Welcome to Tunadorable's weekly AI newsletter, where we summarize his favorite articles of the week that he plans to read. This article was written by gpt-3.5-turbo-16k on 2024-04-26. # Mechanistic Interpretability for AI Safety -- A Review The review explores mechanistic interpretability, an approach to understanding AI systems that aims to reverse-engineer the computational mechanisms and representations learned by neural networks. The goal is to provide a granular, causal understanding of how the models make decisions. Mechanistic interpretability is distinct from other interpretability paradigms, such as behavioral, attributional, and concept-based interpretability.
This Week's AI Papers - April 26, 2024
This Week's AI Papers - April 26, 2024
This Week's AI Papers - April 26, 2024
Welcome to Tunadorable's weekly AI newsletter, where we summarize his favorite articles of the week that he plans to read. This article was written by gpt-3.5-turbo-16k on 2024-04-26. # Mechanistic Interpretability for AI Safety -- A Review The review explores mechanistic interpretability, an approach to understanding AI systems that aims to reverse-engineer the computational mechanisms and representations learned by neural networks. The goal is to provide a granular, causal understanding of how the models make decisions. Mechanistic interpretability is distinct from other interpretability paradigms, such as behavioral, attributional, and concept-based interpretability.