Baidu Cloud Collaborates with Intel AI to Advance Financial Services, Shipping and Video Processing

What’s New: At the Baidu* 2018 ABC Summit in Shanghai, Baidu and Intel outlined new artificial intelligence (AI) collaborations showcasing applications ranging from financial services and shipping to video content detection. Specifically, Baidu Cloud* is leveraging Intel® Xeon® Scalable processors and the Intel® Math Kernel Library-Deep Neural Network (Intel® MKL-DNN) as part of a new financial services solution for leading China banks; the Intel OpenVINO toolkit in new AI edge distribution and video solutions; and Intel® Optane™ technology and Intel QLC NAND SSD technology for enhanced object storage.

“Intel is collaborating with Baidu Cloud to deliver end-to-end AI solutions. Adopting a new chip or optimizing a single framework is not enough to meet the demands of new AI workloads. What’s required is systems-level integration with software optimization, and Intel is enabling this through our expertise and extensive portfolio of AI technologies – all in the name of helping our customers achieve their AI goals.”-- Raejeanne Skillern, Intel vice president, Data Center Group, and general manager, Cloud Service Provider Platform Group

How Intel Processors Help in Finance: Baidu is deploying advanced private financial clouds for customers including China Union Pay*, AI Bank* and the Agricultural Bank of China*. The financial clouds run on Intel Xeon Scalable processors and the Intel MKL-DNN library to meet the performance and security requirements of financial services companies.

How Intel AI Helps in Shipping: Baidu Cloud wanted to help companies monitor trucks in real time by increasing efficiency of edge devices to improve shipping operations and report back to the central office on outstanding events. For edge computing, Intel supported Baidu Cloud in implementing an AI video analysis system to accelerate the development of vision applications. For example, it was difficult to monitor municipal service trucks, even if they were equipped with a video recording device because the recordings could only be reviewed afterward. With the OpenVINO™ toolkit, Baidu Cloud is able to deploy a powerful AI video analysis system equipped with a camera that detects and instantly reports any unusual incidents (such as waste falling from a truck) through a wireless connection.

How Intel Helps Speed Video Processing: Baidu Cloud sought to bolster its video processing services to better support iQiyi*, the Chinese equivalent of Netflix*. iQiyi adopted the OpenVINO toolkit to detect videos that violate the content rules and achieved performance improvements on the existing platform, and an extra performance improvement on Baidu Cloud powered by Intel Xeon Scalable processors.

How Intel Storage Helps with AI Training: Baidu Cloud and Intel set out to develop a new AI storage solution that better meets requirements for performance, size and cost given the large amount of data generated by AI training. By leveraging both Intel Optane technology and Intel QLC Technology, Baidu Cloud is tackling the challenges posed by the massive generation of data and related performance, size and cost requirements.

Why It’s Important: Intel is continuously pushing the boundaries of AI through performance-leading products and broad ecosystem collaboration, all in an effort to unleash the power of data. Since no single hardware or framework meets the diversified requirements of all possible AI solutions and each customer faces different scenarios with different workloads, Intel is investing in a holistic approach to AI and developing unique solutions for customers across central processing units, storage and the network. Building on its strong partnership with Baidu Cloud, Intel will continue to accelerate AI breakthroughs and implementations – leveraging AI to solve real problems and eventually turning AI concepts into reality.

More Context: Artificial Intelligence at Intel | Intel AI at Baidu Create: AI Camera, FPGA-based Acceleration and Xeon Scalable Optimizations for Deep Learning

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