The past few years have seen a flurry of computer vision acquisitions by industry-leading websites- Google got DNNResearch, Pinterest acquired Visual Graph, while Yahoo acquired not one but two image recognition startups- IQ Engines and LookFlow. Now, Twitter has entered the fold, announcing the acquisition of deep learning startup Madbits. Madbits is the brainchild of Clément Farabet and Louis-Alexandre Etezad-Heydari- two former proteges of Yann LeCun, NYU Professor and Director of AI’s Facebook lab.
There are a number of possibilities that Madbits open up for Twitter. Primarly, computer vision technologies allow for sophisticated image search; they could also gain a better sense of what people are actually tweeting about, by gaining automated recognition of what a photo contains.
In line with the other acquisitions, the exact purpose of the acquisition remains nebulous. A statement from the Madbit team offered a high-level explanation of their technology, and why they decided to accept Twitter’s offer.
Over this past year, we’ve built visual intelligence technology that automatically understands, organizes and extracts relevant information from raw media. Understanding the content of an image, whether or not there are tags associated with that image, is a complex challenge. We developed our technology based on deep learning, an approach to statistical machine learning that involves stacking simple projections to form powerful hierarchical models of a signal.
We prototyped and tested about ten different applications, and as we’ve prepared to launch publicly, we’ve decided to bring the technology to Twitter, a company that shares our ambitions and vision and will help us scale this technology.
Details of the acquisition have not been released, but it’s safe to assume another reason Madbits accepted Twitter’s offer was a hefty amount of money on the table. Deep learning has become hot property, garnering more interest and recognition in recent years due to high-profile companies (such as Google and Microsoft) adopting the method, and the increasingly dazzling results it provides. As Microsoft Research director Peter Lee told Businessweek, “Last year, the cost of a top, world-class deep learning expert was about the same as a top NFL quarterback prospect.” It appears the deep learning specialist is going the way of the data scientist.
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