Kalev Leetaru, Forbes
As powerful and capable as current deep learning systems are, they are still only rote pattern extractors. A computer vision system can take a pile of cat photographs and “learn” to recognize cats. Transfer learning can be used to teach it to recognize dogs with a much smaller pile of training images. However, the underlying algorithm is not reasoning about what it is seeing, it is merely breaking the image into distinct colors, patterns and shapes and associating specific visual cues with a textual label. It cannot generalize from what it sees to autonomously expand its vocabulary to new mammals or understand the concept of “fur” or “paws” even as it associates a particular covering texture and four rectangularly distributed shapes with the images it has seen.
Share on Facebook