# Alignment Workshop talks

In February 2023, researchers from a number of top industry AI labs (OpenAI, DeepMind, Anthropic) and universities (Cambridge, NYU) co-organized a two-day workshop on the problem of AI alignment, attended by 80 of the world’s leading machine learning researchers. We’re now making recordings and transcripts of the talks available online. The content ranged from very concrete to highly speculative, and the recordings include the many questions, interjections and debates which arose throughout.

If you're a machine learning researcher interested in attending follow-up workshops similar to the San Francisco alignment workshop, you can fill out [this form](https://forms.gle/wxXn5b8b7afJMVjq7).

**Main Talks**

[**Ilya Sutskever** \- Opening Remarks: Confronting the Possibility of AGI](https://www.alignment-workshop.com/sf-talks-and-transcripts/ilya-sutskever-opening-remarks-confronting-the-possibility-of-agi)  
[**Jacob Steinhardt** \- Aligning Massive Models: Current and Future Challenges](https://www.alignment-workshop.com/sf-talks-and-transcripts/jacob-steinhardt-aligning-massive-models-current-and-future-challenges)  
[**Ajeya Cotra** \- “Situational Awareness” Makes Measuring Safety Tricky](https://www.alignment-workshop.com/sf-talks-and-transcripts/ajeya-cotra-situational-awareness-makes-measuring-safety-tricky)  
[**Paul Christiano** \- How Misalignment Could Lead to Takeover](https://www.alignment-workshop.com/sf-talks-and-transcripts/paul-christiano-how-misalignment-could-lead-to-takeover)  
[**Jan Leike** \- Scaling Reinforcement Learning from Human Feedback](https://www.alignment-workshop.com/sf-talks-and-transcripts/jan-leike-scaling-reinforcement-learning-from-human-feedback)  
[**Chris Olah** \- Looking Inside Neural Networks with Mechanistic Interpretability](https://www.alignment-workshop.com/sf-talks-and-transcripts/chris-olah-looking-inside-neural-networks-with-mechanistic-interpretabili)  
[**Dan Hendrycks** \- Surveying Safety Research Directions](https://www.alignment-workshop.com/sf-talks-and-transcripts/dan-hendrycks-surveying-safety-research-directions)

**Lightning talks (Day 1)**

**Jason Wei** \- Emergent abilities of language models  
**Martin Wattenberg** \- Emergent world models and instrumenting AI systems  
**Been Kim** \- Alignment, setbacks and beyond alignment  
**Jascha Sohl-Dickstein** \- More intelligent agents behave less coherently  
**Ethan Perez** \- Model-written evals  
**Daniel Brown** \- Challenges and progress towards efficient and causal preference-based reward wearning  
**Boaz Barak** \- For both alignment and utility: focus on the medium term  
**Ellie Pavlick** \- Comparing neural networks' conceptual representations to humans’  
**Percy Liang** \- Transparency and standards for language model evaluation

**Lightning talks (Day 2)**

**Sam Bowman** \- Measuring progress on scalable oversight for large language models  
**Zico Kolter** \- "Safe Mode": the case for (manually) verifying the output of LLMs  
**Roger Grosse** \- Understanding LLM generalization using influence functions  
**Scott Niekum** \- Models of human preferences for learning reward functions  
**Aleksander Madry** \- Faster datamodels as a new approach to alignment  
**Andreas Stuhlmuller** \- Iterated decomposition: improving science Q&A by supervising reasoning processes  
**Paul Christiano** \- Mechanistic anomaly detection  
**Lionel Levine** \- Social dynamics of reinforcement learners  
**Vincent Conitzer** \- Foundations of cooperative AI lab  
**Scott Aaronson** \- Cryptographic backdoors in large language models