Students to Beat Google’s Machine-Learning Code

Student programmers' image classification algorithm successfully identifies the object in 93% of cases
13 August 2018   660

Developers-students from Fast.ai which organize free online computer training courses have created an image classification algorithm that successfully identifies the object in 93% of cases and copes with it faster than a similar Google algorithm with a similar configuration. The authors argue that "the creation of breakthrough technologies is not just for big companies". This is reported by MIT Technology Review.

When evaluating performance, the DAWNBench test was used, which calculates the speed and cost of teaching the neural network. During the Fast.ai experiment, the neural network was launched on 16 virtual AWS nodes, each contained 8 NVIDIA V100 graphics cards. This configuration achieved accuracy of 93% in 18 minutes, and the cost of machine time was estimated at $ 40. The result of Fast.ai is faster than the development of Google engineers by 40%, but the corporation uses its own clusters TPU Pod, so the comparison is not entirely objective.

The developers used the PyTorch Python library, as well as their own development - fastai. They were able to achieve this learning speed with the new method of cropping images from the ImageNet dataset: instead of square pictures, they began to use rectangular:

Fast AI
Fast AI

State-of-the-art results are not the exclusive domain of big companies. These are the obvious, dumb things that many researchers wouldn’t even think to do.
 

Jeremy Howard

Founder, Fast.AI

The authors tried to make the project accessible to everyone, so they simplified its infrastructure, refusing to use distributed computing systems and containers. To implement it, developers teamed up with engineers from the innovative division of the Pentagon (DIU) to release software to quickly create and support distributed models on AWS. 

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   119

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.