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Google publishes hundreds of research papers each year. Publishing is important to us; it enables us to collaborate and share ideas with, as well as learn from, the broader scientific community. Submissions are often made stronger by the fact that ideas have been tested through real product implementation by the time of publication. We believe the formal structures of publishing today are changing - in computer science especially, there are multiple ways of disseminating information.  We encourage publication both in conventional scientific venues, and through other venues such as industry forums, standards bodies, and open source software and product feature releases. Open Source We understand the value of a collaborative ecosystem and love open-source software. Product and Feature Launches With every launch, we're publishing progress and pushing functionality. Industry Standards Our researchers are often helping to define not just today's products but also tomorrow's. Resources Resources doesn't just mean tangible assets but also intellectual. Incredible data sets and a great team of colleagues foster a rich and collaborative research environment. Impact Couple big challenges with big resources and Google offers unprecedented research opportunities. Algorithms and Theory 410 Publications Artificial Intelligence and Machine Learning 454 Publications Data Management 102 Publications Data Mining 168 Publications Distributed Systems and Parallel Computing 145 Publications Economics and Electronic Commerce 145 Publications Education Innovation 24 Publications General Science 114 Publications Hardware and Architecture 52 Publications Human-Computer Interaction and Visualization 351 Publications Information Retrieval and the Web 187 Publications Machine Perception 266 Publications Machine Translation 40 Publications Mobile Systems 56 Publications Natural Language.
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There are several major closed-form challenges in Computer Science that are prime targets for research, such asHowever, the hottest topics are broad and intentionally defined with some vagueness, to encourage out-of-the-box thinking. For such topics, zooming in on the right questions often marks significant progress in itself.Here's my list for 2015.Abundant-data applications, algorithms and architectures are a meta-topic that includes research avenues such as data mining (quickly finding relatively simple patterns in massive amounts of loosely structured data, evaluating and labeling data, etc), machine learning (building mathematical models that represent structure and statistical trends in data, with good predictive properties), hardware architectures to process more data than is possible today.Artificial intelligence and robotics - broadly, figuring out how to formalize human capabilities which currently appear beyond the reach of computers and robots, then make computers and robots more efficient at it. Self-driving cars and swarms of search-and-rescue robots are a good illustration. In the past, once good model were found for something (such as computer-aided design of electronic circuits), this research moves into a different field - the design of efficient algorithms, statistical models, computing hardware, etc.Bio-informatics and other uses of CS in biology, biomedical engineering and medicine, including systems biology (modeling interactions of multiple systems in a living organism, including immune systems and cancer development),  computational biophysics (modeling and understanding mechanical, electrical, and molecular-level interactions inside an organism),  computational neurobiology (understanding how organisms process incoming information and react to it, control their bodies, store information, and think). There is a very large gap between what is.
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