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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. 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: 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. 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: 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 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.

But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning.

We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning COCO dataset , a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains.

It contains convolutional layers, an atte Lukasz Kaiser , Aidan N. Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. 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.


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