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.

The following is a guest post authored by David Pitman (@edavepitman), co-founder of Converge (@converge_co). I was told Fred was the man to talk to about inspecting buildings. But I don't know that he's ever used email," my reference said, advising that I call his phone. Surprisingly though, Fred has a website. The sole image on the site shows him standing on a roof in a ten-gallon hat staring jauntily down at the steep angle.

Artificial Intelligence is a fascinating topic for many people nowadays, no matter if they are a consumer or an influencer. Today, I'm happy to be joined by Ilya Gelfenbeyn, CEO and co-founder of Api.ai, a conversational UX platform used to embed natural language understanding capabilities into connected devices, apps and services. Regular readers of SitePoint may recognize the service, as we have covered Api.ai in the past with a series earlier this year on getting started with the platform. Ilya has a background in machine learning, natural language processing and conversational interfaces. Elio: We have covered Api.ai in the past, but could you briefly explain the concept behind it?

The White House wound up a nationwide series of workshops on artificial intelligence today on a cautionary note: Yes, AI promises to ease many of humanity's ills, but humanity needs to make sure that flesh-and-blood policymakers are firmly in charge. Latanya Sweeney, director of the Data Privacy Lab at Harvard's Institute of Quantitative Social Science, said AI programs should be made to reflect the norms agreed upon by human society. "I want the people we elect controlling those norms, not the technology itself. Those norms should include supporting social equity and diversity, said Alicia Glen, New York City's deputy mayor for housing and urban development. "At its best, artificial intelligence can be a tool to promote equity, and it obviously can create huge economic opportunity for a lot of people," she said. "But it can also have discriminatory effects, whether they're intended or unintended.