In this column, we describe the Model AI Assignment "FairKalah: Fair Mancala Competition". After introducing the rules of Mancala (a.k.a. Kalah), we discuss the primary difficulty that its unfairness causes for AI competition assessment, and present a solution along with a description of a set of resources to aid in assignment adoption.
{"title":"AI education matters","authors":"T. Neller","doi":"10.1145/3544897.3544900","DOIUrl":"https://doi.org/10.1145/3544897.3544900","url":null,"abstract":"In this column, we describe the Model AI Assignment \"FairKalah: Fair Mancala Competition\". After introducing the rules of Mancala (a.k.a. Kalah), we discuss the primary difficulty that its unfairness causes for AI competition assessment, and present a solution along with a description of a set of resources to aid in assignment adoption.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"8 1","pages":"9 - 11"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45223152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many of the most significant breakthroughs in artificial intelligence over the past decade have been based on progress in deep neural networks. That progress has been facilitated by deep-learning libraries like Theano (Al-Rfou et al., 2016), TensorFlow (Abadi et al., 2015) and PyTorch (Paszke et al., 2019) that allow rapid prototyping and efficient execution. The key algorithm at the heart of all of these libraries is reverse-mode automatic differentiation. This column introduces the Model AI Assignment ScalarFlow: Implementing Reverse Mode Automatic Differentiation. This assignment gives students the opportunity to gain a deeper understanding of modern deeplearning frameworks by building their own automatic differentiation engine and using it to experiment with some important concepts in deep learning. In this column we will review some basic background on training neural networks, provide a brief overview of the reverse-mode automatic differentiation algorithm, describe the model assignment and provide some pointers to additional resources.
{"title":"AI education matters: Model AI assignment","authors":"N. Sprague","doi":"10.1145/3516418.3516422","DOIUrl":"https://doi.org/10.1145/3516418.3516422","url":null,"abstract":"Many of the most significant breakthroughs in artificial intelligence over the past decade have been based on progress in deep neural networks. That progress has been facilitated by deep-learning libraries like Theano (Al-Rfou et al., 2016), TensorFlow (Abadi et al., 2015) and PyTorch (Paszke et al., 2019) that allow rapid prototyping and efficient execution. The key algorithm at the heart of all of these libraries is reverse-mode automatic differentiation. This column introduces the Model AI Assignment ScalarFlow: Implementing Reverse Mode Automatic Differentiation. This assignment gives students the opportunity to gain a deeper understanding of modern deeplearning frameworks by building their own automatic differentiation engine and using it to experiment with some important concepts in deep learning. In this column we will review some basic background on training neural networks, provide a brief overview of the reverse-mode automatic differentiation algorithm, describe the model assignment and provide some pointers to additional resources.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"8 - 11"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45833622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This section is compiled from reports of recent events sponsored or run in cooperation with ACM SIGAI. In general these reports were written and submitted by the conference organisers.
{"title":"Conference reports","authors":"Louise A. Dennis","doi":"10.1145/3516418.3516421","DOIUrl":"https://doi.org/10.1145/3516418.3516421","url":null,"abstract":"This section is compiled from reports of recent events sponsored or run in cooperation with ACM SIGAI. In general these reports were written and submitted by the conference organisers.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"5 - 7"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44292924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U. Endriss, A. Nowé, Maria L. Gini, V. Lesser, Michael Luck, Ana Paiva, Jaime Simão Sichman
The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.
{"title":"Autonomous agents and multiagent systems","authors":"U. Endriss, A. Nowé, Maria L. Gini, V. Lesser, Michael Luck, Ana Paiva, Jaime Simão Sichman","doi":"10.1145/3511322.3511329","DOIUrl":"https://doi.org/10.1145/3511322.3511329","url":null,"abstract":"The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"29 - 37"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44595047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, L. Medsker, T. Neller, Iolanda Leite, A. Karpatne, Alan Tsang
We have continued adjusting to a "new normal" in the Covid era. In addition to the significant socio-economic challenges of the pandemic, for us as a scientific organization, we continue to grapple with a world with few, if any, in-person conferences for a second year in a row, and continued virtual interactions for the community. We are, however, proud of what we have been able to accomplish in the past year. As part of transparent communication with our membership, we share here the annual report that we provide to ACM each summer. You may notice a slight change in format this year, to focus on areas that ACM is particularly interested in hearing from us about. Also note that we include the report without modifications, so the information is a few months old!
{"title":"SIGAI annual report","authors":"Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, L. Medsker, T. Neller, Iolanda Leite, A. Karpatne, Alan Tsang","doi":"10.1145/3511322.3511324","DOIUrl":"https://doi.org/10.1145/3511322.3511324","url":null,"abstract":"We have continued adjusting to a \"new normal\" in the Covid era. In addition to the significant socio-economic challenges of the pandemic, for us as a scientific organization, we continue to grapple with a world with few, if any, in-person conferences for a second year in a row, and continued virtual interactions for the community. We are, however, proud of what we have been able to accomplish in the past year. As part of transparent communication with our membership, we share here the annual report that we provide to ACM each summer. You may notice a slight change in format this year, to focus on areas that ACM is particularly interested in hearing from us about. Also note that we include the report without modifications, so the information is a few months old!","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"5 - 11"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47731270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The topic for EAAI 2023's Mentored Undergraduate Research Challenge is Human-Aware AI in Sound and Music. What does that mean? Where are the applications? How can you get started? We break down the topic, discuss applications, and explore project ideas in this column.
{"title":"2023 EAAI mentored undergraduate research challenge","authors":"R. Freedman","doi":"10.1145/3511322.3511328","DOIUrl":"https://doi.org/10.1145/3511322.3511328","url":null,"abstract":"The topic for EAAI 2023's Mentored Undergraduate Research Challenge is Human-Aware AI in Sound and Music. What does that mean? Where are the applications? How can you get started? We break down the topic, discuss applications, and explore project ideas in this column.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"21 - 28"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45772282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Welcome to the third issue of this year's AI Matters Newsletter. We open with the annual report of SIGAI by our leadership team. We then bring you a brief report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis. In our regular Education column, Lisa Zhang, Pouria Fewzee, and Charbel Feghali describe a Model AI Assignment where students combine various techniques from a deep learning course to build a denoising autoencoder for news headlines. We end the issue with two article contributions, one by Richard Freedman that describes the 2023 EAAI Mentored Undergraduate Research Challenge, and another by Ulle Endriss, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman, which provides perspectives on the completion of 20 years of AAMAS.
{"title":"Welcome to AI matters 7(3)","authors":"Iolanda Leite, A. Karpatne","doi":"10.1145/3511322.3511323","DOIUrl":"https://doi.org/10.1145/3511322.3511323","url":null,"abstract":"Welcome to the third issue of this year's AI Matters Newsletter. We open with the annual report of SIGAI by our leadership team. We then bring you a brief report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis. In our regular Education column, Lisa Zhang, Pouria Fewzee, and Charbel Feghali describe a Model AI Assignment where students combine various techniques from a deep learning course to build a denoising autoencoder for news headlines. We end the issue with two article contributions, one by Richard Freedman that describes the 2023 EAAI Mentored Undergraduate Research Challenge, and another by Ulle Endriss, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman, which provides perspectives on the completion of 20 years of AAMAS.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"4 - 4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42419738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a Model AI Assignment (Neller et al., 2021) where students combine various techniques from a deep learning course to build a denoising autoencoder (Shen, Mueller, Barzilay, & Jaakkola, 2020) for news headlines. Students then use this denoising autoencoder to query similar headlines, and interpolate between headlines. Building this denoising autoencoder requires students to apply many course concepts, including data augmentation, word and sentence embeddings, autoencoders, recurrent neural networks, sequence-to-sequence networks, and temperature. As such, this assignment can be ideal as a final assessment that synthesizes many topics. This assignment is written in PyTorch, uses the torchtext package, and is intended to be completed on the Google Colab platform.
{"title":"AI education matters","authors":"Lisa Zhang, Pouria Fewzee, Charbel Feghali","doi":"10.1145/3511322.3511327","DOIUrl":"https://doi.org/10.1145/3511322.3511327","url":null,"abstract":"We introduce a Model AI Assignment (Neller et al., 2021) where students combine various techniques from a deep learning course to build a denoising autoencoder (Shen, Mueller, Barzilay, & Jaakkola, 2020) for news headlines. Students then use this denoising autoencoder to query similar headlines, and interpolate between headlines. Building this denoising autoencoder requires students to apply many course concepts, including data augmentation, word and sentence embeddings, autoencoders, recurrent neural networks, sequence-to-sequence networks, and temperature. As such, this assignment can be ideal as a final assessment that synthesizes many topics. This assignment is written in PyTorch, uses the torchtext package, and is intended to be completed on the Google Colab platform.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"18 - 20"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42453577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This section features brief reports from recent events sponsored or run in cooperation with ACM SIGAI.
本节介绍了与ACM SIGAI合作赞助或举办的近期活动的简要报告。
{"title":"Conference reports","authors":"Louise Dennis","doi":"10.1145/3465074.3465077","DOIUrl":"https://doi.org/10.1145/3465074.3465077","url":null,"abstract":"This section features brief reports from recent events sponsored or run in cooperation with ACM SIGAI.","PeriodicalId":91445,"journal":{"name":"AI matters","volume":"7 1","pages":"8 - 9"},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3465074.3465077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46826439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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