The hottest Interpretability Substack posts right now

Large language models like ChatGPT have complex, learned logic that is difficult to interpret due to 'superposition' - where single neurons correspond to multiple functions.
Techniques like sparse dictionary learning can decompose artificial neurons into 'features' that exhibit 'monosemanticity', making the models more interpretable.
Reproducing research on model interpretability shows promise for breakthroughs and indicates a shift towards engineering challenges over scientific barriers.

This post explains key terms in Human-Centered AI, including HCAI concepts, Ethics, and Machine Learning.
Understanding and managing uncertainty is crucial in AI models for performance and reliability.
Explainability methods aim to make AI models transparent, interpretable, and understandable for humans.

Explainable AI (XAI) helps provide transparency in AI models so users can understand the logic behind predictions.
Understanding how AI decisions are made is crucial for accountability, identifying biases, and improving model performance.
Principles of Explainable AI include transparency in outputs, user-centric design, accurate explanations, and awareness of system limitations.

Don't settle for a superficial understanding of AI chatbots.
Real insight on AI chatbots will require research, not just casual use.
Debating whether chatbots have 'world models' is important to understanding how they work.

Transformer models, like LLMs, are often considered black boxes, but recent work is shedding light on the internal processes and interpretability of these models.
Induction heads in transformer models help with in-context learning and the ability to predict information based on the sequence of tokens seen before.
By analyzing hidden states and conducting memory-based experiments, researchers are beginning to understand how transformer models store and manipulate information, providing insights into how these models may represent truth internally.

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A.I. image generators struggle with creating hands due to the complexity of hand shapes and poses
Neural networks power image generators through mathematical transforms
Efforts are being made to improve A.I. image generation by addressing challenges like hand creation through interpretability of neural networks

Sparse dictionary learning helps in managing ambiguity in representing input data.
Applying sparse dictionary learning to language models like BERT and GPT reveals tiers of semantic structures.
Future research could explore improving data curation methods and adapt the technique for larger models like GPT.

The algorithms in deep learning systems are mostly unknown, making it challenging to assess their learning process and how they generate output.
Firms like Anthropic are investing in making AI algorithms more interpretable, but more support is needed.
To promote the development of interpretable AI systems, measures like grants, collaboration across disciplines, and improving existing techniques are crucial.

AI alignment can be viewed as a translation problem between different learning agents.
The AI-human translation problem is more about humans learning from AIs than vice versa.
Equipping AIs with human inductive biases may be crucial for developing more 'natural' AI.

Activation engineering is a new method to steer LLMs during inference without weight modifications.
Choice of metric significantly influences how suddenly an ability seems to appear in large language models.
Bilinear layers offer better interpretability than nonlinear activations in MLPs.