Amoore, Louise, Campolo, Alexander, Jacobsen, Benjamin orcid.org/0000-0002-6656-8892 et al. (1 more author) (2024) A world model:On the political logics of generative AI. Political Geography. 103134. ISSN 0962-6298
Abstract
The computational logics of large language models (LLMs) or generative AI – from the early models of CLIP and BERT to the explosion of text and image generation via ChatGPT and DALL-E − are increasingly penetrating the social and political world. Not merely in the direct sense that generative AI models are being deployed to govern difficult problems, whether decisions on the battlefield or responses to pandemic, but also because generative AI is shaping and delimiting the political parameters of what can be known and actioned in the world. Contra the promise of a generalizable “world model” in computer science, the article addresses how and why generative AI gives rise to a model of the world, and with it a set of political logics and governing rationalities that have profound and enduring effects on how we live today. The article traces the genealogies of generative AI models, how they have come into being, and why some concepts and techniques that animate these models become durable forms of knowledge that actively shape the world, even long after a specific material commercial GPT model has moved on to a new iteration. Though generative AI retains significant traces of former scientific and computational regimes – in statistical practices, probabilistic knowledge, and so on – it is also dislocating epistemological arrangements and opening them to novel ways of perceiving, characterising, classifying, and knowing the world. Four defining aspects of the political logic of generative AI are elaborated: i) generativity as something more than the capacity to generate image or text outputs, so that a generative logic acts upon the world understood as estimates of “underlying distributions” in data; ii) latency as a political logic of compression in which (by contrast with claims to reduction or distortion) the thing that is hidden, unknown or latent becomes surfaced and amenable to being governed; iii) broken and parallelized sequences as the ordering device of the political logic of generative AI, where attention frameworks radically change the possibilities for governing non-linear problems; iv) pretraining and fine-tuning as a computational logic of generative AI that simultaneously shapes a “zero shot politics” oriented towards unencountered data and new tasks. Across each of the four aspects, the article maps the emerging contemporary political logic of generative AI.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > Sociology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 03 Jul 2024 08:00 |
Last Modified: | 03 Apr 2025 23:14 |
Published Version: | https://doi.org/10.1016/j.polgeo.2024.103134 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.polgeo.2024.103134 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214390 |
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Description: A world model: On the political logics of generative AI
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