Artificial intelligence has arrived, with a boost from computer gaming pioneer Nvidia.

Artificial intelligence (AI) has long been used as a story-telling device, but the technology has actually moved well beyond the realm of science fiction. AI-powered chatbots, virtual personal assistants and smart toys are already popular with consumers, and applications such as driverless cars, healthcare diagnostics and advanced robotics are expected to experience rapid growth in the near term.

“A lot of folks view artificial intelligence as this futuristic technology that has no practical application in their businesses, but that’s really not accurate,” said Michael Hritz, Vendor Alliance Manager, ProSys. “We may not have humanoid robots running around the office, but there are a surprising number of AI applications that are useful for businesses today. You might even be using AI without realizing it.”

Siri, Google Now and Cortana virtual assistants are becoming commonplace. Many retailers are using AI predictive analytics to offer personalized advertising, coupons and discounts. Apps such as Spotify, Pandora and Netflix use similar systems to recommend music and movies. Countless other organizations use basic AI apps to automate data entry, analyze contracts, manage investment portfolios, filter job applicants and more.

Tractica, a research firm focused on the AI market, has identified nearly 200 real-world AI uses across 27 industries.  The firm forecasts that revenue for enterprise AI applications will increase from $358 million in 2016 to $31.2 billion by 2025, representing a compound annual growth rate of 64.3 percent.

Processing Power

AI is actually an umbrella term for a number of technologies such as deep learning, machine learning, computer vision and natural language processing. All are aimed at embedding machines with the ability to analyze massive data sets, identify patterns and make autonomous decisions — eliminating the need for programmers to write code for every function.

Rapid advances in AI are being enabled by more powerful hardware, sophisticated algorithms and big data analytics. However, the greatest breakthrough in AI development may have come from a company most closely associated with the video game market.

Nvidia gained prominence for revolutionizing computer gaming through the development of its graphics processing unit (GPU). These specialized circuits perform multiple mathematic calculations simultaneously to manipulate and alter memory in order to produce cleaner, faster and smoother motion in video games. In 2007, Nvidia pioneered the concept of using GPUs in massively parallel processing environments designed to make compute-intensive programs run faster. This brought dramatic improvements over previous methods that relied on linking together multiple computer processing units (CPUs).

Key architectural differences between a CPU and GPU make the difference. A CPU has a few cores with lots of cache memory that can handle a few software threads at a time, but a GPU has hundreds of cores that can handle thousands of threads at the same time. Plus, CPUs are optimized for sequential processing — the execution of processes in the order they are received — as opposed to the GPU’s ability to execute multiple processes at the same time.

“GPU-accelerated computing can run some software 100 times faster than with a CPU alone,” said Hritz. “Plus, it conserves power and is more cost-efficient. That makes it perfect for the deep learning type of algorithms that are powering a range of AI applications.”

Deep Learning

Deep learning is a form of AI designed to loosely mimic the way the human brain works with neurons and synapses. Nvidia’s GPUs are used to create so-called “artificial neural networks” that use a large number of highly interconnected nodes working in unison to analyze large datasets.

“Nvidia’s GPUs provide the computational muscle to unlock the value of big data,” said Hritz. “The ability to discover patterns or trends — and learn from those discoveries — is the essence of artificial intelligence. Deep learning is what allows a personal digital assistance to learn your preferences, a smart thermostat to learn your schedule or a self-driving car to learn the rules of the road.”

Nvidia GPUs are being used to accelerate more than 400 applications for uses such as quantum chemistry, fluid dynamics, video editing, medical imaging and geosciences. Over the past two years, the number of companies collaborating with Nvidia on deep learning has jumped to nearly 20,000.

To fully exploit the capabilities of its GPUs, Nvidia recently introduced the DGX-1 server. This so-called “AI supercomputer in a box” delivers 170 teraflops of processing power in a single system and is purpose-built for deep learning and AI accelerated analytics. It comes fully integrated with hardware, deep learning software and development tools, and runs popular accelerated analytics applications.

The DGX-1 software stack includes DIGITS deep-learning training module, the CUDA programming model and a library of neural network designs. It also includes optimized versions of several widely used deep learning frameworks such as Caffe, Theano and Torch.

AI in the Cloud

Because deep learning involves analysis of large datasets, AI platforms need a cloud element for accessing cloud storage. DGX-1 provides access to cloud management tools, software updates and a repository for containerized applications.

The cloud, in fact, represents a vital intersection for AI. It is inevitable that organizations will look to utilize deep learning and AI applications without implementing an AI framework onsite. This is why Nvidia recently partnered with Microsoft to allow users to run GPU-accelerated workloads in Microsoft’s Azure cloud platform. Customers will be able to use Azure N-Series virtual machines powered by Nvidia Tesla K80 GPUs to run deep learning training jobs, high-performance computing simulations, data rendering, real-time analytics, DNA sequencing and other accelerated tasks.

“We’re working hard to empower every organization with AI, so that they can make smarter products and solve some of the world’s most pressing problems,” said Harry Shum, executive vice president of the Artificial Intelligence and Research Group at Microsoft. “AI is now within reach of any business.”