#monosemanticity
6 related articles
·3 min
Interpretability: The Science and Practice of Opening the Neural Network Black Box
Why do neural networks make the decisions they do? This article explores AI interpretability — mechanistic interpretability, CoT monitoring, and safety auditing — and how researchers reverse-engineer large models for AI safety.
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·2 min
Dissecting a Single Convolutional Neuron: A New Approach to Mechanistic Interpretability
An independent researcher dissects a single 1×1 convolutional neuron in InceptionV1, using Hadamard product clustering to reveal detection patterns and discovers how gradient descent hides concepts in noise.
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·6 min
The Frontier of AI Interpretability Research: Opening the Neural Network Black Box
An in-depth look at AI interpretability research: from chain of thought and probes to sparse autoencoders, exploring how scientists understand neural network internals and assess AI alignment and safety.
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·6 min
Jacobian-Lens in Practice: Manually Reshaping LLM Behavior with J-Space
After Anthropic released Jacobian-Lens, a developer reversed it from an interpretability tool into a behavior editor, manually tuning J-Space to reshape LLM outputs. An in-depth look at the tech, representation engineering, and AI safety risks.
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·6 min
AMD Ryzen AI Halo Dev Kit: A $4,000 Local LLM Powerhouse Taking on Apple and NVIDIA
AMD officially unveils the Ryzen AI Halo local AI dev kit, priced around $4,000 with 128GB unified memory, capable of running 70B LLMs locally. An in-depth look at its specs, pricing, and market competition.
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·6 min
Reverse-Engineering ChatGPT's Source Selection: What Network Traffic Analysis Reveals About AI Citations
By capturing and analyzing ChatGPT's real network traffic during web retrieval, this article reveals the underlying logic of AI source selection—covering the layered mechanisms of retrieval recall and content filtering, domain preference patterns, and practical insights for content creators and SEO professionals.
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