Exciting change is on the way! Please join us at nsf.gov for the latest news on NSF-funded research. While the NSF Science360 page and daily newsletter have now been retired, there’s much happening at nsf.gov. You’ll find current research news on the homepage and much more to explore throughout the site. Best of all, we’ve begun to build a brand-new website that will bring together news, social media, multimedia and more in a way that offers visitors a rich, rewarding, user-friendly experience.

Want to continue to receive email updates on the latest NSF research news and multimedia content? On September 23rd we’ll begin sending those updates via GovDelivery. If you’d prefer not to receive them, please unsubscribe now from Science360 News and your email address will not be moved into the new system.

Thanks so much for being part of the NSF Science360 News Service community. We hope you’ll stay with us during this transition so that we can continue to share the many ways NSF-funded research is advancing knowledge that transforms our future.

For additional information, please contact us at NewsTravels@nsf.gov

Top Story

Can science writing be automated?

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.

Visit Website | Image credit: Chelsea Turner/MIT