What are edge cases in AI?

In AI, edge cases are specific instances or problem scenarios that diverge from normal behavior or expected patterns and present challenges that an AI system must learn to handle. Lex Fridman and Andrew Huberman discussed such examples in the context of semi-autonomous driving. Fridman described edge cases as failure situations where AI systems encounter unexpected or rare incidents in real-world conditions that weren't anticipated during their programming.

He explained that these unexpected incidents, or "weird situations," are crucial for improving AI systems. This process involves continuously collecting data from these edge cases through what is referred to as the "data engine" process. Here, every instance of anomaly detected by AI systems in vehicles, for example, is collected and sent back for retraining the system. Through this iterative learning and updating process, AI systems are trained to get better at handling an increasing range of scenarios by incorporating lessons learned from these edge cases 1.