What is generative in generative artificial intelligence? A design-based perspective

Abstract Generative artificial intelligence (GenAI) models have attracted tremendous interest since the advent of ChatGPT, raising numerous opportunities and challenges. However, their generative power has not yet been studied, leaving open the question of what is truly generated by these tools. This paper addresses this question and precisely characterizes the generativity behind GenAI models. Owing to the latest advancements in engineering design, we first propose a framework for uncovering the various types of generativity. Then, we consider the main families of GenAI models and systematically analyze them to characterize their generativity within this framework. By doing so, we highlight the existence of two distinct generative levels in GenAI: one leading to the generation of new artifacts and the other leading to the generation of GenAI models themselves. We are also able to characterize the generativity of both of these levels, thus specifically confirming the generative power of GenAI and opening research avenues toward human-GenAI collaboration.

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Publication Details

Journal
Research in Engineering Design
Published
2024-10-01
DOI
https://doi.org/10.1007/s00163-024-00441-x
Citations
34
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
11.81
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article

What is generative in generative artificial intelligence? A design-based perspective

Maxime Thomas, Antoine Bordas, Pascal Le Masson, Benoît Weil
34 citations
Research in Engineering Design
Reinforcement Learning in Robotics
11.81
article

What is generative in generative artificial intelligence? A design-based perspective

Maxime Thomas, Antoine Bordas, Pascal Le Masson, Benoît Weil
article en
34 citations

Abstract

Abstract Generative artificial intelligence (GenAI) models have attracted tremendous interest since the advent of ChatGPT, raising numerous opportunities and challenges. However, their generative power has not yet been studied, leaving open the question of what is truly generated by these tools. This paper addresses this question and precisely characterizes the generativity behind GenAI models. Owing to the latest advancements in engineering design, we first propose a framework for uncovering the various types of generativity. Then, we consider the main families of GenAI models and systematically analyze them to characterize their generativity within this framework. By doing so, we highlight the existence of two distinct generative levels in GenAI: one leading to the generation of new artifacts and the other leading to the generation of GenAI models themselves. We are also able to characterize the generativity of both of these levels, thus specifically confirming the generative power of GenAI and opening research avenues toward human-GenAI collaboration.

Research in Engineering DesignVol. 35(4)
École Normale Supérieure Paris-Saclay (FR), Centre National de la Recherche Scientifique (FR), Centre de Gestion Scientifique (FR), EPF - École d'ingénieurs (FR)
Partnerships for the goals
Openalex Percentile: Top 1%
Reinforcement Learning in Robotics
11.81
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