I was involved with a startup system that used image recognition and categorization called Photrade several years ago. There I got an appreciation of how difficult this problem was. Also how closely it matched to the process of 'learning' we had mimicked using AI methods in the enterprise years before. What we did was very primitive compared to what is underway at Carnegie Mellon University with The Never Ending Image Learner (NEIL) program. This also sounds like it should be directly connected to the work at Cyc we also explored. Will continue to watch.In BBC Technology:
" ... The work is being funded by the US Department of Defense's Office of Naval Research and Google. Since July, the NEIL program has looked at three million images. As a result it has managed to identify 1,500 objects in half a million images and 1,200 scenes in hundreds of thousands of images as well as making 2,500 associations ... The team working on the project hopes that NEIL will learn relationships between different items without being taught. Computer programs can already identify and label objects using computer vision, which models what humans can see using hardware and software, but the researchers hope that NEIL can bring extra analysis to the data. ... "
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