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Exploring Responsible Scaling Policy and Model Interpretability in GPT-7 Training
The chapter dives into the responsible scaling policy adopted by a lab and the challenges surrounding GPT-7's evaluation and interpretability progress. Discussions on labeling features in unsupervised manners, searching for deception circuits, and scaling up dictionary learning within a six-month timeframe showcase the team's optimism and progress. The dialogue also delves into understanding advanced AI systems like GPT-7's mind, potential risks of control, and the importance of transparent governance in shaping AI program performance.