Self-driving Audis of 2020 will be powered by Nvidia artificial intelligence


Audi and Nvidia have been collaborating for some time, but at CES 2017, the companies made their biggest joint announcement yet. Using artificial intelligence and deep learning technology, the companies will bring fully automated driving to the roads by 2020. To achieve this, Audi will leverage Nvidia's expertise in artificial intelligence, the fruits of which are already being shown at CES. Audi's Q7 Piloted Driving Concept is fitted with Nvidia's Drive PX 2 processor and after only four days of "training," the vehicle is already driving itself over a complex road course. This is due to the Drive PX 2's incredible ability to learn on the go, which is a far cry from the first driverless cars that needed pre-mapped routes to function properly. "Nvidia is pioneering the use of deep learning AI to revolutionize transportation," Nvidia CEO Jen-Hsun Huang said.

Get ready for Audis with artificial intelligence CTV News


Audi and Nvidia are putting their partnership into high gear with the aim of putting cars with artificial intelligence on the road by 2020. The ambitious project, detailed at CES in Las Vegas is, according to both companies, the best way for cars to become truly autonomous and genuinely capable of coping with the everyday complexities of real-world driving and of learning as they go. At the 2016 NIPS Artificial Intelligence Conference in Barcelona in December, Audi demonstrated a miniature prototype AI car -- a scale model of the Q2 SUV -- that was laden with sensors. It autonomously drove around an alien space learning where obstacles were, developing a real-time map of the space in its digital mind and after some trial and error could drive straight to a confined parking space and navigate into it every time. However, for CES, the demonstrations are very much full-scale.

Nvidia and Mercedes-Benz to bring an AI car to market within a year


Nvidia already announced a partnership at CES to bring a AI self-driving car to production, and now Mercedes-Benz is also teaming up with the GPU-maker on a vehicle with AI on board. Nvidia and Mercedes-Benz are also setting an ambitious timeline for their goal; the two will field this new vehicle within the next 12 months, Nvidia confirmed to TechCrunch. The news came out on stage at a talk between Mercedes-Benz VP of Digital Vehicle and Mobility Sajjad Khan, and Nvidia CEO and co-founder Jen-Hsun Huang on Friday at CES. It's the result of a project the two began together three years ago, which helps explain why the car will be ready to get to customers by 2018. Mercedes and Nvidia have been working together with a specific focus on deep learning and AI.

Nvidia CES 2017 Keynote: Google Home AI, Cloud Gaming Service, AI Co-Pilot For Your Car


Nvidia had a huge 2016 with one of best performing stocks of the year. In the past 12 months, the graphics processing chipmaker's stock value has boomed 230%. This is mostly due to its impressive growth in artificial intelligence applications using its graphics processors in data centers and cars. Meanwhile, Nvidia maintains a fast-growing business in its core gaming market. Partially as a reflection of the growing importance of AI in the tech industry, Nvidia stole the opening Consumer Electronics Show keynote this year from Intel.

Build your own Deep Learning Box


Originally used to generate high-resolution computer images at fast speeds, the GPU's computational efficiency makes it ideal for executing deep learning algorithms. This section lists the main components of your deep learning box. Nvidia Digits is a user-friendly platform that allows you to train prediction models using deep learning techniques. If you're new to deep learning, you can also test the techniques in the cloud first, using Google's Cloud Machine Learning platform.

Audi: Machine Learning to Give Cars Superhuman Capabilities NVIDIA Blog


Machine learning will give cars the ability to analyze and learn from hundreds of thousands, even millions, of driving situations to learn better than any human being can, Audi execs told the press at CES 2016 Wednesday. "But our systems will learn from hundreds of thousands, even millions, of such situations that can be stored, analyzed and improved from, so these cars can learn even better than a human being can." Audi is continuing to work toward fully autonomous vehicles -- it sent a self-piloted A7 sedan from San Francisco to Las Vegas in time for last year's CES -- even as it continues to introduce increasingly sophisticated advanced driver assistance features that have seen a strong uptake among Audi buyers. Audi continues to work closely with NVIDIA, incorporating our Tegra processors into its zFAS driver assistance control unit and MIB infotainment system.



Check if your latest driver exists in the "Proprietary GPU Drivers" PPA. Add the "Proprietary GPU Drivers" PPA repository. Run a test to ensure your Tensorflow installation is successful. Install the requirements, build Caffe, build the tests, run the tests and ensure that all tests pass.

The hottest new technologies are coming to cars


Many of these advancements are being driven by the interest in what's called ADAS (Advanced Driver Assistance Systems), the technology that will eventually lead to self-driving cars. The multiple cameras, LIDAR and other sensors being integrated into new models serve as inputs to sophisticated neural networks that are running inside the car. From more sophisticated entertainment features to better displays to more reliable connectivity, tech performance has largely overtaken driving performance for many modern buyers. USA TODAY columnist Bob O'Donnell is president and chief analyst of TECHnalysis Research, a market research and consulting firm that provides strategic consulting and market research services to the technology industry and professional financial community.

NVIDIA : Toyota Exec Explains Why Simulation Key to Autonomous Driving 4-Traders


With visuals powered by NVIDIA GPUs, Toyota's simulation work is the key to taking the technologies developed by these research teams and rolling them out to Toyota's vast fleet of automobiles. GPU-powered deep learning technology for autonomous systems can help them learn from huge quantities of real-world data. That's because when he did the math - there are about 100 million Toyotas in service every year, driving 10,000 miles each year - he realized that Toyota cars are driven about 1 trillion miles per year. By contrast, parallel autonomy, an idea robotics researchers have been pursuing for years, enables autonomous systems improve safety, without needing to be as perfect.