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U.S.-China Artificial Intelligence Competition: A Conversation with Dr. Jeffrey Ding

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Content provided by CSIS | Center for Strategic and International Studies, CSIS | Center for Strategic, and International Studies. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CSIS | Center for Strategic and International Studies, CSIS | Center for Strategic, and International Studies or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of the ChinaPower Podcast, Dr. Jeffrey Ding joins us to discuss U.S.-China artificial intelligence (AI) competition and his book, Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition. Dr. Ding describes the framework he uses to understand the competition between the US and China on AI and explains that while many assume leading a technological competition comes from developing the next breakthrough invention, it should actually be centered around the diffusion of these technologies throughout their population of users. Technological leadership, therefore, depends on which country can best transfer and spread innovation from its top firms to the entire economy more effectively. Dr. Ding notes that China prioritizes an innovation-centric approach while neglecting broad-based technical and STEM education. He finds that the United States is better positioned than China to adopt and diffuse AI across a broad spectrum of sectors, given that more U.S. training institutions meet a quality baseline compared to China’s. Dr. Ding advises that since the United States is better positioned to diffuse AI technologies throughout its economy, it should focus on “running fast” rather than restricting China’s access to advanced technologies. Finally, Dr. Ding recommends that Washington focus on education policy, widening the base of AI engineers by increasing training sites, supporting public-private partnerships, and helping SMEs develop their AI capabilities.

Dr. Jeffrey Ding is an Assistant Professor of Political Science at George Washington University, and the author of Technology and the Rise of Great Power: How Diffusion Shapes Economic Competition. Previously, he was a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. His research has been published or is forthcoming at European Journal of International Security, Foreign Affairs, Review of International Political Economy, and Security Studies, and his work has been cited in The Washington Post, The Financial Times, and other outlets. He also writes a weekly "ChinAI" newsletter, which features translations of Chinese conversations about AI development, to 12,000+ subscribers including the field's leading policymakers, scholars, and journalists. Dr. Ding holds a Ph.D in international relations from Oxford University, where he studied as a Rhodes scholar.

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255 episodes

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Manage episode 473808902 series 1254885
Content provided by CSIS | Center for Strategic and International Studies, CSIS | Center for Strategic, and International Studies. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CSIS | Center for Strategic and International Studies, CSIS | Center for Strategic, and International Studies or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of the ChinaPower Podcast, Dr. Jeffrey Ding joins us to discuss U.S.-China artificial intelligence (AI) competition and his book, Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition. Dr. Ding describes the framework he uses to understand the competition between the US and China on AI and explains that while many assume leading a technological competition comes from developing the next breakthrough invention, it should actually be centered around the diffusion of these technologies throughout their population of users. Technological leadership, therefore, depends on which country can best transfer and spread innovation from its top firms to the entire economy more effectively. Dr. Ding notes that China prioritizes an innovation-centric approach while neglecting broad-based technical and STEM education. He finds that the United States is better positioned than China to adopt and diffuse AI across a broad spectrum of sectors, given that more U.S. training institutions meet a quality baseline compared to China’s. Dr. Ding advises that since the United States is better positioned to diffuse AI technologies throughout its economy, it should focus on “running fast” rather than restricting China’s access to advanced technologies. Finally, Dr. Ding recommends that Washington focus on education policy, widening the base of AI engineers by increasing training sites, supporting public-private partnerships, and helping SMEs develop their AI capabilities.

Dr. Jeffrey Ding is an Assistant Professor of Political Science at George Washington University, and the author of Technology and the Rise of Great Power: How Diffusion Shapes Economic Competition. Previously, he was a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. His research has been published or is forthcoming at European Journal of International Security, Foreign Affairs, Review of International Political Economy, and Security Studies, and his work has been cited in The Washington Post, The Financial Times, and other outlets. He also writes a weekly "ChinAI" newsletter, which features translations of Chinese conversations about AI development, to 12,000+ subscribers including the field's leading policymakers, scholars, and journalists. Dr. Ding holds a Ph.D in international relations from Oxford University, where he studied as a Rhodes scholar.

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255 episodes

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