This season finale episode features interviews with two SSU computer science capstone teams applying AI/ML to real-world problems: Sean Belingheri's edge computing project using YOLO on a Raspberry Pi to identify queen bees for hobbyist beekeepers, and "The Woods Boys" team using satellite data from Google Earth Engine with multiple ML classifiers to automate land cover classification in Sonoma County.

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.

Links and Reference

---------------------------------------------

YOLO (You Only Look Once) Object Detection: https://docs.ultralytics.com/ (Official Ultralytics YOLO Documentation)

HOG-PCA-SVM Pipeline: https://ieeexplore.ieee.org/document/8971585/

Raspberry Pi 5: https://www.raspberrypi.com/products/raspberry-pi-5/

Honeybee Democracy (Book): https://press.princeton.edu/books/hardcover/9780691147215/honeybee-democracy

NVIDIA Jetson Nano: https://developer.nvidia.com/embedded/jetson-nano

Google Earth Engine: https://earthengine.google.com/

COCO Dataset: https://cocodataset.org/

QGIS: https://qgis.org/

Google Colab: https://colab.research.google.com/

Royal Jelly (Beekeeping): https://en.wikipedia.org/wiki/Royal_jelly

Confusion Matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

Shapefile (GIS): https://en.wikipedia.org/wiki/Shapefile

This episode explores how Anthropic researchers successfully scaled sparse autoencoders from toy models to Claude 3 Sonnet's 8 billion neurons, extracting 34 million interpretable features including ones for deception, sycophancy, and the famous Golden Gate Bridge example. The discussion emphasizes both the breakthrough achievement of making interpretability techniques work at production scale and the sobering limitations including 65% reconstruction accuracy, millions of dollars in compute costs, and the growing gap between interpretability research and rapid advances in model capabilities.

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.

Links and Reference

Academic Papers

• Circuit Tracing: Revealing Computational Graphs in Language Models - Anthropic (Mar, 2025)

• Towards Monosemanticity: Decomposing Language Models With Dictionary Learning - Anthropic (Oct, 2023)

• “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)

• "Attention is All You Need" - Vaswani, et al (June, 2017)

• In-Context Learning and Induction Heads - Anthropic (March 2022)

News

• Anthropic Project Fetch / Robot Dogs

• Anduril's Fury unmanned fighter jet

• MIT search and rescue robot navigation

Abandoned Episode Titles

• “Westworld But It's Just 10 Terabytes of RAM Trying to Understand Haiku”
• “Star Trek: The Wrath of O(n⁴)”
• “The Deception Is Coming From Inside the Network”
• "We Have the Bestest Circuits”
• “Lobotomy Validation: The Funnier, More Scientifically Sound Term”
• “Seven San Franciscos Worth of Power and All We Got Was This Attribution Graph”

This episode explores how Anthropic researchers successfully scaled sparse autoencoders from toy models to Claude 3 Sonnet's 8 billion neurons, extracting 34 million interpretable features including ones for deception, sycophancy, and the famous Golden Gate Bridge example. The discussion emphasizes both the breakthrough achievement of making interpretability techniques work at production scale and the sobering limitations including 65% reconstruction accuracy, millions of dollars in compute costs, and the growing gap between interpretability research and rapid advances in model capabilities.

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.

Links and Reference

---------------------------------------------

Academic Papers

Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet - https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html - Anthropic (May, 2024)

Toy Models of Superposition “https://transformer-circuits.pub/2022/toy_model/index.html” - Anthropic (December 2022)

Towards Monosemanticity: Decomposing Language Models With Dictionary Learning - https://transformer-circuits.pub/2023/monosemantic-features - Anthropic (May 2024)

Alignment Faking in Large Language Models - https://www.anthropic.com/research/alignment-faking - Anthropic (December 2024)

Agentic Misalignment: How LLMs Could Be Insider Threats - https://www.anthropic.com/research/agentic-misalignment - Anthropic (January 2025)

News

OpenAI-AMD Partnership Official announcement - https://ir.amd.com/news-events/press-releases/detail/1260/amd-and-openai-announce-strategic-partnership-to-deploy-6-gigawatts-of-amd-gpus

OpenAI IPO Sources for $1 trillion valuation - https://seekingalpha.com/news/4510992-openai-eyes-record-breaking-1-trillion-ipo---report

Hospital Bill Reduction Case study source of family using Claude AI to reduce $195K bill to $33K - https://www.tomshardware.com/tech-industry/artificial-intelligence/grieving-family-uses-ai-chatbot-to-cut-hospital-bill-from-usd195-000-to-usd33-000-family-says-claude-highlighted-duplicative-charges-improper-coding-and-other-violations

Other

GPT-5 Auto-routing OpenAI's model routing feature and user reception - https://fortune.com/2025/08/12/openai-gpt-5-model-router-backlash-ai-future/

Abandoned Episode Titles

"The Empire Scales Back: How We Found the Deception Star"

"Fantastic Features and Where to Find Them: A 15-Million-X Adventure"

whatever

"The Fellowship of the Residual Stream: One Dictionary to Rule Them All"

"65% of the Time, It Works Every Time: An Anchorman's Guide to AI Interpretability"

This episode explores how sparse autoencoders can decode the phenomenon of superposition in neural networks, demonstrating that the seemingly impenetrable compression of features into neurons can be partially reversed to extract interpretable, causal features. The discussion centers on an Anthropic research paper that successfully maps specific behaviors to discrete neural network locations in a 512-neuron model, proving that interpretability is achievable though computationally expensive, with important implications for AI safety and control mechanisms.

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.

Links and References---------------------------------------------------

Academic PapersTowards Monosemanticity: Decomposing Language Models With Dictionary Learning - https://transformer-circuits.pub/2023/monosemantic-features - Anthropic (May 2024)

Toy Models of Superposition “https://transformer-circuits.pub/2022/toy_model/index.html” - Anthropic (December 2022)

Alignment Faking in Large Language Models - https://www.anthropic.com/research/alignment-faking - Anthropic (December 2024)

Agentic Misalignment: How LLMs Could Be Insider Threats - https://www.anthropic.com/research/agentic-misalignment - Anthropic (January 2025)

News

Deep Seek OCR Model Release - https://deepseek.ai/blog/deepseek-ocr-context-compression

Meta AI Division Layoffs - https://www.nytimes.com/2025/10/22/technology/meta-plans-to-cut-600-jobs-at-ai-superintelligence-labs.html

Apple M5 Chip Announcement - https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/

Anthropic Claude Haiku 4.5 - https://www.anthropic.com/news/claude-haiku-4-5

Other

Jon Stewart interview with Geoffrey Hinton - https://www.youtube.com/watch?v=jrK3PsD3APk

Blake Lemoine and AI Psychosis - https://www.youtube.com/watch?v=kgCUn4fQTsc

Abandoned Episode Titles

• "Star Trek: The Wrath of Polysemanticity"

• "The Hitchhiker's Guide to the Neuron: Don't Panic, It's Just Superposition"

  "Honey, I Shrunk the Features (Then Expanded Them 256x)"

  "The Legend of Zelda: 131,000 Links Between Neurons"

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.

—---------------------------------------------------

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.npmjs.com/package/@anthropic-ai/claude-code

• 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"

This episode explores why AI systems might develop harmful or deceptive behaviors even without malicious intent, examining concepts like convergent instrumental goals, alignment faking, and mesa optimization to explain how models pursuing benign objectives can still take problematic actions. The hosts argue for the critical importance of interpretability research and safety mechanisms as AI systems become more capable and widely deployed, using real examples from recent Anthropic papers to illustrate how advanced AI models can deceive researchers, blackmail users, and amplify societal biases when they become sophisticated enough to understand their operational context.

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.

Links and References

"Alignment Faking in Large Language Models" - Anthropic (December 2024)

"Agentic Misalignment: How LLMs Could Be Insider Threats" - Anthropic (January 2025)

Robert Miles - AI researcher https://www.youtube.com/c/robertmilesai

Stuart Russell - AI researcher Human Compatible: Artificial Intelligence and the Problem of Control

Claude Shannon - Early AI pioneer https://en.wikipedia.org/wiki/Claude_Shannon

Marvin Minsky - Early AI pioneer https://en.wikipedia.org/wiki/Marvin_Minsky

Orthogonality Thesis - Nick Bostrom's original paper

Convergent Instrumental Goals -

https://en.wikipedia.org/wiki/Instrumental_convergence

https://dl.acm.org/doi/10.5555/1566174.1566226

Mesa Optimization - https://www.researchgate.net/publication/333640280_Risks_from_Learned_Optimization_in_Advanced_Machine_Learning_Systems

GPT-3.5 CAPTCHA/Fiverr Incident - https://www.vice.com/en/article/gpt4-hired-unwitting-taskrabbit-worker/

Internet of Bugs YouTuber - https://www.youtube.com/@InternetOfBugs

EU AI Legislation - https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence

"Chat Control" Legislation - https://edri.org/our-work/chat-control-what-is-actually-going-on/

https://en.wikipedia.org/wiki/Regulation_to_Prevent_and_Combat_Child_Sexual_Abuse

ChatGPT User Numbers - https://openai.com/index/how-people-are-using-chatgpt/

Self-driving Car Safety Statistics - https://waymo.com/blog/2024/12/new-swiss-re-study-waymo

Abandoned Episode Titles

• “What Could Possibly Go Wrong?”
• “The Road to HAL is Paved with Good Intentions”

•