With the help of Microsoft, last year Toyota created a new data analytics division called Toyota Connected to bring Internet-connected services into the car. Earlier this year, Renault-Nissan inked a deal to leverage Microsoft's Connected Vehicle Platform and its Azure cloud architecture to collect vehicle sensor and usage data in order to develop "connected driving experiences." Ford recently invested $182 million in Pivotal, a cloud-based software company, in part to create analytics tools and a cloud platform to support the automaker's Smart Mobility initiative. Cadillac introduced the first production vehicle-to-vehicle communication system on its 2017 models, and last year, Audi launched a Traffic Light Information vehicle-to-infrastructure system that lets its cars know how long a light will stay red or green to help improve traffic flow.
SAE International has created the now-standard definitions for the six distinct levels of autonomy, from Level 1 representing only minor driver assistance (like today's cruise control) to Level 6 being the utopian dream of full automation: naps and movie-watching permitted. Many of the features of AI-assisted driving center around increased safety, like automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise control. A connected vehicle could also share performance data directly with the manufacturer (called "cognitive predictive maintenance"), allowing for diagnosis and even correction of performance issues without a stop at the dealer. Although it may not at first appear directly tied to automotive AI, the health and medical industry stands to experience some significant disruptions as well.
Elon Musk's recent remark on Twitter that artificial intelligence (AI) is more dangerous than North Korea is based on his bedrock belief in the power of thought. In the last few years, some famously hard computational problems have been mastered, including identifying objects in images, recognizing the words that people say, and outsmarting human champions in games like Go. If machine learning researchers can create programs that can replace captioners, transcriptionists, and board game masters, maybe it won't be long before they can replace themselves. His primary mistake is in extrapolating from recent successes of machine learning the eventual development of general intelligence.
It won't be long before connected cars serve two masters: owners and manufacturers. As connected car systems evolve, the plan is for vehicles from the same manufacturer to communicate with each other and with the car maker without requiring user action. Xevo, is a tier 1 automotive supplier -- a major league parts and components source for the industry. Xevo is reportedly in talks with other major car manufacturers but is not releasing any names.
Ford has revealed plans to grow and increase profit margins by streamlining and transforming the company into a dominant player in next-generation automotive technology. On Wednesday, the Dearborn, Michigan-based firm said that expansion into the mobile area is moving apace, but more opportunities await the automaker in next-generation technologies -- including electric vehicles (EVs) and self-driving technology. Ford plans to grow the company through an enhanced focus on utility vehicles and heavy investment in "emerging opportunities," with the overall aim of becoming a leader in "electrification, autonomy and mobility," the firm said in a press release. "We expect Ford's performance to be strong through 2018 -- with our core business improving, allowing us to invest in the emerging opportunities that will ensure our future success," said Ford CFO Bob Shanks.
In step 1, analyze available data to narrow down the scope of developing predictive models to achieve objective #2 and explanatory models to achieve objective #3. In step 2, we will need to develop, test and validate predictive analytics models to predict unplanned down times. To complete the use case, we will need to develop, test and validate explanatory models to diagnose root causes for performance variability. His first exposure to predictive models and analytics was in academia, in the field of biomechanics – for identifying correlations and building regression models to predict muscle forces based on electrical activity in muscles.
Tesla Motors's statement last week disclosing the first fatal crash involving its Autopilot automated driving feature opened not with condolences but with statistics. "It has no meaning," says Alain Kornhauser, a Princeton professor and director of the university's transportation program, of Tesla's comparison of U.S.-wide statistics with data collected from its own cars. He, Kornhauser, and other researchers argue that companies working on autonomous driving technology need to drop such comparisons altogether. In California and some other states that permit testing of autonomous cars, companies must report accidents or technology failures that required a human to take over.
In their book Bad Moves, the neurologists Barbara Sahakian and Jamie Nicole LaBuzetta highlight the ethical challenges of using smart drugs to boost academic performance. Why, they ask, do we take such a dim view of athletes who use steroids to cheat in the Olympic Games but ignore students who use smart drugs to boost their performance when they are about to take university entrance exams? "Now is the time to have informed discussion and debate of the ethics of these'smart drugs' and the role they should play in our future society," they conclude. But, as we saw in the run-up to the financial crisis of 2008, private sector institutions can often hide behind a narrow interpretation of the law.