Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles. Potential car buyers spend anywhere between 30 to 50 minutes every day on Facebook and that has helped the social business make significant inroads in digital prospecting and omni-channel commerce. Facebook believes that car companies are focusing more on the connected car, rather than the connected consumer. With every new customer car buying journey now beginning online, it is possible through Facebook's huge data on a customer's social behavior, to make that experience personalized and completely customized.
Enter Timnit Gebru at Stanford University and a few pals, who have used Google Street View images to make remarkably accurate assessments of the demographic breakdown in a wide range of U.S. cities. Gebru and co begin with 50 million Street View images gathered by Google's cars in 200 American cities. The team believes the type of car people own is a strong indicator of their race, income, education levels, occupation, and so on. To find out, the team trained another deep-learning algorithm to learn the correlation between vehicle types and the data from U.S. Census and presidential election voting patterns in each precinct (an area of about 1,000 people).
Engineers at the University of California, Riverside (UCR) have developed a new online energy management system (EMS) that they say can improve PHEV fuel efficiency by more than 30%. In "Development and Evaluation of an Evolutionary Algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles," published in IEEE Transactions on Intelligent Transportation Systems, Xuewei Qi and colleagues explain that improving the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery. The EMS developed by Qi and his team combines vehicle connectivity information (such as cell networks and crowdsourcing platforms) and evolutionary algorithms – a mathematical way to describe natural phenomena such as evolution, insect swarming and bird flocking. "We combined this approach with connected vehicle technology to achieve energy savings of more than 30 percent.
In my last LinkedIn post I spoke of Deep Learning in and as it relates to Web Development (you can read that particular blog post here Deep learning and Web development) today however I want to take a look at deep machine learning. Perhaps one of the most important things to appreciate about multiple layers of representation is that it's overcome much of the previous issues faced when computer scientists modelled neuron networks and today instead of simply classifying data they can instead generate the data models for themselves. Today deep machine learning features end-to-end learning that can allow a computer to learn free from intermediaries and significant human expertise. And as a perfect example of this is the way in which speech recognition advanced with deep learning free from the previously cumbersome (yet necessary) phonetic representations.
We pay a monthly subscription fee to J-Tech. In exchange, we're able to access their pool of hover vehicles on-demand. It knows where and when anyone needs to be at any given point in time and will pool the vehicle with anyone that fits into that schedule and destination. After a lengthy conversation, he excused himself to some quiet time.
Technology company Nauto has entered into agreements with BMW i Ventures and Toyota Research Institute, as well as with Allianz Ventures, part of the leading global financial service provider and insurance company Allianz Group. Under the agreements, Nauto and its auto and insurance industry partners will license data and technologies, including Nauto's artificial intelligence-powered vehicle network. Insurers get a more precise view of each and every driver that can help personalize coverage, deliver precision risk assessments, reduce fraudulent claims and provide enhanced urban mobility services for commercial fleets. Nauto's artificial intelligence platform drives deep learning that goes beyond the basics of recorded events by capturing driver behavior, inside-the-vehicle activity and correlations from road, weather and traffic conditions.
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. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain. If a traditional AI system had no rule for a skateboarding dog, this scenario could result in an accident.
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. 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. This approach allows for learning all aspects of driving the way that people do as they grow older--by initially recognizing road signs, for example, or seeing the differences between cars, buildings, pedestrians or other objects--and later by coming to understand things like braking distance, road handling and the habits of other drivers on the road. But there is one catch to creating such a system--neural networks learn by example, which means they need a lot of examples.
Here's more: A test driver "operating" a Google Lexus-model autonomous vehicle on September 23 was fortunate to escape with no serious damage. We're talking about solving and integrating concepts such as computer vision, deep learning, machine learning, and latency. We're talking about solving and integrating concepts such as computer vision, deep learning, machine learning, and latency. Jon Hilsensrath of The Wall Street Journal, a reporter with particularly strong sources inside the Marriner S. Eccles Federal Reserve Board Building, wrote Friday morning, "The subdued September jobs report ensures the Federal Reserve won't be raising short-term interest rates at its November meeting, a week before the U.S. presidential election, and creates a new thread of uncertainty about its action in mid-December."
A team of NVIDIA engineers working out of a former Bell Labs office in New Jersey decided to use deep learning to teach an automobile how to drive. Instead, they used an NVIDIA DevBox and Torch 7 (a machine learning library) for training and an NVIDIA DRIVE PX self driving car computer to do all the processing. The team trained a CNN with time-stamped video from a front-facing camera in the car synced with the steering wheel angle applied by the human driver. You can read the entire NVIDIA research paper, End To End Learning For Self Driving Cars for yourself or watch the video to learn more about how artificial intelligence is teaching cars how to drive themselves.