Few topics fan the innovation flames more vigorously these days than Artificial Intelligence (AI). Gartner predicts that by 2020, AI will be a top five investment priority for more than 30% of CIOs, and by 2022, AI will be one of three top workloads in the data center. Lurking inevitably nearby in conversation are its cousins, Machine Learning (ML), and “PFNN,” for “Phase Function Neural Networks.”
Organizations across every industry want to extract more intelligence from their data throughout its lifecycle, and AI enables organizations to learn from data and make better, faster decisions.
Take the media and entertainment industry, whose rapid adoption of IP and cloud technology is enabling more real-time capabilities across the media lifecycle from creation to consumption. No wonder that AI/ML is a top 10 technology trend for media and entertainment, according to Devoncroft’s 2018 Big Broadcast Survey.
At IBC 2018, AI will thread through just about all of the major trends, from automation, to production to customer experience. And we find ourselves on another brink. The brink is between what we know now about AI — that it helps companies, broadcasters and content producers, to better manage day-to-day workflows by automating repetitive tasks — and where it’s headed.
Which brings us to AI and media creation. Animation is a good example: Much like how motion capture automated the hand-keying of facial and character animation, AI is enabling new ways to automate the craft through advances like, audio-driven facial animation, and the aforementioned PFNN. Plus, it’s in use now for color matching, re-sampling, and de-noising images and sound.
Another area where AI is intersecting with media is in curation — a major focus for commercial AI companies who want to help broadcasters to better monetize the mountains of content that exists in their archives, old and new. Maybe the need is to up-sample old content, to make it suitable for viewing on higher-resolution devices. Or to auto-tag content, to drive higher relevancy for viewers and advertisers. Or even to auto-generate program schedules.
But how is Cisco helping the media industry and AI go beyond the brink?
Powering AI workloads at industrial scale
Traditional approaches toward data collection, transport, storage and management are not designed to handle the data volume, velocity, and variability of AI at production scale. IT needs new tools to deliver AI at scale.
IT must extend accelerated computing at the right scale to the right locations across an increasingly distributed landscape. The promise is to build a data center and multicloud environment where massive amounts of data can be collected, analyzed and distilled, while at the same time consuming this data to conduct deep learning that result in better decisions.
To address this emerging opportunity and associated challenges, Cisco has expanded the UCS portfolio with the addition of the Cisco UCS C480 ML M5 for deep learning. Cisco now offers a complete array of computing options right-sized to each element of the AI/ML lifecycle: from data collection and analysis near the edge, to data preparation and training in the data center core, to real-time inference at the heart of AI. Deep Learning, a subset of AI and Machine Learning, uses multi layered artificial neural networks to deliver state of the art accuracy in tasks such as object detection, image classification, speech recognition and language translation. These tasks require the compute power available with the solution Cisco and NVIDIA are delivering.
The goal is to enable IT organizations to capitalize on the adaptability, programmability, and manageability of Cisco UCS and power AI at any scale and location.
AI for media at IBC: GPU-accelerated data center
In our booth, we are proud to be partnered with the AI Industry leader NVIDIA, who will be showcasing how we are powering the possible with NVIDIA’s GPU-accelerated processing of data in artificial intelligence, deep learning and machine learning applications that transform content creation and curation. Broadcasters can work with technology companies like NVIDIA to develop customized internal solutions using a suite of SDKs tailored for accelerating the creation of deep neural networks, as well as training and inferencing from desktop to cloud.
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