Applications Let’s finish with some concrete Then, a neural network goes process examples. the texts to find statistical patterns. Image and video recognition From these patterns, it builds equivalences between sequences of Interpreting an image—recognizing a words, which allow it to translate an person or an object and its surrounding unknown text. environment—is a relatively easy task However, translation softwares have for a human being. Every day, our brain fewer documents translated for some process effortlessly complex visual language pairs. For instance, there are information: a family picture, a car, a more documents translated from landscape. However, it is a very French into Spanish than from Danish to challenging task for a computer. Romanian. That’s why the quality of Yet the stakes are high because the translations varies a lot from one development of autonomous cars (for language to another. the perception of its surrounding Content recommendation environment), the automating labeling of images, the improvement of To recommend content to their users, identification systems, the detection of online marketplace or streaming pathologies from medical imaging platforms, use AI systems which devices are all dependent on advances operate according to a different in image and video recognition. approach from those of the learning How do social media platforms techniques exposed above. Here, input recognize faces on pictures? data are composed of all the past choices made by users. This dataset is They use convolutional neural networks. used to create fake user profiles and According to the same principle of product categories, here “fake” means supervised learning for the classification that they represent average user of the pictures of dogs and cats behaviours. Because of the wide variety described above, the system learns to of users and products, it is impossible to distinguish the faces of user’s friends for infer consumption behaviours whom he has labeled images. When a categories directly from their photo is uploaded to the platform, the consumption choices. Each user can system only has to categorize the new then be analyzed from these fake faces present by matching them with categories, which makes it possible to the labelled faces from its database. compute a “fake proximity” between Translation users more reliably than simply counting the products they ordered. In order to build a translation software, Afterwards, the system can recommend we start by building a large database of to a specific user a set of products likely texts translated by human translators to match his/her preferences because that serve as models. These translations they have been selected according to often come from books, official similar fake users' preferences. documents produced by international organizations (United Nations, European Commission) and authoritative websites. We talk about millions of texts…