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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.