Why Multiple AI Models Often Think Like Just One
When deploying multiple AI models for ensemble voting, developers often assume independence, but shared training data leads to heavily correlated mistakes. Analyzing six open models across 1,500 exam questions reveals that ensemble methods yield far fewer truly independent opinions than expected. This mathematical insight is crucial for building reliable AI systems and avoiding false confidence.
Source: Towards Data Science