Latest research papers in artificial intelligence

Recently published articles from Artificial Intelligence. Leadership in singleton congestion games: What is hard and what is easy December From iterated revision to iterated contraction: Extending the Harper Identity December On rational entailment for Propositional Typicality Logic December Proving semantic properties as first-order satisfiability December Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods December Natural strategic ability December Approximate verification of strategic abilities under imperfect information December Democratic fair allocation of indivisible goods December Exploiting reverse target-side contexts for neural machine translation via asynchronous bidirectional decoding December Optimal cruiser-drone traffic enforcement under energy limitation December Coevolutionary systems and PageRank December Determining inference semantics for disjunctive logic programs - Open access December Knowing-how under uncertainty November A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation November Pareto optimal allocation under uncertain preferences: uncertainty models, algorithms, and complexity November How we designed winning algorithms for abstract argumentation and which insight we attained November Distributed monitoring of election winners November Forgetting auxiliary atoms in forks October Syntax-aware entity representations for neural relation extraction October 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.

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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.

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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.

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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.

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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.

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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.

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?

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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.

Recent Artificial Intelligence Articles

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. Abstract: In this paper, the researchers explore various text data augmentation techniques in text space and word embedding space. Abstract : This research paper described a personalised smart health monitoring device using wireless sensors and the latest technology.

Author : A. Anandhakumar and V. Nithin Meenashisundharam. Abstract : This article we discuss about Big data on IoT and how it is interrelated to each other along with the necessity of implementing Big data with IoT and its benefits, job market. Research Methodology : Machine learning, Deep Learning, and Artificial Intelligence are key technologies that are used to provide value-added applications along with IoT and big data in addition to being used in a stand-alone mod. When not writing, she can be seen either reading or staring at a flower.