How to Do Things with Deep Learning Code.
The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model...
| Published in: | DHQ: Digital Humanities Quarterly Vol. 17; no. 3; pp. 1 - 20 |
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| Main Authors: | , |
| Format: | Article |
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Digital Humanities Quarterly
2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=173348895&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173348895 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 19384122 77X0 jtl: DHQ: Digital Humanities Quarterly issn: 19384122 maglogo: N pubinfo: dt: 2023 vid: 17 iid: 3 pid: 47240 pub: Digital Humanities Quarterly artinfo: ui: 173348895 ppf: 1 ppct: 19 formats: tig: atl: How to Do Things with Deep Learning Code. aug: au: Hua, Minh Raley, Rita affil: Johns Hopkins University University of California, Santa Barbara su: Deep learning Language models Computer programming education Machine learning Artificial intelligence Adventure games sug: subj: Deep learning Language models Computer programming education Machine learning Artificial intelligence Adventure games ab: The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model and that which underwrites applications built around the model. We then verify this map through case studies of two popular GPT-2 applications: the text adventure game, AI Dungeon , and the language art project, This Word Does Not Exist. Such an exercise allows us to test the potential of Critical Code Studies when the object of study is deep learning code and to demonstrate the validity of code as an analytical focus for researchers in the subfields of Critical Artificial Intelligence and Critical Machine Learning Studies. More broadly, however, our work draws attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems, and by extension works toward demystifying some of the auratic mystery of AI. What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models, as well as a more expansive sense of their creative capabilities — indeed, understanding how and where engagement occurs allows all of us to become more active participants in the development of machine learning systems. Our work draws attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems, and by extension works toward demystifying some of the auratic mystery of AI. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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