- Corrections
Search Help
Get the most out of Google Scholar with some helpful tips on searches, email alerts, citation export, and more.
Finding recent papers
Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:
- click "Since Year" to show only recently published papers, sorted by relevance;
- click "Sort by date" to show just the new additions, sorted by date;
- click the envelope icon to have new results periodically delivered by email.
Locating the full text of an article
Abstracts are freely available for most of the articles. Alas, reading the entire article may require a subscription. Here're a few things to try:
- click a library link, e.g., "[email protected]", to the right of the search result;
- click a link labeled [PDF] to the right of the search result;
- click "All versions" under the search result and check out the alternative sources;
- click "Related articles" or "Cited by" under the search result to explore similar articles.
If you're affiliated with a university, but don't see links such as "[email protected]", please check with your local library about the best way to access their online subscriptions. You may need to do search from a computer on campus, or to configure your browser to use a library proxy.
Getting better answers
If you're new to the subject, it may be helpful to pick up the terminology from secondary sources. E.g., a Wikipedia article for "overweight" might suggest a Scholar search for "pediatric hyperalimentation".
If the search results are too specific for your needs, check out what they're citing in their "References" sections. Referenced works are often more general in nature.
Similarly, if the search results are too basic for you, click "Cited by" to see newer papers that referenced them. These newer papers will often be more specific.
Explore! There's rarely a single answer to a research question. Click "Related articles" or "Cited by" to see closely related work, or search for author's name and see what else they have written.
Searching Google Scholar
Use the "author:" operator, e.g., author:"d knuth" or author:"donald e knuth".
Put the paper's title in quotations: "A History of the China Sea".
You'll often get better results if you search only recent articles, but still sort them by relevance, not by date. E.g., click "Since 2018" in the left sidebar of the search results page.
To see the absolutely newest articles first, click "Sort by date" in the sidebar. If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.
Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labelled "Year" right below the search button.
Select the "Case law" option on the homepage or in the side drawer on the search results page.
It finds documents similar to the given search result.
It's in the side drawer. The advanced search window lets you search in the author, title, and publication fields, as well as limit your search results by date.
Select the "Case law" option and do a keyword search over all jurisdictions. Then, click the "Select courts" link in the left sidebar on the search results page.
Tip: To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection.
Access to articles
For each Scholar search result, we try to find a version of the article that you can read. These access links are labelled [PDF] or [HTML] and appear to the right of the search result. For example:
A paper that you need to read
Access links cover a wide variety of ways in which articles may be available to you - articles that your library subscribes to, open access articles, free-to-read articles from publishers, preprints, articles in repositories, etc.
When you are on a campus network, access links automatically include your library subscriptions and direct you to subscribed versions of articles. On-campus access links cover subscriptions from primary publishers as well as aggregators.
Off-campus access
Off-campus access links let you take your library subscriptions with you when you are at home or traveling. You can read subscribed articles when you are off-campus just as easily as when you are on-campus. Off-campus access links work by recording your subscriptions when you visit Scholar while on-campus, and looking up the recorded subscriptions later when you are off-campus.
We use the recorded subscriptions to provide you with the same subscribed access links as you see on campus. We also indicate your subscription access to participating publishers so that they can allow you to read the full-text of these articles without logging in or using a proxy. The recorded subscription information expires after 30 days and is automatically deleted.
In addition to Google Scholar search results, off-campus access links can also appear on articles from publishers participating in the off-campus subscription access program. Look for links labeled [PDF] or [HTML] on the right hand side of article pages.
Anne Author , John Doe , Jane Smith , Someone Else
In this fascinating paper, we investigate various topics that would be of interest to you. We also describe new methods relevant to your project, and attempt to address several questions which you would also like to know the answer to. Lastly, we analyze …
You can disable off-campus access links on the Scholar settings page . Disabling off-campus access links will turn off recording of your library subscriptions. It will also turn off indicating subscription access to participating publishers. Once off-campus access links are disabled, you may need to identify and configure an alternate mechanism (e.g., an institutional proxy or VPN) to access your library subscriptions while off-campus.
Email Alerts
Do a search for the topic of interest, e.g., "M Theory"; click the envelope icon in the sidebar of the search results page; enter your email address, and click "Create alert". We'll then periodically email you newly published papers that match your search criteria.
No, you can enter any email address of your choice. If the email address isn't a Google account or doesn't match your Google account, then we'll email you a verification link, which you'll need to click to start receiving alerts.
This works best if you create a public profile , which is free and quick to do. Once you get to the homepage with your photo, click "Follow" next to your name, select "New citations to my articles", and click "Done". We will then email you when we find new articles that cite yours.
Search for the title of your paper, e.g., "Anti de Sitter space and holography"; click on the "Cited by" link at the bottom of the search result; and then click on the envelope icon in the left sidebar of the search results page.
First, do a search for your colleague's name, and see if they have a Scholar profile. If they do, click on it, click the "Follow" button next to their name, select "New articles by this author", and click "Done".
If they don't have a profile, do a search by author, e.g., [author:s-hawking], and click on the mighty envelope in the left sidebar of the search results page. If you find that several different people share the same name, you may need to add co-author names or topical keywords to limit results to the author you wish to follow.
We send the alerts right after we add new papers to Google Scholar. This usually happens several times a week, except that our search robots meticulously observe holidays.
There's a link to cancel the alert at the bottom of every notification email.
If you created alerts using a Google account, you can manage them all here . If you're not using a Google account, you'll need to unsubscribe from the individual alerts and subscribe to the new ones.
Google Scholar library
Google Scholar library is your personal collection of articles. You can save articles right off the search page, organize them by adding labels, and use the power of Scholar search to quickly find just the one you want - at any time and from anywhere. You decide what goes into your library, and we’ll keep the links up to date.
You get all the goodies that come with Scholar search results - links to PDF and to your university's subscriptions, formatted citations, citing articles, and more!
Library help
Find the article you want to add in Google Scholar and click the “Save” button under the search result.
Click “My library” at the top of the page or in the side drawer to view all articles in your library. To search the full text of these articles, enter your query as usual in the search box.
Find the article you want to remove, and then click the “Delete” button under it.
- To add a label to an article, find the article in your library, click the “Label” button under it, select the label you want to apply, and click “Done”.
- To view all the articles with a specific label, click the label name in the left sidebar of your library page.
- To remove a label from an article, click the “Label” button under it, deselect the label you want to remove, and click “Done”.
- To add, edit, or delete labels, click “Manage labels” in the left column of your library page.
Only you can see the articles in your library. If you create a Scholar profile and make it public, then the articles in your public profile (and only those articles) will be visible to everyone.
Your profile contains all the articles you have written yourself. It’s a way to present your work to others, as well as to keep track of citations to it. Your library is a way to organize the articles that you’d like to read or cite, not necessarily the ones you’ve written.
Citation Export
Click the "Cite" button under the search result and then select your bibliography manager at the bottom of the popup. We currently support BibTeX, EndNote, RefMan, and RefWorks.
Err, no, please respect our robots.txt when you access Google Scholar using automated software. As the wearers of crawler's shoes and webmaster's hat, we cannot recommend adherence to web standards highly enough.
Sorry, we're unable to provide bulk access. You'll need to make an arrangement directly with the source of the data you're interested in. Keep in mind that a lot of the records in Google Scholar come from commercial subscription services.
Sorry, we can only show up to 1,000 results for any particular search query. Try a different query to get more results.
Content Coverage
Google Scholar includes journal and conference papers, theses and dissertations, academic books, pre-prints, abstracts, technical reports and other scholarly literature from all broad areas of research. You'll find works from a wide variety of academic publishers, professional societies and university repositories, as well as scholarly articles available anywhere across the web. Google Scholar also includes court opinions and patents.
We index research articles and abstracts from most major academic publishers and repositories worldwide, including both free and subscription sources. To check current coverage of a specific source in Google Scholar, search for a sample of their article titles in quotes.
While we try to be comprehensive, it isn't possible to guarantee uninterrupted coverage of any particular source. We index articles from sources all over the web and link to these websites in our search results. If one of these websites becomes unavailable to our search robots or to a large number of web users, we have to remove it from Google Scholar until it becomes available again.
Our meticulous search robots generally try to index every paper from every website they visit, including most major sources and also many lesser known ones.
That said, Google Scholar is primarily a search of academic papers. Shorter articles, such as book reviews, news sections, editorials, announcements and letters, may or may not be included. Untitled documents and documents without authors are usually not included. Website URLs that aren't available to our search robots or to the majority of web users are, obviously, not included either. Nor do we include websites that require you to sign up for an account, install a browser plugin, watch four colorful ads, and turn around three times and say coo-coo before you can read the listing of titles scanned at 10 DPI... You get the idea, we cover academic papers from sensible websites.
That's usually because we index many of these papers from other websites, such as the websites of their primary publishers. The "site:" operator currently only searches the primary version of each paper.
It could also be that the papers are located on examplejournals.gov, not on example.gov. Please make sure you're searching for the "right" website.
That said, the best way to check coverage of a specific source is to search for a sample of their papers using the title of the paper.
Ahem, we index papers, not journals. You should also ask about our coverage of universities, research groups, proteins, seminal breakthroughs, and other dimensions that are of interest to users. All such questions are best answered by searching for a statistical sample of papers that has the property of interest - journal, author, protein, etc. Many coverage comparisons are available if you search for [allintitle:"google scholar"], but some of them are more statistically valid than others.
Currently, Google Scholar allows you to search and read published opinions of US state appellate and supreme court cases since 1950, US federal district, appellate, tax and bankruptcy courts since 1923 and US Supreme Court cases since 1791. In addition, it includes citations for cases cited by indexed opinions or journal articles which allows you to find influential cases (usually older or international) which are not yet online or publicly available.
Legal opinions in Google Scholar are provided for informational purposes only and should not be relied on as a substitute for legal advice from a licensed lawyer. Google does not warrant that the information is complete or accurate.
We normally add new papers several times a week. However, updates to existing records take 6-9 months to a year or longer, because in order to update our records, we need to first recrawl them from the source website. For many larger websites, the speed at which we can update their records is limited by the crawl rate that they allow.
Inclusion and Corrections
We apologize, and we assure you the error was unintentional. Automated extraction of information from articles in diverse fields can be tricky, so an error sometimes sneaks through.
Please write to the owner of the website where the erroneous search result is coming from, and encourage them to provide correct bibliographic data to us, as described in the technical guidelines . Once the data is corrected on their website, it usually takes 6-9 months to a year or longer for it to be updated in Google Scholar. We appreciate your help and your patience.
If you can't find your papers when you search for them by title and by author, please refer your publisher to our technical guidelines .
You can also deposit your papers into your institutional repository or put their PDF versions on your personal website, but please follow your publisher's requirements when you do so. See our technical guidelines for more details on the inclusion process.
We normally add new papers several times a week; however, it might take us some time to crawl larger websites, and corrections to already included papers can take 6-9 months to a year or longer.
Google Scholar generally reflects the state of the web as it is currently visible to our search robots and to the majority of users. When you're searching for relevant papers to read, you wouldn't want it any other way!
If your citation counts have gone down, chances are that either your paper or papers that cite it have either disappeared from the web entirely, or have become unavailable to our search robots, or, perhaps, have been reformatted in a way that made it difficult for our automated software to identify their bibliographic data and references. If you wish to correct this, you'll need to identify the specific documents with indexing problems and ask your publisher to fix them. Please refer to the technical guidelines .
Please do let us know . Please include the URL for the opinion, the corrected information and a source where we can verify the correction.
We're only able to make corrections to court opinions that are hosted on our own website. For corrections to academic papers, books, dissertations and other third-party material, click on the search result in question and contact the owner of the website where the document came from. For corrections to books from Google Book Search, click on the book's title and locate the link to provide feedback at the bottom of the book's page.
General Questions
These are articles which other scholarly articles have referred to, but which we haven't found online. To exclude them from your search results, uncheck the "include citations" box on the left sidebar.
First, click on links labeled [PDF] or [HTML] to the right of the search result's title. Also, check out the "All versions" link at the bottom of the search result.
Second, if you're affiliated with a university, using a computer on campus will often let you access your library's online subscriptions. Look for links labeled with your library's name to the right of the search result's title. Also, see if there's a link to the full text on the publisher's page with the abstract.
Keep in mind that final published versions are often only available to subscribers, and that some articles are not available online at all. Good luck!
Technically, your web browser remembers your settings in a "cookie" on your computer's disk, and sends this cookie to our website along with every search. Check that your browser isn't configured to discard our cookies. Also, check if disabling various proxies or overly helpful privacy settings does the trick. Either way, your settings are stored on your computer, not on our servers, so a long hard look at your browser's preferences or internet options should help cure the machine's forgetfulness.
Not even close. That phrase is our acknowledgement that much of scholarly research involves building on what others have already discovered. It's taken from Sir Isaac Newton's famous quote, "If I have seen further, it is by standing on the shoulders of giants."
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Research and Writing Guides
Writing a paper? Don't get lost.
The top list of academic search engines
Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind pay walls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of academic search engines.
- 1. Google Scholar
Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.
- Coverage: approx. 200 million articles
- Abstracts: only a snippet of the abstract is available
- Related articles: ✔
- References: ✔
- Cited by: ✔
- Links to full text: ✔
- Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX
- 2. Microsoft Academic
It's Microsoft answer to Google Scholar. Microsoft Academic takes a different approach and generates for each paper that is indexed an overview page that allows to easily explore top citing articles and references of the article.
- Coverage: approx. 210 million articles
- Abstracts: ✔
- Export formats: APA, MLA, BibTeX
BASE is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).
- Coverage: approx. 136 million articles (contains duplicates)
- Related articles: ✘
- References: ✘
- Cited by: ✘
- Export formats: RIS, BibTeX
CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.
- Coverage: approx. 136 million articles
- Links to full text: ✔ (all articles in CORE are open access)
- Export formats: BibTeX
- 5. Science.gov
Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!
- Coverage: approx. 200 million articles and reports
- Links to full text: ✔ (available for some databases)
- Export formats: APA, MLA, RIS, BibTeX (available for some databases)
- 6. Semantic Scholar
Semantic Scholar is the new kid on the block. It's mission is to provide more relevant and impactful search results using AI powered algorithms that find hidden connections and links between research topics.
- Coverage: approx. 40 million articles
- Export formats: APA, MLA, Chicago, BibTeX
- 7. Baidu Scholar
Although Baidu Scholar's interface is in Chinese it's index contains research papers in English as well as Chinese.
- Coverage: no detailed statistics available, approx. 100 million articles
- Abstracts: only snippets of the abstract are available
- Export formats: APA, MLA, RIS, BibTeX
RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.
- Coverage: no detailed statistics available, approx. 1 billion documents
- Abstracts: only snippets of the article are available
- Export formats: not available
- Frequently Asked Questions about academic search enginces
Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.
BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).
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Publication database
Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
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Algorithms and theory.
Google’s mission presents many exciting algorithmic and optimization challenges across different product areas including Search, Ads, Social, and Google Infrastructure. These include optimizing internal systems such as scheduling the machines that power the numerous computations done each day, as well as optimizations that affect core products and users, from online allocation of ads to page-views to automatic management of ad campaigns, and from clustering large-scale graphs to finding best paths in transportation networks. Other than employing new algorithmic ideas to impact millions of users, Google researchers contribute to the state-of-the-art research in these areas by publishing in top conferences and journals.
Data Management
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets , Docs, and many others). The overarching goal is to create a plethora of structured data on the Web that maximally help Google users consume, interact and explore information. Through those projects, we study various cutting-edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc., using a variety of techniques, such as information retrieval, data mining and machine learning.
A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management.
Furthermore, Data Management research across Google allows us to build technologies that power Google's largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service. Some examples of such technologies include F1 , the database serving our ads infrastructure; Mesa , a petabyte-scale analytic data warehousing system; and Dremel , for petabyte-scale data processing with interactive response times. Dremel is available for external customers to use as part of Google Cloud’s BigQuery .
Data Mining and Modeling
The proliferation of machine learning means that learned classifiers lie at the core of many products across Google. However, questions in practice are rarely so clean as to just to use an out-of-the-box algorithm. A big challenge is in developing metrics, designing experimental methodologies, and modeling the space to create parsimonious representations that capture the fundamentals of the problem. These problems cut across Google’s products and services, from designing experiments for testing new auction algorithms to developing automated metrics to measure the quality of a road map.
Data mining lies at the heart of many of these questions, and the research done at Google is at the forefront of the field. Whether it is finding more efficient algorithms for working with massive data sets, developing privacy-preserving methods for classification, or designing new machine learning approaches, our group continues to push the boundary of what is possible.
Distributed Systems and Parallel Computing
No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.
From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model .
Economics and Electronic Commerce
Google is a global leader in electronic commerce. Not surprisingly, it devotes considerable attention to research in this area. Topics include 1) auction design, 2) advertising effectiveness, 3) statistical methods, 4) forecasting and prediction, 5) survey research, 6) policy analysis and a host of other topics. This research involves interdisciplinary collaboration among computer scientists, economists, statisticians, and analytic marketing researchers both at Google and academic institutions around the world.
A major challenge is in solving these problems at very large scales. For example, the advertising market has billions of transactions daily, spread across millions of advertisers. It presents a unique opportunity to test and refine economic principles as applied to a very large number of interacting, self-interested parties with a myriad of objectives.
It is remarkable how some of the fundamental problems Google grapples with are also some of the hardest research problems in the academic community. At Google, this research translates direction into practice, influencing how production systems are designed and used.
Education Innovation
Our Education Innovation research area includes publications on: online learning at scale, educational technology (which is any technology that supports teaching and learning), curriculum and programming tools for computer science education, diversity and broadening participation in computer science the hiring and onboarding process at Google.
General Science
We aim to transform scientific research itself. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning (including deep learning). We aim to accelerate scientific research by applying Google’s computational power and techniques in areas such as drug discovery, biological pathway modeling, microscopy, medical diagnostics, material science, and agriculture. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.
Hardware and Architecture
The machinery that powers many of our interactions today — Web search, social networking, email, online video, shopping, game playing — is made of the smallest and the most massive computers. The smallest part is your smartphone, a machine that is over ten times faster than the iconic Cray-1 supercomputer. The capabilities of these remarkable mobile devices are amplified by orders of magnitude through their connection to Web services running on building-sized computing systems that we call Warehouse-scale computers (WSCs).
Google’s engineers and researchers have been pioneering both WSC and mobile hardware technology with the goal of providing Google programmers and our Cloud developers with a unique computing infrastructure in terms of scale, cost-efficiency, energy-efficiency, resiliency and speed. The tight collaboration among software, hardware, mechanical, electrical, environmental, thermal and civil engineers result in some of the most impressive and efficient computers in the world.
Human-Computer Interaction and Visualization
HCI researchers at Google have enormous potential to impact the experience of Google users as well as conduct innovative research. Grounded in user behavior understanding and real use, Google’s HCI researchers invent, design, build and trial large-scale interactive systems in the real world. We declare success only when we positively impact our users and user communities, often through new and improved Google products. HCI research has fundamentally contributed to the design of Search, Gmail, Docs, Maps, Chrome, Android, YouTube, serving over a billion daily users. We are engaged in a variety of HCI disciplines such as predictive and intelligent user interface technologies and software, mobile and ubiquitous computing, social and collaborative computing, interactive visualization and visual analytics. Many projects heavily incorporate machine learning with HCI, and current projects include predictive user interfaces; recommenders for content, apps, and activities; smart input and prediction of text on mobile devices; user engagement analytics; user interface development tools; and interactive visualization of complex data.
Information Retrieval and the Web
The science surrounding search engines is commonly referred to as information retrieval, in which algorithmic principles are developed to match user interests to the best information about those interests.
Google started as a result of our founders' attempt to find the best matching between the user queries and Web documents, and do it really fast. During the process, they uncovered a few basic principles: 1) best pages tend to be those linked to the most; 2) best description of a page is often derived from the anchor text associated with the links to a page. Theories were developed to exploit these principles to optimize the task of retrieving the best documents for a user query.
Search and Information Retrieval on the Web has advanced significantly from those early days: 1) the notion of "information" has greatly expanded from documents to much richer representations such as images, videos, etc., 2) users are increasingly searching on their Mobile devices with very different interaction characteristics from search on the Desktops; 3) users are increasingly looking for direct information, such as answers to a question, or seeking to complete tasks, such as appointment booking. Through our research, we are continuing to enhance and refine the world's foremost search engine by aiming to scientifically understand the implications of those changes and address new challenges that they bring.
Machine Intelligence
Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.
Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.
Machine Perception
Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: content-based search in Google Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for Google Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of Google Photos. The ability to mine meaningful information from multimedia is broadly applied throughout Google.
Machine Translation
Machine Translation is an excellent example of how cutting-edge research and world-class infrastructure come together at Google. We focus our research efforts on developing statistical translation techniques that improve with more data and generalize well to new languages. Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality. This research backs the translations served at translate.google.com, allowing our users to translate text, web pages and even speech. Deployed within a wide range of Google services like GMail , Books , Android and web search , Google Translate is a high-impact, research-driven product that bridges language barriers and makes it possible to explore the multilingual web in 90 languages. Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages.
Mobile Systems
Mobile devices are the prevalent computing device in many parts of the world, and over the coming years it is expected that mobile Internet usage will outpace desktop usage worldwide. Google is committed to realizing the potential of the mobile web to transform how people interact with computing technology. Google engineers and researchers work on a wide range of problems in mobile computing and networking, including new operating systems and programming platforms (such as Android and ChromeOS); new interaction paradigms between people and devices; advanced wireless communications; and optimizing the web for mobile settings. In addition, many of Google’s core product teams, such as Search, Gmail, and Maps, have groups focused on optimizing the mobile experience, making it faster and more seamless. We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware. The tremendous scale of Google’s products and the Android and Chrome platforms make this a very exciting place to work on these problems.
Some representative projects include mobile web performance optimization, new features in Android to greatly reduce network data usage and energy consumption; new platforms for developing high performance web applications on mobile devices; wireless communication protocols that will yield vastly greater performance over today’s standards; and multi-device interaction based on Android, which is now available on a wide variety of consumer electronics.
Natural Language Processing
Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.
Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.
Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.
On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.
Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.
Networking is central to modern computing, from connecting cell phones to massive Cloud-based data stores to the interconnect for data centers that deliver seamless storage and fine-grained distributed computing at the scale of entire buildings. With an understanding that our distributed computing infrastructure is a key differentiator for the company, Google has long focused on building network infrastructure to support our scale, availability, and performance needs.
Our research combines building and deploying novel networking systems at massive scale, with recent work focusing on fundamental questions around data center architecture, wide area network interconnects, Software Defined Networking control and management infrastructure, as well as congestion control and bandwidth allocation. By publishing our findings at premier research venues, we continue to engage both academic and industrial partners to further the state of the art in networked systems.
Quantum Computing
Quantum Computing merges two great scientific revolutions of the 20th century: computer science and quantum physics. Quantum physics is the theoretical basis of the transistor, the laser, and other technologies which enabled the computing revolution. But on the algorithmic level, today's computing machinery still operates on "classical" Boolean logic. Quantum Computing is the design of hardware and software that replaces Boolean logic by quantum law at the algorithmic level. For certain computations such as optimization, sampling, search or quantum simulation this promises dramatic speedups. We are particularly interested in applying quantum computing to artificial intelligence and machine learning. This is because many tasks in these areas rely on solving hard optimization problems or performing efficient sampling.
Having a machine learning agent interact with its environment requires true unsupervised learning, skill acquisition, active learning, exploration and reinforcement, all ingredients of human learning that are still not well understood or exploited through the supervised approaches that dominate deep learning today.
Our goal is to improve robotics via machine learning, and improve machine learning via robotics. We foster close collaborations between machine learning researchers and roboticists to enable learning at scale on real and simulated robotic systems.
Security, Privacy and Abuse Prevention
The Internet and the World Wide Web have brought many changes that provide huge benefits, in particular by giving people easy access to information that was previously unavailable, or simply hard to find. Unfortunately, these changes have raised many new challenges in the security of computer systems and the protection of information against unauthorized access and abusive usage. At Google, our primary focus is the user, and his/her safety. We have people working on nearly every aspect of security, privacy, and anti-abuse including access control and information security, networking, operating systems, language design, cryptography, fraud detection and prevention, spam and abuse detection, denial of service, anonymity, privacy-preserving systems, disclosure controls, as well as user interfaces and other human-centered aspects of security and privacy. Our security and privacy efforts cover a broad range of systems including mobile, cloud, distributed, sensors and embedded systems, and large-scale machine learning.
Software Engineering
At Google, we pride ourselves on our ability to develop and launch new products and features at a very fast pace. This is made possible in part by our world-class engineers, but our approach to software development enables us to balance speed and quality, and is integral to our success. Our obsession for speed and scale is evident in our developer infrastructure and tools. Developers across the world continually write, build, test and release code in multiple programming languages like C++, Java, Python, Javascript and others, and the Engineering Tools team, for example, is challenged to keep this development ecosystem running smoothly. Our engineers leverage these tools and infrastructure to produce clean code and keep software development running at an ever-increasing scale. In our publications, we share associated technical challenges and lessons learned along the way.
Software Systems
Delivering Google's products to our users requires computer systems that have a scale previously unknown to the industry. Building on our hardware foundation, we develop technology across the entire systems stack, from operating system device drivers all the way up to multi-site software systems that run on hundreds of thousands of computers. We design, build and operate warehouse-scale computer systems that are deployed across the globe. We build storage systems that scale to exabytes, approach the performance of RAM, and never lose a byte. We design algorithms that transform our understanding of what is possible. Thanks to the distributed systems we provide our developers, they are some of the most productive in the industry. And we write and publish research papers to share what we have learned, and because peer feedback and interaction helps us build better systems that benefit everybody.
Speech Processing
Our goal in Speech Technology Research is to make speaking to devices--those around you, those that you wear, and those that you carry with you--ubiquitous and seamless.
Our research focuses on what makes Google unique: computing scale and data. Using large scale computing resources pushes us to rethink the architecture and algorithms of speech recognition, and experiment with the kind of methods that have in the past been considered prohibitively expensive. We also look at parallelism and cluster computing in a new light to change the way experiments are run, algorithms are developed and research is conducted. The field of speech recognition is data-hungry, and using more and more data to tackle a problem tends to help performance but poses new challenges: how do you deal with data overload? How do you leverage unsupervised and semi-supervised techniques at scale? Which class of algorithms merely compensate for lack of data and which scale well with the task at hand? Increasingly, we find that the answers to these questions are surprising, and steer the whole field into directions that would never have been considered, were it not for the availability of significantly higher orders of magnitude of data.
We are also in a unique position to deliver very user-centric research. Researchers are able to conduct live experiments to test and benchmark new algorithms directly in a realistic controlled environment. Whether these are algorithmic performance improvements or user experience and human-computer interaction studies, we focus on solving real problems and with real impact for users.
We have a huge commitment to the diversity of our users, and have made it a priority to deliver the best performance to every language on the planet. We currently have systems operating in more than 55 languages, and we continue to expand our reach to more users. The challenges of internationalizing at scale is immense and rewarding. Many speakers of the languages we reach have never had the experience of speaking to a computer before, and breaking this new ground brings up new research on how to better serve this wide variety of users. Combined with the unprecedented translation capabilities of Google Translate, we are now at the forefront of research in speech-to-speech translation and one step closer to a universal translator.
Indexing and transcribing the web’s audio content is another challenge we have set for ourselves, and is nothing short of gargantuan, both in scope and difficulty. The videos uploaded every day on YouTube range from lectures, to newscasts, music videos and, of course, cat videos. Making sense of them takes the challenges of noise robustness, music recognition, speaker segmentation, language detection to new levels of difficulty. The potential payoff is immense: imagine making every lecture on the web accessible to every language. This is the kind of impact for which we are striving.
Health & Bioscience
Research in health and biomedical sciences has a unique potential to improve peoples’ lives, and includes work ranging from basic science that aims to understand biology, to diagnosing individuals’ diseases, to epidemiological studies of whole populations.
We recognize that our strengths in machine learning, large-scale computing, and human-computer interaction can help accelerate the progress of research in this space. By collaborating with world-class institutions and researchers and engaging in both early-stage research and late-stage work, we hope to help people live healthier, longer, and more productive lives.
Responsible AI
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Learn more about how we do research
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work
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