Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve

In episode 91 of The Gradient Podcast, Daniel Bashir (twitter.com/spaniel_bashir) speaks to Arjun Ramani (twitter.com/arjun_ramani3) and Zhengdong Wang (twitter.com/zhengdongwang).
Arjun is the global business and economics correspondent at The Economist.
Zhengdong is a research engineer at Google DeepMind.
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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:
* Zhengdong’s homepage (zhengdongwang.com) and Twitter (twitter.com/zhengdongwang)
* Arjun’s homepage (arjunramani.com) and Twitter (twitter.com/arjun_ramani3)
* Why transformative artificial intelligence is really, really hard to achieve (thegradient.pub/why-transformative-artificial-…)
* Other resources and links mentioned:
* Allan-Feuer and Sanders: Transformative AGI by 2043 is * On AlphaStar Zero (x.com/banburismus_/status/1697342740764168277?…)
* Hardmaru on AI as applied philosophy (x.com/hardmaru/status/1493026762514890752?s=20)
* Robotics Transformer 2 (deepmind.com/blog/rt-2-new-model-translates-vi…)
* Davis Blalock on synthetic data (dblalock.substack.com/p/models-generating-trai…)
* Matt Clancy on automating invention and bottlenecks (mattsclancy.substack.com/p/what-if-we-could-au…)
* Michael Webb on 80,000 Hours Podcast (80000hours.org/podcast/episodes/michael-webb-a…)
* Bob Gordon: The Rise and Fall of American Growth (goodreads.com/book/show/26634594-the-rise-and-…)
* OpenAI economic impact paper (arxiv.org/abs/2303.10130)
* David Autor: new work paper (economics.mit.edu/sites/default/files/2022-11/…)
* Baumol effect paper (jstor.org/stable/1816292)
* Pew research centre poll, public concern on AI (pewresearch.org/short-reads/2023/08/28/growing…)
* Human premium Economist piece (economist.com/finance-and-economics/2023/05/23…)
* Callum Williams — London tube and AI/jobs (economist.com/finance-and-economics/2023/05/07…)
* Culture Series book 1 (goodreads.com/book/show/8935689-consider-phleb…), Iain Banks

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