Nvidia Corp. NVDA 1.44% has been a force among videogame fans for more than a decade. Now the rest of the world is catching on, as the premier maker of chips that paint scenes of on-screen adventure and mayhem emerges as the kingpin in hardware for artificial intelligence. Sales of Nvidia chips to internet giants like Microsoft Corp. and Facebook Inc. --which rely on AI to do things like automatic image labeling and language translation--have grown by triple digits, year over year, for six quarters straight. Investors have responded by driving up Nvidia's stock roughly sevenfold in the past two years, lately trading at about 45 times earnings, compared with an industry average around 17. The market is rewarding not only the company's dominance in graphics and AI but also its extraordinarily well-balanced operations. In the Drucker Institute's Management Top 250 ranking of the most effectively managed U.S. companies, Nvidia's overall score of 76.8, which puts it in the top 10, is based on strong scores in all five categories that contribute to the overall ranking--customer satisfaction, employee engagement and development, innovation, social responsibility and financial strength.
Nvidia researchers have used a pair of generative adversarial networks (GANs) along with some unsupervised learning to create an image-to-image translation network that could allow for artificial intelligence (AI) training times to be reduced. In a blog post, the company explained how its GANs are trained on different data sets, but share a "latent space assumption" that allows for the generation of images by passing the image representation from one GAN to the next. "The use of GANs isn't novel in unsupervised learning, but the Nvidia research produced results -- with shadows peeking through thick foliage under partly cloudy skies -- far ahead of anything seen before," the company said. The benefits of this work could allow for network training to require less labelled data, it said. "For self-driving cars alone, training data could be captured once and then simulated across a variety of virtual conditions: Sunny, cloudy, snowy, rainy, nighttime, etc," Nvidia said.
Roundup Here's a human-compiled, totally non-robot generated summary of AI news beyond what we've already reported the past month week. By the way, your humble Reg vulture will be at the machine-learning super-conference NIPS in LA next week – please do email in if you want to say hi, or point out any hot talks or gossip. AI Index – A team of AI experts have published this year's annual AI Index report that gathers data to show how the field is progressing and changing over time. The rise of deep learning has accelerated the AI hype, and it can be difficult to have meaningful conversations and shape policy without basic metrics. The report shows that the number of active AI startups has increased 14 times since 2000, and venture capital has risen six times across the same period.
Intel said it has formed a partnership with Warner Bros. to create in-cabin and immersive experience within autonomous cars. The chip giant, which acquired Mobileye for a play in the autonomous vehicle market, also threw a jab at Nvidia, which is a key rival. Speaking at the Los Angeles Auto Show, Intel CEO Brian Krzanich outlined the Warner Bros. partnership. While the focus on autonomous vehicles has revolved around mapping, vision, sensors and the Internet of things, Krzanich argued the in-cabin design and entertainment systems will be just as ground breaking. Intel's Mobileye purchase may really be about thwarting Nvidia's car to cloud, data center connection Intel buys Mobileye for $15.3 billion, eyes autonomous driving market, computer vision In a post that went with Krzanich's keynote, he said: Intel and Warner Bros. will develop new experiences and layouts for autonomous vehicles.
In July, China's government issued a sweeping new strategy with a striking aim: draw level with the US in artificial intelligence technology within three years, and become the world leader by 2030. A call for research projects from China's Ministry of Science and Technology posted online last month fills in some detail on the government's plans. And it puts Silicon Valley chipmaker Nvidia, the leading supplier of silicon for machine-learning projects, in the cross hairs. The Ministry of Science and Technology document lays out 13 "transformative" technology projects where it wants to put government money in coming months, hoping for delivery by 2021. One is to invent new chips to run artificial neural networks, the form of software propelling the AI ambitions of Google and other tech companies.
Governor Andrew Cuomo of the State of New York declared last month that New York City will join 13 other states in testing self-driving cars: "Autonomous vehicles have the potential to save time and save lives, and we are proud to be working with GM and Cruise on the future of this exciting new technology." For General Motors, this represents a major milestone in the development of its Cruise software, since the the knowledge gained on Manhattan's busy streets will be invaluable in accelerating its deep learning technology. In the spirit of one-upmanship, Waymo went one step further by declaring this week that it will be the first car company in the world to ferry passengers completely autonomously (without human engineers safeguarding the wheel). As unmanned systems are speeding ahead toward consumer adoption, one challenge that Cruise, Waymo and others may counter within the busy canyons of urban centers is the loss of Global Positioning System (GPS) satellite data. Robots require a complex suite of coordinating data systems that bounce between orbiting satellites to provide positioning and communication links to accurately navigate our world.
We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.
It attributed that growth to the May launch of its Volta chips as part of its Tesla V100 data center graphic processing unit (GPU) that will power systems ranging from artificial intelligence (AI) to driverless cars. "Shipments of the Tesla V100 GPU began in Q2 and ramped significantly in Q3 driven primarily by demand from cloud service providers and high-performance computing," Chief Financial Officer Colette Kress said on a conference call on Thursday. Inc's Amazon Web Services and Microsoft Corp's Azure, was a point of concern after a disappointing showing last quarter. Please verify you're not a robot by clicking the box. You must select a newsletter to subscribe to.
There's a heated debate among the tech elite about whether artificial intelligence will destroy or enhance human life. Tesla founder Elon Musk has been sounding the alarms over AI for months, saying in September that AI will be the cause for World War III. Facebook founder Mark Zuckerberg, meanwhile, counters that AI will be a benefit to the world. Bryan Borzykowski is a Toronto-based business and investments writer. He's contributed to the New York Times, CNBC, BBC Capital, CNNMoney and several other publications.
There are a number of machine learning (ML) architectures that utilize deep neural networks (DNNs), including AlexNet, VGGNet, GoogLeNet, Inception, ResNet, FCN, and U-Net. These in turn run on frameworks like Berkeley's Caffe, Google's TensorFlow, Torch, Microsoft's Cognitive Toolkit (CNTK), and Apache's mxnet. Of course, support for these frameworks on specific hardware is required to actually run the ML applications. Each framework has advantages and disadvantages. For example, Caffe is an easy platform to start with, especially since ones of its popular uses is image recognition.