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At this week's Computer Vision and Pattern Recognition conference, NVIDIA is demonstrating how an NVIDIA DGX Station running NVIDIA TensorRT and using only one of the four Tesla V100s we've equipped DGX Station with can perform a common inferencing task 100X faster than a system without GPUs. Remarkably, moving faster means fewer costs. One NVIDIA GPU-enabled system doing the same work as 100 CPU-only systems means 100 times fewer cloud servers to rent or buy. To learn more about NVIDIA DGX Station with Tesla V100 GPU accelerators, go to www.nvidia.com/dgx-station.


Image Recognition: Can an Image Recognition App Become the Quality Boost Your Business Needs?

#artificialintelligence

The Image Recognition Technology Is, Usually, Associated with an Array of Security and Surveillance-Related Uses and the Rapidly Developing Autonomous Vehicle Niche. Can Image Recognition Apps Help Businesses in Other Verticals? With Reuters' predictions for the not-so-far-off year of 2022 being in the region of a hefty $43-57 billion, Image Recognition is one big lure for AI outfits, and, simultaneously, a lot of hope for businesses and organizations that depend upon it for their survival and success. These include entities as diverse, as manufacturers of autonomous cars and security systems, national nature parks, border security forces, and companies that produce drones. Be it monitoring the state of a much cherished rainforest or sending drones to remote oil rigs to check if all one's assets are in one piece, almost all of the widely known uses of Image Recognition seem to be related to security and surveillance.


Denso, Toyota collaborate in AI-based image recognition

#artificialintelligence

DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects. Because of the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations.


IBM's new PowerAI tools automate image recognition

PCWorld

IBM is trying to remove some of the complications related to image recognition with new tools to automate critical machine learning tasks. A major update of the company's PowerAI tools has a feature called AI Vision, an auto tuner that makes it easy to identify and classify pictures. It will also speed up image recognition by breaking down tasks over multiple clusters. AI Vision plays a big role in automating machine learning by creating a tuned model, said Sumit Gupta, vice president of machine learning. The software abstracts machine learning, and developers don't need knowledge of low-level access to frameworks to tune, train, and deploy image recognition models.


IBM's new PowerAI tools automate image recognition

#artificialintelligence

IBM is trying to remove some of the complications related to image recognition with new tools to automate critical machine learning tasks. A major update of the company's PowerAI tools has a feature called AI Vision, an auto tuner that makes it easy to identify and classify pictures. It will also speed up image recognition by breaking down tasks over multiple clusters. AI Vision plays a big role in automating machine learning by creating a tuned model, said Sumit Gupta, vice president of machine learning. The software abstracts machine learning, and developers don't need knowledge of low-level access to frameworks to tune, train, and deploy image recognition models.