Book title: Cognitive Approach to Natural Language Processing Authors: Bernadette Sharp, Florence Sedes and Wieslaw Lubaszewski
As natural language processing spans many different disciplines, it is sometimes difficult to understand the contributions and the challenges that each of them presents.
This book explores the special relationship between natural language processing and cognitive science, and the contribution of computer science to these two fields.
It is based on the recent research papers submitted at the international workshops of Natural Language and Cognitive Science which was launched in 2004 in an effort to bring together natural language researchers, computer scientists, and cognitive and linguistic scientists to collaborate together and advance research in natural language processing.
The chapters cover areas related to language understanding, language generation, word association, word sense disambiguation, word predictability, text production and authorship attribution.
This book will be relevant to students and researchers interested in the interdisciplinary nature of language processing.
A team of researchers from Los Alamos National Laboratory and Curtin University in Australia developed a theoretical model to forecast the fundamental chemical reactions involving molecular hydrogen (H2), which after many decades and attempts by scientists had remained largely unpredicted and unsolved
“Chemical reactions are the basis of life so predicting what happens during these reactions is of great importance to science and has major implications in innovation, industry and medicine,” said Mark Zammit, a post-doctorate fellow in the Physics and Chemistry of Materials group at Los Alamos National Laboratory. “Our model is the first to very accurately calculate the probability of fundamental electron-molecular hydrogen reactions.” Continue reading New model predicts once-mysterious chemical reactions
DataRobot customers have surpassed 110 million predictive models using enterprise machine learning platform
Machine learning and data science startup, DataRobot, says its customers have developed more than 110 million models using the SaaS version of the DataRobot automated machine learning platform.
The company says it serves as validation of the company’s mission to “bring automated machine learning to every corner of the enterprise” and made the announcement at the Strata + Hadoop World session, Data Science for Executives, delivered by DataRobot CEO and co-founder, Jeremy Achin.
Enterprises across every industry are embracing machine learning to remain competitive in today’s data-driven world. By allowing more professionals across an organization to use advanced algorithms to derive insights from data, machine learning is helping to alleviate issues caused by a critical shortage of data scientists. Continue reading DataRobot surpass 110 million predictive models
By Sundeep Sanhavi, CEO of Data RPM, who claims data science and machine learning will save lives in this exclusive article for Robotics and Automation News
Recalls happen all too frequently, often as a result of some horrendous accident or incident. But there are ways in which the predictive qualities of data science and machine learning can relegate recalls to the annals of history.
“A new car built by my company leaves somewhere traveling at 60 mph,” explains the Narrator in the film adaptation of Chuck Palahniuk’s Fight Club.
“The rear differential locks up. The car crashes and burns with everyone trapped inside. Now, should we initiate a recall? Take the number of vehicles in the field, A, multiply by the probable rate of failure, B, multiply by the average out-of-court settlement, C. A times B times C equals X. If X is less than the cost of a recall, we don’t do one.”
Laurel Riek, a roboticist at the UC San Diego, will lead a three-year, $1 million project funded by the National Science Foundation to help change the role of robots in factories and make it easier for machines to work alongside people.
The goal of the project is to design an intelligent material delivery system, which supports and closely integrates with skilled workers in factories.
By Abdul Razack, SVP of platforms, big data and analytics at Infosys
In 2011, Apple’s Siri began guiding, following, organizing, informing, taking notes and tailoring search results for millions of mobile users worldwide. She was one of the first mainstream machine learning tools powered by Artificial Intelligence (AI). And though AI has been around for decades behind the scenes and in academic circles, it was the first time the wider public took note of all the things a computer or personal device can learn to do.
Only in the 21st century has AI come to maturity, and today it is completely changing the way the working world functions. AI is everywhere around us, including at the heart of our discussions on innovation.
We are learning to capture this opportunity in the business world, in order to accelerate growth, and only by embracing technology-led innovation will we be able to unleash our true human potential. Routine tasks, which can consume us by eating away at time and resources, are well-suited for AI and automation, freeing workers to pursue new ideas and new ways tackle challenges that can only be solved with human imagination. Continue reading Artificial Intelligence: Toward a technology-powered, human-led revolution
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