Neural Network to be Used For Cooking Receipts

One of the best cooking receipt by an AI is to preheat the oven to 3500 ° C for 8 minutes
06 August 2018   1148

The creator of the AIweirdness blog decided to write a cookbook using a neural network. She used the framework from the GitHub resource and published the results.

Despite its early developments, Janelle Shane decided to literally start from scratch and used the possibilities of the textgenrnn framework, transforming them into recipes. The final data for learning the neural network looked like this:

  • framework: textgenrnn, long text mode;
  • memory: 40 characters (default);
  • Duration: about 15 hours with NVIDIA Tesla K80 graphics card (using Google Cloud);
  • temperature: 0,6.

The latest results were better than they were before. But, nevertheless, there are still absurd combinations of ingredients:

  • 1 long granules sugar;
  • 1 Spanish water;
  • 1 cup of cream cheese seeds.

And the names of recipes resemble computer-generated descriptions for products with AliExpress. More details can be found in the AIweirdness blog.

Initially, in order to teach AI to make culinary recipes, Janelle used 30,000 ready-made recipes, which she collected from various sources. However, this experience was not successful. Considering memory of only a few words in length without a certain selection concept, something extraordinary or simply unreal was obtained. For example:

  • 4.5 kg of broccoli dried in a clay pan;
  • half a pint of spicy pieces;
  • 42 cups of milk;
  • Preheat the oven to 3500 ° C for 8 minutes.

And these examples from the cooking tips perfectly illustrate the ineffectiveness of the original method:

  • mix honey, liquid water of the toes, salt and 3 tablespoons of olive oil;
  • throw a frying pan;
  • tear off part of the pan.

In June 2018 AI was taught to create memes. A month later, in July 2018, scientists attempted to improve the model for recognizing speech accents.

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   127

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.