How Drive.ai Is Mastering Autonomous Driving with Deep Learning

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Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars. Last month, we went out to California to take a ride in one of Drive's cars, and to find out how they're using deep learning to master autonomous driving.


Drive.Ai Deep Learning For Autonomous Cars

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A new tech startup is merging deep learning with autonomous cars. Drive.ai has become the 13th company to be granted a license to test autonomous cars on public roads in California. If you haven't yet heard of them, you're not alone. Drive.ai has been in stealth mode for the past year. However, the company recently closed 12 million in Series A funding while developing deep learning technologies with their team of experts who specialize in everything from natural language processing, computer vision, and autonomous driving.


Garage startup uses deep learning to teach cars to drive

USATODAY - Tech Top Stories

Carol Reiley, president and cofounder of autonomous car tech startup Drive.ai, "How do you create a robot that people can trust? That's what we're working on," says co-founder and president Carol Reiley, 34, a Johns Hopkins University PhD candidate who paused her studies to move west and wrangle her Stanford-trained peers into startup mode. This tidy formula of study hall, garage start-up, technological disruption, is well known and beloved in Silicon Valley. Larry Page and Sergey Brin started Google in a garage near their Palo Alto, Calif.


Looking at the Future of SaaS, AI, and IT Through Experts' Eyes - DZone IoT

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Technology is advancing at record speed as innovations that were a foggy prediction came to life one after the other. This passing month, I decided to explore "future studies" and browsed the web for the latest advancements in the tech world, and especially AI, IT, and SaaS. An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor -- outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. People have started noticing self-driving Uber cars in downtown San Francisco, fueling speculation the ridesharing company could soon be deploying autonomous vehicles for commercial use right where it all started, in the Bay Area.


Video Friday: Deep Learning for Cars, Space Invaders With Drones, and Disagreeable Robot

IEEE Spectrum Robotics

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here's a taste of what's to come: In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers.