Samuel Kounev, N. Herbst, Cristina L. Abad, A. Iosup, Ian T Foster, P. Shenoy, O. Rana, A. Chien
Dispelling the confusion around serverless computing by capturing its essential and conceptual characteristics.
通过捕捉无服务器计算的基本和概念特征，消除围绕无服务器计算的困惑。
{"title":"Serverless Computing: What It Is, and What It Is Not?","authors":"Samuel Kounev, N. Herbst, Cristina L. Abad, A. Iosup, Ian T Foster, P. Shenoy, O. Rana, A. Chien","doi":"10.1145/3587249","DOIUrl":"https://doi.org/10.1145/3587249","url":null,"abstract":"Dispelling the confusion around serverless computing by capturing its essential and conceptual characteristics.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"66 1","pages":"80 - 92"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41700908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers look to cut large GPT models down to size.
研究人员希望缩小大型GPT模型的大小。
{"title":"Distilling What We Know","authors":"Samuel Greengard","doi":"10.1145/3607891","DOIUrl":"https://doi.org/10.1145/3607891","url":null,"abstract":"Researchers look to cut large GPT models down to size.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":" ","pages":"15 - 17"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47731824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM Prize recipient Pieter Abbeel is working to help robots learn, and learn to improve.
ACM奖获得者Pieter Abbeel正在努力帮助机器人学习，并学会改进。
{"title":"How Many Ways Can You Teach a Robot?","authors":"L. Hoffmann","doi":"10.1145/3608109","DOIUrl":"https://doi.org/10.1145/3608109","url":null,"abstract":"ACM Prize recipient Pieter Abbeel is working to help robots learn, and learn to improve.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":" ","pages":"104 - ff"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49276397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By revisiting key algorithms from computing, a team unlocked hidden efficiency in a long-standing computer science problem.
通过重新审视计算中的关键算法，一个团队解开了一个长期存在的计算机科学问题中隐藏的效率。
{"title":"Historic Algorithms Help Unlock Shortest-Path Problem Breakthrough","authors":"C. Edwards","doi":"10.1145/3607866","DOIUrl":"https://doi.org/10.1145/3607866","url":null,"abstract":"By revisiting key algorithms from computing, a team unlocked hidden efficiency in a long-standing computer science problem.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"66 1","pages":"10 - 12"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42712844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Is the ability to think scientifically the defining essence of intelligence?
科学思考的能力是智力的本质定义吗?
{"title":"Cargo Cult AI","authors":"E. Levine","doi":"10.1145/3606946","DOIUrl":"https://doi.org/10.1145/3606946","url":null,"abstract":"Is the ability to think scientifically the defining essence of intelligence?","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":" ","pages":"46 - 51"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45474974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coming of Age","authors":"Stefano Zanero","doi":"10.1145/3608965","DOIUrl":"https://doi.org/10.1145/3608965","url":null,"abstract":"Stressing the importance of threat models.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135570502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bran Knowles, J. D’cruz, John T. Richards, Kush R. Varshney
An effort to bring artificial intelligence into better alignment with our moral aims and finally realize the vision of superior decision making through AI.
努力使人工智能更好地与我们的道德目标相一致，并最终通过人工智能实现卓越决策的愿景。
{"title":"Humble AI","authors":"Bran Knowles, J. D’cruz, John T. Richards, Kush R. Varshney","doi":"10.1145/3587035","DOIUrl":"https://doi.org/10.1145/3587035","url":null,"abstract":"An effort to bring artificial intelligence into better alignment with our moral aims and finally realize the vision of superior decision making through AI.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"66 1","pages":"73 - 79"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42132627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
algorithms for F0 estimation to algorithms for model counting. The authors also show a partial converse, namely, by framing F0 estimation as a special case of model counting, the authors obtain a very general algorithm for F0 estimation and variants. The resulting algorithms can be used to select a minimum cost query plan in database design and are also a key tool for detecting denial-of-service attacks in network monitoring. The starting point of the paper is the observation that a hashing-based technique for model counting1,3 uses the same techniques as an F0 estimation data stream algorithm.2 The idea behind both is to reduce the counting problem to a detection problem. For model counting, one chooses random subsets of possible solutions of geometrically varying size and checks if there is any satisfying assignment to φ in each subset. For F0 estimation in data streams, one chooses random subsets of universe items of geometrically varying size and checks if there is an item in one’s subset that occurs in the stream. In both cases, by finding the size of the smallest set for which there is a satisfying assignment (for model counting) or an element occurring in the stream (for F0 estimation), one can scale back up by the reciprocal of that set’s size to obtain a decent approximation to the number of solutions (for model counting) or number of distinct elements (for data streams).
{"title":"Tapping the Link between Algorithmic Model Counting and Streaming: Technical Perspective","authors":"David P. Woodruff","doi":"10.1145/3607825","DOIUrl":"https://doi.org/10.1145/3607825","url":null,"abstract":"algorithms for F0 estimation to algorithms for model counting. The authors also show a partial converse, namely, by framing F0 estimation as a special case of model counting, the authors obtain a very general algorithm for F0 estimation and variants. The resulting algorithms can be used to select a minimum cost query plan in database design and are also a key tool for detecting denial-of-service attacks in network monitoring. The starting point of the paper is the observation that a hashing-based technique for model counting1,3 uses the same techniques as an F0 estimation data stream algorithm.2 The idea behind both is to reduce the counting problem to a detection problem. For model counting, one chooses random subsets of possible solutions of geometrically varying size and checks if there is any satisfying assignment to φ in each subset. For F0 estimation in data streams, one chooses random subsets of universe items of geometrically varying size and checks if there is an item in one’s subset that occurs in the stream. In both cases, by finding the size of the smallest set for which there is a satisfying assignment (for model counting) or an element occurring in the stream (for F0 estimation), one can scale back up by the reciprocal of that set’s size to obtain a decent approximation to the number of solutions (for model counting) or number of distinct elements (for data streams).","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":" ","pages":"94 - 94"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48742293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evaluating the impact of the conference review rebuttal process.
评估会议审查反驳程序的影响。
{"title":"Rebutting Rebuttals","authors":"N. Dershowitz, Rakesh M. Verma","doi":"10.1145/3584664","DOIUrl":"https://doi.org/10.1145/3584664","url":null,"abstract":"Evaluating the impact of the conference review rebuttal process.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"66 1","pages":"35 - 41"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47534752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel
Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. Our investigations lead us to observe striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and distinct elements computation. We design a recipe for the translation of algorithms developed for distinct elements estimation to that of model counting, resulting in new algorithms for model counting. We then observe that algorithms in the context of distributed streaming can be transformed into distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing distinct elements estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works.
{"title":"Model Counting Meets Distinct Elements","authors":"A. Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel","doi":"10.1145/3607824","DOIUrl":"https://doi.org/10.1145/3607824","url":null,"abstract":"Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. Our investigations lead us to observe striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and distinct elements computation. We design a recipe for the translation of algorithms developed for distinct elements estimation to that of model counting, resulting in new algorithms for model counting. We then observe that algorithms in the context of distributed streaming can be transformed into distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing distinct elements estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works.","PeriodicalId":10594,"journal":{"name":"Communications of the ACM","volume":"66 1","pages":"95 - 102"},"PeriodicalIF":22.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44547282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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