AGI can beat top players in chess, poker, and, now, Diplomacy. In November 2022, a bot named Cicero demonstrated mastery in this game, which requires natural language negotiation and cooperation with humans. In short, Cicero can lie, scheme, build trust, pass as human, and ally with humans. So what does that mean for the future of AGI?

This week’s guest is research scientist Noam Brown. He co-created Cicero on the Meta Fundamental AI Research Team, and is considered one of the smartest engineers and researchers working in AI today.

Co-hosts Sarah Guo and Elad Gil talk to Noam about why all research should be high risk, high reward, the timeline until we have AGI agents negotiating with humans, why scaling isn’t the only path to breakthroughs in AI, and if the Turing Test is still relevant.

Show Links:

• More about Noam Brown
• Read the research article about Cicero (diplomacy) published in Science. 
• Read the research article about Liberatus  (heads-up poker) published in Science. 
• Read the research article about Pluribus (multiplayer poker) published in Science. 
• Watch the AlphaGo Documentary.
• Read “How Smart Are the Robots Getting?” by New York Times reporter Cade Metz 

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Show Notes: 

[01:43] - What sparked Noam’s interest in researching AI that could defeat games

[6:00] - How the AlexaNET and AlphaGo changed the landscape of AI research

[8:09] - Why Noam chose Diplomacy as the next game to work on after poker

[9:51] - What Diplomacy is and why the game was so challenging for an AI bot

[14:50] - Algorithmic breakthroughs and significance of AI bots that win in No-Limit Texas Hold'em poker

[23:29] - The Nash Equilibrium and optimal play in poker

[24:53] - How Cicero interacted with humans 

[27:58] - The relevance and usefulness of the Turing Test

[31:05] - The data set used to train Cicero

[31:54] - Bottlenecks to AI researchers and challenges with scaling

[40:10] - The next frontier in researching games for AI

[42:55] - Domains that humans will still dominate and applications for AI bots in the real world

[48:13] - Reasoning challenges with AI