1 hr 50 min
Seth Lazar: Normative Philosophy of Computing The Gradient: Perspectives on AI
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- Technology
Episode 124
You may think you’re doing a priori reasoning, but actually you’re just over-generalizing from your current experience of technology.
I spoke with Professor Seth Lazar about:
* Why managing near-term and long-term risks isn’t always zero-sum
* How to think through axioms and systems in political philosphy
* Coordination problems, economic incentives, and other difficulties in developing publicly beneficial AI
Seth is Professor of Philosophy at the Australian National University, an Australian Research Council (ARC) Future Fellow, and a Distinguished Research Fellow of the University of Oxford Institute for Ethics in AI. He has worked on the ethics of war, self-defense, and risk, and now leads the Machine Intelligence and Normative Theory (MINT) Lab, where he directs research projects on the moral and political philosophy of AI.
Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
Outline:
* (00:00) Intro
* (00:54) Ad read — MLOps conference
* (01:32) The allocation of attention — attention, moral skill, and algorithmic recommendation
* (03:53) Attention allocation as an independent good (or bad)
* (08:22) Axioms in political philosophy
* (11:55) Explaining judgments, multiplying entities, parsimony, intuitive disgust
* (15:05) AI safety / catastrophic risk concerns
* (22:10) Superintelligence arguments, reasoning about technology
* (28:42) Attacking current and future harms from AI systems — does one draw resources from the other?
* (35:55) GPT-2, model weights, related debates
* (39:11) Power and economics—coordination problems, company incentives
* (50:42) Morality tales, relationship between safety and capabilities
* (55:44) Feasibility horizons, prediction uncertainty, and doing moral philosophy
* (1:02:28) What is a feasibility horizon?
* (1:08:36) Safety guarantees, speed of improvements, the “Pause AI” letter
* (1:14:25) Sociotechnical lenses, narrowly technical solutions
* (1:19:47) Experiments for responsibly integrating AI systems into society
* (1:26:53) Helpful/honest/harmless and antagonistic AI systems
* (1:33:35) Managing incentives conducive to developing technology in the public interest
* (1:40:27) Interdisciplinary academic work, disciplinary purity, power in academia
* (1:46:54) How we can help legitimize and support interdisciplinary work
* (1:50:07) Outro
Links:
* Seth’s Linktree and Twitter
* Resources
* Attention, moral skill, and algorithmic recommendation
* Catastrophic AI Risk slides
Get full access to The Gradient at thegradientpub.substack.com/subscribe
Episode 124
You may think you’re doing a priori reasoning, but actually you’re just over-generalizing from your current experience of technology.
I spoke with Professor Seth Lazar about:
* Why managing near-term and long-term risks isn’t always zero-sum
* How to think through axioms and systems in political philosphy
* Coordination problems, economic incentives, and other difficulties in developing publicly beneficial AI
Seth is Professor of Philosophy at the Australian National University, an Australian Research Council (ARC) Future Fellow, and a Distinguished Research Fellow of the University of Oxford Institute for Ethics in AI. He has worked on the ethics of war, self-defense, and risk, and now leads the Machine Intelligence and Normative Theory (MINT) Lab, where he directs research projects on the moral and political philosophy of AI.
Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
Outline:
* (00:00) Intro
* (00:54) Ad read — MLOps conference
* (01:32) The allocation of attention — attention, moral skill, and algorithmic recommendation
* (03:53) Attention allocation as an independent good (or bad)
* (08:22) Axioms in political philosophy
* (11:55) Explaining judgments, multiplying entities, parsimony, intuitive disgust
* (15:05) AI safety / catastrophic risk concerns
* (22:10) Superintelligence arguments, reasoning about technology
* (28:42) Attacking current and future harms from AI systems — does one draw resources from the other?
* (35:55) GPT-2, model weights, related debates
* (39:11) Power and economics—coordination problems, company incentives
* (50:42) Morality tales, relationship between safety and capabilities
* (55:44) Feasibility horizons, prediction uncertainty, and doing moral philosophy
* (1:02:28) What is a feasibility horizon?
* (1:08:36) Safety guarantees, speed of improvements, the “Pause AI” letter
* (1:14:25) Sociotechnical lenses, narrowly technical solutions
* (1:19:47) Experiments for responsibly integrating AI systems into society
* (1:26:53) Helpful/honest/harmless and antagonistic AI systems
* (1:33:35) Managing incentives conducive to developing technology in the public interest
* (1:40:27) Interdisciplinary academic work, disciplinary purity, power in academia
* (1:46:54) How we can help legitimize and support interdisciplinary work
* (1:50:07) Outro
Links:
* Seth’s Linktree and Twitter
* Resources
* Attention, moral skill, and algorithmic recommendation
* Catastrophic AI Risk slides
Get full access to The Gradient at thegradientpub.substack.com/subscribe
1 hr 50 min
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