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

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:

* Zhengdong’s homepage and Twitter

* Arjun’s homepage and Twitter

* 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

* On AlphaStar Zero

* Hardmaru on AI as applied philosophy

* Robotics Transformer 2

* Davis Blalock on synthetic data

* Matt Clancy on automating invention and bottlenecks

* Michael Webb on 80,000 Hours Podcast

* Bob Gordon: The Rise and Fall of American Growth

* OpenAI economic impact paper

* David Autor: new work paper

* Baumol effect paper

* Pew research centre poll, public concern on AI

* Human premium Economist piece

* Callum Williams — London tube and AI/jobs

* Culture Series book 1, Iain Banks