Data Science a New Pillar of the Scientific Method – Nvidia
We already knew that GPUs are useful for lots of things besides making Fortnite uncomfortably realistic. All the biggest supercomputers in the world use GPUs to accelerate math, and more recently they’ve been used to power deep neural networks in public clouds. But judging from Nvidia’s GPU Technology Conference held this week in San Jose, Nvidia sees GPUs and the entire GPU ecosystem as fertile ground for growing a much broader range of data science activities.
During a marathon keynote yesterday, Nvidia CEO Jensen Huang outlined, in strokes both broad and fine, the full Nvidia product strategy. From rendering farms and autonomous cars to medical imaging and cloud-scale analytics, Huang showcased the impressively full gamut of industries and solutions that the company is building and targeting with its GPU-enabled solutions.
Huang devoted almost an hour of his two-hour-and-forty-five minute keynote to the field of data science — and in particular Nvidia’s growing role in empowering data scientists to build the next generation of machine learning and AI systems.
“Data science is the fastest growing field of computer science today,” Huang told an audience of about 6,000 GTC attendees at the San Jose State University Event Center (the San Jose McEnery Convention Center’s halls were considered too cramped for the event this year). “It’s the most sought-after job. It’s the most over-subscribed course, whether it’s Berkeley or Stanford or CMU or NYU.”
Data science is so popular, Huang says, because it’s become the fourth pillar of the scientific method, allowing scientists to extract insights directly from data in ways that weren’t possible with theoretical, experimental, or simulation. “We’re solving problems that were just previously impossible,” he said.
The rest of this article can be found at sister publication Datanami.