OpenAI recently released Transformer Debugger, a tool that provides in-depth understanding of how Transformer models work. The release of this tool signifies further improvement in the transparency of AI operations. This development comes in the backdrop of criticism faced by OpenAI for not disclosing its research findings and Elon Musk's announcement to open-source the Grok source code. However, OpenAI has already released some open-source models, including GPT-2, Whisper, CLIP, Jukebox, and Point E. Transformer Debugger allows analysis of the internal structure of Transformers. It combines automated interpretability features with sparse autoencoder technology. This combination helps in quickly exploring the model, enabling users to understand various aspects of the model's internal "circuits" without the need for coding. The tool is designed to handle neural network components such as neurons and attention heads, providing a practical way to intervene in the model's forward pass. For example, users can remove specific neurons to observe their impact on the model's output. This feature offers a direct way to manually explore and understand the "circuits" within the neural network, where "circuits" refer to specific functional components and their interconnections. Jan Leike, a machine learning and alignment researcher at OpenAI, stated that this research tool is still in its early stages, but "we are releasing it so that others can use it and build upon it!" Its goal is to help researchers discover why small AI language models behave in specific ways, providing detailed observations of the AI decision-making process. The tool builds upon foundational research, including studies on how language models interpret neurons and individual semantic features within the language model. However, OpenAI emphasizes that this release does not come with new discoveries but provides a platform for ongoing exploration and understanding of AI models.