Research Papers On Artificial Neural Networks

Research Papers On Artificial Neural Networks-82
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.

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.

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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.

"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.

"RUM helps neural networks to do two things very well," Nakov 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.

The team also had help from the Science Daily website, whose articles were used in training some of the AI models in this research.

If your browser does not accept cookies, you cannot view this site.Tatalović was at the time exploring AI in science journalism as his Knight fellowship project."And so we tried a few natural language processing tasks on it," Soljačić says.The work is described in the journal , 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 magazine.From AI for physics to natural language The work came about as a result of an unrelated project, which involved developing new artificial intelligence 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."We have been doing various kinds of work in AI for a few years now," Soljačić says.The researchers have even tried using the system on their own research paper describing these findings -- the paper that this news story is attempting to summarize.Here is the new neural network's summary: Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.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.

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