The resulting summaries were dramatically different. The LSTM system yielded this highly repetitive and fairly technical summary: "Baylisascariasis," kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences.
This infection, termed "baylisascariasis," kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences.
"And so we tried a few natural language processing tasks on it," Soljačić says.
"One that we tried was summarizing articles, and that seems to be working quite well." The proof is in the reading As an example, they fed the same research paper through a conventional LSTM-based neural network and through their RUM-based system.
And as we got to be more familiar with AI, we would notice that every once in a while there is an opportunity to add to the field of AI because of something that we know from physics—a certain mathematical construct or a certain law in physics.
We noticed that hey, if we use that, it could actually help with this or that particular AI algorithm." This approach could be useful in a variety of specific kinds of tasks, he says, but not all.Essentially, the system represents each word in the text by a vector in multidimensional space—a line of a certain length pointing in a particular direction.Each subsequent word swings this vector in some direction, represented in a theoretical space that can ultimately have thousands of dimensions."We can't say this is useful for all of AI, but there are instances where we can use an insight from physics to improve on a given AI algorithm." Neural networks in general are an attempt to mimic the way humans learn certain new things: The computer examines many different examples and "learns" what the key underlying patterns are.Such systems are widely used for pattern recognition, such as learning to identify objects depicted in photos.At the end of the process, the final vector or set of vectors is translated back into its corresponding string of words."RUM helps neural networks to do two things very well," Nakov says.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 is described in the journal Transactions of the Association for Computational Linguistics, in a paper by Rumen Dangovski and Li Jing, both MIT graduate students; Marin Soljačić, a professor of physics at MIT; Preslav Nakov, a senior scientist at the Qatar Computing Research Institute, HBKU; and Mićo Tatalović, a former Knight Science Journalism fellow at MIT and a former editor at New Scientist magazine.A team of scientists at MIT and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can read scientific papers and render a plain-English summary in a sentence or two.Credit: Chelsea Turner The work of a science writer, including this one, 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.