AI to Create Pizza Receipts

MIT researchers used AI to create new pizza receipts; some of them are really interesting
12 September 2018   141

Researchers at MIT conducted an experiment to create recipes for pizza by artificial intelligence. Working within the framework of the project How To Generate (Almost) Anything, scientists used a recurrent neural network with open source called textgenrnn. The training was conducted on hundreds of author's recipes from culinary blogs.

To assess the taste qualities of the generated recipes, the researchers turned to the culinary specialists for help. Pizzeria Crush Pizza in Boston, Massachusetts, agreed to help implement artificial intelligence.

The local chef noted that in some recipes there are no key ingredients of the dish - meat topping, sauce or cheese. In addition, some components are quite difficult to find. In the pizzerias there was no "crushed caramel cheese" or "farmer's filling from a walnut".

As a result of oral testing of prototypes (in other words, having tried a piece), scientists came to the conclusion that some recipes turned out to be rather not bad. The top includes the following pizza recipes:

  • blueberries, spinach and feta cheese;
  • bacon, avocado and peach;
  • apricot, pear, cranberry and ricotta;
  • sweet potatoes, beans and brie;
  • shrimp, jam and assorted Italian sausages.

Pizza Receipts by AI
Pizza Receipts by AI

The last recipe for pizza was so successful that the chef of the pizzeria promised to think about including it in the main menu.

AI to be Used to Create 3D Motion Sculptures

The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018   110

MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.

At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".

At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.

MoSculp 3D Model
MoSculp 3D Model

The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.

The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.

The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.