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DAM Is A Key Ingredient For Your Digital Experience Delivery

Forrester IT

experiences on their digital channels using assets like video or even 3D renderings, an increasing number have turned to digital asset management (DAM) to better manage the […]. Digital customer experience has become a top-3 growing priority according to our recent COVID-19 Service Provider Pulse Survey.

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Google AI researchers use mannequin challenge YouTube videos to improve depth prediction

CTOvision

Google AI researchers today said they used 2,000 “mannequin challenge” YouTube videos as a training data set to create an AI model capable of depth prediction from videos in motion. Applications of such an understanding could help developers craft augmented reality experiences in scenes shot with hand-held cameras and 3D video.

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Nvidia speeds AI, climate modeling

CIO Business Intelligence

For some applications, a simple database may suffice to record a product’s service history—when it was made, who it shipped to, what modifications have been applied—while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain.

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Google Cloud Run now allows AI inferencing on Nvidia GPUs

Network World

Google Cloud has updated its managed compute service Cloud Run with a new feature that will allow enterprises to run their real-time AI inferencing applications serving large language models (LLMs) on Nvidia L4 GPUs. For example, time required by the LLM to reply to a user query via an enterprise application. But are there caveats?

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Google previews Project Starline, a next-gen 3D video chat booth

The Verge

Google is working on a next-gen video chat booth that makes the person you’re chatting with appear in front of you in 3D. The system is called “ Project Starline ,” and it’s basically a really, really fancy video chat setup. In a demo video, people using the tech describe seeing people like they were in the same room together.

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What is an AI server? Why artificial intelligence needs specialized systems

Network World

To balance speed, performance and scalability, AI servers incorporate specialized hardware, performing parallel compute across multiple GPUs or using other purpose-built AI hardware such as tensor processing units (TPUs), field programmable gate array (FPGA) circuits and application-specific integrated circuit (ASIC).

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Nvidia unveils Workbench for managing AI workloads, updates Omniverse

CIO Business Intelligence

Nvidia has recently focused more on its support for AI applications, but it still had plenty of news from CEO Jensen Huang in a keynote address during the annual computer graphics conference, SIGGRAPH. Despite its name, it’s not quite universal, with different vendors implementing it in different ways.

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