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GTC ON-DEMAND

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Abstract:
The potential information buried in sensors is enormous. There is far too much data from sensors to all be actively monitored and managed by agents. Large-scale autonomous monitoring systems require a significant amount of computing resources. Manual configuring of sensors to detect specific activities can be time consuming. Correlating and fusing different sensor modalities in real time for co-presence for anomaly could be computationally intractable. We'll demonstrate how the Omni AI platform uses NVIDIA GPUs to enable high-performance and high-scalability real-time anomaly detection on thousands of sensors using an unsupervised online machine learning engine with the neuro-linguistic cognitive model.
The potential information buried in sensors is enormous. There is far too much data from sensors to all be actively monitored and managed by agents. Large-scale autonomous monitoring systems require a significant amount of computing resources. Manual configuring of sensors to detect specific activities can be time consuming. Correlating and fusing different sensor modalities in real time for co-presence for anomaly could be computationally intractable. We'll demonstrate how the Omni AI platform uses NVIDIA GPUs to enable high-performance and high-scalability real-time anomaly detection on thousands of sensors using an unsupervised online machine learning engine with the neuro-linguistic cognitive model.  Back
 
Topics:
Intelligent Video Analytics, Accelerated Data Science, Intelligent Machines, IoT & Robotics, Federal, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7200
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