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

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

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

Get full access to The Gradient at thegradientpub.substack.com/subscribe

Guilherme Geirinhas (parte 1): O segredo do Bom Partido para pôr os políticos a falar de si