The Gradient
The Gradient: Perspectives on AI
Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve
4
1
0:00
-1:49:32

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

On why "AI will change the world" narratives might be less true than you think.
4
1

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 | RSS
Follow 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:

Discussion about this podcast

The Gradient
The Gradient: Perspectives on AI
Deeply researched, technical interviews with experts thinking about AI and technology. Hosted, recorded, researched, and produced by Daniel Bashir.