| |December 20189THANKS TO THE SHEER AMOUNT OF DATA THAT DEEP LEARNING TECHNOLOGIES COLLECT, END-USER PRIVACY WILL BE MORE IMPORTANT THAN EVER"AI enterprise." So with the amount of data that's at our disposal, cou-pled with the intended applica-tions of the data, enterprises must looking for new ways to manage it all. The whole idea of drowning in your"data deluge," predicates that enterprises look for new solutions. The motivation must be to do more with valuable data assets--start using AI, machine learning and deep learning. Big Data creates several chal-lenges, such as the volume, velocity and variety that stand as a hinder for Big Data analytics - Deep Learn-ing algorithms and architecture can be used to help in Big Data an-alytics. These algorithms are ex-posed to stand out, compared to rel-atively basic learning architectures at extracting global and non-lo-cal patterns and relationships in the data. The extracted representa-tions by Deep Learning can be reflected as a real source of knowledge for decision-making, information retrieval, semantic in-dexing, and for other purposes in Big Data analytics. The whole mindset needs a change from being overwhelmed by the data deluge, to actually be-ing data hungry. AI is opening an insatiable desire for data.Our daily life, economic vital-ity, and national security depend on a stable, safe and resilient cy-berspace. But attacks on IT systems are becoming more complex and relentless, resulting in loss of in-formation and money and disrup-tions to essential services. Thanks to the sheer amount of data that deep learning technologies collect, end-user privacy will be more im-portant than ever.Industrial trends followed within NVIDIAHuman intelligence will be sim-ulated widely, and there will be a strong focus on security, intelli-gence and investigative capabili-ties. This includes advanced search and facial recognition analytics using multiple visual resources. Intelligent video analytics will contribute to safer, more secure communities and infrastructure. These innovations will be driven by a compute platform called the graphics processing unit or GPU. This processor was originally in-vented for immersive 3D graphics in gaming, but its versatile nature has proved a match for many of our most important computing prob-lems, from super computing to ar-tificial intelligence. The secret of the GPU's power is its ability to handle large amounts of information at the same time, an approach known as parallel processing. The know-how to code applications in parallel and un-leash the power of GPU has already become a `must have' skill for ap-plication developers. As a compute model called GPU-accelerated deep learning, in which computers learn to write their own software, ignites the big bang of AI, the skills to ap-ply this technology will be in mas-sive demand. Data scientists and developers with an eye to career development are adding parallel programming and deep learning expertise to their CVs. The Indian data center mar-ket has seen tremendous growth over the last few years. According to Gartner, last year, India became the second fastest growing market in APAC. Currently, the data center market in the country is valued at USD 2.2 billion, and is expected to touch USD 4.5 billion mark by 2018. Main drivers for this huge increase are growth in data and digital in-telligent devices, digitalization and also the government's Digital India campaign.Data centers are proliferating to meet the relentless demand for IT capacity and seeking greater effi-ciency everyday, and each new in-novation is a major step. To meet these requirements, Artificial In-telligence (AI) has arrived, holding tremendous promise for the indus-try. Automation has been an im-portant aspect of the data centre industry for years, but in the near future,deep learning will be uti-lised to allow computing and stor-age decisions to be quickly made and carried out, without the need for communication. C IVishal Dhupar
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