Twitter aims to up its image search cred with Madbits acquisition

Twitter looks set to up its image search game following the acquisition of startup Madbits for an undisclosed sum of money.

Founded by Clément Farabet and Louis-Alexandre Etezad-Heydari, Madbits is the latest in a series of deep learning startups to be bought out by big tech companies in the recent past.

The notice on the Madbits site announcing the acquisition is a little light on detail around what the company actually does, but the information that is there would suggest that Twitter’s acquisition of the startup does make sense:

Today, after a tremendous year of development and iterations, we are excited to announce that we are joining Twitter.

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.

We are excited to join the folks at Twitter to merge our efforts and see this technology grow to its full potential.

As GigaOm notes, images are playing an increasingly important role in Twitter’s offering. Understanding the content and context of an image would only add to that offering.

Indeed, the social network’s been on a bit of a roll when it comes to adding to that offering. In June, for instance, it added animated gifs. In March meanwhile, it added the ability to attach multiple images to a tweet and tag people in photos.

The kind of deep learning that Madbits could help Twitter with would only add to the content and context around the increasing number of images floating around on the social network. That in turn means that it can tailor its overall offering better to individuals.