Up, up and away: Passenger-carrying drone to fly in Dubai

Boston Herald

Up, up and away: Dubai hopes to have a passenger-carrying drone regularly buzzing through the skyline of this futuristic city-state in July. The arrival of the Chinese-made EHang 184 -- which already has had its flying debut over Dubai's iconic, sail-shaped Burj al-Arab skyscraper hotel -- comes as the Emirati city also has partnered with other cutting-edge technology companies, including Hyperloop One. The question is whether the egg-shaped, four-legged craft will really take off as a transportation alternative in this car-clogged city already home to the world's longest driverless metro line. Mattar al-Tayer, the head of Dubai's Roads & Transportation Agency, announced plans to have the craft regularly flying at the World Government Summit. Before his remarks on Monday, most treated the four-legged, eight-propeller craft as just another curiosity at an event that views itself as a desert Davos.

G-7 transport ministers agree to bolster railway, airline sector cooperation

The Japan Times

Transport ministers from the Group of Seven advanced economies agreed Sunday to strengthen cooperation in the railway and airline sectors as they wrapped up their three-day meeting in the resort town of Karuizawa, Nagano Prefecture. Prior to the conclusion of the gathering, ministers from Britain, Canada, France, Germany, Italy, Japan and the United States plus the European Union adopted a declaration Saturday pledging to reinforce international cooperation in creating safety regulations to promote self-driving cars. The conference was the last of the ministerial meetings related to May's G-7 leaders' Ise-Shima summit in Mie Prefecture. "We will cooperate with each other and exercise leadership to support the early commercialization of automated and connected vehicle technologies," the declaration adopted at the Saturday meeting said. "We obtained a common understanding to make efforts in the same direction to create regulation frameworks that (will) tend to vary depending on region," transport minister Keiichi Ishii told a news conference after the meeting.

Intel Completes Tender Offer for Mobileye Intel Newsroom


SANTA CLARA, Calif., and JERUSALEM, Aug. 8, 2017 -- Intel Corporation (NASDAQ: INTC) and Mobileye N.V. (NYSE: MBLY) today announced the completion of Intel's tender offer for outstanding ordinary shares of Mobileye, a global leader in the development of computer vision and machine learning, data analysis, localization and mapping for advanced driver assistance systems and autonomous driving. The acquisition is expected to accelerate innovation for the automotive industry and positions Intel as a leading technology provider in the fast-growing market for highly and fully autonomous vehicles. The combination of Intel and Mobileye will allow Mobileye's leading computer vision expertise (the "eyes") to complement Intel's high-performance computing and connectivity expertise (the "brains") to create automated driving solutions from cloud to car. Intel estimates the vehicle systems, data and services market opportunity to be up to $70 billion by 2030. "With Mobileye, Intel emerges as a leader in creating the technology foundation that the automotive industry needs for an autonomous future," said Intel CEO Brian Krzanich.

Google's controversial DeepMind deal for 1.6 million NHS patients' data called legally 'inappropriate'

The Independent

Google's artificial intelligence division received the medical records of 1.6 million people on an "inappropriate legal basis", according to a leaked letter from a top government adviser. DeepMind controversially struck up a data-sharing deal with the Royal Free Hospital Trust, for the creation of an app called Streams. In February last year, Google said Streams would help hospital staff monitor patients with kidney disease, but a document obtained by New Scientist caused further concern when it revealed that DeepMind was receiving historical medical data, records of the location and status of patients, and even details about visitors. 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.

Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism

arXiv.org Artificial Intelligence

China Abstract: Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynamics and spatial dependency in multistep traffic-condition prediction, we propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the proposed deep learning framework, spatial and temporal dependencies are modeled through the Seq2Seq model and graph convolution network separately, and the attention mechanism along with a newly designed training method based on the Seq2Seq architecture is proposed to overcome the difficulty in multistep prediction and further capture the temporal heterogeneity of traffic pattern. We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models using a real-world dataset. The results indicate that our model yields the best prediction performance in terms of various prediction error measures. Keywords: traffic forecasting; deep learning; attention mechanism; graph convolution; multistep prediction; sequence-to-sequence model 1. INTRODUCTION Automobile use has significantly increased in the past few decades owing to the steady development in both technology and economy. However, the increased automobile use has resulted in a series of social problems such as traffic congestion, traffic accidents, energy overconsumption, and carbon emissions (Gao et al., 2011). The intelligent transportation system (ITS) has been considered as a promising solution to improve transportation management and services (Qureshi and Abdullah, 2013; Lin et al., 2017).