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www.alignmentforum.org | ||
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www.lesswrong.com
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| | | | | A new Anthropic interpretability paper-"Toy Models of Superpostion"-came out last week that I think is quite exciting and hasn't been discussed here... | |
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transformer-circuits.pub
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| | | | | [AI summary] The text discusses the interpretability of features in a machine learning model, focusing on how features like Arabic, base64, and Hebrew are used in interpretable ways. It explores the extent to which these features explain the model's behavior, noting that features with higher activations are more interpretable. The text also addresses the limitations of current methods, such as the computational cost of simulating features and the potential for dataset correlations to influence feature interpretations. Finally, it concludes that the model's learning process creates a richer structure in its activations than the dataset alone, suggesting that feature-based interpretations provide meaningful insights into the model's behavior. | |
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www.lesswrong.com
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| | | | | Text of post based on our blog post as a linkpost for the full paper which is considerably longer and more detailed. ... | |
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scorpil.com
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| | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | ||
