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MinerEye to Demonstrate Interpretive AI Technology for Data Classification on GPUs at Nvidia’s GTC Israel 2018

MinerEye Automatic Data Classification Is Critical to Unstructured Data Intensive Environments Accelerated by GPU, Including Hybrid Cloud and Data Center Storage

MinerEye’s Interpretive AI™ Technology, which facilitates faster GPU data throughput and automatic data classification for massive data applications will be featured at the Nvidia’s GTC Israel 2018 on Oct. 17-19 at the Tel Aviv Convention Center (Booth 13).

Earlier this year, MinerEye was selected to join Nvidia inception program which nurtures dedicated and exceptional startups that are revolutionizing industries with advances in AI and data science. This virtual accelerator program helps startups during critical stages of product development and investment.

Read More: Interview with Ben Goertzel, CEO at SingularityNET

“Data scientists and researchers are harnessing GPUs to create massive amounts of unlabeled training data, all of which needs to be classified and continuously tracked,” says Yaniv Avidan, CEO of MinerEye. “Our AI-powered Data Tracker solution automatically classifies, tracks and protects unstructured data extremely fast, to support significantly more data volume and throughput using less power and compute infrastructure.”

Read More: Interview with Angel Gambino, CEO and Founder of Sensai

With thousands of computational cores and 10-100x application throughput compared to CPUs alone, GPUs are the preferred processor for AI over big data processing. Applications such as cyber security, autonomous vehicles, medical imaging and many others require significant data for image classification, video analytics, speech recognition and natural language processing.

Read More: The AI Gold Rush: How to Make Money off AI and Machine Learning!

“Automatic data classification is critical to unstructured data intensive environments accelerated by GPU including hybrid cloud and data center storage.” Avidan says. “We have a solution that’s easy for Nvidia customers to implement.”

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