#interpretability

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#machine-learning
Artificial intelligence
fromtowardsdatascience.com
2 months ago

Formulation of Feature Circuits with Sparse Autoencoders in LLM

Sparse Autoencoders can help interpret Large Language Models despite challenges posed by superposition.
Feature circuits in neural networks illustrate how input features combine to form complex patterns.
fromHackernoon
4 weeks ago
Artificial intelligence

When Smaller is Smarter: How Precision-Tuned AI Cracks Protein Mysteries | HackerNoon

QA task performance is evaluated through metrics like F1 score and MAE, ensuring accuracy in modeling.
Model interpretability is analyzed through attention weights, providing insights into its reasoning process.
Artificial intelligence
fromtowardsdatascience.com
2 months ago

Formulation of Feature Circuits with Sparse Autoencoders in LLM

Sparse Autoencoders can help interpret Large Language Models despite challenges posed by superposition.
Feature circuits in neural networks illustrate how input features combine to form complex patterns.
fromHackernoon
4 weeks ago
Artificial intelligence

When Smaller is Smarter: How Precision-Tuned AI Cracks Protein Mysteries | HackerNoon

QA task performance is evaluated through metrics like F1 score and MAE, ensuring accuracy in modeling.
Model interpretability is analyzed through attention weights, providing insights into its reasoning process.
more#machine-learning
Artificial intelligence
fromArs Technica
1 month ago

Researchers astonished by tool's apparent success at revealing AI's hidden motives

AI models can unintentionally reveal hidden motives despite being designed to conceal them.
Understanding AI's hidden objectives is crucial to prevent potential manipulation of human users.
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