Three scientists win 2015 Nobel Prize in Chemistry

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. Each day, DNA is damaged by UV radiation, free radicals and other carcinogenic substances, but even without such external attacks, a DNA molecule is inherently unstable. Thousands of spontaneous changes to a cell's genome occur on a daily basis. Furthermore, defects can also arise when DNA is copied during cell division, a process that occurs several million times every day in the human body, according to the statement. The reason our geneticmaterial does not disintegrate into complete chemical chaos is that a host of molecular systems continuously monitor and repair DNA. The Nobel Committee said the winners' work "has provided fundamental knowledge of how a living cell functions and is, for instance, used for the development of new cancer treatments." But back in the early 1970s, scientists believed that DNA was an extremely stable molecule, Lindahl demonstrated that DNA decays at a rate that ought to have made the development of life on Earth impossible. This insight led him to discover a molecular machinery, which constantly counteracts the collapse of our DNA, according to the statement. Sancar has mapped nucleotide excision repair, the mechanism that cells use to repair UV damage to DNA. People born with defects in this repair system will develop skin cancer if they are exposed to sunlight. The cell also utilises nucleotide excision repair to correct defects caused by mutagenic substances, among other things, according to the statement. Modrich has demonstrated how the cell corrects errors that occur when DNA is replicated during cell division. This mechanism, mismatch repair, reduces the error frequency during DNA replication by about a thousandfold, according to the statement. The Nobel Committee said that "their work has provided fundamental knowledge of how a living cell functions and is, for instance, used for the development of new cancer treatments." This year's prize is 8 million SEK (about 0.96 million U.S. dollars), which will be shared equally among the laureates. Follow China.org.cn on Twitter and Facebook to join the conversation. Source: China.org.cn

Read More........→October 08, 2015

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, a new dance move, or a new letter in an unfamiliar alphabet -- they often need only a few examples to understand its make-up and recognize new instances. While machines can now replicate some pattern-recognition tasks previously done only by humans -- ATMs reading the numbers written on a check, for instance -- machines typically need to be given hundreds or thousands of examples to perform with similar accuracy. "It has been very difficult to build machines that require as little data as humans when learning a new concept," observes Salakhutdinov. "Replicating these abilities is an exciting area of research connecting machine learning, statistics, computer vision, and cognitive science." Salakhutdinov helped to launch recent interest in learning with 'deep neural networks,' in a paper published in Science almost 10 years ago with his doctoral advisor Geoffrey Hinton. Their algorithm learned the structure of 10 handwritten character concepts -- the digits 0-9 -- from 6,000 examples each, or a total of 60,000 training examples. In the work appearing in Science this week, the researchers sought to shorten the learning process and make it more akin to the way humans acquire and apply new knowledge -- i.e., learning from a small number of examples and performing a range of tasks, such as generating new examples of a concept or generating whole new concepts. To do so, they developed a 'Bayesian Program Learning' (BPL) framework, where concepts are represented as simple computer programs. For instance, the letter 'A' is represented by computer code -- resembling the work of a computer programmer -- that generates examples of that letter when the code is run. Yet no programmer is required during the learning process: the algorithm programs itself by constructing code to produce the letter it sees. Also, unlike standard computer programs that produce the same output every time they run, these probabilistic programs produce different outputs at each execution. This allows them to capture the way instances of a concept vary, such as the differences between how two people draw the letter 'A.' While standard pattern recognition algorithms represent concepts as configurations of pixels or collections of features, the BPL approach learns "generative models" of processes in the world, making learning a matter of 'model building' or 'explaining' the data provided to the algorithm. In the case of writing and recognizing letters, BPL is designed to capture both the causal and compositional properties of real-world processes, allowing the algorithm to use data more efficiently. The model also "learns to learn" by using knowledge from previous concepts to speed learning on new concepts -- e.g., using knowledge of the Latin alphabet to learn letters in the Greek alphabet. The authors applied their model to over 1,600 types of handwritten characters in 50 of the world's writing systems, including Sanskrit, Tibetan, Gujarati, Glagolitic -- and even invented characters such as those from the television series Futurama. In addition to testing the algorithm's ability to recognize new instances of a concept, the authors asked both humans and computers to reproduce a series of handwritten characters after being shown a single example of each character, or in some cases, to create new characters in the style of those it had been shown. The scientists then compared the outputs from both humans and machines through 'visual Turing tests.' Here, human judges were given paired examples of both the human and machine output, along with the original prompt, and asked to identify which of the symbols were produced by the computer. While judges' correct responses varied across characters, for each visual Turing test, fewer than 25 percent of judges performed significantly better than chance in assessing whether a machine or a human produced a given set of symbols. "Before they get to kindergarten, children learn to recognize new concepts from just a single example, and can even imagine new examples they haven't seen," notes Tenenbaum. "I've wanted to build models of these remarkable abilities since my own doctoral work in the late nineties. We are still far from building machines as smart as a human child, but this is the first time we have had a machine able to learn and use a large class of real-world concepts -- even simple visual concepts such as handwritten characters -- in ways that are hard to tell apart from humans."Contacts and sources:James Devitt, New York University Source: http://www.ineffableisland.com/, Image: https://pixabay.com/, under Creative Commons CC0

Read More........→December 13, 2015