Waymo to Start First Driverless Car Service in December

Waymo has already invested about $ 80,000,000,000 in the project
14 November 2018   641

Waymo, a subsidiary of Alphabet Inc., a subsidiary of Google, plans to launch the first commercial unmanned vehicle under the new brand in early December 2018. The company has not yet disclosed the exact dates and the new name.

UAVs will begin to operate in the vicinity of the city of Phoenix, where closed tests of such cars have been conducted since 2017. About 400 volunteer families have been using Waymo services for a year now. Volunteers who decide to accept the new conditions will be exempted from non-disclosure obligations. This will allow them to share their impressions, to take friends with them and even media representatives. In this way, the company plans to expand its customer base.

Waymo has already invested about $ 80,000,000,000 in the project, another $ 96,000,000,000 is planned to be spent on licensing in the field of cargo transportation and technology, according to analysts from Morgan Stanley.waymo unmanned vehicles
“Pioneering” in the region gives advantages: the company will have the opportunity to quickly implement a network of vehicles, repair bases and support services. According to experts, this will allow the company to poach customers even from Uber and Lyft.

But analysts do not deny the existence of serious competition. Tesla, Daimler, Volkswagen and other companies have their own approach to solving technological and social issues related to the introduction of unmanned vehicles in everyday life.

UAVs built on the basis of Chrysler Pacifica vans and will have a high level of autonomy: 99.9% of the total time they will drive on autopilot, based on data from a test program.

The company plans to take a serious step - remove the engineer on duty from the cockpit of the drone, who controls the condition of the car while driving, and in the event of an emergency pressing the button forces the car to park at the curb. The data shows that the Waymo car can drive about 5,000 miles before it requires human intervention. 

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   672

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.