Artificial intelligence (AI) systems, blending data and advanced algorithms to mimic the cognitive functions of the human mind, have begun to simplify and enhance even the simplest aspects of our everyday experiences -- and the automotive industry is no exception. A Tractica market intelligence study forecasts that the demand for automotive AI hardware, software, and services will explode from $404 million in 2016 to $14 billion by 2025. Semi-autonomous and fully autonomous vehicles must heavily rely on AI systems to guide the dependability of their fail-safe navigation and earn the trust of drivers and passengers. In February 2017, Ford invested $1 billion -- Detroit's biggest investment yet -- in the self-driving car startup Argo AI, which was founded by a partnership between two top engineers from Google and Uber. Tesla founder Elon Musk speculates that AI will surpass solely human-based efforts by the year 2030.
Listen to your vehicle - this is an advice that all car and motorcycle owners are given when they're getting to know more about the vehicle. Now, a new AI service developed by 3Dsignals, an Israel based start-up is doing just that. The AI system can detect an impending failure in cars or other machines, just by listening to the sound. The system depends on deep learning technique to identify the noise patterns of a car. As per a report by IEEE spectrum, 3Dsignals promises to reduce machinery downtime by 40% and improve efficiency.
Don't hold your breath waiting for the first fully autonomous car to hit the streets anytime soon. Car manufacturers have projected for years that we might have fully automated cars on the roads by 2018. But for all the hype that they bring, it may be years, if not decades, before self-driving systems are reliably able to avoid accidents, according to a blog published Tuesday in The Verge. The million-dollar question is whether self-driving cars will keep getting better – like image search, voice recognition and other artificial intelligence "success stories" – or will they run into a "generalization" problem like chatbots (where some chatbots couldn't make unique responses to questions)? Generalization, author Russell Brandom explained in the blog Self-driving cars are headed toward an AI roadblock, can be difficult for conventional deep learning systems.
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100% of the way to the goal of "complete" fairness or perfectly align with our personal belief on which measure of fairness is used. Some measure of fairness being built would still put us in a better position than the status quo. Impediments to getting fairness and ethical concerns applied in real applications, whether they are abstruse philosophical debates or technical overhead such as the introduction of ever more hyper-parameters, should be avoided. In this paper we further elaborate on our argument for this viewpoint and its importance.
Developing an autonomous vehicle requires a massive amount of data. Before any AV can safely navigate on the road, engineers must first train the artificial intelligence (AI) algorithms that enable the car to drive itself. Deep learning, a form of AI, is used to perceive the environment surrounding the car and to make driving decisions with superhuman levels of performance and precision. This is an enormous big data challenge. A single test vehicle can generate petabytes of data a year.
Video: Yandex's autonomous car hits Moscow's streets. Transportation is about to get a technology-driven reboot. The details are still taking shape, but future transport systems will certainly be connected, data-driven and highly automated. With harsh winters, drivers who constantly switch lanes, traffic jams and occasional crashes, the Russian capital of Moscow provides a challenging setting for testing autonomous cars. "In Moscow, the guys behind you honk the horn even before the traffic lights turn green," says Dmitry Polishchuk, head of Yandex's driverless car project.
Video: Yandex's autonomous car hits Moscow's streets. With harsh winters, drivers who constantly switch lanes, traffic jams and occasional crashes, the Russian capital of Moscow provides a challenging setting for testing autonomous cars. "In Moscow, the guys behind you honk the horn even before the traffic lights turn green," says Dmitry Polishchuk, head of Yandex's driverless car project. Polishchuk is taking me on a ride along Moscow's busy streets to show me how far the company's self-driving technology has evolved in the year and a half since it was officially announced. Since local legislation does not allow unmanned cars on public roads, one of his colleagues, Alex, is sitting behind the wheel hoping not to have to touch it.
In medicine, false positives are expensive, scary, and even painful. Yes, the doctor eventually tells you that the follow-up biopsy after that bloop on the mammogram puts you in the clear. But the intervening weeks are excruciating. A false negative is no better: "Go home, you're fine, those headaches are nothing to worry about." The problem with avoiding both false positives and negatives, though, is that the more you do to get away from one, the closer you get to the other.
The co-founder of Google's DeepMind has slammed self-driving cars for not being safe enough, saying current early tests on public roads are irresponsible. Demis Hassabis has urged developers to be cautious with the new technology, saying it is difficult to prove systems are safe before putting them on public roads. The issue of AI in self-driving cars has flared up this year following the death of a women hit but a self-driving Uber in March. The accident was the first time a pedestrian was killed on a public road by an autonomous car, which had previously been praised as the safer alternative to a traditional car. Speaking at the Royal Society in London, Dr Hassabis said current driverless car programmes could be putting people's lives in danger.
The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles are deployed on urban roads a minimum safety level must be assured. Since the autonomous car technology is still under development there is no standard methodology to evaluate such systems. It is important to completely understand the technology that is being developed to design efficient means to evaluate it. In this paper we assume safety-critical systems reliability as a safety measure. We model an autonomous road vehicle as an intelligent agent and we approach its evaluation from an artificial intelligence perspective. Our focus is the evaluation of perception and decision making systems and also to propose a systematic method to evaluate their integration in the vehicle. We identify critical aspects of the data dependency from the artificial intelligence state of the art models and we also propose procedures to reproduce them.