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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.
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.
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.
Various tricks have been used to improve this capability, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU), but these still fall well short of what's needed for real natural-language processing, the researchers say.
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.
"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.
"Although I do not think this paper completely solves this problem, it shows promising results on the long-term dependency tasks such as question-answering, text summarization, and associative recall." Gülçehre adds, "Since the experiments conducted and model proposed in this paper are released as open-source on Github, as a result many researchers will be interested in trying it on their own tasks. To be more specific, potentially the approach proposed in this paper can have very high impact on the fields of natural language processing and reinforcement learning, where the long-term dependencies are very crucial." The research received support from the Army Research Office, the National Science Foundation, the MIT-Sense Time Alliance on Artificial Intelligence, and the Semiconductor Research Corporation. A neural network can read scientific papers and render a plain-English summary." Science Daily. Researchers have developed an add-on module that helps artificial intelligence systems called convolutional neural networks, or CNNs, to fill in the gaps between video frames to greatly improve the ...
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.
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.