1. 1-24 of 28 1 2 »
    1. How Neural Networks Think | Careers

      How Neural Networks Think | Careers

      Share Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data. During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another.

      Read Full Article
    2. Using Machine Learning to Improve Patient Care

      Using Machine Learning to Improve Patient Care

      Using Machine Learning to Improve Patient Care By MIT News Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory are investigating how computers can help medical personnel make better health-related decisions. Credit: Shutterstock Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory are investigating how computers can enhance medical decisions.

      Read Full Article
      Mentions: EHR
    3. Using Machine Learning to Improve Patient Care | Careers

      Using Machine Learning to Improve Patient Care | Careers

      Using Machine Learning to Improve Patient Care By MIT CSAIL Share Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals.

      Read Full Article
    4. The Future of Search Engines

      The Future of Search Engines

      The Future of Search Engines By Texas Advanced Computing Center August 9, 2017 Researchers at The University of Texas at Austin developed a method to incorporate information from WordNet into informational retrieval systems. Credit: VisuWords Researchers at the University of Texas at Austin (UT Austin) aim to improve information retrieval (IR) systems and enhance search engines by integrating artificial intelligence with annotation insights and information encoded in domain-specific resources.

      Read Full Article
    5. $2-Million Donation Supports Launch of Theoretical Machine Learning Program

      $2-Million Donation Supports Launch of Theoretical Machine Learning Program

      $2-Million Donation Supports Launch of Theoretical Machine Learning Program By Institute for Advanced Study August 7, 2017 A $2-million donation from Eric and Wendy Schmidt will support the launch of the Program in Theoretical Machine Learning in the Institute for Advanced Study’s School of Mathematics. Credit: IAS A $2-million donation from Eric and Wendy Schmidt will support the launch of the Program in Theoretical Machine Learning in the Institute for Advanced Study's School of Mathematics.

      Read Full Article
    6. Barbara Grosz Receives 2017 ACL Life Time Achievement Award

      Barbara Grosz Receives 2017 ACL Life Time Achievement Award

      Barbara Grosz Receives 2017 ACL Life Time Achievement Award By Association for Computational Linguistics August 4, 2017 The Association for Computational Linguistics has awarded its 2017 ACL Lifetime Achievement Award to Barbara Grosz. Credit: Association for Computational Linguistics During its 55th annual meeting in Vancouver, Canada (July 30 - August 4, 2017), the Association for Computational Linguistics (ACL) awarded its 2017 ACL Lifetime Achievement Award to professor Barbara Grosz.

      Read Full Article
    7. To Build a Smarter Chatbot, First Teach It a Second Language

      To Build a Smarter Chatbot, First Teach It a Second Language

      To Build a Smarter Chatbot, First Teach It a Second Language By Technology Review Salesforce researchers have found that first teaching a machine-learning algorithm to speak another language makes it perform better in learning other tasks. Credit: Absolute News Researchers at Salesforce have developed a method that could improve the performance of modern language programs by teaching a machine-learning algorithm to speak another language before educating it on other tasks.

      Read Full Article
      Mentions: Richard Socher
    8. The Rise of Social Bots | July 2016

      The Rise of Social Bots | July 2016

      Share Bots (short for software robots) have been around since the early days of computers. One compelling example of bots is chatbots, algorithms designed to hold a conversation with a human, as envisioned by Alan Turing in the 1950s. 33 The dream of designing a computer algorithm that passes the Turing test has driven artificial intelligence research for decades, as witnessed by initiatives like the Loebner Prize, awarding progress in natural language processing.

      Read Full Article
    9. CMU to Harness Power of Collaboration to Advance Artificial Intelligence

      CMU to Harness Power of Collaboration to Advance Artificial Intelligence

      CMU to Harness Power of Collaboration to Advance Artificial Intelligence By TribLIVE.com Carnegie Mellon University is creating what will be one of the largest and most experienced collaborative research groups in the world. Credit: nextstl.com Carnegie Mellon University (CMU) has announced a project to coordinate faculty, students, and staff working on artificial intelligence (AI) in various disciplines to establish one of the largest and most experienced collaborative research groups in the world. CMU Language Technologies Institute director Jaime Carbonell says the idea behind the initiative is to tap these resources' collective knowledge and expertise to push AI forward ...

      Read Full Article
    10. What Exactly Do You Mean When You Say 'Best'?

      What Exactly Do You Mean When You Say 'Best'?

      What Exactly Do You Mean When You Say 'Best'? By American Technion Society American Technion Society researchers have developed a system that can identify and interpret sarcastic statements on social media. Credit: Twitter Researchers at the American Technion Society have developed the sarcasm Sentimental Interpretation GeNerator (Sarcasm SIGN), a system for interpreting sarcastic statements in social media.

      Read Full Article
    11. The Dark Side of AI

      The Dark Side of AI

      Share Artificial intelligence has evolved to the point where machine learning technologies are being combined with natural language processing to imitate how a human interacts and thinks in a number of applications. But we still don't know the specific steps it takes when making a decision. Credit: Mopic/Shutterstock As far back as 1950, Alan Turing posed the question "Can machines think?"

      Read Full Article
    12. Deep Learning Takes on Translation

      Deep Learning Takes on Translation

      Deep Learning Takes on Translation By Don Monroe Communications of the ACM, Vol. 60 No. 6, Pages 12-14 10.1145/3077229 Share Over the last few years, data-intensive machine-learning techniques have made dramatic strides in speech recognition and image analysis. Now these methods are making significant advances on another long-standing challenge: translation of written text between languages.

      Read Full Article
    13. Modeling Gun Violence as a Contagion

      Modeling Gun Violence as a Contagion

      Modeling Gun Violence as a Contagion By Gregory Goth Researchers used machine learning to model the spread of gun violence in Chicago as a social contagion. Credit: Gary Waters/Getty Images Harvard University researcher Ben Green offers a succinct reason why his recent work addresses gun violence as a public health epidemic rather than as a matter strictly for law enforcement personnel "Certainly there is a role for police, though what we've seen is that you can't just arrest your way out of the problem of gun violence," Green, a doctoral student at Harvard, said. "And, notably, what ...

      Read Full Article
    14. U.S. Intelligence Seeks a Universal Translator for Text Search in Any Language

      U.S. Intelligence Seeks a Universal Translator for Text Search in Any Language

      U.S. Intelligence Seeks a Universal Translator for Text Search in Any Language By Ars Technica The Machine Translation for English Retrieval of Information in Any Language program aims to give researchers and analysts a tool to help them search for documents in any of the more than 7,000 languages spoken worldwide.

      Read Full Article
      Mentions: Iarpa
    15. Open Source Challenger Takes on Google Translate

      Open Source Challenger Takes on Google Translate

      Open Source Challenger Takes on Google Translate By InfoWorld A new machine translation framework developed by researchers at Harvard University and the company Systran could serve as an alternative to services such as Google Translate. Credit: CSO Staff A new open source machine translation framework could serve as an alternative to closed-source projects such as Google Translate.

      Read Full Article
    16. Making Sense of Exabytes of Data at SC16

      Making Sense of Exabytes of Data at SC16

      Making Sense of Exabytes of Data at SC16 By Andrew Rosenbloom Katharine Frase. Credit: Inside HPC “Think about how you got here,” said Katharine Frase, formerly IBM CTO and more recently head of strategy and business development for IBM’s Watson Education unit, in her keynote at the SC16 (supercomputing) conference in Salt Lake City last week.

      Read Full Article
    17. Google Translate: 'This Landmark Update Is Our Biggest Single Leap in 10 Years'

      Google Translate: 'This Landmark Update Is Our Biggest Single Leap in 10 Years'

      Share Google says it is has vastly improved the accuracy of Google Translate through its new Neural Machine Translation (NMT) system. NMT utilizes neural networks to train machines how to produce more natural, grammatically correct translations. The new system improves Google Translate's capacity for contextual translation by processing whole sentences or paragraphs at a time, rather than analyzing individual words.

      Read Full Article
    18. Mobile App Behavior Often Appears at Odds With Privacy Policies

      Mobile App Behavior Often Appears at Odds With Privacy Policies

      Mobile App Behavior Often Appears at Odds With Privacy Policies By Carnegie Mellon University Share Mobile applications' privacy policies often are inconsistent with how an app actually collects and shares users' personal information, according to an automated analysis system developed by Carnegie Mellon University (CMU). Several federal and state laws require mobile apps to have privacy policies, but these policies can be incomplete or missing entirely.

      Read Full Article
    19. Machines May Never Master the Distinctly Human Elements of Language

      Machines May Never Master the Distinctly Human Elements of Language

      Machines May Never Master the Distinctly Human Elements of Language By Quartz Artificial intelligence is difficult to develop because of the diffulty in learning and interpreting human language. Credit: Toru Hanai/Reuters Although Google's Neural Machine Translation system is able to produce translations that can sometimes match the accuracy of human translators, artificial intelligence (AI) might never be able to learn and completely understand human language.

      Read Full Article
    20. Making Computers Explain Themselves

      Making Computers Explain Themselves

      Share In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts. But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why.

      Read Full Article
    21. Cancer's Big Data Problem

      Cancer's Big Data Problem

      Share Data is pouring into the hands of cancer researchers, thanks to improvements in imaging, models, and understanding of genetics. Today the data from a single patient's tumor in a clinical trial can add up to one terabyte — the equivalent of 130,000 books. But we don't yet have the tools to efficiently process the mountain of genetic data to make more precise predictions for therapy. And it's needed: treating cancer remains a complex moving target.

      Read Full Article
    1-24 of 28 1 2 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD