Most of us can recognize an object after seeing it once or twice.
They made a few clever tweaks to a deep-learning algorithm that allows it to recognize objects in images and other things from a single example-something known as “One-shot learning.” The team demonstrated the trick on a large database of tagged images, as well as on handwriting and language.
The software still needs to analyze several hundred categories of images, but after that it can learn to recognize new objects-say, a dog-from just one picture. It effectively learns to recognize the characteristics in images that make them unique. Others have developed one-shot learning systems, but these are usually not compatible with deep-learning systems.
Another group at Google DeepMind recently developed a network with a flexible kind of memory, making it capable of performing simple reasoning tasks-for example, learning how to navigate a subway system after analyzing several much simpler network diagrams.
According to both Gershman and Wan Lee, it will be some time yet before machines match human learning.