Ai Magazine最新文献

{"title":"Exploring the role of simulator fidelity in the safety validation of learning-enabled autonomous systems","authors":"Ali Baheri","doi":"10.1002/aaai.12141","DOIUrl":"https://doi.org/10.1002/aaai.12141","url":null,"abstract":"<p>This article presents key insights from the New Faculty Highlights talk given at AAAI 2023, focusing on the crucial role of fidelity simulators in the safety evaluation of learning-enabled components (<span>LECs</span>) within safety-critical systems. With the rising integration of <span>LECs</span> in safety-critical systems, the imperative for rigorous safety and reliability verification has intensified. Safety assurance goes beyond mere compliance, forming a foundational element in the deployment of <span>LECs</span> to reduce risks and ensure robust operation. In this evolving field, simulations have become an indispensable tool, and fidelity's role as a critical parameter is increasingly recognized. By employing multifidelity simulations that balance the needs for accuracy and computational efficiency, new paths toward comprehensive safety validation are emerging. This article delves into our recent research, emphasizing the role of simulation fidelity in the validation of <span>LECs</span> in safety-critical systems.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"453-459"},"PeriodicalIF":0.9,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-aware AI —A foundational framework for human–AI interaction","authors":"Sarath Sreedharan","doi":"10.1002/aaai.12142","DOIUrl":"https://doi.org/10.1002/aaai.12142","url":null,"abstract":"<p>We are living through a revolutionary moment in AI history. Users from diverse walks of life are adopting and using AI systems for their everyday use cases at a pace that has never been seen before. However, with this proliferation, there is also a growing recognition that many of the central open problems within AI are connected to how the user interacts with these systems. To name two prominent examples, consider the problems of explainability and value alignment. Each problem has received considerable attention within the wider AI community, and much promising progress has been made in addressing each of these individual problems. However, each of these problems tends to be studied in isolation, using very different theoretical frameworks, while a closer look at each easily reveals striking similarities between the two problems. In this article, I wish to discuss the framework of human-aware AI (HAAI) that aims to provide a unified formal framework to understand and evaluate human–AI interaction. We will see how this framework can be used to both understand explainability and value alignment and how the framework also lays out potential novel avenues to address these problems.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"460-466"},"PeriodicalIF":0.9,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of consumer health question answering systems","authors":"Anuradha Welivita,&nbsp;Pearl Pu","doi":"10.1002/aaai.12140","DOIUrl":"https://doi.org/10.1002/aaai.12140","url":null,"abstract":"<p>Consumers are increasingly using the web to find answers to their health-related queries. Unfortunately, they often struggle with formulating the questions, further compounded by the burden of having to traverse long documents returned by the search engine to look for reliable answers. To ease these burdens for users, automated consumer health question answering systems try to simulate a human professional by refining the queries and giving the most pertinent answers. This article surveys state-of-the-art approaches, resources, and evaluation methods used for automatic consumer health question answering. We summarize the main achievements in the research community and industry, discuss their strengths and limitations, and finally come up with recommendations to further improve these systems in terms of quality, engagement, and human-likeness.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"482-507"},"PeriodicalIF":0.9,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-definition map automatic annotation system based on active learning","authors":"Chao Zheng,&nbsp;Xu Cao,&nbsp;Kun Tang,&nbsp;Zhipeng Cao,&nbsp;Elena Sizikova,&nbsp;Tong Zhou,&nbsp;Erlong Li,&nbsp;Ao Liu,&nbsp;Shengtao Zou,&nbsp;Xinrui Yan,&nbsp;Shuqi Mei","doi":"10.1002/aaai.12139","DOIUrl":"https://doi.org/10.1002/aaai.12139","url":null,"abstract":"<p>As autonomous vehicle technology advances, high-definition (HD) maps have become essential for ensuring safety and navigation accuracy. However, creating HD maps with accurate annotations demands substantial human effort, leading to a time-consuming and costly process. Although artificial intelligence (AI) and computer vision (CV) algorithms have been developed for prelabeling HD maps, a significant gap remains in accuracy and robustness between AI-based methods and traditional manual pipelines. Additionally, building large-scale annotated datasets and advanced machine learning algorithms for AI-based HD map labeling systems can be resource-intensive. In this paper, we present and summarize the Tencent HD Map AI (THMA) system, an innovative end-to-end, AI-based, active learning HD map labeling system designed to produce HD map labels for hundreds of thousands of kilometers while employing active learning to enhance product iteration. Utilizing a combination of supervised, self-supervised, and weakly supervised learning, THMA is trained directly on massive HD map datasets to achieve the high accuracy and efficiency required by downstream users. Deployed by the Tencent Map team, THMA serves over 1000 labeling workers and generates more than 30,000 km of HD map data per day at its peak. With over 90% of Tencent Map's HD map data labeled automatically by THMA, the system accelerates traditional HD map labeling processes by more than tenfold, significantly reducing manual annotation burdens and paving the way for more efficient HD map production.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"418-430"},"PeriodicalIF":0.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the spirit of a norm: Challenges for norm-learning agents","authors":"Thomas Arnold,&nbsp;Matthias Scheutz","doi":"10.1002/aaai.12138","DOIUrl":"10.1002/aaai.12138","url":null,"abstract":"<p>Social and moral norms are a fabric for holding human societies together and helping them to function. As such they will also become a means of evaluating the performance of future human–machine systems. While machine ethics has offered various approaches to endowing machines with normative competence, from the more logic-based to the more data-based, none of the proposals so far have considered the challenge of capturing the “spirit of a norm,” which often eludes rigid interpretation and complicates doing the right thing. We present some paradigmatic scenarios across contexts to illustrate why the spirit of a norm can be critical to make explicit and why it exposes the inadequacies of mere data-driven “value alignment” techniques such as reinforcement learning <i>RL</i> for interactive, real-time human–robot interaction. Instead, we argue that norm learning, in particular, learning to capture the spirit of a norm, requires combining common-sense inference-based and data-driven approaches.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"524-536"},"PeriodicalIF":0.9,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135928142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groups of experts often differ in their decisions: What are the implications for AI and machine learning? A commentary on Noise: A Flaw in Human Judgment, by Kahneman, Sibony, and Sunstein (2021)","authors":"Derek H. Sleeman,&nbsp;Ken Gilhooly","doi":"10.1002/aaai.12135","DOIUrl":"10.1002/aaai.12135","url":null,"abstract":"<p>Machine Learning systems rely heavily on annotated instances. Such annotations are frequently done by human experts, or by tools developed by experts, and so the central message of this book, <i>Noise: A Flaw in Human Judgment</i> (Kahneman, Sibony, and Sunstein 2021) is of considerable importance to AI/Machine Learning community. The core message is that if a number of experts are asked to annotate tasks that involve judgments, these responses will frequently differ. This observation poses a problem for how analysts choose a particular annotated dataset (from the group), or process the set of responses to give a “balanced” response, or whether to reject all the annotated datasets. A further important aspect of this book is the case studies which demonstrate that differences in judgments between fellow experts have been reported in a significant number of disciplines including, business, the law, government, and medicine. Kahneman, Sibony and Sunstein (2021), referred to as KSS subsequently, discuss how Expert Biases can be reduced, but the main focus of this book is a discussion of Noise, that is, differences that often occur between fellow experts, and how Noise can often be reduced. To address the last point KSS have formulated a set of six decision hygiene principles which include the recommendation that complex tasks should be subdivided, and then each subtask should be solved separately. A further principle is that each task should be solved by individual experts before the various judgments are discussed with fellow experts. Effectively, the book being reviewed covers three main topics: First, it reports several motivating studies that show how judgments of fellow experts varied significantly in the pricing of insurance premiums, and in setting the lengths of custodial sentences. These motivating studies very effectively illustrate the central concepts of Judgment, Noise, and Bias; that section also provides definitions of these core concepts and discusses how Noise is often amplified in group meetings. Secondly, the authors provide detailed discussion of further studies, in a variety of domains, which report the levels of disagreement between experts. Thirdly, KSS discusses how to reduce the levels of Noise between experts, as noted above, the authors refer to these as Principles of Noise Hygiene. These three parts are interwoven in a complex way throughout the book; in our view, the best overview of the book is given in the section Review and Conclusions: Taking Noise Seriously (KSS, p. 361).</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"555-567"},"PeriodicalIF":0.9,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136376496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust internal representations for domain generalization","authors":"Mohammad Rostami","doi":"10.1002/aaai.12137","DOIUrl":"10.1002/aaai.12137","url":null,"abstract":"<p>This paper, which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23, serves as a comprehensive survey of my research in transfer learning by utilizing embedding spaces. The work reviewed in this paper specifically revolves around the inherent challenges associated with continual learning and limited availability of labeled data. By providing an overview of my past and ongoing contributions, this paper aims to present a holistic understanding of my research, paving the way for future explorations and advancements in the field. My research delves into the various settings of transfer learning, including, few-shot learning, zero-shot learning, continual learning, domain adaptation, and distributed learning. I hope this survey provides a forward-looking perspective for researchers who would like to focus on similar research directions.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"467-481"},"PeriodicalIF":0.9,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trustworthy residual vehicle value prediction for auto finance","authors":"Mihye Kim,&nbsp;Jimyung Choi,&nbsp;Jaehyun Kim,&nbsp;Wooyoung Kim,&nbsp;Yeonung Baek,&nbsp;Gisuk Bang,&nbsp;Kwangwoon Son,&nbsp;Yeonman Ryou,&nbsp;Kee-Eung Kim","doi":"10.1002/aaai.12136","DOIUrl":"10.1002/aaai.12136","url":null,"abstract":"<p>The residual value (RV) of a vehicle refers to its estimated worth at some point in the future. It is a core component in every auto finance product, used to determine the credit lines and the leasing rates. As such, an accurate prediction of RV is critical for the auto finance industry, since it can pose a risk of revenue loss by over-prediction or make the financial product incompetent through under-prediction. Although there are a number of prior studies on training machine learning models on a large amount of used car sales data, we had to cope with real-world operational requirements such as compliance with regulations (i.e., monotonicity of output with respect to a subset of features) and generalization to unseen input (i.e., new and rare car models). In this paper, we describe how we addressed these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"394-405"},"PeriodicalIF":0.9,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video Turing Test: A first step towards human-level AI","authors":"Minsu Lee,&nbsp;Yu-Jung Heo,&nbsp;Seongho Choi,&nbsp;Woo Suk Choi,&nbsp;Byoung-Tak Zhang","doi":"10.1002/aaai.12128","DOIUrl":"10.1002/aaai.12128","url":null,"abstract":"<p>The development of artificial intelligence (AI) agents capable of human-level understanding of video content and conducting conversations with humans on this basis is a promising application that people expect. However, this is a challenging task that requires the holistic integration of multimodal information with temporal dependencies and reasoning, as well as social and physical commonsense. In addition, the development of appropriate systematic evaluation methods is essential. In this context, we introduce the Video Turing Test (VTT), a blind test used to evaluate human-likeness in terms of video comprehension ability. Moreover, we propose Vincent as a video understanding AI. We explain the configuration of VTT, the architecture of Vincent to prepare for VTT and the proposed evaluation methods for video comprehension. We also estimate the current intelligence level of AI based on our results and discuss future research directions.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"537-554"},"PeriodicalIF":0.9,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SolderNet: Towards trustworthy visual inspection of solder joints in electronics manufacturing using explainable artificial intelligence","authors":"Hayden Gunraj,&nbsp;Paul Guerrier,&nbsp;Sheldon Fernandez,&nbsp;Alexander Wong","doi":"10.1002/aaai.12129","DOIUrl":"10.1002/aaai.12129","url":null,"abstract":"<p>In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work, we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called <b>SolderNet</b> that we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 4","pages":"442-452"},"PeriodicalIF":0.9,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}