Facebook is using 'near-human' AI to muscle in on Google's home turf
On Wednesday, the social networking giant announced DeepText - "a deep learning-based text understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages."
DeepText is powered by an AI technique called deep learning. Basically, the more input you give it, the better and better it becomes at what it is trained to do - which in this case is parsing human text-based communication.
The aim? Facebook wants its AI to be able to "understand" your posts and messages to help enrich experiences on the social network. This is everything from recognising from a message that you need to call a cab (rather than just discussing your previous cab ride) and giving you the option to do so, or helping sort comments on popular pages for relevancy. (Both are examples Facebook's research team provides.)
The blog post doesn't directly discuss it, but another obvious application for this kind of sophisticated tech is Google's home turf - search. And engineering director Hussein Mehanna told Quartz that this is definitely an area that Facebook is exploring: "We want Deep Text to be used in categorizing content within Facebook to facilitate searching for it and also surfacing the right content to users."
Search is notoriously difficult to get right, and is a problem Google has thrown billions at (and made billions off) trying to solve. Is someone searching "trump" looking for the Presidential candidate or playing cards? Does a search for the word "gift" want for ideas for gifts, or more information about the history of gifts - or even the German meaning of the word, poison? And how do you handle natural-language queries that may not contain any of the key words the searcher is looking for - for example, "what is this weird thing growing on me?"
By analysing untold trillions of private and public posts and messages, Facebook is going to have an unprecedented window into real-time written communication and all the contexts around it.
Google has nothing directly comparable (on the same scale) it can draw upon as a resource as to train AI. It can crawl the web, but static web pages don't have that real-time dynamism that reflect how people really speak - and search - in private conversations. The search giant has repeatedly missed the boat on social, and is now trying to get onboard - very late in the game - with its new messaging app Allo. It will mine conversations for its AI tech and use it to provide contextual info to users - but it hasn't even launched yet.
Facebook has long been working to improve its search capabilities, with tools like Graph Search that let the user enter natural language queries to find people and information more organically: "My friends who went to Stanford University and like rugby and Tame Impala," for example. And in October 2015, it announced it had indexed all 2 trillion-plus of its posts, making them accessible via search.
Using AI will help the Menlo Park company not just to index but to understand the largest private database of human interactions ever created - super-charging these efforts.