The machine-learning system, called a neural network, was designed to learn like a baby. It was based on research on how babies’ brains work, and it did better on certain tasks than its conventional counterparts.
The neural network was trained on a dataset of images that were labeled with the objects’ locations. The system was then tested on a new set of images, and it was able to correctly identify the objects’ locations more often than other machine-learning systems.
The researchers believe that the system’s success is due to its ability to learn from data in a way that is similar to how babies learn. Babies are able to learn from very little data because they are able to generalize from what they have learned. The neural network is able to do this because it has a “memory” that stores information about the images it has seen.
The researchers say that the system could be used to help robots navigate their environment, or to help computers understand the content of images.
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