Çağlar Gülçehre, a research scientist at the British AI company Deepmind Technologies, who was not involved in this work, says this research tackles an important problem in neural networks, having to do with relating pieces of information that are widely separated in time or space."This problem has been a very fundamental issue in AI due to the necessity to do reasoning over long time-delays in sequence-prediction tasks," he says.
"It helps them to remember better, and it enables them to recall information more accurately." After developing the RUM system to help with certain tough physics problems such as the behavior of light in complex engineered materials, "we realized one of the places where we thought this approach could be useful would be natural language processing," says Soljačić, recalling a conversation with Tatalović, who noted that such a tool would be useful for his work as an editor trying to decide which papers to write about.
Tatalović was at the time exploring AI in science journalism as his Knight fellowship project.
This infection, termed "baylisascariasis," kills mice, has endangered the allegheny woodrat.
Based on the same paper, the RUM system produced a much more readable summary, and one that did not include the needless repetition of phrases: Urban raccoons may infect people more than previously assumed.
Now, a team of scientists at MIT and elsewhere 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.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.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 have been doing various kinds of work in AI for a few years now," Soljačić says."We use AI to help with our research, basically to do physics better.The team came up with an alternative system, which instead of being based on the multiplication of matrices, as most conventional neural networks are, is based on vectors rotating in a multidimensional space.The key concept is something they call a rotational unit of memory (RUM).