Never Ending Image Learner gains some common sense

Share this on social media:

Carnegie Mellon University is running a computer program called the Never Ending Image Learner (NEIL), which searches the web for images and characterises them in order to make some 'common sense' judgments about the world.

The project is supported by the Office of Naval Research and Google Inc. It will present its findings on 4 December at the IEEE International Conference on Computer Vision in Sydney, Australia.

NEIL makes use of recent advances in computer vision that enable computer programs to identify and label objects in images, to characterise scenes and to recognise colours, lighting and materials with a minimum of human supervision. In turn, the data it generates will further enhance the ability of computers to understand the visual world.

NEIL also makes associations between these things to obtain common sense information. Based on text references, it might seem that the colour associated with sheep is black, but NEIL can realise that sheep typically are white.

'Images are the best way to learn visual properties,' said Abhinav Gupta, assistant research professor in Carnegie Mellon’s Robotics Institute. 'Images also include a lot of common sense information about the world. People learn this by themselves and, with NEIL, we hope that computers will do so as well.' 

The program has been running since late July and runs on two clusters of computers that include 200 processing cores. It has analysed three million images, identifying 1,500 types of objects in half a million images and 1,200 types of scenes in hundreds of thousands of images. It has made connections to learn 2,500 associations from thousands of instances.

Recent News

25 May 2021

The face recognition imager consumes 10,000 times less energy than a typical camera and processor. CEA-Leti is working with STMicroelectronics on the imager

06 May 2021

The GTOF0503 sensor features a 5µm three-tap iToF pixel, incorporating an array with a resolution of 640 x 480 pixels

30 April 2021

The algorithm can deduce the shape, size and layout of a room by measuring the time it takes for sound from speakers to return to the phone's microphone

20 April 2021

The Kria K26 SOM is built on top of the Zynq UltraScale+ MPSoC architecture. It has 4GB of DDR4 memory and 245 IOs for connecting sensors