Category: interpretability
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Stop Asking if a Model Is Interpretable
Stop Asking if a Model Is Interpretable Start asking what question the explanation should answer. The post Stop Asking if a Model Is Interpretable appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source
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Tips for Setting Expectations in AI Projects
Tips for Setting Expectations in AI Projects If you want your AI project to succeed, mastering expectation management comes first. When working with AI projets, uncertainty isn’t just a side effect, it can make or break the entire initiative. Most people impacted by AI projects don’t fully understand how AI works, or that errors are…
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Circuit Tracing: A Step Closer to Understanding Large Language Models
Circuit Tracing: A Step Closer to Understanding Large Language Models Context Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely…
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Sparse AutoEncoder: from Superposition to interpretable features
Sparse AutoEncoder: from Superposition to interpretable features Disentangle features in complex Neural Network with superpositions Complex neural networks, such as Large Language Models (LLMs), suffer quite often from interpretability challenges. One of the most important reasons for such difficulty is superposition — a phenomenon of the neural network having fewer dimensions than the number of features it…