Ping An Insurance to Use SingularityNET's AI Solutions

Ping An is the largest insurance corporation in the world with capitalization $217B (as for January 2018)
14 March 2019   372

SingularityNET blockchain platform and the Chinese insurance corporation Ping An will cooperate in the field of artificial intelligence and launch a number of joint initiatives. This is reported in the project's blog.

As the head of Ping An's AI direction, Bai Meng, said, the company is interested in the commercial application of optical character recognition technology, cross validation and machine learning offered by the SingularityNET ecosystem.

In the future, platform solutions can be used in Ping An's AI initiatives, such as Smart City and One Minute Clinic.

Ping An is the largest insurance corporation in the world. As of January 2018, the company's capitalization was estimated at $ 217 billion. Ping An shares are included in the Hang Seng Index - a composite index of the 50 largest companies listed on the Hong Kong Stock Exchange.

At the end of 2017, SingularityNET raised $ 32.8 million on ICO. The project token has been listed on Binance cryptoexc.

Against the background of news on cooperation with Ping An, the price of the SingularityNET (AGI) token began to rise rapidly in the morning of Thursday, March 14, rising to a peak by more than 13% in a short period of time, after which it corrected somewhat.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   858

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.