Progress Measurement
Summary
The subtopic of Progress Measurement in AI alignment research emphasizes the need for a more comprehensive approach to evaluating advancements in artificial intelligence. Traditional metrics primarily focus on performance, but this perspective suggests incorporating often-overlooked dimensions such as development and deployment costs. These costs include data, expert knowledge, human oversight, software and hardware resources, computing power, and development time. The paper proposes analyzing AI progress through a multidimensional lens that considers both performance and various cost factors throughout an AI system’s lifecycle. This approach allows for a more nuanced assessment of AI advances, either by collapsing the multidimensional space into a single utility metric for users with well-defined preferences or by evaluating improvements in terms of expanding the Pareto optimal surface. By adopting this broader conception of AI progress, researchers can develop more accurate and holistic methods for measuring success and setting future milestones in the field.