[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119601-en":3,"doc-seo-119601-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119601,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning","Playing Domains explores how machine learning competitions transform cities into benchmark datasets and reshape practitioners’ relationships to real-world impacts. Drawing on scholarship on “AI as a sport,” the article argues that benchmarking turns urban environments into game worlds through viral competition. Focusing on how evaluation depends on shared tasks, datasets, and alignment work, it examines benchmark standardization and uses examples such as the KITTI Vision Benchmark Suite and common computer-vision metrics to illustrate this cultural shift.","The University of Manchester Research  \nPlaying Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning  \nDocument Version  \nAccepted author manuscript  \nLink to publication record in Manchester Research Explorer  \nCitation for published version (APA):  \nHind, S. (2025) . Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning. Mediapolis, 10(3), 1-8 . [https://www.mediapolisjournal.com/2025/11/playing-domains/](https://www.mediapolisjournal.com/2025/11/playing-domains/)  \nPublished in:  \nMediapolis  \nCiting this paper  \nPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTakedown policy  \nIf you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [[http://man.ac.uk/04Y6Bo](http://man.ac.uk/04Y6Bo)] or [contact openresearch@manchester.ac.uk](contact openresearch@manchester.ac.uk) providing relevant details, so  \nwe can investigate your claim.  \nDownload date:31 . Dec. 2025  \nPlaying Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning  \nSam Hind  \nPhoto: A '3D semantic segmentation' image taken from the KITTI-360 Vision Benchmark Suite. Source:  \n[https://www.cvlibs.net/datasets/kitti-360](https://www.cvlibs.net/datasets/kitti-360/)[/](https://www.cvlibs.net/datasets/kitti-360/)  \nWhat happens when cities become datasets for AI competitions? Sam Hind showshow machine learning’s scoreboards distance practitioners from the real-world impacts oftheir work.  \n[Ed. note: This article is part ofa dossier on Playable Cities]  \nThe history of artificial intelligence (AI) is also a history of play. In 1988, acclaimed computer scientist Raj Reddy proposed six ‘grand challenges’ intended to jolt the AI community into life.1 The most famous was the goal of building a ‘world champion chess machine’. After losing the first match in February 1996, IBM’s Deep Blue computer beat reigning world chess champion Garry Kasparov the following May. More recently AI start-up DeepMind – acquired by Google in 2014 – built AlphaGo, a computer program capable of playing Go, a game infinitely more complex than chess. After winning 60 straight online games against professionals an updated version of AlphaGo (referred to as ‘Master’) beat the number one ranked Go player in the world, China’s Ke Jie, in a three-game match – 20 years since Deep Blue’s victory. Realizing there was nothing else  \nleft to win, DeepMind retired all versions of AlphaGo to ‘throw their considerable energy into the next set of grand challenges…such as finding new cures for diseases’.2  \nYet AI’s connection to play goes much deeper. For 20 years competitions, often referred to as challenges, have been organized to drive the development, and more recent commercialization, of AI and machine learning (ML) .3 In this article I draw on recent scholarship on ‘AI as a sport’ to argue that as everyday urban environments – people, streets, situations – are compressed into training datasets, they have begun to function as game worlds for practitioners, attracted by the viral nature of machine learning.4 This practice of ‘playing domains’ – where practitioners treat AI as an arena for gladiatorial-style battles– is becoming a central feature of contemporary machine learning culture driven by this virality.  \nAI as a sport of conflicting codes  \nTo evaluate the performance of ML models, the AI community engage in the practice of benchmarking: a process that allows dev","cbCainNGWqHshisJ","https://ap.wps.com/l/cbCainNGWqHshisJ","pdf",432817,1,9,"English","en",105,"# Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning\n## AI as a sport of conflicting codes","[{\"question\":\"How does the article explain the link between AI and play?\",\"answer\":\"It frames AI history as intertwined with games and “grand challenges,” then extends this idea to machine learning competitions that treat urban data environments as game worlds.\"},{\"question\":\"What role does benchmarking play in this “sport” of machine learning?\",\"answer\":\"Benchmarking enables comparative evaluation by requiring shared tasks and shared datasets so models can be judged against standardized criteria.\"},{\"question\":\"Why are cities important in the article’s discussion of machine learning culture?\",\"answer\":\"Everyday urban settings are compressed into training datasets, allowing practitioners to engage with real environments through competitive, viral benchmark culture rather than direct world impact.\"}]","Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning | 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does the article explain the link between AI and play?","Question",{"text":75,"@type":76},"It frames AI history as intertwined with games and “grand challenges,” then extends this idea to machine learning competitions that treat urban data environments as game worlds.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does benchmarking play in this “sport” of machine learning?",{"text":80,"@type":76},"Benchmarking enables comparative evaluation by requiring shared tasks and shared datasets so models can be judged against standardized criteria.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are cities important in the article’s discussion of machine learning culture?",{"text":84,"@type":76},"Everyday urban settings are compressed into training datasets, allowing practitioners to engage with real environments through competitive, viral benchmark culture rather than direct world 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