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Essays in the Economics of Artificia...

Webb, Michael William.

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Essays in the Economics of Artificial Intelligence.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in the Economics of Artificial Intelligence./
作者:	Webb, Michael William.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	167 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
標題:	Author productivity. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29756108
ISBN:	9798357503992

Essays in the Economics of Artificial Intelligence.
Webb, Michael William.

Essays in the Economics of Artificial Intelligence. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 167 p.

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--Stanford University, 2020.

This dissertation consists of three chapters. In the first chapter, I develop a new method to predict the impacts of any technology on occupations. I use the overlap between the text of job task descriptions and the text of patents to construct a measure of the exposure of tasks to automation. I first apply the method to historical cases such as software and industrial robots. I establish that occupations I measure as highly exposed to previous automation technologies saw declines in employment and wages over the relevant periods. I use the fitted parameters from the case studies to predict the impacts of artificial intelligence. I find that, in contrast to software and robots, AI is directed at high-skilled tasks. Under the assumption that historical patterns of long-run substitution will continue, I estimate that AI will reduce 90:10 wage inequality, but will not affect the top 1%.The second chapter presents work coauthored with Nick Short, Nick Bloom, and Josh Lerner. We show that patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large US technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon, and Fujitsu. Chinese patenting in the US is small but growing rapidly, and world-leading for drone technology. Patenting in machine learning has seen exponential growth since 2010, although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large increases in inventor productivity in software and semiconductors in the late 1990s. In most high-tech fields, Japan is the only country outside the US with significant US patenting activity; however, whereas Japan played an important role in the burst of neural network patenting in the 1990s, it has not been involved in the current acceleration. Comparing the periods 1970-89 and 2000-15, patenting in the current period has been primarily by entrant assignees, with the exception of neural networks.The third chapter presents work coauthored with Nick Bloom, Chad Jones, and John Van Reenen. Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore's Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas --- and the exponential growth they imply --- are getting harder to find.

ISBN: 9798357503992Subjects--Topical Terms:

3773998
Author productivity.

Essays in the Economics of Artificial Intelligence.
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520 $a This dissertation consists of three chapters. In the first chapter, I develop a new method to predict the impacts of any technology on occupations. I use the overlap between the text of job task descriptions and the text of patents to construct a measure of the exposure of tasks to automation. I first apply the method to historical cases such as software and industrial robots. I establish that occupations I measure as highly exposed to previous automation technologies saw declines in employment and wages over the relevant periods. I use the fitted parameters from the case studies to predict the impacts of artificial intelligence. I find that, in contrast to software and robots, AI is directed at high-skilled tasks. Under the assumption that historical patterns of long-run substitution will continue, I estimate that AI will reduce 90:10 wage inequality, but will not affect the top 1%.The second chapter presents work coauthored with Nick Short, Nick Bloom, and Josh Lerner. We show that patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large US technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon, and Fujitsu. Chinese patenting in the US is small but growing rapidly, and world-leading for drone technology. Patenting in machine learning has seen exponential growth since 2010, although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large increases in inventor productivity in software and semiconductors in the late 1990s. In most high-tech fields, Japan is the only country outside the US with significant US patenting activity; however, whereas Japan played an important role in the burst of neural network patenting in the 1990s, it has not been involved in the current acceleration. Comparing the periods 1970-89 and 2000-15, patenting in the current period has been primarily by entrant assignees, with the exception of neural networks.The third chapter presents work coauthored with Nick Bloom, Chad Jones, and John Van Reenen. Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore's Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas --- and the exponential growth they imply --- are getting harder to find.
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