Google Research Blog

Google Search by Voice Learns Mandarin Chinese

2009-11-02T09:00:00.000-08:00

Posted by Pedro J. Moreno, Research Scientist

Google Search by Voice was released more than one year ago as a feature of Google Mobile App, our downloadable application for smartphones. Its performance has been improving consistently and it now understands not only US English, but also UK, Australian, and Indian-English accents. However, this is far from Google's goal to find information and make it easily accessible in any language.

So, almost one year ago a team of researchers and engineers at Google's offices in Bangalore, Beijing, Mountain View, and New York decided we had to fix this problem. Our next question was, which should be our first language to address beyond English? We could have chosen many languages. The decision wasn't easy, but once we looked carefully at demographics and internet populations the choice was clear--we decided to work on Mandarin.

Mandarin is a fascinating language. Over this year we have learned about the differences between traditional and simplified Chinese, tonal characteristics in Chinese, pinyin representations of Chinese characters, sandhi rules, the different accents and languages in China, unicode representations of Chinese character sets...the list goes on and on. It has been a fascinating journey. The conclusion of all this work is today's launch of Mandarin Voice Search, as a part of Google Mobile App for Nokia s60 phones. Google Mobile App places a Google search widget on your Nokia phone's home screen, allowing you to quickly search by voice or by typing.

This is a first version of Mandarin search by voice and it is rough around the edges. It might not work very well if you have a strong southern Chinese accent for example, but we will continue working to improve it. The more you use it, the more it will improve, so please use it and send us your comments. And stay tuned for more languages. We know a lot of people speak neither English nor Mandarin!

To try Mandarin search by voice, download the new version of Google Mobile App on your Nokia S60 phone by visiting m.google.com from your phone's browser.

51 Languages in Google Translate

2009-08-31T09:00:00.000-07:00

Posted by Franz Och, Principal Scientist

Are you using Google Translate to access the world's information? It can help you find and translate local restaurant and hotel reviews into your language when planning a vacation abroad, allow you to read the Spanish or French Editions of Google News, communicate with people who speak different languages using Google Translate chat bots, and more. We're constantly working to improve translation quality, so if you haven't tried it recently, you may be pleasantly surprised with what it can do now.

We're especially excited to announce that we've added 9 new languages to Google Translate: Afrikaans, Belarusian, Icelandic, Irish, Macedonian, Malay, Swahili, Welsh, and Yiddish, bringing the number of languages we support from 42 to 51. Since we can translate between any two of these languages, we offer translation for 2550 language pairs!

How do we decide which languages to add to Google Translate? Our goal is to provide automatic translation for as many languages as possible. So internally we've been collecting data and building systems for more than 100 languages. Whenever a set of languages meets our quality bar we consider it for our next language launch. We've found that one of the most important factors in adding new languages to our system is the ability to find large amounts of translated documents from which our system automatically learns how to translate. As a result, the set of languages that we've been able to develop is more closely tied to the size of the web presence of a language and less to the number of speakers of the language.

We're very happy that our technology allows us to produce machine translation systems for languages that often don't get the attention they deserve. For many of the newly supported languages ours is the only mature and freely available translation system. While translation quality in these languages will be noticeably rougher than for languages we've supported for a longer time like French or Spanish, it is most often good enough to give a basic understanding of the text, and you can be sure that the quality will get better over time.

Remember, you can also use Google Translate from inside other Google products. For example you can translate e-mails within GMail, translate web pages using Google Toolbar, translate RSS news feeds from around the world in Google Reader, and translate documents in Google Docs. (The new languages aren't available in these products yet but will be soon!) And, if you're translating content into other languages, you can use our technology within Google Translator Toolkit to help you translate faster and better. In the future, expect to find our translation technology in more places, making it increasingly simple to get access to information no matter what language it is written in.

On the predictability of Search Trends

2009-08-17T14:19:00.000-07:00

Posted by Yossi Matias, Niv Efron, and Yair Shimshoni, Google Labs, Israel.

Since launching Google Trends and Google Insights for Search, we've been providing daily insight into what the world is searching for. An understanding of search trends can be useful for advertisers, marketers, economists, scholars, and anyone else interested in knowing more about their world and what's currently top-of-mind.

As many have observed, the trends of some search queries are quite seasonal and have repeated patterns. See, for instance, the search trends for the query "ski" hit their peak during the winter seasons in the US and Australia. The search trends for basketball correlate with annual league events, and are consistent year-over-year. When looking at trends of the aggregated volume of search queries related to particular categories, one can also observe regular patterns in some categories like Food & Drink or Automotive. Such trends sequences appear quite predictable, and one would naturally expect the patterns of previous years to repeat looking forward.

On the other hand, for many other search queries and categories, the trends are quite irregular and hard to predict. Examples include the search trends for obama, twitter, android, or global warming, and the trend of aggregate searches in the News & Current Events category.

Having predictable trends for a search query or for a group of queries could have interesting ramifications. One could forecast the trends into the future, and use it as a "best guess" for various business decisions such as budget planning, marketing campaigns and resource allocations. One could identify deviation from such forecasting and identify new factors that are influencing the search volume as demonstrated in Flu Trends.

We were therefore interested in the following questions:

How many search queries have trends that are predictable?
Are some categories more predictable than others? How is the distribution of predictable trends between the various categories?
How predictable are the trends of aggregated search queries for different categories? Which categories are more predictable and which are less so?

To learn about the predictability of search trends, and so as to overcome our basic limitation of not knowing what the future will entail, we characterize the predictability of a Trends series based on its historical performance. In other words, we estimate the a posteriori predictability of a sequence determined by the error of forecasted trends vs the actual performance.

Specifically, we have used a simple forecasting model that learns basic seasonality and general trend. For each trends sequence of interest, we take a point in time, t, which is about a year back, compute a one year forecasting for t based on historical data available at time t, and compare it to the actual trends sequence that occurs since time t. The error between the forecasting trends and the actual trends characterizes the predictability level of a sequence, and when the error is smaller than a pre-defined threshold, we denote the trends query as predictable.

Our work to date is summarized in a paper called On the Predictability of Search Trends which includes the following observations:

Over half of the most popular Google search queries are predictable in a 12 month ahead forecast, with a mean absolute prediction error of about 12%.
Nearly half of the most popular queries are not predictable (with respect to the model we have used).
Some categories have particularly high fraction of predictable queries; for instance, Health (74%), Food & Drink (67%) and Travel (65%).
Some categories have particularly low fraction of predictable queries; for instance, Entertainment (35%) and Social Networks & Online Communities (27%).
The trends of aggregated queries per categories are much more predictable: 88% of the aggregated category search trends of over 600 categories in Insights for Search are predictable, with a mean absolute prediction error of of less than 6%.
There is a clear association between the existence of seasonality patterns and higher predictability, as well as an association between high levels of outliers and lower predictability. For the Entertainment category that has typically less seasonal search behavior as well as relatively higher number of singular spikes of interest, we have seen a predictability of 35%, where as the category of Travel with a very seasonal behavior and lower tendency for short spikes of interest had a predictability of 65%.
One should expect the actual search trends to deviate from forecast for many predictable queries, due to possible events and dynamic circumstances.
We show the forecasting vs actual for trends of a few categories, including some that were used recently for predicting the present of various economic indicators. This demonstrates how forecasting can serve as a good baseline for identifying interesting deviations in actual search traffic.

As we see that many of the search trends are predictable, we are introducing today a new forecasting feature in Insights for Search, along with a new version of the product. The forecasting feature is applied to queries which are identified as predictable (see, for instance, basketball or the trends in the Automotive category) and then shown as an extrapolation of the historical trends and search patterns.

There are many more questions that can be looked at regarding search trends in general, and their predictability in particular, including design and testing more advanced forecasting models, getting other insights into the distributions of sequences, and demonstrating interesting deviations of actual-vs-forecast for predictable trends series. We'd love to hear from you - share with us your findings, published results or insights - email us at insightsforsearch@google.com.

Under the Hood of App Inventor for Android

2009-08-11T08:00:00.000-07:00

Posted by Bill Magnuson, Hal Abelson, and Mark Friedman

We recently announced our App Inventor for Android project on the Google Research Blog. That blog entry was long on vision but short on technological details--details which we think would be of interest to our readers.

Of particular interest is our use of Scheme. Part of our development environment is a visual programming language similar to Scratch. The visual language provides a drag-and-drop interface for assembling procedures and event handlers that manipulate high-level components of Android-based phones. The components are similar to the ones in the recently announced Simple; in fact, the code bases share an ancestor.

We parse the visual programming language into an S-expression intermediate language, which is a domain-specific language expressed as a set of Scheme macros, along with a Scheme runtime library. We did this for a few reasons:

S-expressions are easy to generate and read for both humans and machines.
Scheme macros are a convenient (albeit sometimes arcane) way to express S-expression based syntax.
Scheme is a small, powerful and elegant language well suited to describe and evaluate a large set of programming semantics. Additionally, it provides the flexibility that we require as our language and its semantics grow and develop.
Scheme expertise was readily available among our team.
A pre-existing tool (Kawa by Per Bothner) to create Android compatible output from scheme code was already available.

For now the project is just an experiment we're performing with a dozen colleges and universities, but we hope to eventually open up the development environment to wider use and to open-source parts of the code.

Two Views from the 2009 Google Faculty Summit

2009-08-03T14:59:00.000-07:00

Posted by Alfred Spector, Vice President of Research and Special Initiatives

[cross-posted with the Official Google Blog]

We held our fifth Computer Science Faculty Summit at our Mountain View campus last week. About 100 faculty attendees from schools in the Western hemisphere attended the summit, which focused on a collection of technologies that serve to connect and empower people. Included in the agenda were presentations on technologies for automated translation of human language, voice recognition, responding to crises, power monitoring and collaborative data management. We also talked about technologies to make personal systems more secure, and how to teach programming — even using Android phones. You can see a more complete list of the topics in the Faculty Summit Agenda or check out my introductory presentation for more information.

I asked a few of the faculty to provide us their perspective on the summit, thinking their views may be more valuable than our own: Professor Deborah Estrin, a Professor of Computer Science at UCLA and an expert in large-scale sensing of environmental and other information, and Professor John Ousterhout, an expert in distributed operating systems and scripting languages.

Professor Estrin's perspective:

We all know that Google has produced a spectacular array of technologies and services that has changed the way we create, access, manage, share and curate information. A very broad range of people samples and experiences Google’s enhancements and new services on a daily basis. I, of course, am one of those minions, but last week I had the special opportunity to get a glimpse inside the hive while attending the 2009 Google Faculty Summit. I still haven't processed all of the impressions, facts, figures and URLs that I jotted down over the packed day and a half-long gathering, but here are a few of the things that impressed me most:

The way Google simultaneously launches production services while making great advances in really hard technical areas such as machine translation and voice search, and how these two threads are fully intertwined and feed off of one another.
Their embrace of open source activities, particularly in the Android operating system and programming environment for mobiles. They also seed and sponsor all sorts of creative works, from K-12 computer science learning opportunities to an the open data kit that supports data-gathering projects worldwide.
The company’s commitment to thinking big and supporting their employees in acting on their concerns and cares in the larger geopolitical sphere. From the creation of Flu Trends to the support of a new "Crisis Response Hackathon" (an event that Google, Microsoft and Yahoo are planning to jointly sponsor to help programmers find opportunities to use their technical skills to solve societal problems), Googlers are not just encouraged to donate dollars to important causes — they are encouraged to use their technical skills to create new solutions and tools to address the world's all-too-many challenges.

This was my second Google Faculty Summit — I previously attended in 2007. I was impressed by the 2007 Summit, but not as deeply as I was this year. Among other things, this year I felt that Googlers talked to us like colleagues instead of just visitors. The conversations flowed: Not once did I run up across the "Sorry, can't talk about that... you know our policy on early announcements". I left quite excited about Google's expanded role in the CS research ecosystem. Thanks for changing that API!

Professor Ousterhout's perspective:

I spent Thursday and Friday this week at Google for their annual Faculty Summit. After listening to descriptions of several Google projects and talking with Googlers and the other faculty attendees, I left with two overall takeaways. First, it's becoming clear that information at
scale is changing science and engineering. If you have access to enormous datasets, it opens up whole new avenues for scientific discovery and for solving problems. For example, Google's machine translation tools take advantage of "parallel texts": documents that have been translated by humans from one language to another, with both forms available. By comparing the sentences from enormous numbers of parallel texts, machine translation tools can develop effective translation tools using simple probabilistic approaches. The results are better than any previous attempts at computerized translation, but only if there are billions of words available in parallel texts. Another example of using large-scale information is Flu Trends, which tracks the spread of flu by counting the frequency of certain search terms in Google's search engine; the data is surprisingly accurate and available more quickly than that from traditional approaches.

My second takeaway is that it's crucial to keep as much information as possible publicly available. It used to be that much of science and engineering was driven by technology: whoever had the biggest particle accelerator or the fastest computer had an advantage. From now on, information will be just as important as technology: whoever has access to the most information will make the most discoveries and create the most exciting new products. If we want to maintain the leadership position of the U.S., we must find ways to make as much information as possible freely available. There will always be vested commercial interests that want to restrict access to information, but we must fight these interests. The overall benefit to society of publishing information outweighs the benefit to individual companies from restricting it.

App Inventor for Android

2009-07-31T15:15:00.000-07:00

Posted by Hal Abelson, Visiting Faculty

At Google Research, we are making it easy to build mobile applications, and we're collaborating with faculty from a dozen colleges and universities to explore whether this could change the nature of introductory computing. With the support of Google University Relations, the faculty group will work together this fall to pilot courses where beginning students, including non-computer science majors, create Android applications that incorporate social networking, location awareness, and Web-based data collections.

Mobile applications are triggering a fundamental shift in the way people experience computing and use mobile phones. Ten years ago, people "went to the computer" to perform tasks and access the Internet, and they used a cell phone only to make calls. Today, smartphones let us carry computing with us, have become central to servicing our communication and information needs, and have made the web part of all that we do. Ten years ago, people's use of computing was largely dissociated from real life. With the ubiquity of social networking, online and offline life are becoming fused. This fall's exploration is motivated by the vision that open mobile platforms like Android can bring some of that same change to introductory Computer Science, to make it more about people and their interactions with others and with the world around them. It's a vision where young people—and everyone—can engage the world of mobile services and applications as creators, not just consumers. Through this work, we hope to do the following:

Make mobile application development accessible to anyone.
Enhance introductory learning experiences in computing through the vehicle of Android’s open platform.
Encourage a community of faculty and students to share material and ideas for teaching and exploring.

The collaborative experiment kicked off with a three-day workshop at Google's Mountain View campus in June, where invited faculty shared their plans for the courses they will offer this fall. The group also got an advance look at App Inventor for Android, the prototype development platform that Google is working on and that the faculty and their students will use in their courses. App Inventor for Android lets people assemble Android applications by arranging "components" using a graphical drag-and-drop-interface. One of the goals of the fall experiment is to further shape the system in response to the experience and feedback of students and faculty.

The schools participating in this fall's collaboration are Ball State University, University of Colorado Boulder, Georgia Tech, Harvard, Indiana University, Mills College, MIT, Olin College, University of California at Berkeley, University of Michigan, University of Queensland, University of San Francisco, and Wellesley College.

Questions or comments? Please send us feedback. We look forward to hearing from you!

Predicting Initial Claims for Unemployment Benefits

2009-07-22T17:00:00.001-07:00

Posted by Hal Varian, Chief Economist and Hyunyoung Choi, Sr. Economist

One of the strongest leading indicators of economic activity is the number of people who file for unemployment benefits. Macroeconomists Robert Gordon and James Hamilton have recently examined the historical evidence. According to Hamilton's summary: "...in each of the last six recessions, the recovery began within 8 weeks of the peak in new unemployment claims."

In an earlier blog post, we suggested that Google Trends/Search Insights data could be useful in short term predictions of economic variables. Given the importance of initial claims as a macroeconomic predictor, we thought it would be useful to try to forecast this economic metric. The initial claims data is available from the Department of Labor, while the Google Trends data for relevant categories is available here.

We applied the methodology outlined in our earlier paper, building a model to forecast initial claims using the past values of the time series, and then added the Google Trends variables to see how much they improved the forecast. We found a 15.74% reduction in mean absolute error for one-week ahead out, of sample forecasts. Most economists would consider this to be a significant boost. Details of our analysis may be found in this paper.

The bottom line is that initial claims have been generally declining from their peak and that, so far at least, the Google query data is forecasting further short term declines. It would be good news indeed if this particular Google trend continues.

ACM EC Conference and Workshop on Ad Auctions

2009-07-21T17:00:00.000-07:00

By Jon Feldman and Vahab Mirrokni, Google Research, NY

This month, the 10th ACM Conference on Electronic Commerce (EC 2009) and the 5th Workshop on Ad Auctions took place at Stanford University. This is one of the major forums for economists and computer scientists to share their ideas about mechanism design and algorithmic game theory. Other than co-authoring several papers in the conference and workshops, Google contributed significantly in presenting tutorials.

Among the four tutorials given at the ACM EC conference, we participated in presenting two of them:

In a joint tutorial with Google researcher Muthu Muthukrishnan, we explored research problems in sponsored search inspired by taking the advertiser's perspective. Emphasizing a cross-disciplinary approach, we presented sample research directions in keyword selection, traffic prediction and bidding strategy, encouraging the research community to build upon known auction models in order to tackle these even more challenging domains. We explored in more detail specific examples of research in bidding strategies.
Moreover, in a joint tutorial with H. Roeglin, we presented results in convergence of game dynamics, both to equilibria and nearly-optimal solutions. This was a more algorithm-oriented variant of the tutorial at ICML (which is described in a previous blog post.)

The Ad Auctions Workshop brought together many industry and academic research leaders to discuss ongoing challenges in online advertisement. The topics presented at the workshop included the role of externalities in ad auctions, new truthful ad auction mechanisms with budget constraints, efficiency loss of generalized second-price ad auctions, and complex combinatorial ad auctions. Google researchers co-organized, participated in the discussions, and contributed the following presentations:

Google's chief economist, Hal Varian, gave an enlightening invited talk about using Google Trends data for "predicting the present." In this work, Google Trends data is used to help improve forecasts of various economic time series. Examples illustrating this technique were drawn from the auto industry, real estate, and unemployment. He emphasized that Google Trends data is publicly available and encouraged people to use this data for their research.
We presented two papers in the workshop, one about optimal pricing mechanisms over social networks and one about using offline optimization in stochastic online ad allocation problems. In the latter talk, we presented an algorithm in which we use an optimal offline solution in an "expected instance", and use this solution as a signal in online decision making. Using the idea of the power of two choices from the CS literature, we give a novel theoretical analysis of our method, improving the best previously known result. We also gave some practical insight about using these methods in online ad allocation. The theoretical results will appear in the upcoming FOCS 2009 conference.

Motivated by our various ad systems, there is a large research effort at Google around areas at the intersection of Economics, Computer Science and Machine Learning. The Ad Auctions Workshop and ACM Conference on Electronic Commerce are among the best forums for stimulating ideas and collaboration in these interdisciplinary areas.

Google's Research Awards Program Update

2009-07-14T13:30:00.000-07:00

Posted by Posted by Juan E. Vargas, University Relations

When we think about innovation, it is easy to forget that it took about 55 years to spread automobile usage to 1/4 of the US population, ... 35 years for the telephone, ... 20 years for the radio, ... 15 years for the PC, ... 10 years for the cell phone, ... 7 years for the Internet (Council of Competiveness, Innovate America, 2004).

Recognizing that innovation holds the key to many of the unique technical challenges we face, we remain committed to maintaining strong relations with the academic research community. Our Research Awards Program has experienced phenomenal growth. Of the total number of applications that we received since the program's inception in 2005, more than half were submitted in the past year. To cope with the increased level of interest worldwide, we reorganized the program to accept submissions three times per year: April 15th, August 15th, and December 15th. Proposals are evaluated by teams of engineers and researchers, who make recommendations for funding. We try to move fast. Investigators receive a response about three months after their submission.

Here are some highlights from a recent round of applications:

"Recognition and Modeling of Objects from Street View Scans of Cities"
Thomas Funkhouser, Princeton University
Professor Funkhouser aims to develop methods for automatic construction of semantically-labeled, detailed, and photorealistic 3D models of cities from Street View data. The main efforts will be towards the segmentation and recognition of small objects (e.g. mailboxes, fire hydrants, parking meters, etc.) in Lidar data based on shape classification and contextual reasoning. A second objective will be to construct seamless, photorealistic 3D models of complete cities by extracting and fitting parts from repositories of polygonal models.

"An Ad Auctions Trading Agent Competition"
Michael Wellman, University of Michigan
The University of Michigan will introduce and operate a new game in the Trading Agent Competition (TAC) series of research competitions, in the domain of sponsored search. The TAC Ad Auctions (TAC/AA) game challenges participants to develop bidding strategies for advertisers in a simulated retail home entertainment market. The aim is to spur research and generate insights about advertiser bidding strategy, in a scenario more complex than those considered in the research literature to date. The TAC/AA environment features multiple interrelated keywords, a structured search user model, rich data availability, and a dynamic market context. Since 2000, the annual TAC series has catalyzed research on trading agent design and analysis, produced by a diverse group of researchers from academia and industry.

"A Suite of Automated Tools for Efficient Management and Search in Web-based Arabic Documents"
Adnan Yahya, Birzeit University, Palestine.
This research aims to design text mining and processing tools that are able to efficiently index, process, search, and categorize large quantities of Arabic data. This research addresses the challenges Arabic poses for NLP and information retrieval, automatic Arabic document categorization, root extraction, language detection, and Arabic query correction, suggestion and expansion. The PIs employ a statistical/Corpus-based approach based on contemporary data initially obtained from a local newspaper.

"When Children Search: Understanding what they do and what they could do with Google Search"
Allison Druin, University of Maryland
Children ages 5-13 are among the most frequent users of the Internet; yet, searching and browsing the web can present many challenges. Spelling, typing, query formulation, and deciphering results are all barriers for children in attempting to find the information they need. Professor Druin is trying to understand these issues in more diverse ages of children by focusing on current and ubiquitous search tools, namely, keyword-based web search engines.

"An Educational Camera for Kids"
Shree Nayar, Columbia University
Professor Nayar is desiging a novel digital camera that could be used as an innovative educational medium. His target audience is students between the ages of 10 and 13 years living in poor communities across the globe. The camera, named “Bigshot,” will be presented to students as a kit to expose them to diverse science and engineering concepts. Once assembled, the camera will be used so that students can share their photos with students in other cultures using Picasa and Google Groups.

"Discovering semantic concepts and their relations in large image collections"
Bernt Schiele, Technische Unversitat Darmstadt, Germany
Professor Schiele will investigate to what extent meaningful structures can be discovered from large sets of images both in a fully unsupervised fashion as well with minimal human supervision. To this end, Professor Schiele's work will try to first discover structure and learn multi-feature distance metrics in large collections of images and then to enrich such structure by weak annotations in order to link discovered structure and to derive semantic concepts.

"Dataspace Metrics: Measuring Progress for Pay-as-you-go Information Integration"
Michael Franklin, University of California
The goal of this project is to develop a measurement framework for gauging progress in terms of the quality and accuracy of information integration. The starting point is the development of a set of metrics for judging the “goodness’ of information integration across a number of information types and use cases. These metrics will then be analyzed and where possible, unified, so that a more general measurement framework can be developed. Such a framework will serve as a key component for future Dataspace management systems, and could provide a grounding for other collaborative information integration solutions.

For more information about this program, including submission guidelines, please visit the Research Awards Program page.

International Conference on Machine Learning (ICML 2009) in Montreal

2009-07-02T16:00:00.000-07:00

Posted by Eyal Even Dar and Vahab Mirrokni, Google Research, NY

The 26th International Conference on Machine Learning (ICML 2009) was recently held in Montreal in conjunction with the 22nd Conference On Learning Theory (COLT 2009) and the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). This is one of the major forums for researchers from both industry and academia to share the recent developments in the area of machine learning and artificial intelligence. Machine learning is a central area for Google as it has many applications in extracting useful information from a vast amount of data available on the web. In addition to sponsoring this scientific event, Google contributed intellectually to several scientific forums. Here's a short report of those activities:

There were ten papers co-authored by Googlers in these conferences, which covered several areas of machine learning including domain adaption, online learning, bandits, boosting, sparsity and kernel learning.
Corinna Cortes, the head of Google Research NY gave one of the three invited talks of ICML. She surveyed the last decade of research in learning kernels and highlighted both the successes and the failures in learning kernels with a focus on applications of convex optimization for this purpose. Corinna concluded with a call for applying new ideas and novel techniques to overcome the current obstacles.
We presented a tutorial on Convergence of Natural Game Dynamics. This topic has received a lot of attention recently as it stands at the conflux of many fields such as economics, machine learning and theoretical computer science. In the tutorial, we surveyed the convergence properties of the most natural game dynamics such as the Nash dynamics or the best-response dynamics to the popular no-regret learning-based dynamics. The tutorial highlighted similarities and differences between the approaches in both the time of convergence, the point of convergence, and the quality of the outcome. We believe that the influence of the learning algorithms on the behavior of the users is an exciting and intriguing topic of research for many, and in particular for the analysis of ad auctions.

Google's main mission is "to organize the world's information and make it universally accessible and useful," and machine learning plays a fundamental role in both of these aspects. As a result, Google has invested significant resources in this area of research, and we look forward to continued participation and collaboration at these conferences for many more years.

Speed Matters

2009-06-23T15:15:00.000-07:00

Posted by Jake Brutlag, Web Search Infrastructure

At Google, we've gathered hard data to reinforce our intuition that "speed matters" on the Internet. Google runs experiments on the search results page to understand and improve the search experience. Recently, we conducted some experiments to determine how users react when web search takes longer. We've always viewed speed as a competitive advantage, so this research is important to understand the trade-off between speed and other features we might introduce. We wanted to share this information with the public because we hope it will give others greater insight into how important speed can be.

Speed as perceived by the end user is driven by multiple factors, including how fast results are returned and how long it takes a browser to display the content. Our experiments injected server-side delay to model one of these factors: extending the processing time before and during the time that the results are transmitted to the browser. In other words, we purposefully slowed the delivery of search results to our users to see how they might respond.

All other things being equal, more usage, as measured by number of searches, reflects more satisfied users. Our experiments demonstrate that slowing down the search results page by 100 to 400 milliseconds has a measurable impact on the number of searches per user of -0.2% to -0.6% (averaged over four or six weeks depending on the experiment). That's 0.2% to 0.6% fewer searches for changes under half a second!

Furthermore, users do fewer and fewer searches the longer they are exposed to the experiment. Users exposed to a 200 ms delay since the beginning of the experiment did 0.22% fewer searches during the first three weeks, but 0.36% fewer searches during the second three weeks. Similarly, users exposed to a 400 ms delay since the beginning of the experiment did 0.44% fewer searches during the first three weeks, but 0.76% fewer searches during the second three weeks. Even if the page returns to the faster state, users who saw the longer delay take time to return to their previous usage level. Users exposed to the 400 ms delay for six weeks did 0.21% fewer searches on average during the five week period after we stopped injecting the delay.

While these numbers may seem small, a daily impact of 0.5% is of real consequence at the scale of Google web search, or indeed at the scale of most Internet sites. Because the cost of slower performance increases over time and persists, we encourage site designers to think twice about adding a feature that hurts performance if the benefit of the feature is unproven. To learn more on how to improve the performance of your website visit code.google.com/speed. For more details on our experiments, download this PDF.

A new landmark in computer vision

2009-06-22T07:30:00.000-07:00

Posted by Jay Yagnik, Head of Computer Vision Research

[Cross-posted with the Official Google Blog]

Science fiction books and movies have long imagined that computers will someday be able to see and interpret the world. At Google, we think computer vision has tremendous potential benefits for consumers, which is why we're dedicated to research in this area. And today, a Google team is presenting a paper on landmark recognition (think: Statue of Liberty, Eiffel Tower) at the Computer Vision and Pattern Recognition (CVPR) conference in Miami, Florida. In the paper, we present a new technology that enables computers to quickly and efficiently identify images of more than 50,000 landmarks from all over the world with 80% accuracy.

To be clear up front, this is a research paper, not a new Google product, but we still think it's cool. For our demonstration, we begin with an unnamed, untagged picture of a landmark, enter its web address into the recognition engine, and poof — the computer identifies and names it: "Recognized Landmark: Acropolis, Athens, Greece." Thanks computer.

How did we do it? It wasn't easy. For starters, where do you find a good list of thousands of landmarks? Even if you have that list, where do you get the pictures to develop visual representations of the locations? And how do you pull that source material together in a coherent model that actually works, is fast, and can process an enormous corpus of data? Think about all the different photographs of the Golden Gate Bridge you've seen — the different perspectives, lighting conditions, and image qualities. Recognizing a landmark can be difficult for a human, let alone a computer.

Our research builds on the vast number of images on the web, the ability to search those images, and advances in object recognition and clustering techniques. First, we generated a list of landmarks relying on two sources: 40 million GPS-tagged photos (from Picasa and Panoramio) and online tour guide webpages. Next, we found candidate images for each landmark using these sources and Google Image Search, which we then "pruned" using efficient image matching and unsupervised clustering techniques. Finally, we developed a highly efficient indexing system for fast image recognition. The following image provides a visual representation of the resulting clustered recognition model:

In the above image, related views of the Acropolis are "clustered" together, allowing for a more efficient image matching system.

While we've gone a long way towards unlocking the information stored in text on the web, there's still much work to be done unlocking the information stored in pixels. This research demonstrates the feasibility of efficient computer vision techniques based on large, noisy datasets. We expect the insights we've gained will lay a useful foundation for future research in computer vision.

If you're interested to learn more about this research, check out the paper.

Large-scale graph computing at Google

2009-06-15T11:47:00.000-07:00

Posted by Grzegorz Czajkowski, Systems Infrastructure Team

If you squint the right way, you will notice that graphs are everywhere. For example, social networks, popularized by Web 2.0, are graphs that describe relationships among people. Transportation routes create a graph of physical connections among geographical locations. Paths of disease outbreaks form a graph, as do games among soccer teams, computer network topologies, and citations among scientific papers. Perhaps the most pervasive graph is the web itself, where documents are vertices and links are edges. Mining the web has become an important branch of information technology, and at least one major Internet company has been founded upon this graph.

Despite differences in structure and origin, many graphs out there have two things in common: each of them keeps growing in size, and there is a seemingly endless number of facts and details people would like to know about each one. Take, for example, geographic locations. A relatively simple analysis of a standard map (a graph!) can provide the shortest route between two cities. But progressively more sophisticated analysis could be applied to richer information such as speed limits, expected traffic jams, roadworks and even weather conditions. In addition to the shortest route, measured as sheer distance, you could learn about the most scenic route, or the most fuel-efficient one, or the one which has the most rest areas. All these options, and more, can all be extracted from the graph and made useful — provided you have the right tools and inputs. The web graph is similar. The web contains billions of documents, and that number increases daily. To help you find what you need from that vast amount of information, Google extracts more than 200 signals from the web graph, ranging from the language of a webpage to the number and quality of other pages pointing to it.

In order to achieve that, we have created scalable infrastructure, named Pregel, to mine a wide range of graphs. In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it in the previous iteration, send messages to other vertices, modify its own and its outgoing edges' states, and mutate the graph's topology (experts in parallel processing will recognize that the Bulk Synchronous Parallel Model inspired Pregel).

Currently, Pregel scales to billions of vertices and edges, but this limit will keep expanding. Pregel's applicability is harder to quantify, but so far we haven't come across a type of graph or a practical graph computing problem which is not solvable with Pregel. It computes over large graphs much faster than alternatives, and the application programming interface is easy to use. Implementing PageRank, for example, takes only about 15 lines of code. Developers of dozens of Pregel applications within Google have found that "thinking like a vertex," which is the essence of programming in Pregel, is intuitive.

We've been using Pregel internally for a while now, but we are beginning to share information about it outside of Google. Greg Malewicz will be speaking at the joint industrial track between ACM PODC and ACM SPAA this August on the very subject. In case you aren't able to join us there, here's a spoiler: The seven bridges of Königsberg — inspiration for Leonhard Euler's famous theorem that established the basics of graph theory — spanned the Pregel river.

Google Fusion Tables

2009-06-09T16:00:00.000-07:00

Posted by Alon Halevy, Google Research and Rebecca Shapley, User Experience

Database systems are notorious for being hard to use. It is even more difficult to integrate data from multiple sources and collaborate on large data sets with people outside your organization. Without an easy way to offer all the collaborators access to the same server, data sets get copied, emailed and ftp'd--resulting in multiple versions that get out of sync very quickly.

Today we're introducing Google Fusion Tables on Labs, an experimental system for data management in the cloud. It draws on the expertise of folks within Google Research who have been studying collaboration, data integration, and user requirements from a variety of domains. Fusion Tables is not a traditional database system focusing on complicated SQL queries and transaction processing. Instead, the focus is on fusing data management and collaboration: merging multiple data sources, discussion of the data, querying, visualization, and Web publishing. We plan to iteratively add new features to the systems as we get feedback from users.

In the version we're launching today, you can upload tabular data sets (right now, we're supporting up to 100 MB per data set, 250 MB of data per user) and share them with your collaborators or with the world. You can choose to share all of your data with your collaborators, or keep parts of it hidden. You can even share different portions of your data with different collaborators.

When you edit the data in place, your collaborators always get the latest version. The attribution feature means your data will get credit for its contribution to any data set built with it. And yes, you can export your data back out of the cloud as CSV files.

Want to understand your data better? You can filter and aggregate the data, and you can visualize it on Google Maps or with other visualizations from the Google Visualization API. In this example, an intensity map of the world shows countries that won more than 10 gold medals in the Summer Olympics. You can then embed these visualizations in other properties on the Web (e.g., blogs and discussion groups) by simply pasting some HTML code we provide you.

The power of data is truly harnessed when you combine data from multiple sources. For example, consider combining data about access to fresh water in various countries with data about malaria rates in those countries, or as shown here, showing three sources of GDP data side by side. Fusion Tables enables you to fuse multiple sets of data when they are about the same entities. In database speak, we call this a join on a primary key but the data originates from multiple independent sources. This is just the start, more join capabilities will come soon.

But Fusion Tables doesn't require you and your collaborators to stop there. What if you don't agree on all of the values? Or need to understand the assumptions behind the data better? Fusion Tables enables you to discuss data at different granularity levels -- you can discuss individual rows or columns or even individual cells. If a collaborator with edit permission changes data during the discussion, viewers will see the change as part of the discussion trail.

We hope you find Fusion Tables useful. As usual with first releases, we realize there is much missing, and we look forward to hearing your feedback.

Remembering Rajeev Motwani

2009-06-08T08:06:00.000-07:00

Posted by Alfred Spector, VP of Research

Many hundreds of us at Google were fortunate to have been educated, advised, and inspired by Professor Rajeev Motwani. Six of us were his PhD students and very many others (including our founders) were advised by or took courses from him. Others Googlers, who were not students at Stanford, had close collegial relations. But, no matter what the relationship, we respected Rajeev as a great man. He was not just a mathematically deep computer scientist, not just an entrepreneurial computer scientist who catalyzed value at the intersection of his work and the real world, he was also a thoughtful, caring, and honorable friend.

The words of just a few of us speak louder than any summary I can make:

Sergey Brin wrote in his blog, “Officially, Rajeev was not my advisor, and yet he played just as big a role in my research, education, and professional development. In addition to being a brilliant computer scientist, Rajeev was a very kind and amicable person and his door was always open. No matter what was going on with my life or work, I could always stop by his office for an interesting conversation and a friendly smile.”

Zoltan Gyongyi wrote, “Not only a great educator and one of the brightest researchers of his generation, Rajeev was also a catalyst of Silicon Valley innovation--Google itself standing as a proof. Moreover, he was a mentor, colleague, role model, friend to many Googlers. I am utterly unable to find words that would properly express my personal gratitude to him and the weight of this loss.”

Mayur Datar wrote, “I was fortunate to have Rajeev as my PhD advisor for five years at Stanford. Beyond graduation, he often helped me with priceless career guidance and professional help in terms of meetings with other people in Silicon Valley. There are only a handful of people I can think of who are such high caliber academics and entrepreneurs. His contributions and impact on CS theory community, Stanford CS Dept, and Silicon Valley enterprises and entrepreneurs is unfathomable. I still find it hard to come to terms with his horrible reality. My deepest condolences and prayers go out to his family. He will be fondly remembered and dearly missed by all of us!"

An Zhu wrote, “I am both fortunate and honored to have Rajeev as my PhD advisor. The 5 years at Stanford is very memorable to me. I’m eternally grateful for his advice and support throughout. It is indeed a sad day for many, including his students.”

Alon Halevy wrote, “Rajeev was an inspiration to me and my colleagues on so many levels. As a young graduate student, I remember him working on some of the toughest theoretical computer science problems of the day. Later, his taste for good theory and ability to apply it to practice had a huge impact on various aspects of data management research. As a professor, and now as a Googler, I am awed at the amazing stream of high-caliber students that he mentored. As an entrepreneur, he gave me some generous and well-timed advice. And most of all, as a person, his kindness and willingness to help anyone was a true inspiration.”

Vibhu Mittal wrote, “He was a brilliant researcher and a great professor. And yet the only thing that I can remember right now is that he was a fun, generous, helpful guy who was always willing to sit down and chat for a few minutes. I hope wherever he is, he is still doing it. And I hope there’ll be more people like him in this world to help people like us. I wish his family well — words cannot express what I feel for them.”

Gagan Aggarwal wrote, “I feel extremely fortunate to have had Rajeev as my PhD advisor. He was a wonderful advisor--always very flexible and willing to let his students work at their own pace, while making sure that things are going alright and providing guidance when needed. One of the several striking features of Rajeev's research was his ability to translate real life problems into clean, well-motivated, abstract questions (that he would promptly pose to his students). He was for me an eternal source of fresh problems and great ideas, a source I could tap into whenever my own ideas dried up (and was planning to, just last week). It is impossible to come to terms with the fact that I am never going to do this again. Rajeev had an unmatched clarity of thought and perceptiveness that was evident not only in doing research with him but also in the invaluable advice he gave me about career choices and life in general. ...Rajeev took on many diverse roles: teacher, entrepreneur, advisor and friend, and filled them all as only he could have. His passing will leave an impossible-to-fill void among all those whose lives he touched.”

There are more notes from Googlers, among those of many others, on the Stanford blog commemorating Rajeev.

I’d like to close by noting that Rajeev Motwani’s work on the intersection of theory and practice inspired not only the way Google processes information, but also Google's core scientific values: we fundamentally believe in the power of applying mathematical analysis and algorithmic thinking to challenging real world problems. This philosophy was inherent in Rajeev’s research, the education he gave PhD students, and the advice and classes he provided to many more.

With his and the recent untimely deaths of other influential computer scientists and friends, we are all reminded to seize each day and make the most of it. I think Rajeev would have wanted us to keep this in mind.

The best and the brightest

2009-05-15T09:00:00.001-07:00

Posted by Leslie Yeh Johnson, Google University Relations

[Also posted on the Official Google Blog]

I can't think of a better environment than academia for asking hard questions and trying to solve the unsolvable. It's at universities that graduate students perform some of the most exciting and game-changing research in computer science and technology. These university labs foster the students that are going to be the next innovators and leaders in research.

We started the Google Fellowship Program this year to support graduate students in their quest to discover and achieve great things. Our goal was to find the best and brightest PhD students and award them a unique fellowship that highlights their contributions to research and supports them through their graduate studies. Several top universities submitted their students for consideration by research scientists, distinguished engineers and executives at Google. The breadth of research covered by these students and the scope of their vision was astounding. Learning about them was exciting; choosing from among them was truly difficult.

After careful review, we are proud to announce the 2009 Google Fellowship recipients:

Roxana Geambasu, Google Fellowship in Cloud Computing (University of Washington)
Michael Piatek, Google Fellowship in Computer Networking (University of Washington)
David Sontag, Google Fellowship in Machine Learning (Massachusetts Institute of Technology)
Ali Farhadi, Google Fellowship in Computer Vision Image Interpretation (University of Illinois at Urbana-Champaign)
Nicholas Chen, Google Fellowship in Human-Computer Interaction (University of Maryland)
Siddhartha Sen, Google Fellowship in Fault Tolerant Computing (Princeton University)
Ryan Peterson, Google Fellowship in Distributed Systems (Cornell University)
Eric Gilbert, Google Fellowship in Social Computing (University of Illinois at Urbana-Champaign)
Micha Elsner, Google Fellowship in Natural Language Processing (Brown University)
Subhransu Maji, Google Fellowship in Computer Vision Object Recognition (University of California, Berkeley)
Nicolas Lambert, Google Fellowship in Market Algorithms (Stanford University)
Han Liu, Google Fellowship in Statistics (Carnegie Mellon University)
Lixia Liu, Google Fellowship in Compiler Technology (Purdue University)

These students exemplify excellence in all areas, and we look forward to the impact that they are sure to have on their fields and the world. The Google Fellowship will provide them with funding to cover their tuition and expenses, plus an Android-powered phone and a Google mentor. Our sincere congratulations to all of them!

Google Fellowships, the Nuts and Bolts

2009-05-15T09:00:00.000-07:00

Posted by Leslie Yeh Johnson, Google University Relations

As you may have read, today we announced the recipients of the 2009 Google Fellowships. (You can read the announcement over on the Official Google Blog.) This is fantastic news, and the blog post makes the Google Fellowship Program sound very polished. But the truth is there was a lot more work (and scrambling) done in the background...here's a quick snapshot.

We first conceived of the idea of the fellowships late last year. Google already funds academic research through the Google Research Awards, but we really wanted to support the graduate students who are doing a lot of the research and are the future of their respective fields. Idea: why don't we search out the best and brightest PhD students and pay their tuition and expenses, plus give them an Android phone and hook them up with a Google researcher so we can all share really cool ideas? Done and done.

After we made the decision to do the fellowships in 2009, we were in for some hard work. We quickly spread the word about the fellowships in order to give the universities and students time to prepare and send us information about themselves and their research. The nominated students were doing research on a vast array of subjects: Cloud Computing, Computer Graphics, Market Algorithms, Machine Learning, Natural Language Processing, Social Computing, Information Retrieval, Compilers, and Computer Vision to name a few. I relied upon a small army of research scientists and distinguished engineers to help me review them. In addition to lending their scientific expertise to looking over the Google Research Awards, not to mention their "day job", the forty-five Googlers also were able to provide feedback on the students in record time - these guys are champs. Then a whirlwind review with Alfred Spector, VP of Research and Special Initiatives at Google, and just six months later we are proud to announce the 2009 Google Fellowship recipients.

It was a jam-packed 6 months, and I'm really proud of how the program turned out this year. That said, I'm already looking forward to our sophomore year in 2010. You should expect to see a broader program covering more areas of research, more schools, and more geographies. I can't wait.

ACM Multimedia 2009 Grand Challenges

2009-05-12T07:19:00.000-07:00

Posted by Jay Yagnik, Head of Computer Vision Research

At Google Research we interact with the academic research community closely through various programs like Research Awards, Visiting Faculty Program, and by active participation in various conferences. Dealing with large quantities of data gives us some unique challenges and perspectives on various problems. In many cases entirely new problem classes begin to emerge. These problems often have not received attention from a broad part of the research community. In an effort to bridge this gap for multimedia problems, we participated in setting Grand Challenges for this year's ACM Multimedia Conference. We proposed "Robust, As-Accurate-As-Human Genre Classification for Video" as a challenge.

The majority of research in video analysis today focuses on surveillance video. While this is critical for a lot of security applications, it is incomplete in describing challenges that come up when we tackle a video retrieval and discovery application like YouTube. Analysis work beyond surveillance is often limited to specific categories like News and Sports that have well defined structures that the solution methods can explicitly work with. Our challenge aims to encourage more work in the area of semantic understanding of a broad variety of videos. Genre classification is a problem thats representative of some of the challenges that stem from the sheer diversity that can exist across video categories. The challenge will encourage new methods to solve these problems, as well as attempts at standardizing datasets to represent this problem. With internet video gaining popularity in an astounding magnitude, we believe this challenge will steer the multimedia research community towards challenges posed by the magnitude and variety of this new problem area.

We are grateful to Mor Naaman (Rutgers University) and Tat-Seng Chua (National University of Singapore) for organizing this industry challenge track at ACM Multimedia and inviting us to be a part of it.

Details of our challenge can be found here.

The bar-bet phenomenon: increasing diversity in mobile searches

2009-05-07T11:27:00.001-07:00

Posted by Maryam Kamvar, Melanie Kellar, Rajan Patel and Ya Xu, Google Research

Historically, research suggests that web search on mobile phones has been limited when compared to the diverse set of queries which comprise computer-based search. Researchers attribute the homogeneous mobile search behavior in part to the phone's form factor and browsing capabilities. However, our new logs-based study indicates that high-end phones, like the iPhone, are changing the landscape of mobile search. We found that search from these phones has evolved not only to mimic computer web search patterns, but to exceed the expectations set by conventional web search in some cases.

We see iPhone searches mimicking computer-based search behavior in terms of query length (~3 words per query for computer and iPhone queries, as opposed to 2.5 words per query for conventional mobile queries) and query classification (notably the percentage of Adult and Entertainment searches have decreased on the iPhone relative to conventional mobile phones). But what is most surprising to us is that frequent searchers on iPhone surpass frequent searchers on computers in terms of the diversity of queries they issue. In other words, people are using high-end phones to search for a more diverse set of information needs than computers are used for; we jokingly refer to this as the "bar-bet" phenomenon -- or the "pub-quiz" phenomenon for those of you in the UK.

We devised a metric for quantifying the variability of a user’s search intentions across time. This variability metric, entro-percent, is a normalized entropy metric which compares the number of search tasks issued by a user to the number of categories those search tasks fall under. This user-variability for conventional mobile web search is much lower than for computer-based search, confirming the hypothesis that mobile web users query over a much less diverse set of topics. The surprising news is that iPhone users, on the other hand, had a higher variability than computer based users, indicating their information needs are more diverse! This shows that the challenges posed by a phone's form factor can be outweighed by its "always on, always in your pocket" benefits.

To understand the meaning of the entro-percent equation, read our full paper summarizing the findings of our logs-based study of search patterns on conventional mobile phones, iPhones and conventional computers and get all the juicy details.

Cloud Computing and the Internet

2009-04-28T08:17:00.000-07:00

Posted by Vinton Cerf, Chief Internet Evangelist

[adapted from the speech given on the occasion of the honoris causa ceremony
at the Universidad Politecnico de Madrid]

The Internet is largely a software artifact and a layered one as my distinguished colleague, Sir Tim Berners-Lee has observed on many occasions. The layering has permitted a remarkable versatility in the implementation of the Internet and its applications. New technology can be used to implement each layer and as long as the interfaces between the layers remain static, the changes do not affect the functionality of the system. In this way, the Internet has evolved and adapted new transmission and switching technology into its lower layers and has supported new upper layers such as the HTTP, HTML and SSL protocols of the World Wide Web.

In recent years, the term “cloud computing” has emerged to make reference to the idea that from the standpoint of a device, say a laptop, on the Internet, many of the applications appear to be operating somewhere in the network “cloud.” Google, Amazon, Microsoft and others, as well as enterprise operators, are constructing these cloud computing centers. Generally, each cloud knows only about itself and is unaware of the existence of other cloud computing facilities. In some ways, cloud computing is like the networks of the 1960s when my colleagues and I began to think about connecting computers together on networks. Each network was typically proprietary. IBM had Systems Network Architecture; Digital Equipment Corporation had its DECNET; Hewlett-Packard had its Distributed System. These networks were specific to each manufacturer and did not interconnect nor even have a way to express the idea of connecting to another network. The Internet was the solution that Robert Kahn and I developed to allow all such networks to be interconnected in a uniform way.

Cloud computing is at the same stage. Each cloud is a system unto itself. There is no way to express the idea of exchanging information between distinct computing clouds because there is no way to express the idea of “another cloud.” Nor is there any way to describe the information that is to be exchanged. Moreover, if the information contained in one computing cloud is protected from access by any but authorized users, there is no way to express how that protection is provided and how information about it should be propagated to another cloud when the data is transferred.

Interestingly, my colleague, Sir Tim Berners-Lee, has been pursuing ideas that may inform the so-called “inter-cloud” problem. His idea of data linking may prove to be a part of the vocabulary needed to interconnect computing clouds. The semantics of data and of the actions one can take on the data, and the vocabulary in which these actions are expressed appear to me to constitute the beginning of an inter-cloud computing language. This seems to me to be an extremely open field in which creative minds everywhere can be free to contribute ideas and to experiment with new concepts. It is a new layer in the Internet architecture and, like the many layers that have been invented before, it is an open opportunity to add functionality to an increasingly global network.

There are many unanswered questions that can be posed about this new problem. How should one reference another cloud system? What functions can one ask another cloud system to perform? How can one move data from one cloud to another? Can one request that two or more cloud systems carry out a series of transactions? If a laptop is interacting with multiple clouds, does the laptop become a sort of “cloudlet”? Could the laptop become an unintended channel of information exchange between two clouds? If we implement an inter-cloud system of computing, what abuses may arise? How will information be protected within a cloud and when transferred between clouds. How will we refer to the identity of authorized users of cloud systems? What strong authentication methods will be adequate to implement data access controls?

Because the Internet is primarily a software artifact, there seems to be no end to its possibilities. It is an endless frontier, open to exploration by virtually anyone. I cannot guess what will be discovered in these explorations but I am sure that we will continue to be surprised by the richness of the Internet’s undiscovered territory in the decades ahead.

The Continuing Metamorphosis of the Web

2009-04-27T10:39:00.000-07:00

Posted by Alfred Spector, VP Research and Special Initiatives

I just returned from giving a talk at the 18th World Wide Web Conference in Madrid and was pleased to see a healthy and dynamic conference despite difficult economic conditions. Madrid had beautiful spring weather, and a magnificent modern architecture abounds throughout the city. I will say, though, that the Madrid subway does not vibrate (shake, rattle, and roll) one’s soul quite as much as does our local NYC subway.

My talk was entitled The Continuing Metamorphosis of the Web. In it, I noted that the initial web standards were so simple and sensible that they engendered a path of stepwise innovations, which taken together have aggregated into amazing accomplishments. Metaphorically, I feel our community has been on a kind of pseudo-random walk that has taken us to remarkable places. The truly great results have included the creation of a virtual Library of Alexandria, the creation of the search engine (to be that library’s super-card catalog), the empowerment of the long tail (in diverse communities), and great innovations to doing business. I argued that the bottom up evolution is continuing (perhaps even accelerating) today, and that the current stepwise improvements are still leading to broad innovations, which we will come to view as extraordinary as any that have occurred to-date.

Here are three great achievements currently a-brewing:

“Totally Transparent Processing.” By this, I argued that our use of the web (whether for search, communication, or information access) can increasingly occur in a fluid manner that is independent of the device we are using, independent of the human language we prefer, independent of the modality of the data, and independent of the corpus of information on which our interaction is based. In effect, processing can be transparent ∀d∈D, ∀l∈L, ∀m∈M, ∀c∈C. Our barriers to using information technology are fading away and becoming transparent.
“Ideal Distributed Computing.” While we have known the fundamentals of distributed computing for many decades, only today are we reaching a state where we can achieve a powerful and efficient balance of computation between all end-user devices and a vast collection of shared storage and computational resources. Cloud computing is today’s term d’arte, but I talked more generally about systems with the flexibility that computation and data can move across computers within a cluster, across clusters of computers and—of course—between clusters and all other (say, end user) devices. The result is the efficient, even awesome, capability to provide communication, computation and data to a vast collection of people and applications.
“Hybrid, Not Artificial, Intelligence.” Systems are regularly augmenting the capability of all of us in day-to-day life, and our collective use of those systems is, in turn, augmenting the capabilities of those systems in a beneficial virtuous circle. The virtuous circle is operating already in the search engine, voice recognition systems, recommendation systems, and more. There is every reason to think the effect will become ever more potent as computers are applied to more domains and and used by larger populations. The result may not be artificially intelligent machines that pass the Turing Test, but instead systems that will be ever more capable of helping us achieve our goals in life -- in a kind of partnership. For a related take on this, you might look at a Google Official Blog post, “The Intelligent Cloud,” which Franz Och and I posted last Fall.

More explanation and many examples, based on Google research and services, are available in the slides I used with my talk. A PDF file of those slides is available on the WWW2009 website under the papers and presentations link.

Congratulations to NSF CLuE Grant awardees

2009-04-23T12:03:00.000-07:00

Posted by Jeff Walz and Andrea Held

The first goal of the Academic Cluster Computing Initiative was to familiarize the academic community with the methods necessary to run very large datasets on massive distributed computer networks. By expanding that program to include research grants through the National Science Foundation's Cluster Exploratory (CLuE) program, we're also hoping to enable new and better approaches to data-intensive research across a range of disciplines.

Now that the NSF has announced the 2009 CLuE grants in addition to some previous Small Grant for Exploratory Research (SGER) grants, we're excited to congratulate the recipient researchers and wish them the best as they bring new projects online and continue to run existing SGER projects on the Google/IBM cluster.

The NSF selected projects based on their potential to advance computer science as well as to benefit society as a whole, and researchers at 14 institutions are tackling ambitious problems in everything from computer science to bioinformatics. The institutions receiving CLuE grants are Purdue, UC Santa Barbara, University of Washington, University of Massachussetts-Amherst, UC San Diego, University of Virginia, Yale, MIT, University of Wisconsin-Madison, Carnegie Mellon, University of Maryland- College Park, University of Utah and UC Irvine. Florida International University, Carnegie Mellon and University of Maryland will continue other projects with exiting SGER grants. These grantees will run their projects on a Google/IBM-provided cluster running an open source implementation of Google's MapReduce and File System.

We're excited to help foster new approaches to difficult, data-intensive problems across a range of fields, and we can't wait to see more students and researchers come up with creative applications for massive, highly distributed computing.

Socially Adjusted CAPTCHAs

2009-04-16T10:58:00.000-07:00

Posted by Rich Gossweiler, Maryam Kamvar, Shumeet Baluja

Unfortunately, there is a war going on between humans and 'bots. Software
'bots are attempting to generate massive numbers of computer accounts
which are then sold in bulk to spammers. Spammers use these accounts to
inundate emails and discussion boards. Meanwhile humans are trying to
simply create an account and don't want to spend a lot of time proving
that they are not a program.

Typically we use CAPTCHAs -- we present an image of some distorted text
and then ask the applicant to type in the letters. As image processing gets
more sophisticated, these letter sequences tend to get longer and more
distorted, sometimes to the point where humans fail too.

So we switched the game. We show an image, say an airplane, but it
is randomly rotated and we ask the applicant to rotate it to "up." This
is generally hard for computers but easy for people. Well, for the most
part.

Since computers are good at faces, skies, text, etc. we sift
through our database of images running state-of-the-art up detectors to
remove those images. But of the images that remain, some are too hard
for people to figure out. What is up for a plate or a piece of
abstract art?

So here is where it gets interesting. We show people several images, one
of which is a "candidate" and we see how people do. If everyone rotates
it the same way, it is a keeper. If there is a lot of variation, we
discard it. As extra credit it turns out that even if the original image were
taken at an angle, it does not matter, since people, in large numbers,
socially adjust the CAPTCHA.

Read the full paper here (posted with the permission of WWW'09).

The Grill: Google's Alfred Spector on the hot seat

2009-04-15T11:05:00.000-07:00

Posted by Ben Bayer, Google Research

Alfred Spector, Google's VP of Research, tells COMPUTERWORLD the ins and outs of Research at Google and where it's headed for the future. Read the complete interview here.

Predicting the Present with Google Trends

2009-04-02T14:10:00.000-07:00

Posted by Hal Varian, Chief Economist and Hyunyoung Choi, Decision Support Engineering Analyst

Can Google queries help predict economic activity?

The answer depends on what you mean by "predict." Google Trends and Google Insights for Search provide a real time report on query volume, while economic data is typically released several days after the close of the month. Given this time lag, it is not implausible that Google queries in a category like "Automotive/Vehicle Shopping" during the first few weeks of March may help predict what actual March automotive sales will be like when the official data is released halfway through April.

That famous economist Yogi Berra once said "It's tough to make predictions, especially about the future." This inspired our approach: let us lower the bar and just try to predict the present.

Our work to date is summarized in a paper called Predicting the Present with Google Trends. We find that Google Trends data can help improve forecasts of the current level of activity for a number of different economic time series, including automobile sales, home sales, retail sales, and travel behavior.

Even predicting the present is useful, since it may help identify "turning points" in economic time series. If people start doing significantly more searches for "Real Estate Agents" in a certain location, it is tempting to think that house sales might increase in that area in the near future.

Our paper outlines one approach to short-term economic prediction, but we expect that there are several other interesting ideas out there. So we suggest that forecasting wannabes download some Google Trends data and try to relate it to other economic time series. If you find an interesting pattern, post your findings on a website and send a link to econ-forecast@google.com. We'll report on the most interesting results in a later blog post.

It has been said that if you put a million monkeys in front of a million computers, you would eventually produce an accurate economic forecast. Let's see how well that theory works ...