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www.lesswrong.com | ||
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thesephist.com
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| | | | | [AI summary] The text provides an in-depth overview of research on sparse autoencoders (SAEs) applied to embeddings for automated interpretability. It discusses methods for analyzing and manipulating embeddings, including feature extraction, gradient-based optimization, and visualization tools. The work emphasizes the importance of understanding model representations to improve human-computer interaction with information systems. Key components include: 1) Automated interpretability prompts for generating feature labels, 2) Feature gradients implementation for optimizing embeddings to match desired feature dictionaries, and 3) Visualizations of feature spaces and embedding transformations. The text also includes FAQs addressing the use of embeddings over lan... | |
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goodfire.ai
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| | | | | Goodfire is an AI research company building practical interpretability tools for safe and reliable generative models. | |
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deepmind.google
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| | | | | Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability. | |
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www.analyticsvidhya.com
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| | | Explore RNNs: their unique architecture, working principles, BPTT, pros/cons, and Python implementation using Keras. | ||
