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Research output: Contribution to Journal/Magazine › Editorial › peer-review
Research output: Contribution to Journal/Magazine › Editorial › peer-review
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TY - JOUR
T1 - Machine Learning and the Work of the User
AU - Harper, Richard
AU - Randall, Dave
PY - 2024/6/30
Y1 - 2024/6/30
N2 - This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of ML from the 1950’s through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning. It concludes with the ‘generative AI’ used in such ML technologies as PaLM and GPT-3. The history highlights key changes and developments in ML, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users in an environment when ML can and often is oversold. The paper then explores the ways CSCW research has addressed the social context of organisational systems and how the same can apply for ML tools and techniques. It urges research that focuses on the particular ways that ML comes to fit into ‘real world’ collaborative work sites and hence speaks to the CSCW cannon.
AB - This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of ML from the 1950’s through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning. It concludes with the ‘generative AI’ used in such ML technologies as PaLM and GPT-3. The history highlights key changes and developments in ML, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users in an environment when ML can and often is oversold. The paper then explores the ways CSCW research has addressed the social context of organisational systems and how the same can apply for ML tools and techniques. It urges research that focuses on the particular ways that ML comes to fit into ‘real world’ collaborative work sites and hence speaks to the CSCW cannon.
KW - CSCW, Users, ML, AI
U2 - 10.1007/s10606-023-09483-6
DO - 10.1007/s10606-023-09483-6
M3 - Editorial
AN - SCOPUS:85184172409
VL - 33
SP - 103
EP - 136
JO - Computer Supported Cooperative Work: CSCW: An International Journal
JF - Computer Supported Cooperative Work: CSCW: An International Journal
SN - 0925-9724
IS - 2
ER -