OpenAI to Create Fake News Creating Algorithm

On the basis of one or two phrases that set the theme, it is able to “write” a fairly plausible story
18 February 2019   530

The GPT-2 algorithm, created by OpenAI for working with language and texts, turned out to be a master in creating fake news. On the basis of one or two phrases that set the theme, it is able to “compose” a fairly plausible story. For example:

  • an article about scientists who have found a herd of unicorns in the Andes;
  • news about pop star Miley Cyrus caught on shoplifting;
  • artistic text about Legolas and Gimli attacking the orcs;
  • an essay on how waste recycling harms the economy, nature, and human health.

The developers did not publish the source code of the model entirely, fearing abuse by unscrupulous users. For fellow researchers, they posted on GitHub a simplified version of the algorithm and gave a link to the preprint of the scientific article. The overall results are published on the OpenAI blog.

GPT-2 is a general purpose algorithm. The developers taught it to answer questions, “understand” the logic of a text, a sentence, finish building phrases. In this case, the algorithm worked worse than the model of a specific purpose. Researchers suggest that the indicators can be improved by expanding the training datasets and choosing computers more efficiently.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   634

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.