Startup develops AI that can detect machine failure just by listening to sounds


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.

Why fully automated cars are a lot further away than you think


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.

What About Applied Fairness? Artificial Intelligence

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.

NVIDIAVoice: Building The AI Architecture To Train, Simulate And Test AI Self-Driving Cars

Forbes Technology

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.

Inside Yandex self-driving car: Here's what it's like to ride on Moscow's crazy roads ZDNet


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.

Inside Yandex self-driving car: Here's what it's like to ride on Moscow's crazy roads


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.

Self-Driving Cars and the Agony of Knowing What Matters


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.

Self-driving cars are NOT safe 'while in the wild', says the co-founder of Google's DeepMind

Daily Mail

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.

Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment Artificial Intelligence

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.

How artificial intelligence is transforming the world


Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it.1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations. Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance. In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values.2 Although there is no uniformly agreed upon definition, AI generally is thought to refer to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention."3 According to researchers Shubhendu and Vijay, these software systems "make decisions which normally require [a] human level of expertise" and help people anticipate problems or deal with issues as they come up.4 As such, they operate in an intentional, intelligent, and adaptive manner. Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.