Facebook to Use AI to Find & Understand Memes

Facebook is going to use machine learning system called Rosetta to deliver a more personalized news feed, as well as tracking spam, offensive or banned content
13 September 2018   170

Facebook introduced Rosetta - machine learning system, which in real time extracts text from more than a billion publicized images and videos in social networks in different languages, and then recognizes their context.

Rosetta performs simultaneously two independent processes: detection of areas that can contain text, and word recognition using the Faster R-CNN convolutional neural network on the ResNet18 architecture.

The algorithm recognizes English, Arabic, Hindi, German, Spanish and other languages, including those that have horizontal right-to-left writing, diacritics and other specific characters.

In the future, the corporation will try to teach the system to recognize more languages, types of text and image templates.

Facebook is going to use Rosetta to deliver a more personalized news feed, as well as tracking spam, offensive or banned content. Now it is sorted by operators and it takes a long time.

In June, 2018, researchers from Stanford talked about a model of machine learning that could create memes in the style of "advising animals." The authors noted that on average, an "artificial" meme is difficult to distinguish from "real" in the context of the quality of the joke in it.

AI to be Used to Create 3D Motion Sculptures

The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018   110

MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.

At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".

At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.

MoSculp 3D Model
MoSculp 3D Model

The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.

The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.

The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.