Dean Evans of Tech Radar reports, "How, for example, does a computer know what a car looks like? We just know. We've built up that knowledge over time by observing lots of cars. Consequently, we know that not all cars look the same. We know that they come in different shapes, sizes and colours. But we can generally recognise a car because they have consistent and definable elements - wheels, tyres, an engine, windscreen and wing mirrors, they travel on roads, and so on. Could a computer learn all this information in the same way? A team working at Carnegie Mellon University in the United States believes so. It has developed a system called NEIL (Never Ending Image Learner), an ambitious computer program that can decipher the content of photos and make visual connections between them without being taught. Just like a human would."
Evans continues, "According to Xinlei Chen, a PHd student who works with NEIL, the software 'uses a semi-supervised learning algorithm that jointly discovers common sense relationships - e.g 'Corolla is a kind of/looks similar to Car', 'Wheel is part of Car' - and labels instances of the given visual categories… The input is a large collection of images and the desired output is extracting significant or interesting patterns in visual data - e.g. car is detected frequently in raceways. These patterns help us to extract common sense relationships.' As the 'never ending' part of its name suggests, NEIL is being run continuously, and it works by plundering Google Image Search data to amass a library of objects, scenes and attributes. The current array of information includes everything from aircraft carriers to zebras, basilicas to hospitals, speckled textures to distinctive tartan patterns."
Image: Courtesy NEIL