Neural nets are so named because they roughly approximate the structure of the human brain. Typically, they’re arranged into layers, and each layer consists of many simple processing units - nodes - each of which is connected to several nodes in the layers above and below. Data is fed into the lowest layer, whose nodes process it and pass it to the next layer. The connections between layers have different "weights," which determine how much the output of any one node figures into the calculation performed by the next.
Neural networks , which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems. During training, however, a neural net continually adjusts its internal settings in ways that even its creators can't interpret. Much recent work in computer science has focused on clever techniques for determining just how neural nets do what they do. In several recent papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Computing Research Institute have used a recently developed interpretive technique, which had been applied in other areas, to analyze neural networks trained to do machine translation and speech recognition. They find empirical support for some common intuitions about how the networks probably work. For example, the systems seem to concentrate on lower-level tasks, such as sound recognition or part-of-speech recognition, before moving on to higher-level tasks, such as transcription or semantic interpretation. But the researchers also find a surprising omission in the type of data the translation network considers, and they show that correcting that omission improves the network's performance.
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