Recently, OpenAI made a splash by announcing a new "Superalignment" team. Lead by Jan Leike and Ilya Sutskever, the team would consist of top researchers, attempting to solve alignment for superintelligent AIs in four years by figuring out how to build a trustworthy human-level AI alignment researcher, and then using it to solve the rest of the problem. But what does this plan actually involve? In this episode, I talk to Jan Leike about the plan and the challenges it faces.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

Episode art by Hamish Doodles: hamishdoodles.com/

Topics we discuss, and timestamps:

 - 0:00:37 - The superalignment team

 - 0:02:10 - What's a human-level automated alignment researcher?

   - 0:06:59 - The gap between human-level automated alignment researchers and superintelligence

   - 0:18:39 - What does it do?

   - 0:24:13 - Recursive self-improvement

 - 0:26:14 - How to make the AI AI alignment researcher

   - 0:30:09 - Scalable oversight

   - 0:44:38 - Searching for bad behaviors and internals

   - 0:54:14 - Deliberately training misaligned models

 - 1:02:34 - Four year deadline

   - 1:07:06 - What if it takes longer?

 - 1:11:38 - The superalignment team and...

   - 1:11:38 - ... governance

   - 1:14:37 - ... other OpenAI teams

   - 1:18:17 - ... other labs

 - 1:26:10 - Superalignment team logistics

 - 1:29:17 - Generalization

 - 1:43:44 - Complementary research

 - 1:48:29 - Why is Jan optimistic?

   - 1:58:32 - Long-term agency in LLMs?

   - 2:02:44 - Do LLMs understand alignment?

 - 2:06:01 - Following Jan's research

The transcript: axrp.net/episode/2023/07/27/episode-24-superalignment-jan-leike.html

Links for Jan and OpenAI:

 - OpenAI jobs: openai.com/careers

 - Jan's substack: aligned.substack.com

 - Jan's twitter: twitter.com/janleike

Links to research and other writings we discuss:

 - Introducing Superalignment: openai.com/blog/introducing-superalignment

 - Let's Verify Step by Step (process-based feedback on math): arxiv.org/abs/2305.20050

 - Planning for AGI and beyond: openai.com/blog/planning-for-agi-and-beyond

 - Self-critiquing models for assisting human evaluators: arxiv.org/abs/2206.05802

 - An Interpretability Illusion for BERT: arxiv.org/abs/2104.07143

 - Language models can explain neurons in language models https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html

 - Our approach to alignment research: openai.com/blog/our-approach-to-alignment-research

 - Training language models to follow instructions with human feedback (aka the Instruct-GPT paper): arxiv.org/abs/2203.02155