A team of researchers from Tel Aviv consisted of three people taught neural network a very interesting nontrivial skill: generate images of ready-made food from the recipe text. Literally it means that this AI can take any text which contain different ingredients and figure out what the finished food product will look like.
However, the results are not very reliable yet – in the sense that the images of really cooked dishes are sometimes quite different from what the network imagined based on reading the recipe.
Researcher Ori Bar El told:
«It all started when I asked my grandmother for a recipe of her legendary fish cutlets with tomato sauce. Due to her advanced age she didn’t remember the exact recipe. So, I was wondering if I can build a system that given a food image, can output the recipe. After thinking about this task for a while I concluded that it is too hard for a system to get an exact recipe with real quantities and with “hidden” ingredients such as salt, pepper, butter, flour etc.
Then, I wondered if I can do the opposite. Namely, generating food images based on the recipes. We believe that this task is very challenging to be accomplished by humans, all the more so for computers. Since most of the current AI systems try replace human experts in tasks that are easy for humans, we thought that it would be interesting to solve a kind of task that is even beyond humans’ ability. As you can see, it can be done in a certain extent of success.»
Although these images are far from real, tests on people showed that they like the generated pictures and they find them even appetizing. The authors of the article, which was posted on TNW web site, find this result depressing. As you know, there is a lot of food pictures in Instagram, and now there is a danger that these pictures may turn out to be fake. For many people, it is even more distressing than fake news or portraits of non-existent people. It seems that nothing sacred is left in this world where even a photo of snacks can turn out to be a realistic-looking fantasy of a neural network.
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