Roman Yampolskiy, Professor of Computer Science at the University of Louisville, joins us to discuss whether we can control, comprehend, and explain AI systems, and how this constrains the project of AI safety.

 Topics discussed in this episode include:

-Roman’s results on the unexplainability, incomprehensibility, and uncontrollability of AI

-The relationship between AI safety, control, and alignment

-Virtual worlds as a proposal for solving multi-multi alignment

-AI security

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0:00 Intro 

2:35 Roman’s primary research interests 

4:09 How theoretical proofs help AI safety research 

6:23 How impossibility results constrain computer science systems

10:18 The inability to tell if arbitrary code is friendly or unfriendly 

12:06 Impossibility results clarify what we can do 

14:19 Roman’s results on unexplainability and incomprehensibility 

22:34 Focusing on comprehensibility 

26:17 Roman’s results on uncontrollability 

28:33 Alignment as a subset of safety and control 

30:48 The relationship between unexplainability, incomprehensibility, and uncontrollability with each other and with AI alignment 

33:40 What does it mean to solve AI safety? 

34:19 What do the impossibility results really mean? 

37:07 Virtual worlds and AI alignment 

49:55 AI security and malevolent agents 

53:00 Air gapping, boxing, and other security methods 

58:43 Some examples of historical failures of AI systems and what we can learn from them 

1:01:20 Clarifying impossibility results

1:06 55 Examples of systems failing and what these demonstrate about AI 

1:08:20 Are oracles a valid approach to AI safety? 

1:10:30 Roman’s final thoughts

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