Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Artificial Intelligence In Digital Storage

May 30, 2017 | 2778 Views

Unstructured data, that is ordinary information, videos and sensor measurements not in a formal structure; such as a database, is growing by leaps and bounds. Factors such as higher resolution, higher frame rate, multi-camera video projects and the internet of Things will lead to about a 56%annual increase in this type of data each year according to a keynote presentation by Dave Elliot from Google Cloud at the 2017 Creative Storage Conference (CS 2017). This will lead to huge challenges managing this unstructured content and finding what you need, when you need it.

Various technologies under the general name of Artificial Intelligence (AI) can provide ways to organize this avalanche of unstructured data. Using technologies such as Machine Learning (ML), content can be made available to meet individualized needs. In a world where people have come to expect instant responses from their queries using their personal electronic devices AI technologies provide a way to provide what people want, when they want it.

The cloud is emerging as a way to provide services such as storage and AI that can make content, including valuable video content, available for repurposing and monetization. Cloud storage allows moving a Capex expense to an ongoing Opex expense, it provides the ability to scale on demand and it makes other on-line resources available to understand and use this content. For video content Machine Learning can impact the entire supply chain using various cloud-based resources.

Google's Machine Learning tools include TensorFlow and its Cloud Machine Learning Engine. These tools allow speed recognition and conversion to text, image recognition, translation capabilities and various APIs to use ML data for further actions. The company's video intelligence API allows fining entities, content and moments, which can be broken down to the video shot or frame level-all available with a simple REST API call. ML technologies can even be used to look at the audience at a live event to create metadata (information about the data being displayed) reflecting human reactions to what is happening. This allows advanced video content recommendation and filtering as well as building a full media portal using a Content Delivery Network (CDN) as shown below.

In addition to the Google keynote there was an entire session at CS 2017 looking at the role of AI in creating metadata for video content. Aaron Edell from GrayMeta said that according to a Harvard Business Review article in December 2013, knowledge workers were wasting up to 50%of their time hunting for data, identifying and correcting errors and seeking confirmatory sources for data they do not trust. Aaron suggested applying cognitive services to index, extract and classify data to make "dark data" discoverable. As for the Google keynote, GrayMeta said that much of this big data analysis will happen in the cloud.

Aaron gave an example of transcription of a 60 minute video that can now be done by Google. For a human it costs $15 per hour to transcribe and it takes 4-6 hours for a 60 minute transcription of audio content with 99%accuracy. Google charges $0.005 per 15 minute and with parallel processing 60 minutes of audio can be transcribed in 5 minutes. The cloud based solution is approaching 99%accuracy. GrayMeta believes that by implementing a high-quality enterprise search solution, you can reduce "search time" by 54%, impowering greater use of unstructured data, such as video.

Primary Data, another speaker in the Rise of the Machines session, spoke about the value of combining data virtualization with metadata intelligence to improve performance, reduce costs and increase data protection across various storage systems including all flash arrays, NAS and cloud storage. Eliot Sakhartov from Microsoft spoke about their Azure Media Services offering encoding and data analytics to support live an on-demand streaming from a CDN. Microsoft also supports various cognitive services including face and speech recognition and search.

Video content is part of the fast-growing unstructured data that modern system administrators and data analysts must deal with. At the same time that this flood of data is growing and the demands to use and monetize it are increasing, the budgets to provide these capabilities is generally stagnant. This is leading to increasing interest in using new metadata tools based upon sophisticated big data analysis to organize and categorize this data, making it available when and where it is needed. Read More

Source: Forbes
Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

Full Bio 
Follow on

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

Industy seeks clear Artifical Intelligence policy: Ravi Shankar Prasad
today

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yesterday

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yesterday

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yesterday

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2 days ago

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