Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

AI Safety Fundamentals: Alignment

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Did you know there’s an actual science to uncovering your hidden genius? It’s not about filling out a “dream job” worksheet—it’s about understanding how your brain is wired, identifying your natural aptitudes, and using them to thrive. This isn’t just a self-discovery exercise. It’s a game-changer for your career, your relationships, and how you show up in the world. Betsy Wills and Alex Ellison are redefining how we approach career discovery, proving that finding the right path isn’t just about landing a job—it’s about creating a life that aligns with who you actually are. ✅ Betsy Wills – Cofounder of YouScience, a groundbreaking psychometric assessment platform reshaping how we understand our talents. She’s also the Director of Marketing & Branding at Diversified Trust and a frequent lecturer at Vanderbilt University and NYU’s Stern School of Business. ✅ Alex Ellison – Founder of Throughline Guidance, a global college and career counseling practice. She’s a sought-after writer, speaker, and expert in college readiness and career development. ✅ Together, they co-authored Your Hidden Genius: The Science-Backed Strategy to Uncovering and Harnessing Your Innate Talents. Discovering your hidden genius isn’t just about career success—it’s about tapping into what makes you, you . Connect with Betsy & Alex: Website (Free Downloads): www.yourhiddengenius.com Book: https://www.harpercollins.com/products/your-hidden-genius-elizabeth-m-willsalexandra-ellison Related Podcast Episodes: How To Be You, But Better with Olga Khazan | 288 Finding Purpose Through Human Design with Emma Dunwoody | 228 195 / Finding (And Using) Your Voice with Amy Green Smith Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…

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Research in mechanistic interpretability seeks to explain behaviors of machine learning (ML) models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task called indirect object identification (IOI). Our explanation encompasses 26 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches relying on causal interventions. To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model. We evaluate the reliability of our explanation using three quantitative criteria–faithfulness, completeness, and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. Our work provides evidence that a mechanistic understanding of large ML models is feasible, pointing toward opportunities to scale our understanding to both larger models and more complex tasks. Code for all experiments is available at https://github.com/redwoodresearch/Easy-Transformer.
Source:
https://arxiv.org/pdf/2211.00593.pdf
Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

فصل ها

1. Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small (00:00:00)

2. ABSTRACT (00:00:17)

3. 1 INTRODUCTION (00:01:34)

4. 2 BACKGROUND (00:05:58)

5. 2.1 CIRCUITS AND KNOCKOUTS (00:10:41)

6. 3 DISCOVERING THE CIRCUIT (00:14:06)

7. 3.1 WHICH HEADS DIRECTLY AFFECT THE OUTPUT? (NAME MOVER HEADS) (00:19:08)

85 قسمت

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AI Safety Fundamentals: Alignment

Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

AI Safety Fundamentals: Alignment

published حدود یک سال پیش

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بایگانی مجموعه ها ("فیدهای غیر فعال" status)

When? This feed was archived on February 21, 2025 21:08 (2M ago). Last successful fetch was on January 02, 2025 12:05 (3M ago)

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A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

فصل ها

1. Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small (00:00:00)

2. ABSTRACT (00:00:17)

3. 1 INTRODUCTION (00:01:34)

4. 2 BACKGROUND (00:05:58)

5. 2.1 CIRCUITS AND KNOCKOUTS (00:10:41)

6. 3 DISCOVERING THE CIRCUIT (00:14:06)

7. 3.1 WHICH HEADS DIRECTLY AFFECT THE OUTPUT? (NAME MOVER HEADS) (00:19:08)

85 قسمت

#Tech #Society #Philosophy #Blue Dot Impact

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AI Safety Fundamentals: Alignment

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AI Safety Fundamentals: Alignment « » Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

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فصل ها

1. Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small (00:00:00)

2. ABSTRACT (00:00:17)

3. 1 INTRODUCTION (00:01:34)

4. 2 BACKGROUND (00:05:58)

5. 2.1 CIRCUITS AND KNOCKOUTS (00:10:41)

6. 3 DISCOVERING THE CIRCUIT (00:14:06)

7. 3.1 WHICH HEADS DIRECTLY AFFECT THE OUTPUT? (NAME MOVER HEADS) (00:19:08)

Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small

بایگانی مجموعه ها ("فیدهای غیر فعال" status)

فصل ها

1. Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small (00:00:00)

2. ABSTRACT (00:00:17)

3. 1 INTRODUCTION (00:01:34)

4. 2 BACKGROUND (00:05:58)

5. 2.1 CIRCUITS AND KNOCKOUTS (00:10:41)

6. 3 DISCOVERING THE CIRCUIT (00:14:06)

7. 3.1 WHICH HEADS DIRECTLY AFFECT THE OUTPUT? (NAME MOVER HEADS) (00:19:08)

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Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small