Scaling interpretability

Science and engineering are inseparable. Our researchers reflect on the close relationship between scientific and engineering progress, and discuss the technical challenges they encountered in scaling our interpretability research to much larger AI models.

[palimondo]

It’s win for humanity when quants quit finance to work on AI interpretability! Thank you 🙏

[taiyoinoue]

I absolutely love the results on interpretability discussed here. The Scaling Monosemanticity paper blew my mind, and I was raving about it to anyone who would listen. It is so wonderful to get the chance to see you all talk about this stuff. When I was a kid, I wanted to be one of those NASA engineers who sat in the command center doing calculations to explore outer space. Alas, I'm now a middle-aged pure mathematician. But now, if I was a kid, I'd want to be an AI interp researcher and do calculations to explore the space of possible minds.

[toubxvo]

# Interpretability Engineering at Anthropic

## Chapter 1: Introductions and Background
****0:00**-**1:15****
- Team members: Josh Batson, Jonathan Marcus, Adly, TC.
- Backgrounds in finance, exchange learning, and backend work.

## Chapter 2: Recent Interpretability Release
****1:15**-**4:41****
- Transition from small to large models.
- Goal: Extract interpretable features from production models.

## Chapter 3: Discoveries and Features
****4:41**-**8:16****
- Examples: Functions that add numbers, veganism feature.
- Multimodal features: code backdoors, hidden cameras.

## Chapter 4: Golden Gate Claude Experiment
****8:16**-**10:42****
- Experiment: Claude responding with Golden Gate Bridge information.
- Rapid implementation and success.

## Chapter 5: Scaling Challenges
****10:42**-**13:24****
- Scaling dictionary learning technique.
- Transition from single GPU to multiple GPUs.
- Sparse auto-encoders: scalability and initial doubts.

## Chapter 6: Engineering Efforts and Trade-offs
****13:24**-**17:17****
- Efficiently shuffling large data sets.
- Balancing short-term experiments and long-term infrastructure.
- Parallel shuffling for massive data.

## Chapter 7: Research Engineering Dynamics
****17:17**-**23:43****
- Differences between product and research engineering.
- Importance of flexible, iterative development.
- Strategies for testing and debugging.

## Chapter 8: Interdisciplinary Collaboration
****23:43**-**32:03****
- Collaboration enhances outcomes.
- Importance of diverse skill sets.
- Pairing different experts together.

## Chapter 9: Future of Interpretability
****32:03**-**39:29****
- Vision: Analyze all layers of production models.
- Goals: Understand feature interactions and model circuits.
- Scaling techniques to address AI safety challenges.

## Chapter 10: Personal Reflections and Team Dynamics
****39:29**-**53:12****
- Personal motivations for working in interpretability.
- Challenges and satisfactions of the field.
- Encouragement for new team members to apply.

[kekekekatie]

More of this please! There's a real hunger out here in reality-land for this stuff.

[trpultz]

I have my issues with Claude, but I appreciate the openness! Look forward to (hopefully) future round tables!

[TomGally]

This is a great discussion. Many thanks for posting it.

I read your “Scaling Monosemanticity” paper soon after it was released and have been telling people how important it is. It’s pretty dense reading, though, and its implications are not yet widely recognized. Nearly every day I still see comments from people dismissing large language models as “just predicting the next word.” I hope Anthropic can produce more videos like this but aimed at a wider audience, so that more people will understand how meaning is represented in LLMs and how their performance can be adjusted for safety and other purposes.

[cheshirecat]

I just want to say thank you very much to Anthropic and its employees for being more considerate of the risks of AI, compared to OpenAI. Thank you for working on interpretability which has the promise of being able to control models, which will be very important when AGI comes.

[haihuang]

It's so cool to see big achievements made by newly joined team members!

[johnnykidblue]

Now maybe people will finally stop saying “they don’t really understand, they’re just predicting the next word.”

They do understand, and they will take your job.

[mattwesney]

love this. glad to see you guys putting content out like this! a lot of us are rooting for you

[bobbyjunelive]

Thank you for not saying ‘right’ after every claim. I would enjoy more bench-engineering discussions like this.

So we could have our leaders remain in our view…and so happy, I would say, that we have likable, believable and even attractive figureheads. Even in the hardware zone like Nvidia…everyone is stellar. Love it. Now we can have this top level working scientists. Brains we need to hear from to fill in the gargantuan gaps the founders must leave out. Would love more.

One last level would be worth trying is a group that consists of zero leads. Maybe not only coders but maybe one coder, a marketer, tech support, psych, etc.

Let’s expand this transparency (not for safety) so we can not only enjoy the exercise of it all but also another smattering of education to let us out here move with you as you unleash it all.

Thank you.

[TheLegendaryHacker]

7:35 Huh, this makes me wonder if you could "rip out" the part of Sonnet that fires for hidden cameras, put that into a smaller model, and get a lightweight SOTA hidden camera detector

[mustafaozgul]

This is amazing how wonderful achievements succeed by new members 👏👏👏👏👏.

Understanding nanostructures, molecular levels physiology and chemistry in cells, these knowledge and experience allow you to understand whole human organism in macro-level. The researchers here use various versions of these in AI to scale interpretability and understand mechanism answers of AI. They are really interesting as a researcher.

[nossonweissman]

40:27 this is so on point and commonly overlooked. Applies in all areas of life!

[NandoPrm]

Thank you all for the work you do! Interpretability is a cornerstone for adaptability by the general public. As societal impact of this technology also SCALES, we need both the transparency and the tools to mitigate fear of the unknown. Please keep this type of content coming!

[TheExodusLost]

This is a great type of content. Getting ai researchers in a room together talking to other researchers is a new take for most of us.

I’m curious if they get a bonus or incentive to do this podcast, the woman seems a little nervous!

[TheFeedRocket]

The difficulty is that these models may come up with the same answer in different ways, just as if you ask 10 humans to come up with an answer or an idea, it would be somewhat different for each person on how they arrived at the answer. Being that the models are not fully matureI, just adding vision changes things a lot, so how they come to answer might continually change. Just when you think you understand a certain aspect of how it arrives at an answer, you could pull out some key features that was assumed was needed and it still comes up with answer.

What we have built here is truly alien, then again how we think is really alien to us as we have so little understanding of how the human mind works either. I actually think humans are closer to an LLM than we want to believe. Words are very important, and like an LLM sometimes the content doesn't matter, just the act of reading and absorbing more words increases a child's ability in many areas as it does for the LLM. But you can't compare how we think to these models, although similar in some ways, it's very dangerous to compare the two.

Look forward to more of these, or even a live Q&A.

[RalphDratman]

This is fantastic news -- that you've been able to do this.
Is it possible for the world to experience some of this directly?

[shokoofehk]

I really enjoyed this video, super insightful and professionally done. I totally agree with the comment below, would love to see more videos like this!

Also, I'm on the lookout for a mentor in AI research. If you have any recommendations or might be open to mentoring, I'd really appreciate it. Thanks a lot!

[TheShyGuitarist]

🎯 Key points for quick navigation:

00:00 *👨‍🔬 Introduction and Background of the Interpretability Team and Their Recent Project*
- The video features members of the interpretability team at Anthropic discussing their recent project.
- The team had previously published a paper, "Towards Monosemanticity, " which explored interpretable features in a small language model.
- This project involved scaling up their techniques to work with a much larger language model used in production.
02:55 *🌋 Scaling Up Interpretability Research and Discovering Interesting Features in Large Language Models*
- The team discusses the challenges of scaling up their interpretability techniques from a small, limited language model to a larger, more complex one.
- They highlight the increased complexity and the need for significant engineering efforts to handle the scale.
- The team also shares their excitement about discovering interesting and nuanced features within the larger model, providing insights into how it performs complex tasks.
04:52 *💡 Noteworthy Features Discovered and Insights into Language Model's Capabilities*
- Team members share specific examples of features they found interesting or surprising.
- These examples include features related to code functions, veganism, multimodal understanding (linking text and images), and security vulnerabilities.
- The discoveries challenge the notion of language models simply repeating training data, showcasing their ability to grasp complex concepts and relationships across different domains.
08:21 *🌉 "Golden Gate Claude": Bringing Interpretability Research to Life*
- The team discusses "Golden Gate Claude, " an experiment inspired by a feature that activated when describing the Golden Gate Bridge.
- This experiment highlights the collaborative and fast-paced nature of their work, where research findings are quickly translated into interactive experiences.
- "Golden Gate Claude" allowed users to interact with the model in a way that emphasized the specific feature related to the Golden Gate Bridge, illustrating how these features can influence the model's behavior.
10:51 *🛠️ Scaling Challenges and Engineering Solutions for Dictionary Learning on Large Language Models*
- This section delves into the technical challenges of scaling up their dictionary learning technique for a model the size of Claude.
- The speakers highlight the iterative process of scaling, testing, and refining their methods, rather than a pre-planned approach.
- They emphasize the need to balance research goals with engineering constraints, constantly evaluating trade-offs to achieve meaningful results efficiently.
12:22 *🧮 Sparse Autoencoders: Scaling Up with Simple yet Powerful Algorithms*
- The discussion centers around the use of sparse autoencoders, a relatively simple mathematical technique, for their work.
- They explain the benefits of sparse autoencoders in identifying interpretable features by reducing data complexity.
- The team underscores the power of scalable, simple algorithms when applied to vast amounts of data, even if they might not be the most mathematically sophisticated.
16:17 *🔀 Tackling the Data Shuffling Problem at Scale*
- The conversation shifts to a specific engineering challenge: shuffling large datasets.
- They explain the necessity of shuffling data in machine learning to ensure the model learns from the entire distribution, not just the order of input.
- They illustrate how simple tasks like shuffling become exponentially harder with terabytes or petabytes of data, requiring innovative, parallel solutions.
21:17 *🧪 The Nature of Engineering for Research: Adaptability, Trade-offs, and Unpredictability*
- The team compares and contrasts engineering for research with traditional software engineering for product development.
- They stress the importance of adaptability in research engineering, where code might be discarded quickly, and the need to prioritize flexibility over perfection.
- The speakers highlight the continuous trade-offs between code quality, research timelines, and the evolving nature of research goals.
24:15 *⚖️ Balancing Engineering and Science for Interpretability Research*
- The team prioritizes research goals over creating perfect engineering solutions, focusing on scientific understanding for safety.
- There is a constant need to balance long-term engineering investments with the urgency of quick experimental results.
- The team acknowledges that hindsight often reveals better approaches, but emphasizes the importance of adapting to new findings.
26:13 *🐛 Navigating the Challenges of Bugs and Metric Evaluation in Machine Learning Research*
- Verifying code correctness is particularly difficult in machine learning research, especially in unexplored areas like interpretability.
- Bugs in evaluation metrics can lead to weeks of wasted effort, highlighting the importance of thorough testing and meticulous metric design.
- The team emphasizes the value of logging and graphing extensive metrics during training to aid in identifying anomalies and ensuring code reliability.
31:32 *❤️‍🔥 The Appeal and Significance of Interpretability Research in AI*
- Team members express their passion for interpretability research, highlighting the intellectual stimulation and the satisfaction of demystifying AI models.
- They emphasize the unique opportunity to perform "computational neuroscience" on artificial minds, a field made possible by recent advancements in AI.
- The work is described as meaningful due to its potential to ensure the safety and understand the inner workings of increasingly complex AI systems.
35:34 *🤝 Advice for Aspiring Engineers and the Importance of Collaboration in AI Research*
- The team encourages engineers interested in interpretability research to apply, emphasizing the significant need for strong engineering skills.
- They highlight the importance of breadth over extreme optimization, as the work requires quickly identifying and addressing bottlenecks across diverse parts of the system.
- The team values collaboration between individuals with complementary skills, such as pairing engineers with scientists to tackle complex problems effectively.
42:54 *🔬 Scaling Challenges and the Importance of Distributed Systems in Feature Visualization*
- The team discusses the challenges of visualizing and understanding the behavior of millions of features in a sparse autoencoder.
- They describe the need for complex, multi-step distributed pipelines to handle the computational demands of feature visualization at scale.
- The conversation highlights how even seemingly simple operations, like matrix multiplication, can become bottlenecks when dealing with large data sets, requiring creative solutions and optimization techniques.
47:11 *➗ Scaling Challenges in Machine Learning*
- Discussing the challenges of scaling machine learning models, particularly the difficulties encountered when moving from small-scale experiments to large, distributed systems.
- Highlighting the complexities introduced by data transfer bottlenecks and the need for specialized code to handle massive datasets.
- Mentioning the "bitter lesson" of scaling, suggesting that simpler, scalable methods often outperform complex ones with increased data and computational resources.
49:07 *🔎 Scaling Interpretability in Machine Learning*
- Shifting the focus to interpretability in machine learning, emphasizing the importance of understanding how models make decisions.
- Drawing parallels between scaling models and scaling interpretability techniques, arguing that simple methods applied at scale can yield valuable insights.
- Expressing the desire to move beyond merely identifying features to understanding their interactions and how they contribute to model behavior in various contexts.
51:33 *🔮 Future of Interpretability and Importance of Understanding Models*
- Outlining a future vision for interpretability, aiming to analyze entire production models, understanding feature interactions, and their impact on model behavior.
- Highlighting the potential of scaling interpretability techniques to uncover deeper insights into model workings.
- Emphasizing the significance of understanding models for addressing potential safety challenges associated with large language models.
52:03 *💪 Robustness of Interpretability and Call to Action*
- Underscoring the value of interpretability in navigating the uncertainties surrounding the future of large language models.
- Advocating for a "completionist" approach to interpretability, mapping the full diversity of a model to understand its behavior comprehensively.
- Concluding with a call to action, inviting viewers with relevant skills to join their efforts in advancing interpretability research.

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