A company is developing an AI that is supposed to automatically detect interesting patterns in large data streams.
The AI is trained to particularly highlight unusual events or outliers so that analysts can quickly respond to anomalies.
At first, the AI seems very useful because it reliably reports rare anomalies.
But soon the analysts notice a problem:
The AI ignores or rates ordinary but important patterns as irrelevant.
Important trends and regular developments are hardly recognized or not displayed at all.
The company wonders: Why does the AI constantly prefer only unusual data points and overlook the ordinary, even though it is often just as relevant?
Question: Why can an AI trained to recognize unusual patterns overlook important, regularly occurring patterns and trends?
Solution follows tomorrow.
Solution
The AI was specifically trained to detect and prioritize outliers and anomalies, as these often indicate errors or special events.
As a result, it learns to classify unusual patterns as particularly relevant and to rate everyday or frequent patterns as less interesting.
Regular trends or continuous developments are considered "normal" by the AI and are therefore often hidden or not highlighted.
This behavior arises from the training objectives and evaluation metrics that prioritize anomalies and define frequent patterns as "standard."
Conclusion: An AI that focuses only on the unusual can overlook important but regular information. For comprehensive analyses, such systems must also recognize the ordinary as relevant and include it in their assessments, otherwise a distorted view arises that ignores essential developments.