[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119564-en":3,"doc-seo-119564-105":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},119564,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning","Playing Domains: Codes, Cities, and Cultures in the Viral World of Machine Learning examines how AI competitions reshape urban life into training datasets, turning everyday places into “game worlds” for machine learning practitioners. Drawing on scholarship on “AI as a sport,” the paper argues that benchmarking practices—shared tasks, shared datasets, and alignment work—compress real-world complexity into standardized arenas. Examples such as KITTI’s role in autonomous-vehicle development illustrate how comparative performance culture distances practitioners from the impacts of their work.","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:21 . Nov. 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","cbCaivGWfKuYKgjh","https://ap.wps.com/l/cbCaivGWfKuYKgjh","pdf",432713,1,9,"English","en",105,"# Introduction\n## AI as play and grand challenges\n## Playing domains and viral machine learning culture\n# AI as a sport of conflicting codes\n## Benchmarking: shared tasks and standards\n## Benchmark datasets and alignment work\n## Urban datasets and KITTI Vision Benchmark Suite","[{\"question\":\"What does “playing domains” mean in this paper?\",\"answer\":\"It describes how practitioners treat AI as an arena for competitive battles, as everyday urban environments get compressed into training datasets that function like game worlds.\"},{\"question\":\"How does benchmarking work in machine learning according to the document?\",\"answer\":\"It evaluates model performance by comparing models against each other using established standards, requiring shared tasks and shared datasets.\"},{\"question\":\"Why was the KITTI Vision Benchmark Suite important?\",\"answer\":\"It addressed a lack of benchmarks that could mimic the messiness of real driving, enabling autonomous-vehicle development beyond what lab images could support.\"}]","Playing 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