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Summary
This post gives my personal take on “AI for epistemics” and how important it might be to work on.
Some background context:
AI capabilities are advancing rapidly and I think it's important to think ahead and prepare for the possible development of AI that could automate almost all economically relevant tasks that humans can do.[1]
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Outline:
(00:04) Summary
(04:36) Structure of the post
(05:33) Previous work
(07:29) Why work on AI for epistemics?
(07:34) Summary
(09:38) Importance
(12:15) Path-dependence
(14:45) To be more concrete
(15:37) Good norms and practices for AI-as-knowledge-producers
(17:12) Good norms and practices for AI-as-communicators
(19:30) Differentially high epistemic capabilities
(27:12) Heuristics for good interventions
(28:01) Direct vs. indirect strategies
(29:24) Indirect value generation
(33:30) On long-lasting differential capability improvements
(36:11) Painting a picture of the future
(41:04) Concrete projects for differentially advancing epistemic capabilities
(43:47) Evals/benchmarks for forecasting (or other ambitious epistemic assistance)
(44:27) Automate forecasting question-generation and -resolution
(45:59) Logistics of past-casting
(50:22) Start efforts inside of AI companies for AI forecasting or other ambitious epistemic assistance
(51:34) Scalable oversight / weak-to-strong-generalization / ELK
(53:53) Experiments on what type of arguments and AI interactions tend to lead humans toward truth vs. mislead them
(56:12) Concluding thoughts
The original text contained 22 footnotes which were omitted from this narration.
---
First published:
Source:
Narrated by TYPE III AUDIO.
Summary
This post gives my personal take on “AI for epistemics” and how important it might be to work on.
Some background context:
AI capabilities are advancing rapidly and I think it's important to think ahead and prepare for the possible development of AI that could automate almost all economically relevant tasks that humans can do.[1]
---
Outline:
(00:04) Summary
(04:36) Structure of the post
(05:33) Previous work
(07:29) Why work on AI for epistemics?
(07:34) Summary
(09:38) Importance
(12:15) Path-dependence
(14:45) To be more concrete
(15:37) Good norms and practices for AI-as-knowledge-producers
(17:12) Good norms and practices for AI-as-communicators
(19:30) Differentially high epistemic capabilities
(27:12) Heuristics for good interventions
(28:01) Direct vs. indirect strategies
(29:24) Indirect value generation
(33:30) On long-lasting differential capability improvements
(36:11) Painting a picture of the future
(41:04) Concrete projects for differentially advancing epistemic capabilities
(43:47) Evals/benchmarks for forecasting (or other ambitious epistemic assistance)
(44:27) Automate forecasting question-generation and -resolution
(45:59) Logistics of past-casting
(50:22) Start efforts inside of AI companies for AI forecasting or other ambitious epistemic assistance
(51:34) Scalable oversight / weak-to-strong-generalization / ELK
(53:53) Experiments on what type of arguments and AI interactions tend to lead humans toward truth vs. mislead them
(56:12) Concluding thoughts
The original text contained 22 footnotes which were omitted from this narration.
---
First published:
Source:
Narrated by TYPE III AUDIO.
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