Twitter Acquires Madbits To Boost Deep Image Learning

Twitter has acquired Madbits, a deep-learning-based computer vision startup founded by proteges of Facebook AI director Yann LeCun. It’s the latest in a spate of deep learning and computer vision acquisitions that also includes Google, Yahoo, Dropbox and Pinterest.

Twitter

 

Twitter has acquired a stealthy computer vision startup called Madbits, which was founded by former New York University researchers. Clément Farabet and Louis-Alexandre Etezad-Heydari. Farabet is a protégé of Facebook AI Lab director and New York University professor Yann LeCun, while Etezad-Heydari was advised by Larry Maloney and Eero Simoncelli.

The notice on the Madbits website, which is dated Monday, is light on detail about the company’s technology, but includes this brief explanation of what it was doing and why it 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.

It’s not at all surprising that Twitter would want to acquire the company, given the tremendous amount of images published on Twitter every day. If Twitter wants to roll out functions such as image search, improve its search rankings based on image content, or perhaps even analyze images to get a better sense of what people are tweeting about, it will need people who understand how to do it. Yahoo, Dropbox and Pinterest have also made acquisitions in the computer vision space likely to glean the same types of capabilities.

Read more on this story at Giga OM.

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