Jeremie Harris: Realistic Alignment and AI Policy

Jeremie Harris: Realistic Alignment and AI Policy

Author: Daniel Bashir June 29, 2023 Duration: 1:30:35

In episode 79 of The Gradient Podcast, Daniel Bashir speaks to Jeremie Harris.

Jeremie is co-founder of Gladstone AI, author of the book Quantum Physics Made Me Do It, and co-host of the Last Week in AI Podcast. Jeremy previously hosted the Towards Data Science podcast and worked on a number of other startups after leaving a PhD in physics.

Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub

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Outline:

* (00:00) Intro

* (01:37) Jeremie’s physics background and transition to ML

* (05:19) The physicist-to-AI person pipeline, how Jeremie’s background impacts his approach to AI

* (08:20) A tangent on inflationism/deflationism about natural laws (I promise this applies to AI)

* (11:45) How ML implies a particular viewpoint on the above question

* (13:20) Jeremie’s first (recommendation systems) company, how startup founders can make mistakes even when they’ve read Paul Graham essays

* (17:30) Classic startup wisdom, different sorts of startups

* (19:35) OpenAI’s approach in shipping features for DALL-E 2 and generation vs. discrimination as an approach to product

* (24:55) Capabilities and risk

* (26:43) Commentary on fundamental limitations of alignment in LLMs

* (30:45) Intrinsic difficulties in alignment problems

* (41:15) Daniel tries to steel man / defend anti-longtermist arguments (nicely :) )

* (46:23) Anthropic’s paper on asking models to be less biased

* (47:20) Why Jeremie is excited about Anthropic’s Constitutional AI scheme

* (51:05) Jeremie’s thoughts on recent Eliezer discourse

* (56:50) Cheese / task vectors and steerability/controllability in LLMs

* (59:50) Difficulty of one-shot solutions in alignment work, better strategies

* (1:02:00) Lack of theoretical understanding of deep learning systems / alignment

* (1:04:50) Jeremie’s work and perspectives on AI policy

* (1:10:00) Incrementality in convincing policymakers

* (1:14:00) How recent developments impact policy efforts

* (1:16:20) Benefits and drawbacks of open source

* (1:19:30) Arguments in favor of (limited) open source

* (1:20:35) Quantum Physics (not Mechanics) Made Me Do It

* (1:24:10) Some theories of consciousness and corresponding physics

* (1:29:49) Outro

Links:

* Jeremie’s Twitter

* Quantum Physics Made Me Do It

* Gladstone AI



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Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
Author: Language: English Episodes: 100

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