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In today’s world, we see data anywhere and everywhere. Data comes in different shapes and sizes, such as video, voice, text, photos, objects, maps, charts and spreadsheets. Can you imagine life without a smartphone, social apps, GPS, ride-hailing, or e-commerce? Data is at the center of how we consume and experience all these services.

Beyond the gathering of data, we need to determine how to use it. That’s where artificial intelligence and machine learning (AI/ML) come in. In services like ride-hailing, navigation/wayfinding, video communications and many others, AI/ML has been designed built in. For example:

• Today, a luxury car is built with 100 microprocessors to control various functions
• An autonomous vehicle (AV) may have 200 or more microprocessors and generates 10 GB of data per mile
• Tens of thousands of connected cars will create a massive distributed computing system at the edge of the network
• 3D body scanners and cameras generate 80 Gbps of massive raw data for the streaming games
• A Lytro camera, equipped with light field technology, generates 300 GB of data per second

Computer systems now perform tasks requiring human intelligence – from visual perception to speech recognition, from decision making to pattern recognition. As more data is generated, better algorithms get developed. When better services are offered, the usage of the services goes up. Think of it as a fly wheel that keeps moving.

The AI solutions are only as limited as the number of high-quality datasets you have. Real-world scenarios showing how the technology is used include:

• Autonomous driving: Data storage on vehicle versus in the data center for neuro map analysis and data modeling
• Autonomous business planning: Decision and business models for manufacturing and distribution
• Data disaggregation: Uncover hidden patterns in shifting consumer tastes and buying behavior in retail space
• Video games: Real-time player level adjustment using low-latency data for enhanced experiences

Enabling AI infrastructure in data centers

Because data centers sit right in the middle of compute, storage, networking and AI, they are the hub that those other technologies revolve around. So as a data center company, how can we make AI/ML computation affordable and accessible for enterprises to keep their innovation engines running?

At NTT's Global Data Centers, enabling AI infrastructure is an important pillar of our growth plans. GPUs, memory, storage, and the network are the key components of ‘AI Compute’ infrastructure. Our goal is to make AI Infrastructure-as-a-Service accessible and affordable to forward-looking small, medium and large businesses.

Modern enterprises must have an AI engine at the core of their business decision making, operations and P&L to stay competitive in the marketplace. But AI projects can be an expensive undertaking … from hiring talent to hardware procurement to deployment, your budget may skyrocket. However, if the upfront costs of AI infrastructure can be eliminated, the entire value proposition shifts in a positive way.

So how do NTT's Global Data Centers help our customers build AI solutions?

1. We de-construct our customer’s problem statement and design the solution
2. Our AI experts build tailored models for the computation
3. Our customers have full access to AI-ready infrastructure and talent

AI is transforming the way that organizations conduct their business. With data centers in the middle, GPUs, hardware infrastructure, algorithms and networks, will change the DNA of these organizations.