Facebook's Andrew Tulloch says deep learning has enabled the company's news-feed ranking algorithm to capture more nuance in posts, with textual content interpreted by neural network-based natural-language processing programs. Facebook is heavily leveraging deep-learning models to further its user engagement efforts, with the company's Andrew Tulloch noting predictive analytics has become less relevant as more Facebook posts embed video and images, and the volumes of data analyzed grow exponentially. Tulloch says deep learning also has enabled Facebook's news-feed ranking algorithm to capture more nuance in posts, with textual content interpreted by neural network-based natural-language processing programs. He also notes deep-learning models are being applied to product development by enabling large-scale comprehension of content. For example, Tulloch cites the use of computer-vision, neural-network, deep-learning models to interpret the content of photos posted by users and select those to surface in the "on this day" feature, without spotlighting potentially negative memories.

Facebook achieved web dominance by riding a business model of understanding users and feeding them tailored content and advertising. And as the social networking company further builds on its strong position, it leans heavily on deep learning models. "These kinds of deep learning techniques have been really important over the last couple years," said Andrew Tulloch, an artificial intelligence researcher at Facebook. In a presentation at the Deep Learning Summit in Boston, Tulloch said traditional predictive analytics techniques like logistic regressions used to be the state of the art at Menlo Park, Calif.-based Facebook. In particular, this sort of analytics powered the ranked news feed, in which users are shown posts they're likely to find interesting, as determined by an algorithm.