This episode of "Two Minds, One Model" explores the critical concept of interpretability in AI systems, focusing on Anthropic's research paper "Toy Models of Superposition." Hosts John Jezl and Jon Rocha from Sonoma State University's Computer Science Department delve into why neural networks are often "black boxes" and what this means for AI safety and deployment.
Credits
Cover Art by Brianna Williams
TMOM Intro Music by Danny Meza
A special thank you to these talented artists for their contributions to the show.
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Links and Reference
Academic Papers
“Toy Models of Superposition” - Anthropic (December 2022)
"Alignment Faking in Large Language Models" - Anthropic (December 2024)
"Agentic Misalignment: How LLMs Could Be Insider Threats" - Anthropic (January 2025)
News
https://www.wired.com/story/thinking-machines-lab-first-product-fine-tune/
https://www.wired.com/story/chatbots-play-with-emotions-to-avoid-saying-goodbye/
Harvard Business School study on companion chatbots
Misc
“Words are but vague shadows of the volumes we mean”' - Theodore Dreiser
3Blue1Brown video about vectors - https://www.youtube.com/shorts/FJtFZwbvkI4
GPT-3 parameter count Correction: https://en.wikipedia.org/wiki/GPT-3#:~:text=GPT%2D3%20has%20175%20billion,each%20parameter%20occupies%202%20bytes.
ImageNet: ImageNet: A Large-Scale Hierarchical Image Database
We mention Waymo a lot in this episode and felt it was important to link to their safety page: https://waymo.com/safety/
Abandoned Episode Titles
"404: Interpretation Not Found"
"Neurons Gone Wild: Spring Break Edition"
"These Aren't the Features You're Looking For”
"Bigger on the Inside"
