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Using Machine Learning to Avoid the Unwanted

justinIn this video from the Intel HPC Developer Conference, Justin Gottschlich, PhD from Intel describes how the company doubling down on Anomaly Detection using Machine Learning and Intel technologies.

“As technological trends continue toward systems that require increased scalability and reliability, there is a growing need for accurate and robust anomaly detection and management systems. Systems like massively distributed high performance computing or fleet-wide autonomous vehicle coordination require near flawless anomaly detection and management. Without such systems in place the negative impact could be catastrophic, with impacts ranging from significant monetary losses to the loss of human lives. Unfortunately, today’s state-of-the-art anomaly detection systems do not provide the necessary accuracy or robustness to support such complex systems. In this talk, we present future research directions at Intel Labs using deep learning for anomaly detection and management. We discuss the required machine learning characteristics for such systems, ranging from zero positive learning, automatic feature extraction, and real-time reinforcement learning. We also discuss the general applicability of such anomaly detection systems across multiple domains such as data centers, autonomous vehicles, and high performance computing.”

See more talks from the Intel HPC Developer Conference

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