[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126007-en":3,"doc-seo-126007-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126007,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Artificial Intelligence and Machine Learning: technologies in search of a perspective","The paper introduces the special issue of the Journal on AI and ML and situates AI history from the 1950s to contemporary generative systems. It traces key technological shifts, emphasizing deep learning’s importance alongside its limitations, and how user expectations and attitudes evolve with new capabilities. It examines how CSCW research addresses the social context of organisational systems, then argues for research on how AI and ML tools fit into real-world collaborative work sites.","Artificial Intelligence and Machine Learning: technologies in search of a perspective  \nby  \nRichard Harper* and Dave Randall**  \n*Lancaster University, England  \n**Siegen University, Germany  \nContact details: first author: [r.harper@lancater.ac.uk](r.harper@lancater.ac.uk)[ ](r.harper@lancater.ac.uk)School of Communication and Computing, Lancaster, LA1 4YD  \nTel +44 7971 670304  \nArtificial Intelligence and Machine Learning: technologies in search of a  \nperspective  \nAbstract  \nThis paper introduces the special issue of the Journal on AI and ML. It provides a summary history of AI from the 1950’s through to the current time, sketching the nature of the kinds of AI used in expert systems to the emergence deep learning and ultimately the ‘generative AI’ used in such technologies as PaLM and GPT-3 . It highlights the key changes and developments in the technology, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users. It explores the ways CSCW research has addressed the social context of organisational systems and how the same applies for AI and ML tools and techniques. It urges research that focuses on the particular ways that AI and ML come to fit into ‘real world’ collaborative work sites.  \n1. Introduction  \nThe history of artificial intelligence (AI) and, latterly, its offshoot, machine learning (ML) has been a history of two sides: exaggerated hype and unnecessary fears on one, and , on the other, steady, if slow progress in technology on the other. Any attempt to grapple with AI and ML needs to separate these two concerns before it outlines a third concern, the kind of relationship CSCW might have with them. CSCW is a perspective more than anything else, and so it might offer interesting views on both these‘sides’ of AI and ML. It is our view that a new perspective , apposite for the issues at hand , needs developing. Such a view will, we argue, owe something to CSCW because CSCW is founded on the interactional issues that are sometimes overlooked in the AI and ML literature. However, (and we remain agnostic for the moment) , it may be that the peculiar features of AI and ML might require ‘special’ treatment.  \nAs we approach these and other possibilities, it is perhaps worth sketching what the issues at hand might be. First of all, there is the technology. This has , as we say, made steady but slow progress over the years , but is , in many ways , relatively straight forward. The basic concept of machine learning was devised in the 1950s; the notions behind deep learning a decade later. Computer hardware that would make these computational techniques were theoretically conceived in the 1980s but only became practical in about 2010.  \nTimelines aside , certain aspects of this development need appreciating. For example, AI and ML depend in very large part on innovations in data. The term data might imply something that is self-evident, a thing that isuncontentious in its capturing. But over the past twenty year or so, acts by users such as keyboard entries, say, or visual signals from a camera, or webs of datum to do with crowd behaviours on the internet , have been transformed by engineers who sought out the ‘AI-relevant features’ of the phenomena in question. Extracting these features took years in some instances, but once done has almost become taken for granted, as Crawford notes (2021) . Feature extraction (or feature engineering, an alternative but perhaps better term for the way data had to be developed) , turns around how data might map its own internal relationships – between instances of data. It is through discovering what the relationships might be and hence their potential for interpretation and ‘learning’ that determines for engineers the properties that need storing. Acts by individual users, asa case in point, have to be defined as instances of types (or categories) that apply for all users (or at least large numbers of users) and not expressions of","cbCaivwziZ3gTTON","https://ap.wps.com/l/cbCaivwziZ3gTTON","pdf",397397,6,1,61,"English","en",105,"# Introduction\n## AI and ML development as hype, fear, and steady progress\n## Technology: data, feature engineering, and ontologies\n## Distances, likelihood, and transfer learning\n## Data stores, tensors, and application engines","[{\"question\":\"What main perspective does the paper propose for understanding AI and ML?\",\"answer\":\"It argues that CSCW offers a perspective more than a technical solution, drawing attention to interactional issues often overlooked in AI and ML literature.\"},{\"question\":\"How does the paper explain the role of data in AI and machine learning progress?\",\"answer\":\"It stresses that data has to be engineered into AI-relevant features and organized through generic categories, such as an ontology, so user actions can be linked and interpreted across many users.\"},{\"question\":\"What does the paper say about deep learning’s significance and limitations?\",\"answer\":\"Deep learning is highlighted as a key development with major impact, but the paper also points to limitations and the need to understand changing user expectations as technologies evolve.\"}]","Artificial Intelligence and Machine Learning: technologies in search of a perspective | 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main perspective does the paper propose for understanding AI and ML?","Question",{"text":77,"@type":78},"It argues that CSCW offers a perspective more than a technical solution, drawing attention to interactional issues often overlooked in AI and ML literature.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the paper explain the role of data in AI and machine learning progress?",{"text":82,"@type":78},"It stresses that data has to be engineered into AI-relevant features and organized through generic categories, such as an ontology, so user actions can be linked and interpreted across many users.",{"name":84,"@type":75,"acceptedAnswer":85},"What does the paper say about deep learning’s significance and limitations?",{"text":86,"@type":78},"Deep learning is highlighted as a key development with major impact, but the paper also points to limitations and the need to understand changing user expectations as technologies 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