A press conference is held to announce the winners of the Nobel Prize in Chemistry 2015 in Stockholm, Sweden, Oct. 7, 2015. The Nobel Prize in Chemistry 2015 was awarded jointly to Tomas Lindahl, Paul Modrich and Aziz Sancar "for mechanistic studies of DNA repair," ie. for having mapped, at a molecular level, how cells repair damaged DNA and safeguard the genetic information. [Xinhua]
Three scientists share 2015 Nobel Prize in Chemistry, the Royal Swedish Academy of Sciences announced Wednesday. The Nobel Prize in Chemistry 2015 was awarded to Tomas Lindahl, Paul Modrich and Aziz Sancar "for mechanistic studies of DNA repair," ie. for having mapped, at a molecular level, how cells repair damaged DNA and safeguard the genetic information. "Their work has provided fundamental knowledge of how a living cell functions and is, for instance, used for the development of new cancer treatments," the statement said. In a telephone interview after the announcement, Lindahl said he was "surprised" and "proud to be selected" for the prize this year. Recalling why choosing this field of research, Lindahl said it is "important to have DNA repair, as damages in cells are unavoidable." "As we understand the mechanism better," it provides "better hope" for cancer treatments, said Lindahl, talking on potential applications of his discovery....
Three scientists win 2015 Nobel Prize in Chemistry
Researchers Teach Machines To Learn Like Humans

A team of scientists has developed an algorithm that captures our learning abilities, enabling computers to recognize and draw simple visual concepts that are mostly indistinguishable from those created by humans. The work, which appears in the latest issue of the journal Science, marks a significant advance in the field -- one that dramatically shortens the time it takes computers to 'learn' new concepts and broadens their application to more creative tasks. A team of scientists has developed an algorithm that captures our learning abilities, enabling computers to recognize and draw simple visual concepts that are mostly indistinguishable from those created by humans. "Our results show that by reverse engineering how people think about a problem, we can develop better algorithms," explains Brenden Lake, a Moore-Sloan Data Science Fellow at New York University and the paper's lead author. "Moreover, this work points to promising methods to narrow the gap for other machine learning tasks." The paper's other authors were Ruslan Salakhutdinov, an assistant professor of Computer Science at the University of Toronto, and Joshua Tenenbaum, a professor at MIT in the Department of Brain and Cognitive Sciences and the Center for Brains, Minds and Machines. When humans are exposed to a new concept -- such as new piece of kitchen equipment,...
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