- 7 Great Educational Search Engines for Students
- 2. Microsoft Academic
- 7 Great Educational Search Engines for Students
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. 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: 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.
7 Great Educational Search Engines for Students
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.
2. Microsoft Academic
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.
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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. 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 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. 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.
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. 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. 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. 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.slurothlichhand.gq
7 Great Educational Search Engines for Students
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. 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.
Can they be evaluated with Can they be evaluated with standard evaluation tools? Even though some evaluation methods have been proposed in the literature it is still not clear yet which of them. Evaluation of an Internet-based smoking cessation program: Lessons learned from a pilot study. Library Network in Indonesia. Emphasises our effort and the current status of the Indonesian library network. The Indonesian AI3 network, supported by the AI3 project, has become the main backbone of the network.
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Library network in Indonesia. Rajesh K. Keywords positoin report.
NGS: a framework for multi-domain query answering. Within the last two years alone, the Department of Justice has held hearings on the appropriate scope of Section 2 of the Sherman Act and has issued, then Within the last two years alone, the Department of Justice has held hearings on the appropriate scope of Section 2 of the Sherman Act and has issued, then repudiated, a comprehensive Report.
During the same time, the European Commission has become an aggressive leader in single-firm conduct enforcement by bringing abuse of dominance actions and assessing heavy fines against firms including Qualcomm, Intel, and Microsoft. In the United States, We describe the underlying architecture, distributed database, along with the complex We describe the underlying architecture, distributed database, along with the complex search algorithms based on multimodal workflows, aggregating index and content information.
We exemplify the utilization of such a distributed infrastructure in diabetic retinopathy research and clinical trials, for the. In this paper we present mechanisms for imaging and spectral data dis- covery, as applied to the early detection of pathologic mechanisms underlying dia- betic retinopathy in research and clinical trial scenarios. We discuss the Alchemist We discuss the Alchemist framework, built using a generic peer-to-peer architecture, supporting distributed database queries and complex search algorithms based on workflow.
The Alchemist is a domain-independent search.