The article also discusses the use of the Elo rating system, originally developed for chess rankings, to measure the relative performance of AI solutions. It suggests that this system can provide a clear, numerical way to compare different models or versions of AI applications. The author concludes by recommending that measurement should be an integral part of the AI development process, with clear benchmarks and transparent results to support ongoing quality improvement.
Key takeaways:
- Measurement is crucial for AI applications to ensure they are delivering value and meeting their intended goals. This includes establishing a baseline for performance, tracking progress, making data-driven decisions, and demonstrating value to customers.
- Understanding how an AI model serves a specific use case is nuanced and may require custom metrics that reflect customers' perceptions of success.
- Gong uses the Elo rating system to measure the relative performance of their generative AI solutions. This system assigns a numerical rating to each competitor, and their ratings are updated based on the outcome and the expected probability of that outcome.
- Measurement should be an integral part of the development process for AI applications. This includes continuously comparing different versions of the end-to-end algorithm, preparing a "gold set" of examples to measure against, and publishing the results for transparency.