MIT to Propose to Teach AI Like a Baby

The machine must independently observe people, “listen” to their conversations and form vocabulary
02 November 2018   578

MIT has developed a parser for artificial intelligence learning language. Its feature is learning through observation - as children do. This is reported by Engadget Com.

The method is not based on clear descriptions of words and concepts, but on the method of “weak control” and passive learning. The machine must independently observe people, “listen” to their conversations and form vocabulary. In the same way, children learn to speak, listening and learning words.

It is assumed that this approach will simplify the accumulation of vocabulary and allow programs and robots to more accurately perceive human speech and respond to it.

People in conversation often use only part of the sentence and violate the rules of grammar. Word analysis 'on the run' is supposed to improve the performance of AI systems and parsers. The parser does not rely on a specific context, and therefore, allows robots to perceive implicitly formulated orders.

The analyzer will help to find out how your child learns the language, which will help not only the developers of robots, but also specialists working with children.

MIT used the passive method of teaching the AI ​​network underlying the parser. Neural networks see video and text descriptions of actions, and the system correlated data and linked words with objects and actions. Researchers used 400 videos.

Scientists argue that the technology is easily scaled, and can be used where voice control or communication with AI is necessary.

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   120

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.