Never mind having robots deliver pizza -- if MIT and QCRI researchers have their way, the automatons will make your pizza as well. They've developed a neural network, PizzaGAN (Generative Adversarial Network), that learns how to make pizza using pictures. After training on thousands of synthetic and real pizza pictures, the AI knows not only how to identify individual toppings, but how to distinguish their layers and the order in which they need to appear. From there, the system can create step-by-step guides for making pizza using only one example photo as the starting point. The result is a system that isn't perfect (it's better at ordering synethetic pizza images than real ones), but it's still reasonably accurate. The scientists found that PizzaGAN could determine the right order 88 percent of the time, albeit using pizzas with just two toppings.

Artificial intelligence can now figure out recipes based on images of pizza. New York thin-crust topped with pesto chicken. What do you think makes for the perfect pizza? A recent study suggests neural networks could create the ultimate pie. The study out of MIT, which appeared earlier this month on Arxiv.org, Generative adversarial networks (GANs) use models to make decisions.

Cooking well takes patience, time, practice, and skill, and so is it possible for a machine to do what professional human chefs take years to perfect? A new study in deep neural networking, titled "How to make a pizza: Learning a compositional layer-based GAN model" and recently published on arxiv.org The PizzaGAN project is described as an experiment in how to teach a machine to make a pizza by recognizing aspects of cooking, such as adding and subtracting ingredients or cooking the dish. The Generative Adversarial Network (GAN) deep learning model is trained to recognize these different steps and objects, and by doing so, is able to view a single image of a pizza, dissect and peel apart each object or change'layer,' and recreate a step-by-step guide to cook it. "Given only weak image-level supervision, the operators are trained to generate a visual layer that needs to be added to or removed from the existing image," the research paper explains.