Results


Nvidia looks to reduce AI training material through 'imagination'

ZDNet

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's new Silicon Valley Autonomous Driving Garage is primed for partnerships

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Intel just launched a new Autonomous Driving Garage facility in San Jose, at a facility that used to house Altera, the company it acquired in 2015. Davis explained that the new facility supplements Intel's existing efforts at developing and testing autonomous vehicles, which include a significant and ongoing effort in Arizona, where other companies (including Waymo, Uber and more) have also set up similar efforts. In San Jose, Intel gets access to another state that's generally friendly to autonomous testing from a regulatory standpoint, but they also get to be neighbors with some key technology partners, including car companies, many of whom have Silicon Valley R&D facilities and autonomous tech development centers. One project, shown to me by Intel's Matt Yurdana, who works as a creative director in the company's IoT Experiences Group, includes a fully functional demonstration system for how a self-driving car operating as part of an on-demand ridesharing fleet might interact with a human rider.


How deep learning will transform the future of the auto industry ZDNet

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The biggest threat to artificial intelligence: Human stupidity Snowden says Petraeus shared'far more highly classified material than I ever did' Snowden says Petraeus shared'far more highly classified material than I ever did' Deep learning is an important enabler of building self-driving vehicles that can operate without human intervention. We're at the start of what Silberg calls a new era in automotive product development and manufacturing -- one that emphasizes a vehicle's nervous system including a computer "brain," sensors, controls, driver interaction, and data storage even more than the powertrain. Because of deep learning, autonomy and mobility, car ownership is moving from individually-owned vehicles to shared driving experiences, with a growing consumer focus on mobility and transportation on demand. Automotive product development and manufacturing will emphasize vehicles' smarts: the computers, sensors, controls, driver interfaces, and data storage components.


How deep learning will transform the future of the auto industry

ZDNet

One of CES' major trends over the last few years has been the connected car -- the concept of adding Internet connectivity and networking to our vehicles. Stealing the spotlight this year was Nvidia, which launched the Drive PX 2 -- an in-car artificial intelligence system. PX 2 is designed for automakers exploring autonomous driving and includes 360-degree situational awareness, deep learning and the processing power of 150 MacBook Pros. Deep learning -- an advanced type of artificial intelligence (AI) -- is driving significant change for autonomous vehicles and for the automotive and transportation industries in general, according to a new report from advisory firm KPMG. The study predicts that by 2030 a new mobility services segment linked to products and services related to autonomy, mobility, and connectivity will be worth more than $1 trillion worldwide.


FiveAI win equity funding to develop Level 5 vehicle autonomy - Artificial Intelligence Online

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The funding, led by Amadeus Capital Partners with Spring Partners and Notion Capital, will enable Bristol-based FiveAI to grow its team, step-up its development and begin simulator and road testing of its software. According to FiveAI, early approaches to autonomous vehicles have required accurate, 3D maps built using point cloud technology. The company is now planning a system using much stronger AI and ML to ensure that autonomous vehicles can safely and accurately navigate all environments, including complex urban ones, with simpler maps. FiveAI said it expects to provide its software to major players in the global mobility industry, including automotive OEMs, automotive industry suppliers, rental companies and transportation operators in both public and private spheres.


Drive.ai Brings Deep Learning to Self-Driving Cars

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The company has been in stealth mode for the past year, working on applying deep learning techniques to self-driving cars. Its core team is made up of experts with a wealth of experience developing deep learning systems in all kinds of different domains, including natural language processing, computer vision, and (most recently) autonomous driving. "Drive.ai is a deep learning company," Reiley says. "We're solving the problem of a self driving car by using deep learning for the full autonomous integrated driving stack--from perception, to motion planning, to controls--as opposed to just bits and pieces like other companies have been using for autonomy.


Drive.ai Brings Deep Learning to Self-Driving Cars

#artificialintelligence

The company has been in stealth mode for the past year, working on applying deep learning techniques to self-driving cars. Its core team is made up of experts with a wealth of experience developing deep learning systems in all kinds of different domains, including natural language processing, computer vision, and (most recently) autonomous driving. "Drive.ai is a deep learning company," Reiley says. "We're solving the problem of a self driving car by using deep learning for the full autonomous integrated driving stack--from perception, to motion planning, to controls--as opposed to just bits and pieces like other companies have been using for autonomy.