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MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
Apply sound data-based anomalous behavior detection, diagnose the root cause via object detection concurrently, and inform the user via SMS.
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