Python to Reach 3rd Place at TIOBE Index

The historical event, to which Python was approaching more than 25 years, finally happened
05 September 2018   1233

According to the new TIOBE index, Python is on the third line. TIOBE experts noted that they consider Python to be easy to learn and deploy applications. They also prophesy it the first place in the ranking.

The historical event, to which Python was approaching more than 25 years, finally happened. The language appeared in the rating in the early 90's and within 10 years won the line for the line to be in the top 10. Then slowly, but surely he was getting to the top-5. And now Python entered the top 3, ahead of C ++.

TIOBE Index September 2018
TIOBE Index September 2018

Notable changes in the top 50 occurred with three languages. Rust climbed five lines and took the 31st position. Groove is now on the 34th place, leaving behind 10 lines. Julia added 11 points and was placed on the 39th line.

The TIOBE index is updated monthly and reflects the popularity of the language among representatives of the IT industry. At the beginning of June, 2018, the TypeScript language was included in the top 100, which is a JavaScript add-in. Researchers believed that its popularity is associated with the use of Google on a par with Dart. A month later, in early July 2018, TypeScript overcame the top-50 mark and lagged behind the direct competitor Dart by 26 positions. However, in early August 2018, it was again outside the first fifty lines.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   673

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.