Artificial intelligence – Challenges and chances.

More and more companies are investing in artificial intelligence (AI) to help them gain a competitive advantage by improving services, making information available more quickly or implementing new business models. But the clock is ticking: 80% of companies have already invested in AI* and this number will only increase. According to McKinsey**, 39 billion dollars was invested globally in the technology in 2016. Companies that do not take the necessary steps now will be left by the wayside.

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What are the advantages of AI?

Capgemini provides insights on the real-life benefits of AI in their study “Turning AI into concrete value: the successful implementers’ toolkit”***:


  • 2930% increase in sales leads experienced by Harley Davidson in three months
  • 20% improvement in emission value at a Siemens gas turbine
  • 35% improvement in customer service efficiency at KLM

Artificial intelligence? Machine learning? Deep learning?

Artificial intelligence is the attempt to reproduce or simulate intelligent behaviour with machine learning and deep learning forming sub-categories..


Programmes using machine learning can employ algorithms to predict people's actions in order to, for example, detect credit card fraud. Deep learning goes a step further and uses hierarchical layers in order to carry out the process of machine learning. That means that artificial neural networks are developed that are akin to the human brain. Complex concepts can be learned by building them from simple, hidden layers. Data is processed in the first layer before being moved up to the next where it is processed further, before being moved up to the next, and so on. This model can have endless numbers of layers and go to incredible depths, and is the reason behind the name “deep learning”.


Deep learning technologies is being utilised, for example, in driverless cars to differentiate between road signs, other cars and pedestrians. It also takes the form of intelligent voice control in computers and smartphones. The application possibilities are practically infinite and much more precise than machine learning. The only disadvantage is that deep learning needs a lot of computing power.


Challenge: Performance.

Artificial intelligence requires a lot more computing power than any other current applications, in particular deep learning algorithms that rely on high performance capacities to analyse and interpret information piece by piece. This cannot be achieved without acceleration using Graphics Processing Units (GPUs). Powerful GPUs currently on the market include the NVIDIA® Tesla® graphics processor This enables companies to accelerate their most demanding high-performance computing (HPC) and hyperscale workloads in the data centre. The advantage being that petabytes of data can be handled much faster than when using standard CPUs. Whether deep learning, energy research, virtual desktops or driverless cars, Tesla GPUs deliver enormous performance to enable extensive calculations and simulations in in next to no time.

Business opportunities.

Deep learning has already been implemented in a lot of industries:

  • Connected Car: Driverless cars would never have got off the drawing board without deep learning.
  • Traffic engineering: Traffic jams can be predicted faster and stolen cars more quickly found thanks to artificial intelligence.
  • Aerospace: Whether for satellite detection or aviation technology, deep learning is playing an increasingly important role.
  • Medicine: These days cancer cells can be detected more quickly and easily thanks to deep learning.
  • Industry: Occupational safety is increased by identifying potential hazards early enough.
  • Smart Home and communication: It won't be long before AI is managing buildings and numerous every day processes at home and in the office.

A strong partner: NVIDIA.

Extensive computing power is required in order to be able to take advantage of AI, and one system that can meet this challenge is the NVIDIA® DGX™, based on the high performance NVIDIA Volta™ GPU platform.  The NVIDIA DGX offers companies GPU-optimised software and simplified management all in one compact system.


Benefits of the DGX Station and DGX Server:

  • Integrated hardware and software
  • Based on the latest NVIDIA Volta™ GPU architecture
  • Deep learning training, inference and acceleration of analyses in one single system
  • Unmatched performance for faster iterations and innovations

Tried and tested:

Some of the world's biggest data centres are already using Tesla GPU to improve performance. The Tesla platform also supports standard applications and system management tools, making it easier for CIOs to ensure maximum system availability and performance.

Which GPUs offer the best performance?

There are two processors that are particularly suitable for AI: NVIDIA® Tesla® V100 is the most advanced data centre GPU ever built to accelerate AI, HPC, and graphics. Best of all, The new Volta™ GV100 GPU is already integrated meaning even more performance. GPUs with Volta architecture come with over 640 Tensor Cores and have 100 teraFLOPS (TFLOPS) of deep learning performance—more than five times that of its predecessor, Pascal.


The Tesla P40, on the other hand, is designed for maximum throughput for ever faster growing data volumes such as big data.


The NVIDIA® Tesla® V100 is recommended particularly for High Performance Computing (HPC), whereas the NVIDIA® Tesla® P100 is ideal to accelerate HPC and AI.


For graphics virtualisation, the NVIDIA® Tesla P6 is designed specifically for blade servers and supports multiple data centre workloads and also deep learning and HPC. The NVIDIA® Tesla® P4taps into the industry-leading NVIDIA Pascal™ architecture to deliver up to twice the professional graphics performance of the NVIDIA® Tesla® M60.

*Teradata: 80 Percent of Enterprises Investing in AI, but Cite Significant Challenges Ahead.
Read here: 
(20 October 2017)


**McKinsey: The growth of AI: 39 billion dollars already invested globally
Read here: 
(20 June 2017)


***Capgemini: Turning AI into concrete value: the successful implementers’ toolkit“.
Read here:
(6 September 2017)