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Company Designs Driverless Car Deep Learning Kit

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Drive.ai is a Silicon Valley startup working on a kit to retrofit your ride If Drive.ai is a success, your first self-driving car might already be parked in the driveway. The Silicon Valley start-up, founded recently by a team of former Stanford University Artificial Intelligence Lab products, is working on a software kit that can be used to retrofit existing vehicles. "We started Drive.ai because we believe there's a real opportunity to make our roads, our commutes, and our families safer," the company announced in a statement on its blog, citing a statistic that more than one million people die each year worldwide in automobile accidents caused by human error. At its foundation, Drive.ai is looking to use deep learning -- which its founders consider the most effective form of artificial intelligence ever developed -- to key a breakthrough in a field that giant companies such as Google and General Motors have been trying to master for years. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain.


Israeli company developing system to allow cars to learn how to drive through experience

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This means that programmers must account for every type of road situation a car may encounter. MIT's Technology Review spoke with Amnon Shashua, CTO and cofounder of the technology firm to learn more about the initiative. Mobileye has been in the news of late for another reason--its system was the one being used by the Tesla vehicle that was involved in a car crash in Florida recently--the incident is still under investigation by the NHTSA. Tesla publicly blamed Mobileye, and because of that, a rift developed between the companies, which are now no longer partners. Shashua does not believe that will harm the company's new initiative, though--building a system based on neural networking, which, if all goes according to plan, will allow a car or truck to learn how to drive in much the same way that humans do.


Intelligent vision systems and AI for the development of autonomous driving

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Maintaining the highest level of user safety will be non-negotiable when it comes to the deployment of autonomous vehicles whether they are used for personal or mass transport, or logistics in industrial environments. However, for reasons of sheer volume, it will be road vehicles where the biggest changes will be felt. Vehicle efficiency and road safety will be improved and congestion will come down and the technology and legislation is in development to make it a reality. It is generally agreed that the transition to autonomous driving will be gradual. In the US, the National Highway Traffic Safety Administration (NHTSA) has defined five levels of automation, from 0 to 4, which it refers to as the automation continuum.


Artificial Intelligence: The Race Is On to Smarten Our Cars

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Uber's Pittsburgh Experiment, featuring semi-autonomous vehicles, is up and running. If only its fleet could distinguish the proper path down a one-way street. And Google is reporting smashing results for its autonomous vehicle program. This is a public service alert for all you Yinzers out there: Get off the road; you're in danger. While we're at it, to unemployed tech bros desperate to get a foot in the Silicon Valley door: Don't take a gig as a Google autonomous vehicle test driver.


NVIDIA DRIVE auto-pilot and cockpit computers

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NVIDIA gives automakers, tier-1 suppliers, automotive research institutions, and start-ups the power and flexibility to develop and deploy breakthrough artificial intelligence (AI) systems for self-driving vehicles. NVIDIA's unified AI computing architecture enables training deep neural networks in the data center on the NVIDIA DGX-1, and then seamlessly runs them on NVIDIA DRIVE PX 2 inside the vehicle. This end-to-end approach leverages NVIDIA DriveWorks software and allows cars to receive over-the-air updates to add new features and capabilities throughout the life of a vehicle.


Impact of deep learning on computer vision

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A rather high profile area generating headlines this year has been connected vehicles. The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Vision is one critical factor; your car needs to be able to identify all road hazards as well as navigating from A to B. So, how can a car achieve that in an often over-crowded highway space? Computer vision can be described as graphics in reverse. Rather than us viewing the computer's world, the computer turns around to look at ours.


Can AI and Sensors Power the Next Generation of Traffic Lights? - DZone IoT

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While traffic lights do use sensors to try and make slightly more intelligent decisions than perhaps they once did, they are still fairly dumb tools for regulating the flow of traffic. A recent Chinese study explores whether machine learning can do a better job. The Elephant & Castle roundabout near where I live is notorious for its complexity, with rush hour traffic bustling onto it from several directions. It's perhaps understandable therefore that humans struggle to program traffic lights to function effectively. Of course, automating the process is no mean feat either, both because it requires an accurate model of the traffic flow, and then the challenges inherent in optimizing the flow.


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

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A UK start-up developing artificial intelligence and machine learning for fully autonomous vehicles has received 2.7m in equity funding. 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. In use, each vehicle then correlates against that map to work out where it is and establish a track to follow. 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.


The Current State of AI - IT News

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Artificial Intelligence has been around virtually since programmers started coding in the early 1950s. Alan Turing had proposed the Turing test in 1950 and the following year the first chess and checkers programs appeared. In 1956 AI gained its name and the next 20 years was spent, in the end fruitlessly trying to create an intelligent machine. At this time machines were unable to recognise human faces or understand speech. However come 1980 the Japanese Government funded the 5th Generation computer project to create a massively parallel computer.


How IoT and machine learning can make our roads safer

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Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015.