Facebook Going To Be A Lot Smarter With Its New DeepText AI

Facebook Going To Be A Lot Smarter With Its New DeepText AI

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Facebook is going to become a lot smarter with its latest DeepText AI project. DeepText – Facebook’s new artificial intelligence system that will help Facebook to analyze several thousands posts a second across 20 languages with near-human accuracy.

About 400,000 new stories and 125,000 comments on public posts are shared every minute on Facebook. So the goal of the DeepText  is to more precisely understand the content and context of text on Facebook, in order to improve its overall user experience.

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DeepText AI – Facebook’s New Text Understanding Engine

DeepText is already being tested on some Facebook experiences. In the case of Messenger, for example, it is beginning to give us a better understanding of when someone might want to go somewhere. DeepText is used for intent detection and entity extraction to help realize that a person is not looking for a taxi when he or she says something like, “I just came out of the taxi,” as opposed to “I need a ride.”

DeepText also help people find the right tools for their purpose. For example, someone could write a post that says, “I would like to sell my old bike for $200, anyone interested?” DeepText would be able to detect that the post is about selling something, extract the meaningful information such as the object being sold and its price, and prompt the seller to use existing tools that make these transactions easier through Facebook.

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DeepText has the potential to further improve Facebook experiences by understanding posts better to extract intent, sentiment, and entities (e.g., people, places, events), using mixed content signals like text and images, and automating the removal of objectionable content like spam.

Facebook is not yet open sourcing this technology. And the company is only beginning to use DeepText with its own services. But as described by Facebook, DeepText shows how the giants of the Internet hope to accelerate the progress of natural language understanding in the months and years to come. In building these systems, they aim to rely far less on humans and far more on data—enormous troves of online data.

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