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Combinatorial Inventions in Artificial Intelligence : = Empirical Evidence and Implications for Science, Technology, and Organizations.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Combinatorial Inventions in Artificial Intelligence :/
Reminder of title:	Empirical Evidence and Implications for Science, Technology, and Organizations.
Author:	Wang, Jieshu.
Description:	1 online resource (302 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
Contained By:	Dissertations Abstracts International85-02A.
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380109888

Combinatorial Inventions in Artificial Intelligence : = Empirical Evidence and Implications for Science, Technology, and Organizations.
Wang, Jieshu.

Combinatorial Inventions in Artificial Intelligence :Empirical Evidence and Implications for Science, Technology, and Organizations. - 1 online resource (302 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: A.

Thesis (Ph.D.)--Arizona State University, 2023.

Includes bibliographical references

Artificial Intelligence (AI) is a rapidly advancing field with the potential to impact every aspect of society, including the inventive practices of science and technology. The creation of new ideas, devices, or methods, commonly known as inventions, is typically viewed as a process of combining existing knowledge. To understand how AI can transform scientific and technological inventions, it is essential to comprehend how such combinatorial inventions have emerged in the development of AI.This dissertation aims to investigate three aspects of combinatorial inventions in AI using data-driven and network analysis methods. Firstly, how knowledge is combined to generate new scientific publications in AI; secondly, how technical components are combined to create new AI patents; and thirdly, how organizations create new AI inventions by integrating knowledge within organizational and industrial boundaries. Using an AI publication dataset of nearly 300,000 AI publications and an AI patent dataset of almost 260,000 AI patents granted by the United States Patent and Trademark Office (USPTO), this study found that scientific research related to AI is predominantly driven by combining existing knowledge in highly conventional ways, which also results in the most impactful publications. Similarly, incremental improvements and refinements that rely on existing knowledge rather than radically new ideas are the primary driver of AI patenting. Nonetheless, AI patents combining new components tend to disrupt citation networks and hence future inventive practices more than those that involve only existing components.To examine AI organizations' inventive activities, an analytical framework called the Combinatorial Exploitation and Exploration (CEE) framework was developed to measure how much an organization accesses and discovers knowledge while working within organizational and industrial boundaries. With a dataset of nearly 500 AI organizations that have continuously contributed to AI technologies, the research shows that AI organizations favor exploitative over exploratory inventions. However, local exploitation tends to peak within the first five years and remain stable, while exploratory inventions grow gradually over time.Overall, this dissertation offers empirical evidence regarding how inventions in AI have emerged and provides insights into how combinatorial characteristics relate to AI inventions' quality. Additionally, the study offers tools to assess inventive outcomes and competence.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380109888Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

InnovationIndex Terms--Genre/Form:

554714
Electronic books.

Combinatorial Inventions in Artificial Intelligence : = Empirical Evidence and Implications for Science, Technology, and Organizations.
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