In episode 91 of The Gradient Podcast, Daniel Bashir speaks to Arjun Ramani and Zhengdong Wang.
Arjun is the global business and economics correspondent at The Economist.
Zhengdong is a research engineer at Google DeepMind.
Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub
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Outline:
(00:00) Intro
(03:53) Arjun intro
(06:04) Zhengdong intro
(09:50) How Arjun and Zhengdong met in the woods
(11:52) Overarching narratives about technological progress and AI
(14:20) Setting up the claim: Arjun on what “transformative” means
(15:52) What enables transformative economic growth?
(21:19) From GPT-3 to ChatGPT; is there something special about AI?
(24:15) Zhengdong on “real AI” and divisiveness
(27:00) Arjun on the independence of bottlenecks to progress/growth
(29:05) Zhengdong on bottleneck independence
(32:45) More examples on bottlenecks and surplus wealth
(37:06) Technical arguments—what are the hardest problems in AI?
(38:00) Robotics
(40:41) Challenges of deployment in high-stakes settings and data sources / synthetic data, self-driving
(45:13) When synthetic data works
(49:06) Harder tasks, process knowledge
(51:45) Performance art as a critical bottleneck
(53:45) Obligatory Taylor Swift Discourse
(54:45) AI Taylor Swift???
(54:50) The social arguments
(55:20) Speed of technology diffusion — “diffusion lags” and dynamics of trust with AI
(1:00:55) ChatGPT adoption, where major productivity gains come from
(1:03:50) Timescales of transformation
(1:10:22) Unpredictability in human affairs
(1:14:07) The economic arguments
(1:14:35) Key themes — diffusion lags, different sectors
(1:21:15) More on bottlenecks, AI trust, premiums on human workers
(1:22:30) Automated systems and human interaction
(1:25:45) Campaign text reachouts
(1:30:00) Counterarguments
(1:30:18) Solving intelligence and solving science/innovation
(1:34:07) Strengths and weaknesses of the broad applicability of Arjun and Zhengdong’s argument
(1:35:34) The “proves too much” worry — how could any innovation have ever happened?
(1:37:25) Examples of bringing down barriers to innovation/transformation
(1:43:45) What to do with all of this information?
(1:48:45) Outro
Links:
Why transformative artificial intelligence is really, really hard to achieve
Other resources and links mentioned:
Allan-Feuer and Sanders: Transformative AGI by 2043 is <1% likely
David Autor: new work paper
Culture Series book 1, Iain Banks
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