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. 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. 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.
Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU. 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.
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. Our Education Innovation research area includes publications on: Google's highest leverage is in transforming scientific research itself.
Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning including deep learning. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.
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.
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. HCI researchers at Google have enormous potential to impact the experience of Google users as well as conduct innovative research.
We declare success only when we positively impact our users and user communities, often through new and improved Google products. 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.
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: 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: 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.
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.
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: 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 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.
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 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. We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware.
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. Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products. Researchers across Google are innovating across many domains. We challenge conventions and reimagine technology so that everyone can benefit. Heart attacks, strokes and other cardiovascular CV diseases continue to be among the top public health issues.
Assessing this risk is critical first step toward reducing the likelihood that a patient suffers a CV event in the future. Learn more about PAIR, an initiative using human-centered research and design to make AI partnerships productive, enjoyable, and fair. The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. We generate human-like speech from text using neural networks trained using only speech examples and corresponding text transcripts.
With motion photos, a new camera feature available on the Pixel 2 and Pixel 2 XL phones, you no longer have to choose between a photo and a video so every photo you take captures more of the moment. Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud.
TensorFlow Lattice is a set of prebuilt TensorFlow Estimators that are easy to use, and TensorFlow operators to build your own lattice models. Get to know Magenta, a research project exploring the role of machine learning in the process of creating art and music.
Our teams advance the state of the art through research, systems engineering, and collaboration across Google. Research Advancing the state of the art. We work on computer science problems that define the technology of today and tomorrow. End-to-End Learning of Semantic Grasping. Unsupervised Perceptual Rewards for Imitation Learning. Large-Scale Evolution of Image Classifiers. Our approach Google AI tackles the most challenging problems in computer science.
An open-source framework for NISQ algorithms. The Building Blocks of Interpretability. Generating Human-like Speech from Text. Open Sourcing the Hunt for Exoplanets. Behind the Motion Photos Technology in Pixel 2. Flexibility Empowered by Prior Knowledge. See our teams See our people. Join Us Our researchers work across the world.
Our global reach means that research teams across the company tackle tough problems together.
- INTRODUCTION OF GOOGLE: Figure 1: GOOGLE LOGO (olimpiadageograficzna2015.ga, ) Google is considered as the leading search engine around the globe. The Google Inc was founded in the year by the graduate students of Stanford University which are .
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Google Google is named after the mathematical term "googol,” defined as the value represented by a one followed by zeros. Google is the leading Internet search engine; its primary service is offering consumers targeted search results which is selected from more than 8 billion web pages. 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 .
Google is the most widely used search engine and is the most successful company in online business today. Google is a global technology leader focused on improving the ways people find and use information. Essay UK offers professional custom essay writing, dissertation writing and coursework writing service. Our work is high quality, plagiarism-free and delivered on time. Essay UK is a trading name of Student Academic Services Limited, a company registered in England and Wales under Company Number VAT Number