Elon Musk and many of the world's most respected artificial intelligence researchers have committed not to build autonomous killer robots. The public pledge not to make any "lethal autonomous weapons" comes amid increasing concern about how machine learning and AI will be used on the battlefields of the future. The signatories to the new pledge – which includes the founders of DeepMind, a founder of Skype, and leading academics from across the industry – promise that they will not allow the technology they create to be used to help create killing machines. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.
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.
Americans spend 8 billion hours stuck in traffic every year. Deep neural networks can help! DeepTraffic is a deep reinforcement learning competition. The goal is to create a neural network to drive a vehicle (or multiple vehicles) as fast as possible through dense highway traffic. What you see above is all you need to succeed in this competition.
There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.
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.
So, if AI have existed since 1950 why it is matter to Automotive industry now? there are two answers for this question. A more detailed answer which reflect all these technologies together. The huge advance in machine learning algorithms due to the deep learning; moreover, With AI as an raising common technology platform, the automotive industry is set to test various changes in the following years. As several issues considered during the manufacturing process in terms of AI: vehicles become more integrated, and complex systems. New functions are added according to standards.
To meet the goal of autonomous vehicles that can operate safely and without any need for human input -- that is, L5 automation -- automakers must train AI systems to navigate myriad conditions they'll run into in the real world so that they don't actually run into anything in the real world). Our highways and roads are, as we all know from experience behind the wheel, wholly unpredictable places, and they'll continually require self-driving cars to instantly interpret and react to "edge case" scenarios. While machine learning can guide AI to develop a recognition of, and reaction to, scenarios that it has seen many times before, there's an immense hurdle in training AI for one-in-a-million (or billion) situations. For example, AI may be well-versed in basic freeway driving, or identifying pedestrians under expected circumstances. Freeways may be littered with everything from tire scraps to sofas to grandmothers chasing after ducks; Halloween costumes can make pedestrians difficult to detect; you can set traps for autonomous vehicles; and even electric scooters can prove problematic for AVs.
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such sensor data in a low-dimensional yet actionable form. Our method is based on stacked gated recurrent units (GRUs). It accepts a short interval of automobile sensor data as input and computes a low-dimensional representation of that data, which can then be used to accurately solve a range of tasks. With this representation, we (1) predict the exact values of the sensors in the short term (up to three seconds in the future), (2) forecast the long-term average values of these same sensors, (3) infer additional contextual information that is not encoded in the data, including the identity of the driver behind the wheel, and (4) build a knowledge base that can be used to auto-label data and identify risky states. We evaluate our approach on a dataset collected by Audi, which equipped a fleet of test vehicles with data loggers to store all sensor readings on 2,098 hours of driving on real roads. We show in several experiments that our method outperforms other baselines by up to 90%, and we further demonstrate how these embeddings of sensor data can be used to solve a variety of real-world automotive applications.
Land Rover builds cars with two principles in mind: off-road capability and in-car comfiness. When you buy a Range Rover or Discovery, you're paying for a vehicle that can clamber over boulder-strewn trails and give you a back massage at the same time. So it shouldn't be surprising that last week the automaker announced it is developing the ultimate combination of these qualities: self-driving cars that can go off-road. The $5 million project, called Cortex, will give customers "autonomous cars capable of all-terrain, off-road driving in any weather condition." Now, these won't be Robo Rovers that can plow through streams and scramble over hulking tree roots--at least not anytime soon.