DeepMind to Test AI's IQ

Most models answered questions with an accuracy of 75%
12 July 2018   865

DeepMind, a subsidiary of Google, talked about the experiment with testing artificial intelligence models on generalization skills and abstract thinking. Specialists have developed a generator that formulates questions based on the notion of progression, color properties, shapes or sizes and their interrelationships. Similar tasks are encountered in IQ tests for people.

Most models answered questions with an accuracy of 75%. At the same time, researchers found a strict correlation between the effectiveness of tasks and the ability to identify the underlying abstractions. They managed to increase efficiency by training algorithms to explain their answers, to show what interrelations and properties should be considered in one or another issue.

However, in some models it is difficult to "transfer" the studied relationships to new properties, for example, if it trained to identify logical sequences relative to the color of objects, and in the task it is required to establish the dependence on their form.

The team found out that if the neural network correctly extrapolated its knowledge of the relationship to a new combination of values, then the accuracy of the tasks was increased to 87%. In the case of incorrect extrapolation, it fell to 32%.

Neural Network to Create Landscapes from Sketches

Nvidia created GauGAN model that uses generative-competitive neural networks to process segmented images and create beautiful landscapes from peoples' sketches
20 March 2019   172

At the GTC 2019 conference, NVIDIA presented a demo version of the GauGAN neural network, which can turn sketchy drawings into photorealistic images.

The GauGAN model, named after the famous artist Paul Gauguin, uses generative-competitive neural networks to process segmented images. The generator creates an image and transfers it to the discriminator trained in real photographs. He in turn pixel-by-pixel tells the generator what to fix and where.

Simply put, the principle of the neural network is similar to the coloring of the coloring, but instead of children's drawings, it produces beautiful landscapes. Its creators emphasize that it does not just glue pieces of images, but generates unique ones, like a real artist.

Among other things, the neural network is able to imitate the styles of various artists and change the times of the day and year in the image. It also generates realistic reflections on water surfaces, such as ponds and rivers.

So far, GauGAN is configured to work with landscapes, but the neural network architecture allows us to train it to create urban images as well. The source text of the report in PDF is available here.

GauGAN can be useful to both architects and city planners, and landscape designers with game developers. An AI that understands what the real world looks like will simplify the implementation of their ideas and help you quickly change them. Soon the neural network will be available on the AI ​​Playground.