Celebrating Computer Science Education Week

Posted by Alfred Spector, VP Research and Special Initiatives and Maggie Johnson, Director of Education and University Relations

[cross-posted with the Official Google Blog]

Today kicks off the nation’s first Computer Science Education Week. The goal of this week is to encourage students to learn about the discipline that powers the computers, applications and technology they use everyday. Computer Science Education Week emphasizes that our society's aspirations will be met by individuals who have an increasingly deep understanding of computer technology.

We've been thinking about ways that Google could help with computer science education for several years. After all, our search engine has been used in education since its inception — how many essays, research papers and theses begin with a Google search? Today, we'd like to summarize some of what we've been doing at Google to advance CS education. Our efforts focus on four strategic areas, with an emphasis on computing in core curriculum.

Use of Google tools to support teaching and learning
Having a web-based shared document, spreadsheet or presentation that students in a group or class can all view and edit online has had an enormous impact on collaboration in education. So we provide a free suite of our communication & collaboration applications designed especially for schools and universities. We also used our tools and infrastructure to build and support a community of teachers who have developed classroom content and activities around these applications.

Increasing the access to and quality of Computer Science curriculum
We have many people at Google who know about all areas of computer science, many with backgrounds and experience in education. With this deep base of computer science knowledge, we developed Google Code University to help faculty update their undergraduate computer science curriculum, and the Summer of Code, which gives students the opportunity to develop programs for various open source software projects.

Integrating computing curriculum across K-12 core subjects
A group of Google engineers and K-12 "teaching fellows" is working on building and testing models of curriculum to encourage innovation. These curriculum models revolve around "computational thinking", a problem-solving technique that draws on the thinking and analysis skills that computer scientists use everyday. Our goal is to integrate computational thinking across subject areas in K-12 by connecting these skills, which are already a part of core curriculum, more explicitly to computer science. We're also taking this a step further by integrating simple programming concepts in appropriate areas of core K-12 curriculum, such as algebra. Our hope is that by making computer science more visible and showing its connection to every subject area, students will experience the full power and utility of technology in areas of interest to them. Integrating CS into other subjects will also have the key added benefit of leveling the playing field, so that many more students will have the opportunity to gain a deeper understanding of computing.

Supporting organizations and individuals through community outreach
We've also worked for years with teachers and nonprofits to build early interest in the Science, Technology, Engineering and Math (STEM) fields. Besides providing financial support and sponsorship for many external organizations, we've developed a number of scholarship and intern programs to increase the number of women and underrepresented minorities in STEM and computer science. In addition to these formal programs, every day Googlers all over the world organize visits with students at nearby schools and community centers to teach, present workshops and tech talks, and to share their personal stories on how they became computer scientists and engineers.

We're absolutely delighted to be a co-sponsor of the first Computer Science Education Week. As a company, we've benefited so much from advances in computer science and the creativity of computer scientists. We also know that the next great innovators in computer science are out there, ready to be inspired to create technologies that change our world and benefit our society. We urge our children, parents, teachers and educational institutions to pay more attention to this critical field, and we will continue to do our share.

Join us for the 2010 Google GRAD CS Forum!

Posted by Hanah Kim, University Programs

[cross-posted with the Google Student Blog]

As part of Google’s ongoing commitment to encouraging students of underrepresented backgrounds in technology to pursue graduate study, we are pleased to host the first annual 2010 Google Graduate Researchers in Academia of Diverse backgrounds (GRAD) CS Forum. This forum will bring together students who are historically underrepresented in the field to connect with one another and with Google.

Up to 75 computer scientists will be invited to an all-expenses paid forum that will run Thursday evening through Saturday afternoon on January 21–23 at Google’s headquarters in Mountain View, CA.

The Google GRAD CS Forum will include technical talks from established researchers – both from Google and universities – and a unique occasion to build and strengthen networks with other emerging researchers. Students will also enjoy tours of the Googleplex, have the opportunity to meet with Google engineers in their focus areas, and have fun exploring the San Francisco Bay Area.

Eligibility Requirements

Applicants must:

• be a computer science (or related technical discipline) graduate student currently enrolled in a Masters or PhD program at a university in North America
• demonstrate academic excellence and leadership in the computing field
• maintain a cumulative GPA of at least 3.3 on a 4.0 scale or 4.3 on a 5.0 scale or equivalent in their current program

The forum is open to all qualified graduate students, and is committed to addressing diversity in our company and in the technology industry. Students who are a member of a group that is historically under-represented in the technology industry are encouraged to apply, including female, Native American, African American and Hispanic students as well as students with disabilities.

Selection Process

Google engineers will select up to 75 attendees based on each applicant’s academic and technical achievements. Evidence of academic achievement and leadership experience should be evident from the resume.

How to Apply

Complete the online application and submit all required documents online. First-time users will be required to register and create an account. Please note that recommendation letters are not required.

Application Deadline: December 12, 2009

Apply now at www.google.com/jobs/students/gradforum.

Note: letters of recommendation are not required

Automatic Captioning in YouTube

Posted by Christopher Alberti and Michiel Bacchiani, Google Research

On November 19, we launched our new automatic captioning and automatic alignment feature for YouTube. These features significantly reduce the effort it takes to create captions for videos on YouTube.

With YouTube expanding its index at a breakneck speed of about 20 hours of new material uploaded each minute, access to this vast body of video material becomes increasingly challenging. This is particularly true for people with hearing disabilities. A 2005 US census showed that 7.8 million people (or about 3 percent of the US population) have difficulty hearing a normal conversation, with 1 million unable to hear at all. Hence, increased accesibility by adding captions to YouTube videos makes the corpus available to a much larger audience.

In addition to expanded accessibility for those with hearing disabilities, the combination of captions with machine translation expands YouTube accessibility across the globe. If a caption track is available, it can be translated automatically in any of the 51 currently available languages. As a result, video content otherwise not accessible due to a language barrier can now be understood by a significantly larger user population.

Although captions are available in YouTube for hundreds of thousands of videos, it remains only a fraction of the the available corpus. Furthermore, only a tiny fraction of the avalanche of new video material getting uploaded is captioned. One reason for this lack of coverage is the effort it takes for a video uploader to generate captions. And this is where our new auto captioning and auto alignment features can benefit our uploaders. Auto-captioning uses automatic speech recognition technology to produce machine generated captions. Auto-alignment requires only a transcript--the uploader no longer has to sync that text with the video stream. To more concisely illustrate the use of these features, check out our help center article or this short video:



Modern-day speech recognition systems are big statistical machines trained on large sets of data. They do the best job recognizing speech in domains similar to their training data. Both the auto captioning and the auto alignment features use the speech recognition infrastructure that underlies Google Voice and Voice Search, but trained on different data. As an intial installment, for YouTube we use models trained on publicly available English broadcast news data. As a result, for now, the new features only work well on English material that is similar in style (i.e. an individual speaker who is speaking clearly).

The auto alignment features is available for all new video uploads, however the scope is limited to English material. The auto captioning feature is initially rolled out to a set of educational partners only. Although this is very limited in scope, the early launch makes the results of the system available to the viewers of this material instantly and it allows us to gauge early feedback which can aid in improving the features. We will release automatic captions more widely as quickly as possible.

Over time, we will work on improving the quality as well as the coverage of these features. Expansion will take place along two axes: additional languages will be made available and within each language we will cover much broader domains (beyond just broadcast news-like material). Since the content available in YouTube is so varied, it is difficult to set a timeline for this expansion. Automatic speech recognition remains challenging, in particular for the varied types of speech and background sounds and noise we see in the YouTube corpus. Therefore, to reach a high level of quality, we need to make advances in core technology. Although this will take time, we are committed to making that happen and to providing the larger community with the benefits of those developments.

Four Googlers elected ACM Fellows

Posted by Alfred Spector, VP of Research

I'm excited to share that the Association for Computing Machinery (ACM) has just announced that four Googlers have been elected ACM Fellows in its class of 2009. Jeff Dean, Tom Dean, Urs Hoelzle and Yossi Matias were chosen for their achievements in computer science and information technology and for their significant contributions to the mission of the ACM. Here at Google, we take great pride in having a tremendously talented workforce, and the talent of our team is exemplified by the addition of Jeff, Tom, Urs and Yossi to the six other ACM Fellows already at Google.

All of these Googlers are being recognized for successes both inside and outside Google. Urs' and Jeff's achievements are most directly related to innovations made while at Google, specifically in our large data centers and in harnessing their inherent parallel computation and vast storage. Tom and Yossi, on the other hand, were elected more for work done prior to Google — respectively, on how to use prediction in planning, control, and decision-making where there is both uncertainty and time constraints, and on theoretically and practically interesting techniques for analyzing and managing large data sets and data streams.

We at Google congratulate our colleagues. They serve as an inspiration to us and to our colleagues in computer science globally and remind us to continue to push the limits of computing, which has enormous benefits to our field and to society at large.

You can read more about Jeff, Tom, Urs and Yossi's achievements and the reasons for this recognition by the ACM below. The citations are the official ones from the ACM.

Jeff Dean, Google Fellow
For contributions to the science and engineering of large-scale distributed computer systems

Dr. Jeff Dean has made great contributions to the programming and use of loosely-coupled multiprocessing systems and cloud computing. Jeff is probably best known for his work (with Sanjay Ghemawat) on the parallel/distribution computing infrastructure called MapReduce, a tremendously influential programming model for batch jobs on loosely coupled multiprocessing systems. Working with others, Jeff has also been a leading contributor to many other Google systems: the BigTable record storage system, which reliably stores diverse record data records (via portioning and replication) in vast quantities, at least two production real-time indexing systems, and several versions of Google's web serving system. The breadth of Jeff's work is quite amazing: At Digital, he co-developed a leading Java compiler and the Continuous Profiling Infrastructure (DCPI). Beyond this core systems work, Jeff has had exceedingly diverse additional activities; for example, he co-designed Google's first ads serving system, made significant quality improvements to the search system, and even has been involved in user-visible efforts such as the first production version of Google News and the production implementation of Google's machine translation system. Despite his primary accomplishments as a designer and implementer of innovative systems that solve hard problems in a practical way, Jeff also has over 20 publications in peer-reviewed publications, more than 25 patents, and is one of Google's most sought-after public speakers.

Thomas L. Dean, Staff Research Scientist
For the development of dynamic Bayes networks and anytime algorithms

Dr. Tom Dean is known in AI for his work on the role of prediction in planning, control and decision-making where uncertainty and the limited time available for deliberation complicate the problem, particularly his work on temporal graphical models and their application in solving robotics and decision-support problems. His temporal Bayesian networks, later called dynamic Bayes networks, made it possible to factor very large state spaces and their corresponding transition probabilities into compact representations, using the tools and theory of graphical models. He was the first to apply factored Markov decision processes to robotics and, in particular, to the problem of simultaneous localization and map building (SLAM). Faced with the need to solve what were essentially intractable problems in real-time, Dean coined the name "anytime algorithm" to describe a class of approximate inference algorithms and the associated (meta) decision problem of deliberation scheduling to address the challenges of bounded-time decision making. These have been applied to large-scale problems at NASA, Honeywell, and elsewhere. At Google, Tom has worked on extracting stable spatiotemporal features from video and developed new, improved features for video understanding, categorization and ranking. During his twenty-year career as a professor at Brown University, he published four books and over 100 technical articles, while serving terms as department chair, acting vice president for computing and information services, and deputy provost.

Urs Hoelzle, Senior Vice President of Engineering
For the design, engineering and operation of large scale cloud computing systems

Dr. Urs Hoelzle has made significant contributions to the literature, theory, and practice in many areas of computer science. His publications are found in areas such as compilers, software and hardware architecture, dynamic dispatch in processing systems, software engineering and garbage collection. Much of this work took place during his time at Stanford and later at UC Santa Barbara as a member of the faculty. Urs' most significant contribution to computer science and its application is found in his work and leadership at Google. Since 1999 he has had responsibility for leading engineering and operations of one of the largest systems of data centers and networks on the planet. That it has been able to scale up to meet the demands of more than a billion users during the past 10 years is an indication of his leadership ability and remarkable design talent. Urs works best in collaborative environments, as evidenced by his publications and in his work at Google. While it would be incorrect to credit Urs alone for the success of the Google computing and communications infrastructure, his ability to lead a large number of contributors to a coherent and scalable result is strong evidence of his qualification for advancement to ACM Fellow. The philosophy behind Google's system of clustered, distributed computing systems reflects a powerful pragmatic: assume things will break; use replication, not gold-plating, for resilience; reduce power requirements where ever possible; create general platforms that can be harnessed in myriad ways; eschew specialization except where vitally necessary (e.g., no commercial products fit the requirement). Much of this perspective can be attributed to Urs Hoelzle.

Yossi Matias, Director of R&D Center in Israel
For contributions to the analysis of large data sets and data streams

Dr. Yossi Matias has made significant contributions to the analysis of large data sets and data streams. He pioneered (with Phillip Gibbons) a new research direction into the study of small-space (probabilistic) “synopses” of large data sets, motivating their study and making key contributions in this area. Yossi’s 1996 paper (with Noga Alon and Mario Szegedy) won the 2005 Gödel Prize, the top ACM prize in Theoretical Computer Science, awarded annually. The award citation describes the paper as having “laid the foundations of the analysis of data streams using limited memory." Further, “It demonstrated the design of small randomized linear projections, subsequently referred to as ‘sketches,’ that summarize large amounts of data and allow quantities of interest to be approximated to user-specified precision.” Additionally, Yossi has made several key contributions to lossless data compression of large data sets, including a “flexible parsing” technique that improves upon the Lempel-Ziv dictionary-based compression algorithm, and novel compression schemes for images and for network packets. Large scale data analysis requires effective use of multi-core processors. For example, his JACM paper (with Guy Blelloch and Phillip Gibbons) provided the first provably memory- and cache-efficient thread scheduler for fine-grained parallelism. In addition to his academic and scientific impact, Yossi has been heavily involved in the high tech industry and in technology and product development, pushing the commercial frontiers for analyzing large data sets and data streams. He is also the inventor on 23 U.S. patents. Yossi joined Google in 2006 to establish the Tel-Aviv R&D Center, and to be responsible for its strategy and operation. Yossi has overall responsibility for Google R&D and technology innovation in Israel.

Explore Images with Google Image Swirl

Posted by Yushi Jing and Henry Rowley, Google Research

Earlier this week, we announced the Labs launch of Google Image Swirl, an experimental search tool that organizes image-search results. We hope to take this opportunity to explain some of the research underlying this feature, and why it is an important area of focus for computer vision research at Google.

As the Web becomes more "visual," it is important for Google to go beyond traditional text and hyperlink analysis to unlock the information stored in the image pixels. If our search algorithms can understand the content of images and organize search results accordingly, we can provide users with a more engaging and useful image-search experience.

Google Image Swirl represents a concrete step towards reaching that goal. It looks at the pixel values of the top search results and organizes and presents them in visually distinctive groups. For example, in ambiguous queries such as "jaguar," Image Swirl separates the top search results into categories such as jaguar the animal and jaguar the brand of car. The top-level groups are further divided into collections of subgroups, allowing users to explore a broad set of visual concepts associated with the query, such as the front view of a Jaguar car or Eiffel Tower at night or from a distance. This is a distinct departure from the way images are ranked by the Google Similar Images, which excels at finding images very visually similar to the query image.

No matter how much work goes into engineering image and text features to represent the content of images, there will always be errors and inconsistencies. Sometimes two images share many visual or text features, but have little real-world connection. In other cases, objects that look similar to the human eye may appear drastically different to computer vision algorithms. Most difficult of all, the system has to work at Web Scale -- it must cover a large fraction of query traffic, and handle ambiguities and inconsistencies in the quality of information extracted from Web images.

In Google Image Swirl, we address this set of challenges by organizing all available information about an image set into a pairwise similarity graph, and applying novel graph-analysis algorithms to discover higher-order similarity and category information from this graph. Given the high dimensionality of image features and the noise in the data, it can be difficult to train a monolithic categorization engine that can generalize across all queries. In contrast, image similarities need only be defined for similar enough objects and trained with limited sets of data. Also, invariance to certain transformations or typical intra-class variation can be built into the perceptual similarity function. Different features or similarity functions may be selected, or learned, for different types of queries or image contents. Given a robust set of similarity functions, one can generate a graph (nodes are images and edges are similarity values) and apply graph analysis algorithms to infer similarities and categorical relationships that are not immediately obvious. In this work, we combined multiple sources of similarity such as those used in Google Similar Images, landmark recognition, Picasa's face recognition, anchor text similarity, and category-instance relationships between keywords similar to that in WordNet. It is a continuation of our prior effort [paper] to rank images based on visual similarity.

As with any practical application of computer vision techniques, there are a number of ad hoc details which are critical to the success of the system but are scientifically less interesting. One important direction of our future work will be to generalize some of the heuristics present in the system to make them more robust, while at the same time making the algorithm easier to analyze and evaluate against existing state-of-the-art methods. We hope that this work will lead to further research in the area of content-based image organization and look forward to your feedback.

UPDATE:  Due to the shutdown of Google Labs, this service is longer active.

Cara Mudah dalam Mengaktifkan Print Spooler

ditulis oleh Basyarah

Dalam setiap kegiatan, manusia mengharapkan kemudahan dalam melaksanakannya. Salah satu kegiatan tersebut adalah membuat dokumen. Bayangkan pada saat zaman mesin ketik, salah ketik satu huruf saja harus mengganti kertas, namun setelah adanya komputer semuanya terasa lebih mudah.

Terkait dengan dokumen, salah satu alat untuk mencetak atau printer sudah sangat dibutuhkan, dimana sudah berbagai macam jenis printer seperti laser jet, bubble jet, dot matrix, dan jenis lainnya. Namun dalam pelaksanaannya dibalik kemudahan itu selalu ada kesulitan yang dapat kita temukan. Misalnya tidak bisa mencetak dokumen. Banyak masalah yang harus di analisa terlebih dahulu, dan tidak instant dapat menebak kesalahannya.

Nah, dalam tulisan ini saya membahas salah satu masalah kenapa driver printer tidak dapat di install. Sebetulnya caranya mudah namun jika belum menemukan solusinya akan terasa lebih sulit. Pada saat anda meng-install driver printer namun tidak bisa, dimana terdapat pesan error “Operation could not be completed. The print spooler service is not running” maka terdapat permasalahan pada service print spooler.

Apa itu print spooler?

Print spooler adalah suatu service di windows yang berfungsi untuk memuat file atau dokumen ke memory untuk di cetak. Jika service ini tidak dalam kondisi aktif maka proses mencetak tidak dapat dilaksanakan, termasuk menginstall printer driver. Sekarang anda mungkin mempunyai pertanyaan seperti ini “bagaimana cara mengaktifkan print spooler ini?” sebetunya caranya sangat mudah yaitu:

1. buka control panel >> Administrative Tools >> services
2. cari print spooler kemudian klik dua kali
3. set startup type ke “Automatic”
4. klik tombol “Start”

Maksunya apa ya startup type itu?

Untuk pemula memang terdapat istilah-istilah komputer yang membingungkan, tapi ini memang harus diketahui. Kamu dapat melihat tulisan saya mengenai startup ini.

The 50th Symposium on Foundations of Computer Science (FOCS)

Posted by Jon Feldman and Vahab Mirrokni, Google Research, NY

The 50th Annual Symposium on Foundations of Computer Science (FOCS) was held a couple of weeks ago in Atlanta. This conference (along with STOC and SODA) is one of the the major venues for recent advances in algorithm design and computational complexity. Computation is now a major ingredient of almost any field of science, without which many of the recent achievements would not have happened (e.g., Human Genome decoding). As the 50th anniversary of FOCS, this event was a landmark in the history of foundations of computer science. Below, we give a quick report of some highlights from this event and our research contribution:

• In a special one-day workshop before the conference, four pioneer researchers of theoretical computer science talked about historical, contemporary, and future research directions. Richard Karp gave an interesting survey on "Great Algorithms," where he discussed algorithms such as the simplex method for linear programming and fast matrix multiplication; he gave examples of algorithms with high impact on our daily lives, as well as algorithms that changed our way of thinking about computation. As an example of an algorithm with great impact on our lives, he gave the PageRank algorithm designed by Larry and Sergey at Google. Mihalis Yannakakis discussed the recent impact of studying game theory and equilibria from a computational perspective and discussed the relationships between the complexity classes PLS, FIXP, and PPAD. In particular he discussed completeness of computing pure and mixed Nash equilibria for PLS, and for FIXP and PPAD respectively. Noga Alon gave a technical talk about efficient routing on expander graphs, and presented a clever combinatorial algorithm to route demand between multiple pairs of nodes in an online fashion. Finally, Manuel Blum gave an entertaining and mind-stimulating talk about the potential contribution of computer science to the study of human consciousness, educating the community on the notion of "Global Workspace Theory."
• The conference program included papers in areas related to algorithm and data structure design, approximation and optimization, computational complexity, learning theory, cryptography, quantum computing, and computational economics. The best student paper awards went to Alexander Shrstov and Jonah Sherman for their papers "The intersection of two halfspaces has high threshold degree" and "Breaking the multicommodity flow barrier for O(sqrt(log n))-approximations to sparsest cut." The program included many interesting results like the polynomial-time smoothed analysis of the k-means clustering algorithm (by David Arthur, Bodo Manthey and Heiko Roeglin), and a stronger version of Azuma's concentration inequality used to show optimal bin-packing bounds (by Ravi Kannan). The former paper studies a variant of the well-known k-means algorithm that works well in practice, but whose worst-case running time can be exponential. By analyzing this algorithm in the smoothed analysis framework, the paper gives a new explanation for the success of the k-means algorithm in practice.
• We presented our recent result about online stochastic matching in which we improve the approximation factor of computing the maximum cardinality matching in an online stochastic setting. The original motivation for this work is online ad allocation which was discussed in a previous blog post. In this algorithm, using our prior on the input (or our historical stochastic information), we compute two disjoint solutions to an instance that we expect to happen; then online, we try one solution first, and if it fails, we try the the other solution. The algorithm is inspired by the idea of "power of two choices," which has proved useful in online load balancing and congestion control. Using this method, we improve the worst-case guarantee of the online algorithm past the notorious barrier of 1-1/e. We hope that employing this idea and our technique for online stochastic optimization will find other applications in related stochastic resource allocation problems.
  

The FOCS conference (along with STOC and SODA) has been the birthplace for many popular data structures and efficient algorithms, with far-reaching applications. Many researchers and engineers at Google are trained in these research communities, and apply these techniques whenever possible. Google researchers will continue to contribute and learn from these conferences.

A 2x Faster Web

Posted by Mike Belshe, Software Engineer and Roberto Peon, Software Engineer

Cross-posted with the Chromium Blog.

Today we'd like to share with the web community information about SPDY, pronounced "SPeeDY", an early-stage research project that is part of our effort to make the web faster. SPDY is at its core an application-layer protocol for transporting content over the web. It is designed specifically for minimizing latency through features such as multiplexed streams, request prioritization and HTTP header compression.

We started working on SPDY while exploring ways to optimize the way browsers and servers communicate. Today, web clients and servers speak HTTP. HTTP is an elegantly simple protocol that emerged as a web standard in 1996 after a series of experiments. HTTP has served the web incredibly well. We want to continue building on the web's tradition of experimentation and optimization, to further support the evolution of websites and browsers. So over the last few months, a few of us here at Google have been experimenting with new ways for web browsers and servers to speak to each other, resulting in a prototype web server and Google Chrome client with SPDY support.

So far we have only tested SPDY in lab conditions. The initial results are very encouraging: when we download the top 25 websites over simulated home network connections, we see a significant improvement in performance - pages loaded up to 55% faster. There is still a lot of work we need to do to evaluate the performance of SPDY in real-world conditions. However, we believe that we have reached the stage where our small team could benefit from the active participation, feedback and assistance of the web community.

For those of you who would like to learn more and hopefully contribute to our experiment, we invite you to review our early stage documentation, look at our current code and provide feedback through the Chromium Google Group.

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Google Search by Voice Learns Mandarin Chinese

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.