Posted by Shankar Kumar and Wolfgang MachereyAt Google, we like search. So it's no surprise that we treat language translation as a search problem. We build statistical models of how one language maps to another (the translation model) and models of what the target language is supposed to look like (the language model) and then we search for the best translation according to those models (combined into one big log linear model for those of you taking notes).But, just as putting all of your money in the investment with...
Tuesday, 30 December 2008
Monday, 10 November 2008
plop: Probabilistic Learning of Programs
Posted on 16:11 by Unknown
Posted by Moshe LooksCross-posted with Open Source at Google blogTraditional machine learning systems work with relatively flat, uniform data representations, such as feature vectors, time-series, and probabilistic context-free grammars. However, reality often presents us with data which are best understood in terms of relations, types, hierarchies, and complex functional forms. The best representational scheme we computer scientists have for coping with this sort of complexity is computer programs. Yet there are comparatively few machine learning...
Friday, 3 October 2008
New Technology Roundtable Series
Posted on 10:28 by Unknown
Posted by Alfred Spector, VP of Research and Special InitiativesWe've just posted the first three videos in the Google Technology Roundtable Series. Each one is a discussion with senior Google researchers and technologists about one of our most significant achievements. We use a talk show format, where I lead a discussion on the technology. While the videos are intended for a reasonably technical audience, I think they may be interesting to many as an overview of the key challenges and ideas underlying Google's systems. And...
Monday, 29 September 2008
Doubling Up
Posted on 20:06 by Unknown
Posted by Franz Josef OchMachine translation is hard. Natural languages are so complex and haveso many ambiguities and exceptions that teaching a computer totranslate between them turned out to be a much harder problem thanpeople thought when the field of machine translation was born over 50years ago. At Google Research, our approach is to have the machineslearn to translate by using learning algorithms on gigantic amounts ofmonolingual and translated data. Another knowledge source is usersuggestions. This approach allows us to constantly improve...
Saturday, 26 July 2008
Remembering Randy Pausch
Posted on 00:51 by Unknown
Posted by Kevin McCurley, Research TeamIt is with great sadness that we note the passing of Randy Pausch, who taught computer science at Carnegie Mellon University. Randy was well-known by many within the research community, including quite a number of us here at Google. Alfred Spector, our Vice President of Research, was his Ph.D. advisor. Rich Gossweiler, a Senior Research Scientist, was his first Ph.D. student. Several other former colleagues and coauthors (Joshua Bloch, Adam Fass, and Ning Hu) now work here.All of us strive to make an impact...
Tuesday, 20 May 2008
Machine Learning Meeting
Posted on 17:32 by Unknown
Posted by Phil LongMachine Learning is a branch of Artificial Intelligence in which, naturally enough, the aim is to get computers to learn: things like improving performance over time, and recognizing general tendencies among a number of specific cases. We have many ways to exploit Machine Learning programs, and a lot of data to give them. Machine Learning helps us to estimate what content users like most, what content is even legitimate, and how to match ads to content. It also plays key roles...
Tuesday, 6 May 2008
Can You Publish at Google?
Posted on 15:10 by Unknown
Posted by Rich GossweilerAs part of the interview process at Google we try to describe what it is like to do research here. A common question I get is "How hard is it to publish at Google?" I want to dispel the myth that it is hard. It is easy to publish, easy to put code into open source, easy to give talks, etc. But it is also easy for great research to become great engineering, and that is an incredible lure. Great barriers of despair exist between research and development at many companies; researchers can find it hard to have impact beyond...
Thursday, 1 May 2008
VisualRank
Posted on 13:30 by Unknown
Posted by Shumeet Baluja and Yushi JingAt WWW-2008, in Beijing, China, we presented our paper "PageRank for Product Image Search". In this paper, we presented a system that used visual cues, instead of solely text information, to determine the rank of images. The idea was simple: find common visual themes in a set of images, and then find a small set of images that best represented those themes. The resulting algorithm wound up being PageRank, but on an entirely inferred graph of image similarities. Since the release of the paper, we've noticed...
Wednesday, 23 April 2008
Research in the Cloud: Providing Cutting Edge Computational Resources to Scientists
Posted on 14:13 by Unknown
Posted by Christophe Bisciglia, Senior Software Engineer, and Alfred Spector, Vice President of ResearchThe emergence of extremely large datasets, well beyond the capacity of almost any single computer, has challenged traditional and contemporary methods of analysis in the research world. While a simple spreadsheet or modest database remains sufficient for some research, problems in the domain of "computational science," which explores mathematical models via computational simulation, require systems that provide huge amounts of data storage...
Friday, 28 March 2008
Deploying Goog411
Posted on 15:34 by Unknown
Posted by Francoise BeaufaysA couple of years ago, a few of us got together and decided to build Goog411. It would be a free phone service that users could call to connect to any business in the US, or simply to browse through a list of businesses such as "bookstores" in a given city. Everything would be fully automated, with no operator in the background, just a speech recognition system to converse with the user, and Google Maps to execute the business search.We knew that speech recognition is not a solved problem; there would be users for whom...
Monday, 11 February 2008
This year's scalability conference
Posted on 11:53 by Unknown
Posted by Andrew Schwerin, Software EngineerManaging huge repositories of data and large clusters of machines is no easy task -- and building systems that use those clusters to usefully process that data is even harder. Last year, we held a conference on scalable systems so a bunch of people who work on these challenges could get together and share ideas. Well, it was so much fun that we've decided to do it again.This year, the conference is taking place in Seattle on Saturday, June 14. (Registration is free.) If you'd like to talk about a topic...
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