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Autonomous Vehicle Performance Supported by AI, ML For Stable Operation

#artificialintelligence

The performance levels in an automated driving vehicle, from Level 1-5 need to be as accurate as possible, so as to enable smooth operation of the vehicle in the real world. Even the amount of disengagement of the vehicle from its autonomous mode to manual intervention needs to be highly-linear in nature. A number of these requirements are being met by new-age technologies of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. In auto mode, the vehicle needs to monitor its surroundings, and then take in all the information received through the various sensors and finally be able to take necessary actions for various scenarios. The algorithms built into these autonomous vehicles need to work accurately, learn new attributes of the environment, and finally react to different scenarios differently.



Head of TSA security operations removed from position

U.S. News

"Darby LaJoye will serve as the Acting Assistant Administrator of the Office of Security Operations," Neffenger wrote in the memo addressed to TSA senior leaders. "Darby LaJoye is an experienced Federal Security Director with successful leadership tours at two of the nation's largest airports, Los Angeles International Airport in California and John F. Kennedy International Airport in New York."


Artificial Intelligence in IT Operations Management

#artificialintelligence

IT operations analytics scan all the data that has been gathered using different tools and provides resolutions to problems and does root-cause analysis.


Advancing Application Performance with NVMe Storage, Part 1 - DZone Performance

#artificialintelligence

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications. AI/ML can be applied across a broad spectrum of applications and industries. Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing. Real-time image recognition is a key enabler for self-driving vehicles, while facial recognition is used by law enforcement across the globe.