Sealy, Texas bridge collapses on mother and son (NBC) SEALY, Texas (KXAN/KPRC) - A bridge collapsed in Sealy, Texas around 10 a.m. after a tractor trailer collided with it. Parts of the bridge landed on a car killing a child and injuring th...

We're excited to announce that Evercontact has created the first app with crowdsourced contact updates, Evercontact . Evercontact users now benefit from immediate updates whenever a new public email signature is detected for one of their contacts, without having to wait for an email from them directly. Picture this: Ellen is not one of your contacts, but you and Ellen are both in touch with Ben. Ben gets a new phone number, changes his email signature and emails Ellen right away. Evercontact will now update your address book even if you haven't yet received a mail from Ben yourself.

There is general agreement that AI is already having a huge impact on our work and leisure lives, as well as society as a whole -- and the effects are escalating rapidly. More open to debate is exactly what to expect and when. Long-term projections for many economists and robotics designers go only as far as the next five, or maybe 20 years, when driverless cars, elder-case assistants and automated factories will be the norm. Robin Hanson takes another tack. The associate professor of economics at George Mason University is also a research associate at the Future of Humanity Institute of Oxford University.

Facebook's new Deep Text system, launched last month, promises to revolutionise the social media experience for both individual users and brands. According to the company, its artificial intelligence (AI) engine can analyse and understand the meaning and sentiment in the text of thousands of posts per second across 20 languages. It is said to be able to achieve "near human accuracy" in understanding the deeper meaning behind written words and distinguishing between slang, colloquialisms and brand names. Facebook has already used the technology in its Messenger app to help users hail taxis and on its main site to advise users who are making sales. But Deep Text could also have a huge impact on the way brands target and communicate with their customers.

The aim of their collaboration is to achieve machine-based translation between the languages of the European Union so that comprehensible texts are achieved for as many language combinations as possible. Two of the EU-funded research projects are being led by the Saarbrücken computer linguist Josef van Genabith. Anyone who wants to learn Finnish has to be prepared to deal with a complex grammar that includes fifteen different cases. The grammatical cases are marked in part by appending syllables to nouns resulting in a dizzying array of word forms and expressive possibilities. "Teaching a computer to understand all these grammatical nuances and to translate them correctly into another language is exceptionally difficult," says Josef van Genabith, Professor of Translation-Oriented Language Technologies at Saarland University and a Scientific Director at the German Research Center for Artificial Intelligence (DFKI). His team is therefore following a different path.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. Application intelligence is the process of using machine learning technology to create apps that use historical and real-time data to make predictions and decisions to deliver rich, adaptive, personalized experiences for users. We believe that every successful new application built today will be an intelligent application. The armies of chat bots and virtual assistants, the e-commerce sites that show the right recommendations at the right time and the latest dating apps are all built to learn and create continuously improving experiences. In addition, legacy applications are becoming more and more intelligent to compete and keep pace with this new wave of applications.

Machine learning enhances Oracle's enterprise offerings. At a recent meeting of the Oracle Product Architects Community, Stephen Green, head of the Information Retrieval and Machine Learning Group within Oracle Labs, was prepared to give a general overview of his organization's activities. He got through only his first three slides, and then spent the rest of the hour answering a barrage of questions about machine learning and how this specialized programming technique might be applied to the product areas for which attendees were responsible. The interest reflects an extraordinary surge in experimentation and investment in the technology category known as artificial intelligence (AI), of which machine learning is a building block. AI in the form of self-driving cars, game-show-playing computers, and smartphone personal assistants may have grabbed the public's attention.

In an effort to apply data-driven insights to one of the most fundamental aspects of running a health care system, UPMC announced today that it has formed Pensiamo, an independent company that aims to help hospitals improve supply chain performance through a comprehensive source-to-pay offering, including cognitive analytics with IBM Watson Health technologies. IBM (NYSE:IBM) is a minority owner of Pensiamo. Supply chain costs are the second-largest and fastest-growing expense behind labor costs for health care providers [1]. The Institute of Medicine estimates that nearly one-third of health care spending is waste [2]. In today's dynamic environment, providers face mounting pressure to improve the effectiveness of patient care while controlling costs.

We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.

Our speaker for the night is Pierre Lafortune. During this meetup Pierre will help us cover all of the basics to get started using R for data manipulation and machine learning. Everything that is essential to extracting, re-coding, and feature engineering. These fundamental concepts will allow you to maneuver confidently through any data set. Lastly, Pierre will introduce some popular machine learning algorithms for statistical analysis.