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Teaching machines to see and understand – A Summary of Facebook Engineering Post James Smith As Facebook has grown in popularity, it also has expanded its areas of interest. Today, it is not only focused on social media, but also the cutting edge of technology, such as artificial intelligence. In particular, the Facebook AI Research group studies image recognition and natural language understanding. Image Recognition Engineers at Facebook are attempting to write software that can look at a picture and identify all of its parts. Their code is artificially intelligent; that is, it does not require explicitly coded rules to make decisions. It can learn how to classify objects by experience, and then generalize this knowledge to the next picture. Due to the wide variety of images it encounters, the system needs to work in this experience-based manner. It may need to identify people, animals, furniture, buildings, cars, food, decorations, etc. Facebook’s latest system works on ten times less training data than other systems while segmenting the image 30 percent faster. Moreover, the model was very effective at generalization, performing well on images different than the training data. This system’s unique design contributes to its speed and efficiency. It differs greatly from all previous systems because it does not utilize any form of low-level segmentation. All previous systems relied heavily on low-level segmentation, such as searching edges or grouping pixels into groups (called super pixels). On the other hand, Facebook’s new model functions on a particular section of the image and finds both a segmentation mask for that area as well as the probability that the section is centered on the object. After this model is applied to all areas of the image and probabilities are found, the most likely object candidates are returned. Applications Giving computers the ability to recognize objects in images has unlimited potential. Facebook will use it to populate each user’s news feed more effectively with a greater understanding of the content of each image. Additionally, understanding images will allow Facebook to better understand the user who posted the image and present him or her with more useful information. For an individual image, it could also be used to predict hashtags. In a different area, this technology will be very useful for organizations like Google who are developing autonomous cars. Software of that purpose needs to know what types of obstacles and environment surround the car. Image recognition benefits wide variety of applications. Natural Language Understanding Facebook is not only developing image recognition software, but also natural language software called Memory Networks, or MemNets. This software had been limited by amount of memory, only understanding quick question and response forms. However, this latest version uses memory much more efficiently and is able to answer thousands of questions over long texts. Facebook has used this natural language understand to begin creation of M, a robust artificial assistant that can send texts, order food, plan flights, or make dinner reservations. Summary Facebook has already created a simple, yet exciting application called Visual Question & Answer that combines these two technologies. It answers spoken questions about the contents of an image. One possibility for this software is helping the visually impaired understand a picture in a similar way to their friends. The combination of image recognition and natural language understanding will reshape the way society uses technology.