件来隐式传达情绪。在心理学中，被称为评估理论的情感理论类别旨在解释事件与情感之间的联系。评估可以被形式化为变量，通过他们认为相关的事件的人们的认知评估来衡量认知评估。其中包括评估事件是否是新颖的，如果该人认为自己负责，是否与自己的目标以及许多其他人保持一致。这样的评估解释了哪些情绪是基于事件开发的，例如，新颖的情况会引起惊喜或不确定后果的人可能引起恐惧。我们在文本中分析了评估理论对情绪分析的适用性，目的是理解注释者是否可以可靠地重建评估概念，如果可以通过文本分类器预测，以及评估概念是否有助于识别情感类别。为了实现这一目标，我们通过要求人们发短信描述触发特定情绪并披露其评估的事件来编译语料库。然后，我们要求读者重建文本中的情感和评估。这种设置使我们能够衡量是否可以纯粹从文本中恢复情绪和评估，并为判断模型的绩效指标提供人体基准。我们将文本分类方法与人类注释者的比较表明，两者都可以可靠地检测出具有相似性能的情绪和评估。我们进一步表明，评估概念改善了文本中情绪的分类。

社交媒体有可能提供有关紧急情况和突然事件的及时信息。但是，在每天发布的数百万帖子中找到相关信息可能很困难，并且开发数据分析项目通常需要时间和技术技能。这项研究提出了一种为分析社交媒体的灵活支持的方法，尤其是在紧急情况下。引入了可以采用社交媒体分析的不同用例，并讨论了从大量帖子中检索信息的挑战。重点是分析社交媒体帖子中包含的图像和文本，以及一组自动数据处理工具，用于过滤，分类和使用人类的方法来支持数据分析师的内容。这种支持包括配置自动化工具的反馈和建议，以及众包收集公民的投入。通过讨论Crowd4SDG H2020欧洲项目中开发的三个案例研究来验证结果。

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

随着AI系统表现出越来越强烈的预测性能，它们的采用已经在许多域中种植。然而，在刑事司法和医疗保健等高赌场域中，由于安全，道德和法律问题，往往是完全自动化的，但是完全手工方法可能是不准确和耗时的。因此，对研究界的兴趣日益增长，以增加人力决策。除了为此目的开发AI技术之外，人民AI决策的新兴领域必须采用实证方法，以形成对人类如何互动和与AI合作做出决定的基础知识。为了邀请和帮助结构研究努力了解理解和改善人为 - AI决策的研究，我们近期对本课题的实证人体研究的文献。我们总结了在三个重要方面的100多篇论文中的研究设计选择：（1）决定任务，（2）AI模型和AI援助要素，以及（3）评估指标。对于每个方面，我们总结了当前的趋势，讨论了现场当前做法中的差距，并列出了未来研究的建议。我们的调查强调了开发共同框架的需要考虑人类 - AI决策的设计和研究空间，因此研究人员可以在研究设计中进行严格的选择，研究界可以互相构建并产生更广泛的科学知识。我们还希望这项调查将成为HCI和AI社区的桥梁，共同努力，相互塑造人类决策的经验科学和计算技术。

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.

通过整合人类的知识和经验，人在循环旨在以最低成本培训准确的预测模型。人类可以为机器学习应用提供培训数据，并直接完成在基于机器的方法中对管道中计算机中的难以实现的任务。在本文中，我们从数据的角度调查了人类循环的现有工作，并将它们分为三类具有渐进关系：（1）从数据处理中提高模型性能的工作，（2）通过介入模型培训提高模型性能，（3）系统的设计独立于循环的设计。使用上述分类，我们总结了该领域的主要方法;随着他们的技术优势/弱点以及自然语言处理，计算机愿景等的简单分类和讨论。此外，我们提供了一些开放的挑战和机遇。本调查打算为人类循环提供高级别的摘要，并激励有兴趣的读者，以考虑设计有效的循环解决方案的方法。

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.

构建用于仇恨语音检测的基准数据集具有各种挑战。首先，因为仇恨的言论相对少见，随机抽样对诠释的推文是非常效率的发现仇恨。为了解决此问题，先前的数据集通常仅包含匹配已知的“讨厌字”的推文。然而，将数据限制为预定义的词汇表可能排除我们寻求模型的现实世界现象的部分。第二个挑战是仇恨言论的定义往往是高度不同和主观的。具有多种讨论仇恨言论的注释者可能不仅可能不同意彼此不同意，而且还努力符合指定的标签指南。我们的重点识别是仇恨语音的罕见和主体性类似于信息检索（IR）中的相关性。此连接表明，可以有效地应用创建IR测试集合的良好方法，以创建更好的基准数据集以进行仇恨语音。为了智能和有效地选择要注释的推文，我们应用{\ em汇集}和{em主动学习}的标准IR技术。为了提高注释的一致性和价值，我们应用{\ EM任务分解}和{\ EM注释器理由}技术。我们在Twitter上共享一个用于仇恨语音检测的新基准数据集，其提供比以前的数据集更广泛的仇恨覆盖。在这些更广泛形式的仇恨中测试时，我们还表现出现有检测模型的准确性的戏剧性降低。注册器理由我们不仅可以证明标签决策证明，而且还可以在建模中实现未来的双重监督和/或解释生成的工作机会。我们的方法的进一步细节可以在补充材料中找到。

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (StyleGAN2:FFHQ) from nondeepfake images (FFHQ), and to assess the effectiveness of simple interventions intended to improve detection accuracy. Using an online survey, 280 participants were randomly allocated to one of four groups: a control group, and 3 assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake and 50 real images of human faces. Participants were asked if each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Participants' confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals participants consistently found certain images harder to label correctly, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85% and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat.

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

深度学习属于人工智能领域，机器执行通常需要某种人类智能的任务。类似于大脑的基本结构，深度学习算法包括一种人工神经网络，其类似于生物脑结构。利用他们的感官模仿人类的学习过程，深入学习网络被送入（感官）数据，如文本，图像，视频或声音。这些网络在不同的任务中优于最先进的方法，因此，整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如，只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集，用于搜索术语“深度学习”，其中大约90％来自过去三年。因此，对深度学习领域的完全概述已经不可能在不久的将来获得，并且在不久的将来可能会难以获得难以获得子场的概要。但是，有几个关于深度学习的综述文章，这些文章专注于特定的科学领域或应用程序，例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础，这一贡献的目的是提供对不同科学学科的深度学习的第一个高级，分类的元调查。根据底层数据来源（图像，语言，医疗，混合）选择了类别（计算机愿景，语言处理，医疗信息和其他工程）。此外，我们还审查了每个子类别的常见架构，方法，专业，利弊，评估，挑战和未来方向。