Machine Learning Street Talk (MLST)
Neel Nanda - Mechanistic Interpretability
Episode guests
Podcast summary created with Snipd AI
Quick takeaways
- Models can represent their thoughts using motifs, circuits, and linear directional features communicated via a 'residual stream' as an information highway.
- Superposition, the ability for models to represent more features than neurons, is a key challenge in interpretability.
- Models likely have goals and agency, executing long-term plans towards objectives, although techniques to build models with specific goals are lacking.
- Different models, even with different random seeds, can learn different representations, suggesting variation in simplicity and structure in neural network learning.
- Superposition, representing multiple features as almost orthogonal directions, is a fundamental aspect of how models function and balance trade-offs.
- Pitfalls of interpreting distributed representations and the importance of linear probing for extracting meaningful representations in neural networks.
Deep dives
The Fourier multiplication algorithm: Understanding the mechanism behind groking
The paper explores the algorithm behind groking in a one-layer transformer trained to perform modular addition. They discover the Fourier multiplication algorithm, which uses trigonometric functions and composition to perform modular addition. The model gradually transitions from memorization to generalization, with the help of regularization techniques like weight decay. The paper also highlights the importance of mechanistic understanding in disentangling memorization and generalization. The progress measures they develop shed light on the different phases of training: memorization, space circuit formation, and groking, confirming that groking is not sudden generalization but rather gradual generalization followed by sudden cleanup in test loss.
Interpretability and Algorithmic Generalization in Neural Networks
The research explores whether neural networks learn universal algorithms or idiosyncratic ones. They focus on group operations and group representations, with modular addition as the specific algorithm studied. The paper shows that models can learn representations that correspond to group symmetries, and it argues for the existence of intrinsic solutions and universality in neural network learning. They find that different models, even with different random seeds, can learn different representations, suggesting variation in simplicity and structure across solutions.
Understanding the Mechanism Behind Groking and Universality in Neural Networks
The study investigates the mechanism behind groking and the presence of universality in neural networks. They uncover the Fourier multiplication algorithm used by a one-layer transformer to perform modular addition, which involves trigonometric functions, composition, and representations of groups. The research demonstrates the progression from memorization to generalization, with regularization techniques playing a crucial role. They also explore the concept of universality, with models learning different group representations randomly, highlighting the existence of intrinsic solutions and the complexity of simplicity in neural network learning.
Exploring the Hypothesis of Superposition in Neural Networks
The paper discusses the phenomenon of superposition in neural networks, which refers to the representation of multiple features as almost orthogonal directions. The authors argue that superposition is a core aspect of how models function and that it exists in both computational and representational forms. They propose the superposition hypothesis to explain why models use superposition to balance the trade-off between representing more features and dealing with interference. The paper critiques existing research on superposition and presents empirical evidence from case studies in language models, showing that superposition occurs in early layers for simple features and decreases in middle layers for complex features.
Challenges and Limitations of Probing Neural Networks
The paper highlights the challenges and limitations of probing neural networks to understand their representations. It emphasizes the need for careful interpretation and consideration of the differences between linear and nonlinear probing techniques. The authors argue that linear probing is more reliable and meaningful for extracting representations, while nonlinear probing can be misleading. They also discuss the pitfalls of interpreting distributed representations and the difficulty in distinguishing between representational and computational superposition. Despite these challenges, the paper acknowledges the importance of probing in gaining insights into the inner workings of neural networks.
Empirical Study of Superposition in Language Models
The paper presents an empirical study on superposition in language models, specifically focusing on sparse probing techniques. The study demonstrates that superposition exists in language models, and that the prevalence of features affects the occurrence of superposition. The authors explore the effects of activation range on superposition, highlighting the benefits of binary features over continuous features. Furthermore, the paper discusses the limitations and caveats of the study, including the emphasis on representational rather than computational superposition, and the need for further research on real models. Overall, the study contributes to our understanding of superposition and its implications in language models.
The significance of induction heads in in-context learning
Induction heads are a key circuit in models that enable in-context learning, where the model can track long-range dependencies in text. These induction heads emerge in a sudden phase transition during training and are crucial for the model's ability to perform in-context learning. By mapping current tokens and previous tokens to the same latent space, induction heads allow the model to efficiently represent and process information that spans multiple positions in the text. The discovery of induction heads provides a deeper understanding of how models learn and reason, and their presence is linked to the emergence of in-context learning.
The importance of surgical interventions for interpretability
Surgical interventions, such as activation patching and causal interventions, are valuable techniques for gaining insight into the inner workings of models. These interventions allow researchers to isolate and analyze specific circuits or components of the model. By causally intervening on the model, it becomes possible to identify necessary and sufficient components for specific tasks or behaviors. Metrics used in these interventions play a crucial role in the effectiveness of the analysis, and metrics like log probability can provide more accurate and surgical results compared to metrics like accuracy or rank. Surgical interventions are a promising avenue for advancing the understanding and interpretability of machine learning models.
The need for a scientific understanding of emergence and better prediction techniques
Emergence, the sudden and unexpected change in model behavior or capabilities, is a topic of great interest and importance. Understanding the underlying mechanisms and circuits that drive emergent phenomena is crucial for predicting and comprehending how models learn and evolve. Induction heads, along with other circuits, have been linked to the emergence of specific behaviors. There is a need for further research and scientific investigation into emergent phenomena, including the development of better prediction techniques. This will enable the discovery and analysis of novel emergent capabilities and mitigate potential risks associated with these phenomena.
The importance of understanding AI's capabilities and risks
Understanding the capabilities and risks of AI is crucial to address its potential dangers. While there may be multiple problems to focus on, the podcast highlights the significance of AI's existential risks. Mechanistic interpretability, which involves understanding AI systems that are smarter than us, is viewed as a valuable approach to mitigate these risks. The emphasis is on discussing whether AI catastrophic and existential risks are significant, rather than debating their ranking among other pressing global problems.
Debunking misconceptions about AI goals and intelligence growth
The podcast challenges misconceptions around AI goals and intelligence growth. It argues that the focus should not be on recursive self-improvement or a sudden intelligence explosion. Instead, attention should be directed towards concerns about aligning AI goals with human values and the potential dangers of systems that exhibit goal-directed behavior. The discussion addresses the debate around whether intelligence is a significant advantage and the ambiguity of intelligence growth. The importance of addressing these misconceptions and defining goals for AI systems is highlighted to ensure safety and responsible deployment.
In this wide-ranging conversation, Tim Scarfe interviews Neel Nanda, a researcher at DeepMind working on mechanistic interpretability, which aims to understand the algorithms and representations learned by machine learning models. Neel discusses how models can represent their thoughts using motifs, circuits, and linear directional features which are often communicated via a "residual stream", an information highway models use to pass information between layers.
Neel argues that "superposition", the ability for models to represent more features than they have neurons, is one of the biggest open problems in interpretability. This is because superposition thwarts our ability to understand models by decomposing them into individual units of analysis. Despite this, Neel remains optimistic that ambitious interpretability is possible, citing examples like his work reverse engineering how models do modular addition. However, Neel notes we must start small, build rigorous foundations, and not assume our theoretical frameworks perfectly match reality.
The conversation turns to whether models can have goals or agency, with Neel arguing they likely can based on heuristics like models executing long term plans towards some objective. However, we currently lack techniques to build models with specific goals, meaning any goals would likely be learned or emergent. Neel highlights how induction heads, circuits models use to track long range dependencies, seem crucial for phenomena like in-context learning to emerge.
On the existential risks from AI, Neel believes we should avoid overly confident claims that models will or will not be dangerous, as we do not understand them enough to make confident theoretical assertions. However, models could pose risks through being misused, having undesirable emergent properties, or being imperfectly aligned. Neel argues we must pursue rigorous empirical work to better understand and ensure model safety, avoid "philosophizing" about definitions of intelligence, and focus on ensuring researchers have standards for what it means to decide a system is "safe" before deploying it. Overall, a thoughtful conversation on one of the most important issues of our time.
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TOC
[00:00:00] Introduction and Neel Nanda's Interests (walk and talk)
[00:03:15] Mechanistic Interpretability: Reverse Engineering Neural Networks
[00:13:23] Discord questions
[00:21:16] Main interview kick-off in studio
[00:49:26] Grokking and Sudden Generalization
[00:53:18] The Debate on Systematicity and Compositionality
[01:19:16] How do ML models represent their thoughts
[01:25:51] Do Large Language Models Learn World Models?
[01:53:36] Superposition and Interference in Language Models
[02:43:15] Transformers discussion
[02:49:49] Emergence and In-Context Learning
[03:20:02] Superintelligence/XRisk discussion
Transcript: https://docs.google.com/document/d/1FK1OepdJMrqpFK-_1Q3LQN6QLyLBvBwWW_5z8WrS1RI/edit?usp=sharing
Refs: https://docs.google.com/document/d/115dAroX0PzSduKr5F1V4CWggYcqIoSXYBhcxYktCnqY/edit?usp=sharing
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