The work of a science writer includes reading journal papers filled with specialized technical terminology and figuring out how to explain their contents in language that readers without a scientific background can understand. Now, a team of National Science Foundation-funded scientists has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent: It can read scientific papers and render a plain-English summary in a sentence or two. Even in this limited form, such a neural network could be useful for helping editors, writers and scientists scan a large number of papers to get a preliminary sense of what they’re about. But the approach the team developed could also find applications in a variety of other areas besides language processing, including machine translation and speech recognition. The work came about as a result of an unrelated project that involved developing new AI approaches based on neural networks, aimed at tackling certain thorny problems in physics. However, the researchers soon realized that the same approach could be used to address other difficult computational problems, including natural language processing, in ways that might outperform existing neural network systems.
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