Data volumes are soaring to unimaginable heights. The quantity of data is doubling in size every two years and by 2020, analyst firm IDC expects the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes. As this trend accelerates, it will unleash a new wave of opportunities for businesses and people around the world. So how do we take the next step: from gathering data to analysing it and deriving meaning that informs decision making? For as long as we have been designing computers, AI has been the final frontier.

Enterprises already appear to be embracing the idea of businesses accessing, and processing, and taking advantage of data by using AI machine learning or deep learning, in other words—becoming an “AI enterprise.” So with the amount of data that’s at our disposal, coupled with the intended applications 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 challenges, such as the volume, velocity and variety that stand as a hinder for Big Data analytics - Deep Learning algorithms and architecture can be used to help in Big Data analytics. These algorithms are exposed to stand out, compared to relatively basic learning architectures at extracting global and non-local patterns and relationships in the data.

The extracted representations by Deep Learning can be reflected as a real source of knowledge for decision-making, information retrieval, semantic indexing, and for other purposes in Big Data analytics.

The whole mindset needs a change from being overwhelmed by the data deluge, to actually being data hungry. AI is opening an insatiable desire for data.

Our daily life, economic vitality, and national security depend on a stable, safe and resilient cyberspace. But attacks on IT systems are becoming more complex and relentless, resulting in loss of information and money and disruptions to essential services. Thanks to the sheer amount of data that deep learning technologies collect, end-user privacy will be more important than ever.

Industrial trends followed within NVIDIA

Human intelligence will be simulated widely, and there will be a strong focus on security, intelligence and investigative capabilities. 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 invented for immersive 3D graphics in gaming, but its versatile nature has proved a match for many of our most important computing problems, from super computing to artificial 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 unleash the power of GPU has already become a ‘must have’ skill for application 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 apply this technology will be in massive 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 market 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 intelligent 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 efficiency everyday, and each new innovation is a major step. To meet these requirements, Artificial Intelligence (AI) has arrived, holding tremendous promise for the industry. Automation has been an important aspect of the data centre industry for years, but in the near future, deep learning will be utilised to allow computing and storage decisions to be quickly made and carried out, without the need for communication.