Dr. Keith Duggar from Machine Learning Street Talk was the subject of my recent reaction episode about whether GPT o1 can reason. But instead of ignoring or blocking me, Keith was brave enough to come into the lion’s den and debate his points with me… and his P(doom) might shock you!
First we debate whether Keith’s distinction between Turing Machines and Discrete Finite Automata is useful for understanding limitations of current LLMs. Then I take Keith on a tour of alignment, orthogonality, instrumental convergence, and other popular stations on the “doom train”, to compare our views on each.
Keith was a great sport and I think this episode is a classic!
00:00 Introduction
00:46 Keith’s Background
03:02 Keith’s P(doom)
14:09 Are LLMs Turing Machines?
19:09 Liron Concedes on a Point!
21:18 Do We Need >1MB of Context?
27:02 Examples to Illustrate Keith’s Point
33:56 Is Terence Tao a Turing Machine?
38:03 Factoring Numbers: Human vs. LLM
53:24 Training LLMs with Turing-Complete Feedback
1:02:22 What Does the Pillar Problem Illustrate?
01:05:40 Boundary between LLMs and Brains
1:08:52 The 100-Year View
1:18:29 Intelligence vs. Optimization Power
1:23:13 Is Intelligence Sufficient To Take Over?
01:28:56 The Hackable Universe and AI Threats
01:31:07 Nuclear Extinction vs. AI Doom
1:33:16 Can We Just Build Narrow AI?
01:37:43 Orthogonality Thesis and Instrumental Convergence
01:40:14 Debating the Orthogonality Thesis
02:03:49 The Rocket Alignment Problem
02:07:47 Final Thoughts
Show Notes
Keith’s show: https://www.youtube.com/@MachineLearningStreetTalk
Keith’s Twitter: https://x.com/doctorduggar
Keith’s fun brain teaser that LLMs can’t solve yet, about a pillar with four holes: https://youtu.be/nO6sDk6vO0g?si=diGUY7jW4VFsV0TJ&t=3684
Eliezer Yudkowsky’s classic post about the “Rocket Alignment Problem”: https://intelligence.org/2018/10/03/rocket-alignment/
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