If fully automated vehicles become a reality someday – and it seems like we're moving in that direction at a fast clip – artificial intelligence will play an instrumental role. That's why Ford is pouring $1 billion into a Pittsburgh-based artificial-intelligence (AI) startup called Argo AI. And Toyota is collaborating with one of the biggest AI players – Nvidia – to develop AI hardware and software. The thinking is that AI has the power to recognize and react to the nearly infinite number of scenarios encountered on the road, because it has the capability to interpret the massive amount of data generated by the sensors and cameras in a vehicle – and make intelligent decisions based on that data. Nvidia says its Nvidia Drive PX platform can use AI to understand the 360-degree environment surrounding the car, localize itself on an HD map and anticipate potential hazards while driving – in a device that can fit in your hand.
AI has become part of the public consciousness. Researchers and data scientists have been sharing their groundbreaking work -- at what is officially known as the Conference and Workshop on Neural Information Processing Systems -- for three decades. But it's only with the recent explosion of interest in deep learning that NIPS has really taken off. We had two papers accepted to the conference this year, and contributed to two others. The researchers involved are among the 120 people on the NVIDIA Research team focused on pushing the boundaries of technology in machine learning, computer vision, self-driving cars, robotics, graphics, computer architecture, programming system, and other areas.
Looking for a way to turn your home computer into a deep-learning AI super-monster? Nvidia has an expensive answer. The new Titan V GPU promises a crazy amount of processing for deep learning and AI applications. It's nine times more powerful -- at 110 teraflops -- than last year's Titan X, Nvidia's last massive desktop graphics processor aimed at machine learning applications. The Titan V is based on Nvidia's newer Volta chip architecture, which is also being used in Nvidia's Xavier self-driving car system and for data centers.
Rockets, electric cars, solar panels, batteries--whirlwind industrialist Elon Musk has set about reinventing one after another. Thursday, he added another ambitious project to the list: Future Tesla vehicles will run their self-driving AI software on a chip designed by the automaker itself. "We are developing customized AI hardware chips," Musk told a room of AI experts from companies such as Alphabet and Uber on the sidelines of the world's leading AI conference. Musk claimed that the chips' processing power would help Tesla's Autopilot automated-driving function save more lives, more quickly, by hastening the day it can drive at least 10 times more safely than a human. "We get there faster if we have dedicated AI hardware," he said.
Tesla is taking its self-driving future into its own hands, which Elon Musk thinks will help usher the company into an era of fully autonomous vehicles in just two years. Musk confirmed the company has a team hard at work developing its own AI chips, which future Teslas will depend on in place of the Nvidia units currently used in the automaker's all-electric vehicles. Musk dropped the news at a private company party in Long Beach, according to CNBC. The team is being headed by Jim Keller, a former AMD and Apple chip architect who helped to design the iPhone maker's A4 and A5 processors. A Keller-led Tesla chip project was rumored back in September, but reports then claimed the automaker was working closely with chipmaker AMD to test the tech, which both companies denied.
DUBLIN--(BUSINESS WIRE)--The "Automotive Artificial Intelligence - Global Market Outlook (2017-2023)" report has been added to Research and Markets' offering. The Global Automotive Artificial Intelligence Market accounted for $563.58 million in 2016 and is expected to reach $5,265.81 million by 2023 growing at a CAGR of 37.6% during the forecast period. The automotive industry has seen the promise of artificial intelligence (AI) technology, and is among the industries at the forefront of using AI to augment human actions and to mimic the actions of humans. The arrival of standards such as the adaptive cruise control (ACC), blind spot alert, and advanced driver assistance systems (ADAS) and rising demand for convenience and safety presents an opportunity for OEMs to build up novel and innovative artificial intelligence systems that would attract customers. Although 2016 was spoiled by some technological failures in self-driving cars, the year als-observed a couple of successful test runs in the US.
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.
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.
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.
Nvidia reported a stellar quarter for the three months ended October 31. Nvidia had $2.6 billion in revenue in the quarter, and $1.5 billion of it came from graphics chips for gaming PCs. But the company's investment in artificial intelligence chips is paying off, with data center growing beyond $500 million in revenue for the first time. Jensen Huang, CEO of Santa Clara, California-based Nvidia, said his company started investing in AI seven years ago, and that its latest AI chips are the result of years of work by several thousand engineers. That has given the company an edge in AI, and other rivals are scrambling to keep up, he said.