At Altair, chief technology officer Sam Mahalingam is heads-down testing the company's newest software for designing cars, buildings, windmills, and other complex systems. The engineering and design software company, whose customers include BMW, Daimler, Airbus, and General Electric, is developing software that combines computer models of wind and fluid flows with machine design in the same process--so an engineer could design a turbine blade while simultaneously seeing its draft's effect on neighboring mills in a wind farm. What Altair needs for a job as hard as this, though, is a particular kind of computing power, provided by graphics processing units (GPUs) made by Silicon Valley's Nvidia and others. "When solving complex design challenges like the interaction between wind structures in windmills, GPUs help expedite computing so faster business decisions can be made," Mahalingam says. An aerodynamics simulation performed with Altair ultraFluidX on the Altair CX-1 concept design, modeled in Altair Inspire Studio. Altair, which offers its computational fluid dynamics software on Oracle Cloud Infrastructure and other cloud computing services, is testing new GPU capabilities to improve how its software solves the complex physics problems that simulate a car's behavior in a wind tunnel, see how windmills' turbulence affect each another, or understand wind flows around skyscrapers long before construction begins.

Suppose there was an exchange traded fund that focused on the single most important technology trend in the world today. You might think that I was smoking California's largest export (it's not grapes). But such a fund DOES exist. The Global X Robotics & Artificial Intelligence ETF (BOTZ) drops a golden opportunity into investors' laps as a way to capture part of the growing movement behind automation. The fund currently has an impressive $2.2 billion in assets under management.

GPU technology has been around for decades, but only recently has it gained traction among enterprises. It was traditionally used to enhance computer graphics, as the name suggests. But as deep learning and artificial intelligence have grown in prominence, the need for fast, parallel computation to train models has increased. "A couple years ago, we wouldn't be looking at special hardware for this," said Adrian Bowles, founder of analyst firm STORM Insights Inc. in Boston. "But with [deep learning], you have a lot of parallel activities going on, and GPU-based tools are going to give you more cores."

Google is bringing graphics processing unit (GPU) chips to its Compute Engine and Cloud Machine Learning to boost performance for intensive computing tasks like rendering and large-scale simulations. GPUs will be available from Google Cloud Platform worldwide in early 2017, according to an announcement this week. In a separate announcement on Tuesday the company announced a new Cloud Machine Learning group, and a series of moves related to machine learning and artificial intelligence. Google introduced its new Cloud Jobs API, which applies machine learning to the hiring process, as well as significantly reduced prices for its Cloud Vision API, and a premium edition of its Cloud Translation (formerly Google Translate) API. A blog post outlining Google's machine learning-related updates also announces the general availability of the Cloud Natural Language API.

Fujitsu has introduced a new technology to improve the internal memory of GPUs in order to increase the machine learning accuracy. Fujitsu claims that the new technology has doubled the efficiency of machine learning compared with previous technology. According to the company, in recent years the use of GPUs in machine learning has increased. GPUs have been providing the raw power to complete the complex calculations required for machine learning. The calculations can be done seamlessly when the data is stored in the GPU chip, but there is a limit on the GPU chips to store information.