Waymo to Start First Driverless Car Service in December

Waymo has already invested about $ 80,000,000,000 in the project
14 November 2018   439

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 StyleGan Source Code

This machine learning project allows to create of people faces by imitating photographs
11 February 2019   640

NVIDIA has open source code if developments related to the StyleGAN project, which allows generating images of new faces of people by imitating photographs. The system automatically takes into account aspects of the placement of individuals and makes the result indistinguishable from real photos (most of the respondents could not distinguish the original photos from the generated ones). For the synthesis of individuals, a machine learning system based on a generative-competitive neural network (GAN) is used. The code is written in Python using the TensorFlow framework and published under the Creative Commons BY-NC 4.0 license (for non-commercial use only).

Both ready-made trained models and collections of images for self-learning of a neural network are available for download. The basic model was trained on the basis of the Flickr-Faces-HQ (FFHQ) collection, which includes 70,000 high-quality (1024x1024) PNG images of people's faces. At the same time, the system is not tied to persons - as an example, the variants trained on collections of photographs of cars, cats and beds are shown. It requires one or more NVIDIA graphics cards (Tesla V100 GPU recommended), at least 11 GB of RAM, NVIDIA 391.35+ drivers, CUDA 9.0+ tools and the cuDNN 7.3.1 library.

The system allows you to synthesize the image of a new face based on interpolation of features of several faces, combining features characteristic of them, as well as adapting the final image to the required age, gender, hair length, smile character, nose shape, skin color, glasses, face rotation in the photo. The generator considers the image as a collection of styles, automatically separates the characteristic details (freckles, hair, glasses) from common high-level attributes (posture, gender, age changes) and allows you to combine them in an arbitrary form with the definition of the dominant properties through weights.