With the AI revolution spreading across industries everywhere, NVIDIA founder and CEO Jensen Huang took the stage Wednesday to unveil the latest technology for speeding its mass adoption.
His talk — to more than 6,000 scientists, engineers and entrepreneurs gathered for this week’s GPU Technology Conference in Suzhou, two hours west of Shanghai — touched on advancements in AI deployment, as well as NVIDIA’s work in the automotive, gaming, and healthcare industries.
“We build computers for the Einsteins, Leonardo di Vincis, Michaelngelos of our time,” Huang told the crowd, which overflowed into the aisles. “We build these computers for all of you.”
Huang explained that demand is surging for technology that can accelerate the delivery of AI services of all kinds. And NVIDIA’s deep learning platform — which the company updated Wednesday with new inferencing software — promises to be the fastest, most efficient way to deliver these services.
It’s the latest example of how NVIDIA achieves spectacular speedups by applying a combination of GPUs optimized for parallel computation, work across the entire computing stack, and algorithm and ecosystem expertise in focused vertical markets.
“It is accepted now that GPU accelerated computing is the path forward as Moore’s law has ended,” Huang said.
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The latest challenge for accelerated computing: driving a new generation of powerful systems, known as recommender systems, able to connect individuals with what they’re looking for in a world where the options available to them is spiraling exponentially.
“The era of search has ended: if I put out a trillion, billion million things and they’re changing all the time, how can you find anything,” Huang asked. “The era of search is over. The era of recommendations has arrived.
Baidu — one of the world’s largest search companies – is harnessing NVIDIA technology to power advanced recommendation engines.
“It solves this problem of taking this enormous amount of data, and filtering it through this recommendation system so you only see 10 things,” Huang said…
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