A world model: On the political logics of generative AI

IF 4.7 1区 社会学 Q1 GEOGRAPHY
Louise Amoore , Alexander Campolo , Benjamin Jacobsen , Ludovico Rella
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引用次数: 0

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) pre-training 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.

世界模型:论生成式人工智能的政治逻辑
从早期的 CLIP 和 BERT 模型,到 ChatGPT 和 DALL-E 所带来的文本和图像生成的爆炸式增长,大型语言模型(LLM)或生成式人工智能的计算逻辑正日益渗透到社会和政治领域。这不仅仅是指生成式人工智能模型被直接用于解决困难问题,无论是战场上的决策还是对流行病的应对,还因为生成式人工智能正在塑造和限定世界上可知和可行动的政治参数。与计算机科学中可通用的 "世界模型 "的承诺相反,文章探讨了生成式人工智能如何以及为什么会产生一种世界模型,并随之产生一套政治逻辑和治理理性,对我们今天的生活方式产生深远而持久的影响。文章追溯了生成式人工智能模型的谱系,它们是如何产生的,以及为什么使这些模型产生活力的一些概念和技术会成为积极塑造世界的持久的知识形式,即使在特定的物质商业 GPT 模型进入新的迭代之后也是如此。尽管生成式人工智能在统计实践、概率知识等方面保留了大量前科学和计算制度的痕迹,但它同时也在颠覆认识论的安排,并向感知、描述、分类和认识世界的新方式敞开大门。本文阐述了生成式人工智能政治逻辑的四个决定性方面:i) 生成性不仅仅是生成图像或文本输出的能力,因此生成性逻辑作用于被理解为对数据中 "潜在分布 "的估计的世界;ii) 潜伏性是一种压缩的政治逻辑,在这种逻辑中(与减少或扭曲的主张相反),隐藏、未知或潜伏的事物变得浮出水面并可被管理;iii)作为生成式人工智能政治逻辑排序工具的破碎化和并行化序列,注意力框架从根本上改变了治理非线性问题的可能性;iv)作为生成式人工智能计算逻辑的预训练和微调,同时塑造了面向未遇到的数据和新任务的 "零射门政治"。文章从这四个方面描绘了生成式人工智能的当代政治逻辑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.60
自引率
14.60%
发文量
210
期刊介绍: Political Geography is the flagship journal of political geography and research on the spatial dimensions of politics. The journal brings together leading contributions in its field, promoting international and interdisciplinary communication. Research emphases cover all scales of inquiry and diverse theories, methods, and methodologies.
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