Understanding Neural Networks: Mechanistic Interpretability Explained

Neural networks have become increasingly impressive, but a significant challenge remains: understanding what they truly learn. This video by Rational Animations delves into the field of mechanistic interpretability, which aims to reverse-engineer the algorithms encoded in neural network parameters. The video uses InceptionV1, a convolutional neural network, as a case study to explore how these models recognize images.

The video begins by explaining how convolutional neural networks (CNNs) are structured and how they process images through layers of neurons. Each layer detects more abstract features, progressing from simple edges to complex objects like dog heads or car parts. The key element in CNNs is the convolutional layer, which uses filters to detect specific patterns in the input image.

Researchers have employed techniques such as feature visualization and optimization to understand what individual neurons are doing. By maximizing neuron activation and applying conditions like transformation robustness, they can generate images that reveal what specific neurons respond to. For example, neurons might detect curves, dog heads, or car parts.

The video also discusses the concept of polysemanticity, where a single neuron or channel tracks multiple features simultaneously, complicating interpretability. Despite these challenges, significant progress has been made in understanding how neurons form circuits to perform complex tasks.

Recent advancements include using language models to interpret neurons in other models and extracting information directly from model activations. OpenAI’s project to use GPT-4 to interpret all neurons in GPT-2 is highlighted as an example of ongoing efforts in mechanistic interpretability.

The ultimate goal is to make AI decisions more transparent and understandable, which is crucial as these systems become more integrated into critical areas such as healthcare, criminal justice, and content recommendation. The video emphasizes the importance of this research for ensuring the safe and effective deployment of AI technologies.