plop: Probabilistic Learning of Programs

Posted by Moshe Looks

Cross-posted with Open Source at Google blog

Traditional 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 methods that operate directly on programmatic representations, due to the extreme combinatorial explosions involved and the semantic complexities of programs.

The plop project is part a new approach to learning programs being developed at Google and elsewhere that takes on the challenges of learning programs through a unified approach based on reducing programs to a hierarchical normal form, building sequences of specialized representations for programs as search progresses, maintaining alternative representations, and managing uncertainty probabilistically by applying estimation-of-distribution algorithms over program spaces, and exploiting probabilistic background knowledge.

For more information on this approach to learning programs, see my doctoral dissertation. For more on the overall philosophy and where things are going, see the plop wiki on Google Code.

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New Technology Roundtable Series

Posted by Alfred Spector, VP of Research and Special Initiatives

We'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 of course they offer a glimpse into the people behind Google.

The first one we made is Large-Scale Search System Infrastructure and Search Quality." I interview Google Fellows Jeff Dean and Amit Singhal on their insights in how search works at Google.

The next title is "Map Reduce," a discussion of this key technology (first, at Google, and now having a great impact across the field) for harnessing parallelism provided by very large-scale clusters computers, while mitigating the component failures that inevitably occur in such big systems. My discussion is with four of our Map Reduce expert engineers: Sanjay Ghemawat and Jeff Dean again, plus Software Engineers Jerry Zhao and Matt Austern, who discuss the origin, evolution and future of Map Reduce. By the way, this type of infrastructure underlies the infrastructure concepts in our recent post on "The Intelligent Cloud."

The third video, "Applications of Human Language Technology," is a discussion of our enormous progress in large-scale automated translation of languages and speech recognition. Both of these technology domains are coming of age with capabilities that will truly impact what we expect of computers on a day-to-day basis. I discuss these technologies with human language technology experts Franz Josef Och, an expert in the automated translation of languages, and Mike Cohen, an expert in speech processing.

We hope to produce more of these, so please leave feedback at YouTube (in the comments field for each video), and we will incorporate your ideas into our future efforts.

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Doubling Up

Posted by Franz Josef Och

Machine translation is hard. Natural languages are so complex and have
so many ambiguities and exceptions that teaching a computer to
translate between them turned out to be a much harder problem than
people thought when the field of machine translation was born over 50
years ago. At Google Research, our approach is to have the machines
learn to translate by using learning algorithms on gigantic amounts of
monolingual and translated data. Another knowledge source is user
suggestions. This approach allows us to constantly improve the
quality of machine translations as we mine more data and
get more and more feedback from users.

A nice property of the learning algorithms that we use is that they
are largely language independent -- we use the same set of core
algorithms for all languages. So this means if we find a lot of
translated data for a new language, we can just run our algorithms and
build a new translation system for that language.

As a result, we were recently able to significantly increase the number of
languages on translate.google.com. Last week, we launched eleven new
languages: Catalan, Filipino, Hebrew, Indonesian, Latvian, Lithuanian, Serbian,
Slovak, Slovenian, Ukrainian, Vietnamese. This increases the
total number of languages from 23 to 34.  Since we offer translation
between any of those languages this increases the number of language
pairs from 506 to 1122 (well, depending on how you count simplified
and traditional Chinese you might get even larger numbers). We're very
happy that we can now provide free online machine translation for many
languages that didn't have any available translation system before.

So how far can we go with adding new languages in the future? Can we
go to 40, 50 or even more languages?  It is certainly getting harder,
as less data is available for those languages and as a result it is
harder to build systems that meet our quality bar.  But we're working
on better learning algorithms and new ways to mine data and so even if
we haven't covered your favorite language yet, we hope that we will have
it soon.

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Remembering Randy Pausch

Posted by Kevin McCurley, Research Team

It 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 with our research, and Randy was no exception. He will be remembered for his work, but also for his contributions to humanity at large. Millions have watched the video on YouTube from his lecture titled Achieving your Childhood Dreams. The strength of his character was already known to his family, his colleagues, and the broader computer science research community. The courage and optimism that he displayed at the end of his life became inspirational to millions more.

I've seen Randy repeatedly go to bat for what is right. As a leader, he consistently evoked incredible enthusiasm and optimism for the subjects he embraces. Randy had a very human passion about people and not just who they are, but their potential, despite any flaws or obstacles in their way. His contributions will be remembered for generations to come. - Rich Gossweiler

Randy was one of the most vibrant, passionate people I've ever known. His passion was inspirational not only to his family and colleagues, but also, because of his courageous presentations beginning with his well-known Last Lecture, he has influenced millions more. - Alfred Spector

We will miss Randy very much, and remember him fondly.

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Machine Learning Meeting

Posted by Phil Long

Machine 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 in products such as Google Translate and 1-800-GOOG-411.

Last week, Google held a Machine Learning Summit in New York, gathering together engineers from around the world, including participants from Beijing, Zurich, Boston, Haifa, and Mountain View. The program included invited talks by CJ Lin and Peng Xu, and many shorter presentations by Googlers on topics including broadly applicable Machine Learning software, especially challenging applications, and some mathematical analysis.

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Can You Publish at Google?

Posted by Rich Gossweiler

As 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 demos, papers, or patents.

Here at Google it is not uncommon for researchers to work on products (and is modus operandi for me); you can come up with something interesting, experiment to convince yourself and others that it is worthwhile, and then work as part of the team to build it and help create world-changing products. But are you willing to do that? That decision is the hard part, not publishing a paper.

I think from a Google standpoint, we need to make sure these barriers don't form, that making products, experimenting and having a venue for trying bold new approaches continues to be part of the culture.

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VisualRank

Posted by Shumeet Baluja and Yushi Jing

At 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 lots of coverage in the press and have received quite a few questions. We thought we could answer a few of them here.


"Why did we choose to use products for our test case?" First and foremost, product queries are popular in actual usage; addressing them is important. Second, users have strong expectations of what results we should return for these queries; therefore, this category provides an important set of examples that we need to address especially carefully. Third, on a pragmatic note, they lend themselves well to the type of "image features" that we selected in this study. Since the publication of the paper, we've also extended our results to other query types, including travel-related queries. One of the nice features of the approach is that (we hope) it will be easy to extend to new domains; as research in measuring image or object similarity continues, the advances can easily be incorporated into the similarity calculation to compute the underlying graph; the computations on the graph do not change.

"Where are we going from here?" Besides broadening the sets of queries (and sets of features) for which we can use this approach, there are three directions we're exploring. First, estimating similarity measures for all of the images on the web is computationally expensive; approximations or alternative computations are needed. Second, we hope to evaluate our approach with respect to the large number of recently proposed alternative clustering methods. Third, many variations of PageRank can be used in quite interesting ways for image search. For example, we can use some of these previously published methods to reintroduce, in a meaningful manner, the textual information that the VisualRank algorithm removed. In the end, we have an approach that has an easy integration with both text and visual clues. Stay tuned for more on that in the coming months.

And now to answer the most commonly asked question, "Is it live?" Not yet. Currently, it is research in progress (click here to help speed up the process). In the meantime, though, if you'd like another sneak peek of our research on large graphs, this time in the context of YouTube datamining, just follow the link.

Finally, we want to extend our deepest thanks to the people who helped on this project, especially the image-search team; without their help, this research would not have been possible.

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